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.cache/uv/archive-v0/4GZLBJvk803hh_euIbHls/huggingface_hub/inference/_generated/types/__init__.py

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+# This file is auto-generated by `utils/generate_inference_types.py`.
+# Do not modify it manually.
+#
+# ruff: noqa: F401
+
+from .audio_classification import (
+    AudioClassificationInput,
+    AudioClassificationOutputElement,
+    AudioClassificationOutputTransform,
+    AudioClassificationParameters,
+)
+from .audio_to_audio import AudioToAudioInput, AudioToAudioOutputElement
+from .automatic_speech_recognition import (
+    AutomaticSpeechRecognitionEarlyStoppingEnum,
+    AutomaticSpeechRecognitionGenerationParameters,
+    AutomaticSpeechRecognitionInput,
+    AutomaticSpeechRecognitionOutput,
+    AutomaticSpeechRecognitionOutputChunk,
+    AutomaticSpeechRecognitionParameters,
+)
+from .base import BaseInferenceType
+from .chat_completion import (
+    ChatCompletionInput,
+    ChatCompletionInputFunctionDefinition,
+    ChatCompletionInputFunctionName,
+    ChatCompletionInputGrammarType,
+    ChatCompletionInputJSONSchema,
+    ChatCompletionInputMessage,
+    ChatCompletionInputMessageChunk,
+    ChatCompletionInputMessageChunkType,
+    ChatCompletionInputResponseFormatJSONObject,
+    ChatCompletionInputResponseFormatJSONSchema,
+    ChatCompletionInputResponseFormatText,
+    ChatCompletionInputStreamOptions,
+    ChatCompletionInputTool,
+    ChatCompletionInputToolCall,
+    ChatCompletionInputToolChoiceClass,
+    ChatCompletionInputToolChoiceEnum,
+    ChatCompletionInputURL,
+    ChatCompletionOutput,
+    ChatCompletionOutputComplete,
+    ChatCompletionOutputFunctionDefinition,
+    ChatCompletionOutputLogprob,
+    ChatCompletionOutputLogprobs,
+    ChatCompletionOutputMessage,
+    ChatCompletionOutputToolCall,
+    ChatCompletionOutputTopLogprob,
+    ChatCompletionOutputUsage,
+    ChatCompletionStreamOutput,
+    ChatCompletionStreamOutputChoice,
+    ChatCompletionStreamOutputDelta,
+    ChatCompletionStreamOutputDeltaToolCall,
+    ChatCompletionStreamOutputFunction,
+    ChatCompletionStreamOutputLogprob,
+    ChatCompletionStreamOutputLogprobs,
+    ChatCompletionStreamOutputTopLogprob,
+    ChatCompletionStreamOutputUsage,
+)
+from .depth_estimation import DepthEstimationInput, DepthEstimationOutput
+from .document_question_answering import (
+    DocumentQuestionAnsweringInput,
+    DocumentQuestionAnsweringInputData,
+    DocumentQuestionAnsweringOutputElement,
+    DocumentQuestionAnsweringParameters,
+)
+from .feature_extraction import FeatureExtractionInput, FeatureExtractionInputTruncationDirection
+from .fill_mask import FillMaskInput, FillMaskOutputElement, FillMaskParameters
+from .image_classification import (
+    ImageClassificationInput,
+    ImageClassificationOutputElement,
+    ImageClassificationOutputTransform,
+    ImageClassificationParameters,
+)
+from .image_segmentation import (
+    ImageSegmentationInput,
+    ImageSegmentationOutputElement,
+    ImageSegmentationParameters,
+    ImageSegmentationSubtask,
+)
+from .image_text_to_image import (
+    ImageTextToImageInput,
+    ImageTextToImageOutput,
+    ImageTextToImageParameters,
+    ImageTextToImageTargetSize,
+)
+from .image_text_to_video import (
+    ImageTextToVideoInput,
+    ImageTextToVideoOutput,
+    ImageTextToVideoParameters,
+    ImageTextToVideoTargetSize,
+)
+from .image_to_image import ImageToImageInput, ImageToImageOutput, ImageToImageParameters, ImageToImageTargetSize
+from .image_to_text import (
+    ImageToTextEarlyStoppingEnum,
+    ImageToTextGenerationParameters,
+    ImageToTextInput,
+    ImageToTextOutput,
+    ImageToTextParameters,
+)
+from .image_to_video import ImageToVideoInput, ImageToVideoOutput, ImageToVideoParameters, ImageToVideoTargetSize
+from .object_detection import (
+    ObjectDetectionBoundingBox,
+    ObjectDetectionInput,
+    ObjectDetectionOutputElement,
+    ObjectDetectionParameters,
+)
+from .question_answering import (
+    QuestionAnsweringInput,
+    QuestionAnsweringInputData,
+    QuestionAnsweringOutputElement,
+    QuestionAnsweringParameters,
+)
+from .sentence_similarity import SentenceSimilarityInput, SentenceSimilarityInputData
+from .summarization import (
+    SummarizationInput,
+    SummarizationOutput,
+    SummarizationParameters,
+    SummarizationTruncationStrategy,
+)
+from .table_question_answering import (
+    Padding,
+    TableQuestionAnsweringInput,
+    TableQuestionAnsweringInputData,
+    TableQuestionAnsweringOutputElement,
+    TableQuestionAnsweringParameters,
+)
+from .text2text_generation import (
+    Text2TextGenerationInput,
+    Text2TextGenerationOutput,
+    Text2TextGenerationParameters,
+    Text2TextGenerationTruncationStrategy,
+)
+from .text_classification import (
+    TextClassificationInput,
+    TextClassificationOutputElement,
+    TextClassificationOutputTransform,
+    TextClassificationParameters,
+)
+from .text_generation import (
+    TextGenerationInput,
+    TextGenerationInputGenerateParameters,
+    TextGenerationInputGrammarType,
+    TextGenerationOutput,
+    TextGenerationOutputBestOfSequence,
+    TextGenerationOutputDetails,
+    TextGenerationOutputFinishReason,
+    TextGenerationOutputPrefillToken,
+    TextGenerationOutputToken,
+    TextGenerationStreamOutput,
+    TextGenerationStreamOutputStreamDetails,
+    TextGenerationStreamOutputToken,
+    TypeEnum,
+)
+from .text_to_audio import (
+    TextToAudioEarlyStoppingEnum,
+    TextToAudioGenerationParameters,
+    TextToAudioInput,
+    TextToAudioOutput,
+    TextToAudioParameters,
+)
+from .text_to_image import TextToImageInput, TextToImageOutput, TextToImageParameters
+from .text_to_speech import (
+    TextToSpeechEarlyStoppingEnum,
+    TextToSpeechGenerationParameters,
+    TextToSpeechInput,
+    TextToSpeechOutput,
+    TextToSpeechParameters,
+)
+from .text_to_video import TextToVideoInput, TextToVideoOutput, TextToVideoParameters
+from .token_classification import (
+    TokenClassificationAggregationStrategy,
+    TokenClassificationInput,
+    TokenClassificationOutputElement,
+    TokenClassificationParameters,
+)
+from .translation import TranslationInput, TranslationOutput, TranslationParameters, TranslationTruncationStrategy
+from .video_classification import (
+    VideoClassificationInput,
+    VideoClassificationOutputElement,
+    VideoClassificationOutputTransform,
+    VideoClassificationParameters,
+)
+from .visual_question_answering import (
+    VisualQuestionAnsweringInput,
+    VisualQuestionAnsweringInputData,
+    VisualQuestionAnsweringOutputElement,
+    VisualQuestionAnsweringParameters,
+)
+from .zero_shot_classification import (
+    ZeroShotClassificationInput,
+    ZeroShotClassificationOutputElement,
+    ZeroShotClassificationParameters,
+)
+from .zero_shot_image_classification import (
+    ZeroShotImageClassificationInput,
+    ZeroShotImageClassificationOutputElement,
+    ZeroShotImageClassificationParameters,
+)
+from .zero_shot_object_detection import (
+    ZeroShotObjectDetectionBoundingBox,
+    ZeroShotObjectDetectionInput,
+    ZeroShotObjectDetectionOutputElement,
+    ZeroShotObjectDetectionParameters,
+)

