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fix: correct spelling mistakes in vertexai docstrings and comments #7265

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fix: correct spelling mistakes in vertexai docstrings and comments #7265

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2 changes: 1 addition & 1 deletion google/cloud/aiplatform/datasets/image_dataset.py
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Expand Up @@ -108,7 +108,7 @@ def create(
compared using their binary data (bytes), not on their content.
If annotation labels are referenced in a schema specified by the
`import_schema_url` parameter, then the labels in the
`data_item_labels` dictionary are overriden by the annotations.
`data_item_labels` dictionary are overridden by the annotations.
project (str):
Optional. The name of the Google Cloud project to which this
`ImageDataset` is uploaded. This overrides the project that
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2 changes: 1 addition & 1 deletion google/cloud/aiplatform/datasets/text_dataset.py
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Expand Up @@ -118,7 +118,7 @@ def create(
compared using their binary data (bytes), not on their content.
If annotation labels are referenced in a schema specified by the
`import_schema_url` parameter, then the labels in the
`data_item_labels` dictionary are overriden by the annotations.
`data_item_labels` dictionary are overridden by the annotations.
project (str):
Optional. The name of the Google Cloud project to which this
`TextDataset` is uploaded. This overrides the project that
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2 changes: 1 addition & 1 deletion google/cloud/aiplatform/datasets/video_dataset.py
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Expand Up @@ -111,7 +111,7 @@ def create(
compared using their binary data (bytes), not on their content.
If annotation labels are referenced in a schema specified by the
`import_schema_url` parameter, then the labels in the
`data_item_labels` dictionary are overriden by the annotations.
`data_item_labels` dictionary are overridden by the annotations.
project (str):
The name of the Google Cloud project to which this
`VideoDataset` is uploaded. This overrides the project that
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2 changes: 1 addition & 1 deletion google/cloud/aiplatform/utils/__init__.py
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Expand Up @@ -731,7 +731,7 @@ class FeatureOnlineStoreClientWithOverride(ClientWithOverride):
class FeatureRegistryClientWithOverride(ClientWithOverride):
"""Adds function override for client classes to support new Feature Store.

`feature_path()` and `parse_feature_path()` are overriden here to compensate
`feature_path()` and `parse_feature_path()` are overridden here to compensate
for the auto-generated GAPIC class which only supports Feature Store
Legacy's feature paths.
"""
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2 changes: 1 addition & 1 deletion vertexai/generative_models/_function_calling_utils.py
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Expand Up @@ -55,7 +55,7 @@ def _generate_json_schema_from_function_using_pydantic(
function_description = func.__doc__

# Parse parameter descriptions from the docstring.
# Also parse the function descripton in a better way.
# Also parse the function description in a better way.
parameter_descriptions = {}
if docstring_parser:
parsed_docstring = docstring_parser.parse(func.__doc__)
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6 changes: 3 additions & 3 deletions vertexai/generative_models/_generative_models.py
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Expand Up @@ -209,7 +209,7 @@ def _reconcile_model_name(model_name: str, project: str, location: str) -> str:
else:
raise ValueError(
"model_name must be either a Model Garden model ID or a full resource name."
f"recieved model_name {model_name}"
f"received model_name {model_name}"
)


Expand Down Expand Up @@ -1162,7 +1162,7 @@ def start_chat(
is incomplete due to going over the max token limit.
If set to `False`, the chat session history will always
accumulate the request and response messages even if the
reponse if blocked or incomplete. This can result in an unusable
response if blocked or incomplete. This can result in an unusable
chat session state.

Returns:
Expand Down Expand Up @@ -1797,7 +1797,7 @@ def __init__(
max_output_tokens: The maximum number of output tokens to generate per message.
stop_sequences: A list of stop sequences.
presence_penalty: Positive values penalize tokens that have appeared in the generated text,
thus increasing the possibility of generating more diversed topics. Range: [-2.0, 2.0]
thus increasing the possibility of generating more diverse topics. Range: [-2.0, 2.0]
frequency_penalty: Positive values penalize tokens that repeatedly appear in the generated
text, thus decreasing the possibility of repeating the same content. Range: [-2.0, 2.0]
response_mime_type: Output response mimetype of the generated
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2 changes: 1 addition & 1 deletion vertexai/language_models/_evaluatable_language_models.py
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Expand Up @@ -230,7 +230,7 @@ class EvaluationTextSummarizationSpec(_EvaluationTaskSpec):

@dataclasses.dataclass
class _EvaluationMetricBase:
"""Base class for returned evaulation metrics."""
"""Base class for returned evaluation metrics."""