.cache/uv/archive-v0/4GZLBJvk803hh_euIbHls/huggingface_hub/inference/_generated/types/audio_classification.py

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+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Literal, Optional
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+AudioClassificationOutputTransform = Literal["sigmoid", "softmax", "none"]
+
+
+@dataclass_with_extra
+class AudioClassificationParameters(BaseInferenceType):
+    """Additional inference parameters for Audio Classification"""
+
+    function_to_apply: Optional["AudioClassificationOutputTransform"] = None
+    """The function to apply to the model outputs in order to retrieve the scores."""
+    top_k: int | None = None
+    """When specified, limits the output to the top K most probable classes."""
+
+
+@dataclass_with_extra
+class AudioClassificationInput(BaseInferenceType):
+    """Inputs for Audio Classification inference"""
+
+    inputs: str
+    """The input audio data as a base64-encoded string. If no `parameters` are provided, you can
+    also provide the audio data as a raw bytes payload.
+    """
+    parameters: AudioClassificationParameters | None = None
+    """Additional inference parameters for Audio Classification"""
+
+
+@dataclass_with_extra
+class AudioClassificationOutputElement(BaseInferenceType):
+    """Outputs for Audio Classification inference"""
+
+    label: str
+    """The predicted class label."""
+    score: float
+    """The corresponding probability."""

.cache/uv/archive-v0/4GZLBJvk803hh_euIbHls/huggingface_hub/inference/_generated/types/audio_to_audio.py

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+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class AudioToAudioInput(BaseInferenceType):
+    """Inputs for Audio to Audio inference"""
+
+    inputs: Any
+    """The input audio data"""
+
+
+@dataclass_with_extra
+class AudioToAudioOutputElement(BaseInferenceType):
+    """Outputs of inference for the Audio To Audio task
+    A generated audio file with its label.
+    """
+
+    blob: Any
+    """The generated audio file."""
+    content_type: str
+    """The content type of audio file."""
+    label: str
+    """The label of the audio file."""

.cache/uv/archive-v0/4GZLBJvk803hh_euIbHls/huggingface_hub/inference/_generated/types/automatic_speech_recognition.py

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+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Literal, Union
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+AutomaticSpeechRecognitionEarlyStoppingEnum = Literal["never"]
+
+
+@dataclass_with_extra
+class AutomaticSpeechRecognitionGenerationParameters(BaseInferenceType):
+    """Parametrization of the text generation process"""
+
+    do_sample: bool | None = None
+    """Whether to use sampling instead of greedy decoding when generating new tokens."""
+    early_stopping: Union[bool, "AutomaticSpeechRecognitionEarlyStoppingEnum"] | None = None
+    """Controls the stopping condition for beam-based methods."""
+    epsilon_cutoff: float | None = None
+    """If set to float strictly between 0 and 1, only tokens with a conditional probability
+    greater than epsilon_cutoff will be sampled. In the paper, suggested values range from
+    3e-4 to 9e-4, depending on the size of the model. See [Truncation Sampling as Language
+    Model Desmoothing](https://hf.co/papers/2210.15191) for more details.
+    """
+    eta_cutoff: float | None = None
+    """Eta sampling is a hybrid of locally typical sampling and epsilon sampling. If set to
+    float strictly between 0 and 1, a token is only considered if it is greater than either
+    eta_cutoff or sqrt(eta_cutoff) * exp(-entropy(softmax(next_token_logits))). The latter
+    term is intuitively the expected next token probability, scaled by sqrt(eta_cutoff). In
+    the paper, suggested values range from 3e-4 to 2e-3, depending on the size of the model.
+    See [Truncation Sampling as Language Model Desmoothing](https://hf.co/papers/2210.15191)
+    for more details.
+    """
+    max_length: int | None = None
+    """The maximum length (in tokens) of the generated text, including the input."""
+    max_new_tokens: int | None = None
+    """The maximum number of tokens to generate. Takes precedence over max_length."""
+    min_length: int | None = None
+    """The minimum length (in tokens) of the generated text, including the input."""
+    min_new_tokens: int | None = None
+    """The minimum number of tokens to generate. Takes precedence over min_length."""
+    num_beam_groups: int | None = None
+    """Number of groups to divide num_beams into in order to ensure diversity among different
+    groups of beams. See [this paper](https://hf.co/papers/1610.02424) for more details.
+    """
+    num_beams: int | None = None
+    """Number of beams to use for beam search."""
+    penalty_alpha: float | None = None
+    """The value balances the model confidence and the degeneration penalty in contrastive
+    search decoding.
+    """
+    temperature: float | None = None
+    """The value used to modulate the next token probabilities."""
+    top_k: int | None = None
+    """The number of highest probability vocabulary tokens to keep for top-k-filtering."""
+    top_p: float | None = None
+    """If set to float < 1, only the smallest set of most probable tokens with probabilities
+    that add up to top_p or higher are kept for generation.
+    """
+    typical_p: float | None = None
+    """Local typicality measures how similar the conditional probability of predicting a target
+    token next is to the expected conditional probability of predicting a random token next,
+    given the partial text already generated. If set to float < 1, the smallest set of the
+    most locally typical tokens with probabilities that add up to typical_p or higher are
+    kept for generation. See [this paper](https://hf.co/papers/2202.00666) for more details.
+    """
+    use_cache: bool | None = None
+    """Whether the model should use the past last key/values attentions to speed up decoding"""
+
+
+@dataclass_with_extra
+class AutomaticSpeechRecognitionParameters(BaseInferenceType):
+    """Additional inference parameters for Automatic Speech Recognition"""
+
+    generation_parameters: AutomaticSpeechRecognitionGenerationParameters | None = None
+    """Parametrization of the text generation process"""
+    return_timestamps: bool | None = None
+    """Whether to output corresponding timestamps with the generated text"""
+
+
+@dataclass_with_extra
+class AutomaticSpeechRecognitionInput(BaseInferenceType):
+    """Inputs for Automatic Speech Recognition inference"""
+
+    inputs: str
+    """The input audio data as a base64-encoded string. If no `parameters` are provided, you can
+    also provide the audio data as a raw bytes payload.
+    """
+    parameters: AutomaticSpeechRecognitionParameters | None = None
+    """Additional inference parameters for Automatic Speech Recognition"""
+
+
+@dataclass_with_extra
+class AutomaticSpeechRecognitionOutputChunk(BaseInferenceType):
+    text: str
+    """A chunk of text identified by the model"""
+    timestamp: list[float]
+    """The start and end timestamps corresponding with the text"""
+
+
+@dataclass_with_extra
+class AutomaticSpeechRecognitionOutput(BaseInferenceType):
+    """Outputs of inference for the Automatic Speech Recognition task"""
+
+    text: str
+    """The recognized text."""
+    chunks: list[AutomaticSpeechRecognitionOutputChunk] | None = None
+    """When returnTimestamps is enabled, chunks contains a list of audio chunks identified by
+    the model.
+    """

.cache/uv/archive-v0/4GZLBJvk803hh_euIbHls/huggingface_hub/inference/_generated/types/base.py