@property
def input_dataset_paths(self) -> str:
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14 changes: 7 additions & 7 deletions vertexai/language_models/_language_models.py
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Expand Up @@ -1377,7 +1377,7 @@ def predict(
The maximum value for `logprobs` is 5.
presence_penalty:
Positive values penalize tokens that have appeared in the generated text,
thus increasing the possibility of generating more diversed topics.
thus increasing the possibility of generating more diverse topics.
Range: [-2.0, 2.0]
frequency_penalty:
Positive values penalize tokens that repeatedly appear in the generated
Expand Down Expand Up @@ -1466,7 +1466,7 @@ async def predict_async(
The maximum value for `logprobs` is 5.
presence_penalty:
Positive values penalize tokens that have appeared in the generated text,
thus increasing the possibility of generating more diversed topics.
thus increasing the possibility of generating more diverse topics.
Range: [-2.0, 2.0]
frequency_penalty:
Positive values penalize tokens that repeatedly appear in the generated
Expand Down Expand Up @@ -1547,7 +1547,7 @@ def predict_streaming(
The maximum value for `logprobs` is 5.
presence_penalty:
Positive values penalize tokens that have appeared in the generated text,
thus increasing the possibility of generating more diversed topics.
thus increasing the possibility of generating more diverse topics.
Range: [-2.0, 2.0]
frequency_penalty:
Positive values penalize tokens that repeatedly appear in the generated
Expand Down Expand Up @@ -1633,7 +1633,7 @@ async def predict_streaming_async(
The maximum value for `logprobs` is 5.
presence_penalty:
Positive values penalize tokens that have appeared in the generated text,
thus increasing the possibility of generating more diversed topics.
thus increasing the possibility of generating more diverse topics.
Range: [-2.0, 2.0]
frequency_penalty:
Positive values penalize tokens that repeatedly appear in the generated
Expand Down Expand Up @@ -1727,7 +1727,7 @@ def _create_text_generation_prediction_request(
The maximum value for `logprobs` is 5.
presence_penalty:
Positive values penalize tokens that have appeared in the generated text,
thus increasing the possibility of generating more diversed topics.
thus increasing the possibility of generating more diverse topics.
Range: [-2.0, 2.0]
frequency_penalty:
Positive values penalize tokens that repeatedly appear in the generated
Expand Down Expand Up @@ -1831,7 +1831,7 @@ def _parse_text_generation_model_multi_candidate_response(
"""Converts the raw text_generation model response to `MultiCandidateTextGenerationResponse`."""
# The contract for the PredictionService is that there is a 1:1 mapping
# between request `instances` and response `predictions`.
# Unfortunetely, the text-bison models violate this contract.
# Unfortunately, the text-bison models violate this contract.

prediction_count = len(prediction_response.predictions)
candidates = []
Expand Down Expand Up @@ -3299,7 +3299,7 @@ def __init__(
class CodeChatSession(_ChatSessionBase):
"""CodeChatSession represents a chat session with code chat language model.

Within a code chat session, the model keeps context and remembers the previous converstion.
Within a code chat session, the model keeps context and remembers the previous conversation.
"""

__module__ = "vertexai.language_models"
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16 changes: 8 additions & 8 deletions vertexai/resources/preview/ml_monitoring/model_monitors.py
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Expand Up @@ -482,19 +482,19 @@ def create(
data form the dataset.
tabular_objective_spec (objective.TabularObjective):
Optional. The default tabular monitoring objective spec for
the model monitor. It can be overriden in the ModelMonitoringJob
the model monitor. It can be overridden in the ModelMonitoringJob
objective spec.
output_spec (output.OutputSpec):
Optional. The default monitoring metrics/logs export spec, it
can be overriden in the ModelMonitoringJob output spec.
can be overridden in the ModelMonitoringJob output spec.
If not specified, a default Google Cloud Storage bucket will be
created under your project.
notification_spec (notification.NotificationSpec):
Optional. The default notification spec for monitoring result.
It can be overriden in the ModelMonitoringJob notification spec.
It can be overridden in the ModelMonitoringJob notification spec.
explanation_spec (explanation.ExplanationSpec):
Optional. The default explanation spec for feature attribution
monitoring. It can be overriden in the ModelMonitoringJob
monitoring. It can be overridden in the ModelMonitoringJob
explanation spec.
project (str):
Optional. Project to retrieve model monitor from. If not set,
Expand Down Expand Up @@ -608,17 +608,17 @@ def update(
extract data form the dataset.
tabular_objective_spec (objective.TabularObjective):
Optional. The default tabular monitoring objective spec for
the model monitor. It can be overriden in the ModelMonitoringJob
the model monitor. It can be overridden in the ModelMonitoringJob
objective spec.
output_spec (output.OutputSpec):
Optional. The default monitoring metrics/logs export spec, it
can be overriden in the ModelMonitoringJob output spec.
can be overridden in the ModelMonitoringJob output spec.
notification_spec (notification.NotificationSpec):
Optional. The default notification spec for monitoring result.
It can be overriden in the ModelMonitoringJob notification spec.
It can be overridden in the ModelMonitoringJob notification spec.
explanation_spec (explanation.ExplanationSpec):
Optional. The default explanation spec for feature attribution
monitoring. It can be overriden in the ModelMonitoringJob
monitoring. It can be overridden in the ModelMonitoringJob
explanation spec.

Returns:
Expand Down