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+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains a base class for all inference types."""
+
+import inspect
+import json
+import types
+from dataclasses import asdict, dataclass
+from typing import Any, TypeVar, get_args
+
+from typing_extensions import dataclass_transform
+
+
+T = TypeVar("T", bound="BaseInferenceType")
+
+
+def _repr_with_extra(self):
+    fields = list(self.__dataclass_fields__.keys())
+    other_fields = list(k for k in self.__dict__ if k not in fields)
+    return f"{self.__class__.__name__}({', '.join(f'{k}={self.__dict__[k]!r}' for k in fields + other_fields)})"
+
+
+@dataclass_transform()
+def dataclass_with_extra(cls: type[T]) -> type[T]:
+    """Decorator to add a custom __repr__ method to a dataclass, showing all fields, including extra ones.
+
+    This decorator only works with dataclasses that inherit from `BaseInferenceType`.
+    """
+    cls = dataclass(cls)
+    cls.__repr__ = _repr_with_extra  # type: ignore[method-assign]
+    return cls
+
+
+@dataclass
+class BaseInferenceType(dict):
+    """Base class for all inference types.
+
+    Object is a dataclass and a dict for backward compatibility but plan is to remove the dict part in the future.
+
+    Handle parsing from dict, list and json strings in a permissive way to ensure future-compatibility (e.g. all fields
+    are made optional, and non-expected fields are added as dict attributes).
+    """
+
+    @classmethod
+    def parse_obj_as_list(cls: type[T], data: bytes | str | list | dict) -> list[T]:
+        """Alias to parse server response and return a single instance.
+
+        See `parse_obj` for more details.
+        """
+        output = cls.parse_obj(data)
+        if not isinstance(output, list):
+            raise ValueError(f"Invalid input data for {cls}. Expected a list, but got {type(output)}.")
+        return output
+
+    @classmethod
+    def parse_obj_as_instance(cls: type[T], data: bytes | str | list | dict) -> T:
+        """Alias to parse server response and return a single instance.
+
+        See `parse_obj` for more details.
+        """
+        output = cls.parse_obj(data)
+        if isinstance(output, list):
+            raise ValueError(f"Invalid input data for {cls}. Expected a single instance, but got a list.")
+        return output
+
+    @classmethod
+    def parse_obj(cls: type[T], data: bytes | str | list | dict) -> list[T] | T:
+        """Parse server response as a dataclass or list of dataclasses.
+
+        To enable future-compatibility, we want to handle cases where the server return more fields than expected.
+        In such cases, we don't want to raise an error but still create the dataclass object. Remaining fields are
+        added as dict attributes.
+        """
+        # Parse server response (from bytes)
+        if isinstance(data, bytes):
+            data = data.decode()
+        if isinstance(data, str):
+            data = json.loads(data)
+
+        # If a list, parse each item individually
+        if isinstance(data, list):
+            return [cls.parse_obj(d) for d in data]  # type: ignore
+
+        # At this point, we expect a dict
+        if not isinstance(data, dict):
+            raise ValueError(f"Invalid data type: {type(data)}")
+
+        init_values = {}
+        other_values = {}
+        for key, value in data.items():
+            key = normalize_key(key)
+            if key in cls.__dataclass_fields__ and cls.__dataclass_fields__[key].init:
+                if isinstance(value, dict) or isinstance(value, list):
+                    field_type = cls.__dataclass_fields__[key].type
+
+                    # if `field_type` is a `BaseInferenceType`, parse it
+                    if inspect.isclass(field_type) and issubclass(field_type, BaseInferenceType):
+                        value = field_type.parse_obj(value)
+
+                    # otherwise, recursively parse nested dataclasses (if possible)
+                    # `get_args` returns handle Union and Optional for us
+                    else:
+                        expected_types = get_args(field_type)
+                        for expected_type in expected_types:
+                            if (
+                                isinstance(expected_type, types.GenericAlias) and expected_type.__origin__ is list
+                            ) or getattr(expected_type, "_name", None) == "List":
+                                expected_type = get_args(expected_type)[
+                                    0
+                                ]  # assume same type for all items in the list
+                            if inspect.isclass(expected_type) and issubclass(expected_type, BaseInferenceType):
+                                value = expected_type.parse_obj(value)
+                                break
+                init_values[key] = value
+            else:
+                other_values[key] = value
+
+        # Make all missing fields default to None
+        # => ensure that dataclass initialization will never fail even if the server does not return all fields.
+        for key in cls.__dataclass_fields__:
+            if key not in init_values:
+                init_values[key] = None
+
+        # Initialize dataclass with expected values
+        item = cls(**init_values)
+
+        # Add remaining fields as dict attributes
+        item.update(other_values)
+
+        # Add remaining fields as extra dataclass fields.
+        # They won't be part of the dataclass fields but will be accessible as attributes.
+        # Use @dataclass_with_extra to show them in __repr__.
+        item.__dict__.update(other_values)
+        return item
+
+    def __post_init__(self):
+        self.update(asdict(self))
+
+    def __setitem__(self, __key: Any, __value: Any) -> None:
+        # Hacky way to keep dataclass values in sync when dict is updated
+        super().__setitem__(__key, __value)
+        if __key in self.__dataclass_fields__ and getattr(self, __key, None) != __value:
+            self.__setattr__(__key, __value)
+        return
+
+    def __setattr__(self, __name: str, __value: Any) -> None:
+        # Hacky way to keep dict values is sync when dataclass is updated
+        super().__setattr__(__name, __value)
+        if self.get(__name) != __value:
+            self[__name] = __value
+        return
+
+
+def normalize_key(key: str) -> str:
+    # e.g "content-type" -> "content_type", "Accept" -> "accept"
+    return key.replace("-", "_").replace(" ", "_").lower()

.cache/uv/archive-v0/4GZLBJvk803hh_euIbHls/huggingface_hub/inference/_generated/types/chat_completion.py

@@ -0,0 +1,347 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any, Literal, Union
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class ChatCompletionInputURL(BaseInferenceType):
+    url: str
+
+
+ChatCompletionInputMessageChunkType = Literal["text", "image_url"]
+
+
+@dataclass_with_extra
+class ChatCompletionInputMessageChunk(BaseInferenceType):
+    type: "ChatCompletionInputMessageChunkType"
+    image_url: ChatCompletionInputURL | None = None
+    text: str | None = None
+
+
+@dataclass_with_extra
+class ChatCompletionInputFunctionDefinition(BaseInferenceType):
+    name: str
+    parameters: Any
+    description: str | None = None
+
+
+@dataclass_with_extra
+class ChatCompletionInputToolCall(BaseInferenceType):
+    function: ChatCompletionInputFunctionDefinition
+    id: str
+    type: str
+
+
+@dataclass_with_extra
+class ChatCompletionInputMessage(BaseInferenceType):
+    role: str
+    content: list[ChatCompletionInputMessageChunk] | str | None = None
+    name: str | None = None
+    tool_calls: list[ChatCompletionInputToolCall] | None = None
+
+
+@dataclass_with_extra
+class ChatCompletionInputJSONSchema(BaseInferenceType):
+    name: str
+    """
+    The name of the response format.
+    """
+    description: str | None = None
+    """
+    A description of what the response format is for, used by the model to determine
+    how to respond in the format.
+    """
+    schema: dict[str, object] | None = None
+    """
+    The schema for the response format, described as a JSON Schema object. Learn how
+    to build JSON schemas [here](https://json-schema.org/).
+    """
+    strict: bool | None = None
+    """
+    Whether to enable strict schema adherence when generating the output. If set to
+    true, the model will always follow the exact schema defined in the `schema`
+    field.
+    """
+
+
+@dataclass_with_extra
+class ChatCompletionInputResponseFormatText(BaseInferenceType):
+    type: Literal["text"]
+
+
+@dataclass_with_extra
+class ChatCompletionInputResponseFormatJSONSchema(BaseInferenceType):
+    type: Literal["json_schema"]
+    json_schema: ChatCompletionInputJSONSchema
+
+
+@dataclass_with_extra
+class ChatCompletionInputResponseFormatJSONObject(BaseInferenceType):
+    type: Literal["json_object"]
+
+
+ChatCompletionInputGrammarType = Union[
+    ChatCompletionInputResponseFormatText,
+    ChatCompletionInputResponseFormatJSONSchema,
+    ChatCompletionInputResponseFormatJSONObject,
+]
+
+
+@dataclass_with_extra
+class ChatCompletionInputStreamOptions(BaseInferenceType):
+    include_usage: bool | None = None
+    """If set, an additional chunk will be streamed before the data: [DONE] message. The usage
+    field on this chunk shows the token usage statistics for the entire request, and the
+    choices field will always be an empty array. All other chunks will also include a usage
+    field, but with a null value.
+    """
+
+
+@dataclass_with_extra
+class ChatCompletionInputFunctionName(BaseInferenceType):
+    name: str
+
+
+@dataclass_with_extra
+class ChatCompletionInputToolChoiceClass(BaseInferenceType):
+    function: ChatCompletionInputFunctionName
+
+
+ChatCompletionInputToolChoiceEnum = Literal["auto", "none", "required"]
+
+
+@dataclass_with_extra
+class ChatCompletionInputTool(BaseInferenceType):
+    function: ChatCompletionInputFunctionDefinition
+    type: str
+
+
+@dataclass_with_extra
+class ChatCompletionInput(BaseInferenceType):
+    """Chat Completion Input.
+    Auto-generated from TGI specs.
+    For more details, check out
+    https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-tgi-import.ts.
+    """
+
+    messages: list[ChatCompletionInputMessage]
+    """A list of messages comprising the conversation so far."""
+    frequency_penalty: float | None = None
+    """Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing
+    frequency in the text so far,
+    decreasing the model's likelihood to repeat the same line verbatim.
+    """
+    logit_bias: list[float] | None = None
+    """UNUSED
+    Modify the likelihood of specified tokens appearing in the completion. Accepts a JSON
+    object that maps tokens
+    (specified by their token ID in the tokenizer) to an associated bias value from -100 to
+    100. Mathematically,
+    the bias is added to the logits generated by the model prior to sampling. The exact
+    effect will vary per model,
+    but values between -1 and 1 should decrease or increase likelihood of selection; values
+    like -100 or 100 should
+    result in a ban or exclusive selection of the relevant token.
+    """
+    logprobs: bool | None = None
+    """Whether to return log probabilities of the output tokens or not. If true, returns the log
+    probabilities of each
+    output token returned in the content of message.
+    """
+    max_tokens: int | None = None
+    """The maximum number of tokens that can be generated in the chat completion."""
+    model: str | None = None
+    """[UNUSED] ID of the model to use. See the model endpoint compatibility table for details
+    on which models work with the Chat API.
+    """
+    n: int | None = None
+    """UNUSED
+    How many chat completion choices to generate for each input message. Note that you will
+    be charged based on the
+    number of generated tokens across all of the choices. Keep n as 1 to minimize costs.
+    """
+    presence_penalty: float | None = None
+    """Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they
+    appear in the text so far,
+    increasing the model's likelihood to talk about new topics
+    """
+    response_format: ChatCompletionInputGrammarType | None = None
+    seed: int | None = None
+    stop: list[str] | None = None
+    """Up to 4 sequences where the API will stop generating further tokens."""
+    stream: bool | None = None
+    stream_options: ChatCompletionInputStreamOptions | None = None
+    temperature: float | None = None
+    """What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the
+    output more random, while
+    lower values like 0.2 will make it more focused and deterministic.
+    We generally recommend altering this or `top_p` but not both.
+    """
+    tool_choice: Union[ChatCompletionInputToolChoiceClass, "ChatCompletionInputToolChoiceEnum"] | None = None
+    tool_prompt: str | None = None
+    """A prompt to be appended before the tools"""
+    tools: list[ChatCompletionInputTool] | None = None
+    """A list of tools the model may call. Currently, only functions are supported as a tool.
+    Use this to provide a list of
+    functions the model may generate JSON inputs for.
+    """
+    top_logprobs: int | None = None
+    """An integer between 0 and 5 specifying the number of most likely tokens to return at each
+    token position, each with
+    an associated log probability. logprobs must be set to true if this parameter is used.
+    """
+    top_p: float | None = None
+    """An alternative to sampling with temperature, called nucleus sampling, where the model
+    considers the results of the
+    tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10%
+    probability mass are considered.
+    """
+
+
+@dataclass_with_extra
+class ChatCompletionOutputTopLogprob(BaseInferenceType):
+    logprob: float
+    token: str
+
+
+@dataclass_with_extra
+class ChatCompletionOutputLogprob(BaseInferenceType):
+    logprob: float
+    token: str
+    top_logprobs: list[ChatCompletionOutputTopLogprob]
+
+
+@dataclass_with_extra
+class ChatCompletionOutputLogprobs(BaseInferenceType):
+    content: list[ChatCompletionOutputLogprob]
+
+
+@dataclass_with_extra
+class ChatCompletionOutputFunctionDefinition(BaseInferenceType):
+    arguments: str
+    name: str
+    description: str | None = None
+
+
+@dataclass_with_extra
+class ChatCompletionOutputToolCall(BaseInferenceType):
+    function: ChatCompletionOutputFunctionDefinition
+    id: str
+    type: str
+
+
+@dataclass_with_extra
+class ChatCompletionOutputMessage(BaseInferenceType):
+    role: str
+    content: str | None = None
+    reasoning: str | None = None
+    tool_call_id: str | None = None
+    tool_calls: list[ChatCompletionOutputToolCall] | None = None
+
+
+@dataclass_with_extra
+class ChatCompletionOutputComplete(BaseInferenceType):
+    finish_reason: str
+    index: int
+    message: ChatCompletionOutputMessage
+    logprobs: ChatCompletionOutputLogprobs | None = None
+
+
+@dataclass_with_extra
+class ChatCompletionOutputUsage(BaseInferenceType):
+    completion_tokens: int
+    prompt_tokens: int
+    total_tokens: int
+
+
+@dataclass_with_extra
+class ChatCompletionOutput(BaseInferenceType):
+    """Chat Completion Output.
+    Auto-generated from TGI specs.
+    For more details, check out
+    https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-tgi-import.ts.
+    """
+
+    choices: list[ChatCompletionOutputComplete]
+    created: int
+    id: str
+    model: str
+    system_fingerprint: str
+    usage: ChatCompletionOutputUsage
+
+
+@dataclass_with_extra
+class ChatCompletionStreamOutputFunction(BaseInferenceType):
+    arguments: str
+    name: str | None = None
+
+
+@dataclass_with_extra
+class ChatCompletionStreamOutputDeltaToolCall(BaseInferenceType):
+    function: ChatCompletionStreamOutputFunction
+    id: str
+    index: int
+    type: str
+
+
+@dataclass_with_extra
+class ChatCompletionStreamOutputDelta(BaseInferenceType):
+    role: str
+    content: str | None = None
+    reasoning: str | None = None
+    tool_call_id: str | None = None
+    tool_calls: list[ChatCompletionStreamOutputDeltaToolCall] | None = None
+
+
+@dataclass_with_extra
+class ChatCompletionStreamOutputTopLogprob(BaseInferenceType):
+    logprob: float
+    token: str
+
+
+@dataclass_with_extra
+class ChatCompletionStreamOutputLogprob(BaseInferenceType):
+    logprob: float
+    token: str
+    top_logprobs: list[ChatCompletionStreamOutputTopLogprob]
+
+
+@dataclass_with_extra
+class ChatCompletionStreamOutputLogprobs(BaseInferenceType):
+    content: list[ChatCompletionStreamOutputLogprob]
+
+
+@dataclass_with_extra
+class ChatCompletionStreamOutputChoice(BaseInferenceType):
+    delta: ChatCompletionStreamOutputDelta
+    index: int
+    finish_reason: str | None = None
+    logprobs: ChatCompletionStreamOutputLogprobs | None = None
+
+
+@dataclass_with_extra
+class ChatCompletionStreamOutputUsage(BaseInferenceType):
+    completion_tokens: int
+    prompt_tokens: int
+    total_tokens: int
+
+
+@dataclass_with_extra
+class ChatCompletionStreamOutput(BaseInferenceType):
+    """Chat Completion Stream Output.
+    Auto-generated from TGI specs.
+    For more details, check out
+    https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-tgi-import.ts.
+    """
+
+    choices: list[ChatCompletionStreamOutputChoice]
+    created: int
+    id: str
+    model: str
+    system_fingerprint: str
+    usage: ChatCompletionStreamOutputUsage | None = None

.cache/uv/archive-v0/4GZLBJvk803hh_euIbHls/huggingface_hub/inference/_generated/types/depth_estimation.py

@@ -0,0 +1,28 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class DepthEstimationInput(BaseInferenceType):
+    """Inputs for Depth Estimation inference"""
+
+    inputs: Any
+    """The input image data"""
+    parameters: dict[str, Any] | None = None
+    """Additional inference parameters for Depth Estimation"""
+
+
+@dataclass_with_extra
+class DepthEstimationOutput(BaseInferenceType):
+    """Outputs of inference for the Depth Estimation task"""
+
+    depth: Any
+    """The predicted depth as an image"""
+    predicted_depth: Any
+    """The predicted depth as a tensor"""

.cache/uv/archive-v0/4GZLBJvk803hh_euIbHls/huggingface_hub/inference/_generated/types/document_question_answering.py

@@ -0,0 +1,80 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class DocumentQuestionAnsweringInputData(BaseInferenceType):
+    """One (document, question) pair to answer"""
+
+    image: Any
+    """The image on which the question is asked"""
+    question: str
+    """A question to ask of the document"""
+
+
+@dataclass_with_extra
+class DocumentQuestionAnsweringParameters(BaseInferenceType):
+    """Additional inference parameters for Document Question Answering"""
+
+    doc_stride: int | None = None
+    """If the words in the document are too long to fit with the question for the model, it will
+    be split in several chunks with some overlap. This argument controls the size of that
+    overlap.
+    """
+    handle_impossible_answer: bool | None = None
+    """Whether to accept impossible as an answer"""
+    lang: str | None = None
+    """Language to use while running OCR. Defaults to english."""
+    max_answer_len: int | None = None
+    """The maximum length of predicted answers (e.g., only answers with a shorter length are
+    considered).
+    """
+    max_question_len: int | None = None
+    """The maximum length of the question after tokenization. It will be truncated if needed."""
+    max_seq_len: int | None = None
+    """The maximum length of the total sentence (context + question) in tokens of each chunk
+    passed to the model. The context will be split in several chunks (using doc_stride as
+    overlap) if needed.
+    """
+    top_k: int | None = None
+    """The number of answers to return (will be chosen by order of likelihood). Can return less
+    than top_k answers if there are not enough options available within the context.
+    """
+    word_boxes: list[list[float] | str] | None = None
+    """A list of words and bounding boxes (normalized 0->1000). If provided, the inference will
+    skip the OCR step and use the provided bounding boxes instead.
+    """
+
+
+@dataclass_with_extra
+class DocumentQuestionAnsweringInput(BaseInferenceType):
+    """Inputs for Document Question Answering inference"""
+
+    inputs: DocumentQuestionAnsweringInputData
+    """One (document, question) pair to answer"""
+    parameters: DocumentQuestionAnsweringParameters | None = None
+    """Additional inference parameters for Document Question Answering"""
+
+
+@dataclass_with_extra
+class DocumentQuestionAnsweringOutputElement(BaseInferenceType):
+    """Outputs of inference for the Document Question Answering task"""
+
+    answer: str
+    """The answer to the question."""
+    end: int
+    """The end word index of the answer (in the OCR’d version of the input or provided word
+    boxes).
+    """
+    score: float
+    """The probability associated to the answer."""
+    start: int
+    """The start word index of the answer (in the OCR’d version of the input or provided word
+    boxes).
+    """

.cache/uv/archive-v0/4GZLBJvk803hh_euIbHls/huggingface_hub/inference/_generated/types/feature_extraction.py

@@ -0,0 +1,36 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Literal, Optional
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+FeatureExtractionInputTruncationDirection = Literal["left", "right"]
+
+
+@dataclass_with_extra
+class FeatureExtractionInput(BaseInferenceType):
+    """Feature Extraction Input.
+    Auto-generated from TEI specs.
+    For more details, check out
+    https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-tei-import.ts.
+    """
+
+    inputs: list[str] | str
+    """The text or list of texts to embed."""
+    normalize: bool | None = None
+    prompt_name: str | None = None
+    """The name of the prompt that should be used by for encoding. If not set, no prompt
+    will be applied.
+    Must be a key in the `sentence-transformers` configuration `prompts` dictionary.
+    For example if ``prompt_name`` is "query" and the ``prompts`` is {"query": "query: ",
+    ...},
+    then the sentence "What is the capital of France?" will be encoded as
+    "query: What is the capital of France?" because the prompt text will be prepended before
+    any text to encode.
+    """
+    truncate: bool | None = None
+    truncation_direction: Optional["FeatureExtractionInputTruncationDirection"] = None

.cache/uv/archive-v0/4GZLBJvk803hh_euIbHls/huggingface_hub/inference/_generated/types/fill_mask.py

@@ -0,0 +1,47 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class FillMaskParameters(BaseInferenceType):
+    """Additional inference parameters for Fill Mask"""
+
+    targets: list[str] | None = None
+    """When passed, the model will limit the scores to the passed targets instead of looking up
+    in the whole vocabulary. If the provided targets are not in the model vocab, they will be
+    tokenized and the first resulting token will be used (with a warning, and that might be
+    slower).
+    """
+    top_k: int | None = None
+    """When passed, overrides the number of predictions to return."""
+
+
+@dataclass_with_extra
+class FillMaskInput(BaseInferenceType):
+    """Inputs for Fill Mask inference"""
+
+    inputs: str
+    """The text with masked tokens"""
+    parameters: FillMaskParameters | None = None
+    """Additional inference parameters for Fill Mask"""
+
+
+@dataclass_with_extra
+class FillMaskOutputElement(BaseInferenceType):
+    """Outputs of inference for the Fill Mask task"""
+
+    score: float
+    """The corresponding probability"""
+    sequence: str
+    """The corresponding input with the mask token prediction."""
+    token: int
+    """The predicted token id (to replace the masked one)."""
+    token_str: Any
+    fill_mask_output_token_str: str | None = None
+    """The predicted token (to replace the masked one)."""

.cache/uv/archive-v0/4GZLBJvk803hh_euIbHls/huggingface_hub/inference/_generated/types/image_classification.py

@@ -0,0 +1,43 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Literal, Optional
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+ImageClassificationOutputTransform = Literal["sigmoid", "softmax", "none"]
+
+
+@dataclass_with_extra
+class ImageClassificationParameters(BaseInferenceType):
+    """Additional inference parameters for Image Classification"""
+
+    function_to_apply: Optional["ImageClassificationOutputTransform"] = None
+    """The function to apply to the model outputs in order to retrieve the scores."""
+    top_k: int | None = None
+    """When specified, limits the output to the top K most probable classes."""
+
+
+@dataclass_with_extra
+class ImageClassificationInput(BaseInferenceType):
+    """Inputs for Image Classification inference"""
+
+    inputs: str
+    """The input image data as a base64-encoded string. If no `parameters` are provided, you can
+    also provide the image data as a raw bytes payload.
+    """
+    parameters: ImageClassificationParameters | None = None
+    """Additional inference parameters for Image Classification"""
+
+
+@dataclass_with_extra
+class ImageClassificationOutputElement(BaseInferenceType):
+    """Outputs of inference for the Image Classification task"""
+
+    label: str
+    """The predicted class label."""
+    score: float
+    """The corresponding probability."""

.cache/uv/archive-v0/4GZLBJvk803hh_euIbHls/huggingface_hub/inference/_generated/types/image_segmentation.py

@@ -0,0 +1,51 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Literal, Optional
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+ImageSegmentationSubtask = Literal["instance", "panoptic", "semantic"]
+
+
+@dataclass_with_extra
+class ImageSegmentationParameters(BaseInferenceType):
+    """Additional inference parameters for Image Segmentation"""
+
+    mask_threshold: float | None = None
+    """Threshold to use when turning the predicted masks into binary values."""
+    overlap_mask_area_threshold: float | None = None
+    """Mask overlap threshold to eliminate small, disconnected segments."""
+    subtask: Optional["ImageSegmentationSubtask"] = None
+    """Segmentation task to be performed, depending on model capabilities."""
+    threshold: float | None = None
+    """Probability threshold to filter out predicted masks."""
+
+
+@dataclass_with_extra
+class ImageSegmentationInput(BaseInferenceType):
+    """Inputs for Image Segmentation inference"""
+
+    inputs: str
+    """The input image data as a base64-encoded string. If no `parameters` are provided, you can
+    also provide the image data as a raw bytes payload.
+    """
+    parameters: ImageSegmentationParameters | None = None
+    """Additional inference parameters for Image Segmentation"""
+
+
+@dataclass_with_extra
+class ImageSegmentationOutputElement(BaseInferenceType):
+    """Outputs of inference for the Image Segmentation task
+    A predicted mask / segment
+    """
+
+    label: str
+    """The label of the predicted segment."""
+    mask: str
+    """The corresponding mask as a black-and-white image (base64-encoded)."""
+    score: float | None = None
+    """The score or confidence degree the model has."""

.cache/uv/archive-v0/4GZLBJvk803hh_euIbHls/huggingface_hub/inference/_generated/types/image_text_to_image.py

@@ -0,0 +1,67 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class ImageTextToImageTargetSize(BaseInferenceType):
+    """The size in pixels of the output image. This parameter is only supported by some
+    providers and for specific models. It will be ignored when unsupported.
+    """
+
+    height: int
+    width: int
+
+
+@dataclass_with_extra
+class ImageTextToImageParameters(BaseInferenceType):
+    """Additional inference parameters for Image Text To Image"""
+
+    guidance_scale: float | None = None
+    """For diffusion models. A higher guidance scale value encourages the model to generate
+    images closely linked to the text prompt at the expense of lower image quality.
+    """
+    negative_prompt: str | None = None
+    """One prompt to guide what NOT to include in image generation."""
+    num_inference_steps: int | None = None
+    """For diffusion models. The number of denoising steps. More denoising steps usually lead to
+    a higher quality image at the expense of slower inference.
+    """
+    prompt: str | None = None
+    """The text prompt to guide the image generation. Either this or inputs (image) must be
+    provided.
+    """
+    seed: int | None = None
+    """Seed for the random number generator."""
+    target_size: ImageTextToImageTargetSize | None = None
+    """The size in pixels of the output image. This parameter is only supported by some
+    providers and for specific models. It will be ignored when unsupported.
+    """
+
+
+@dataclass_with_extra
+class ImageTextToImageInput(BaseInferenceType):
+    """Inputs for Image Text To Image inference. Either inputs (image) or prompt (in parameters)
+    must be provided, or both.
+    """
+
+    inputs: str | None = None
+    """The input image data as a base64-encoded string. If no `parameters` are provided, you can
+    also provide the image data as a raw bytes payload. Either this or prompt must be
+    provided.
+    """
+    parameters: ImageTextToImageParameters | None = None
+    """Additional inference parameters for Image Text To Image"""
+
+
+@dataclass_with_extra
+class ImageTextToImageOutput(BaseInferenceType):
+    """Outputs of inference for the Image Text To Image task"""
+
+    image: Any
+    """The generated image returned as raw bytes in the payload."""

.cache/uv/archive-v0/4GZLBJvk803hh_euIbHls/huggingface_hub/inference/_generated/types/image_text_to_video.py

@@ -0,0 +1,65 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class ImageTextToVideoTargetSize(BaseInferenceType):
+    """The size in pixel of the output video frames."""
+
+    height: int
+    width: int
+
+
+@dataclass_with_extra
+class ImageTextToVideoParameters(BaseInferenceType):
+    """Additional inference parameters for Image Text To Video"""
+
+    guidance_scale: float | None = None
+    """For diffusion models. A higher guidance scale value encourages the model to generate
+    videos closely linked to the text prompt at the expense of lower image quality.
+    """
+    negative_prompt: str | None = None
+    """One prompt to guide what NOT to include in video generation."""
+    num_frames: float | None = None
+    """The num_frames parameter determines how many video frames are generated."""
+    num_inference_steps: int | None = None
+    """The number of denoising steps. More denoising steps usually lead to a higher quality
+    video at the expense of slower inference.
+    """
+    prompt: str | None = None
+    """The text prompt to guide the video generation. Either this or inputs (image) must be
+    provided.
+    """
+    seed: int | None = None
+    """Seed for the random number generator."""
+    target_size: ImageTextToVideoTargetSize | None = None
+    """The size in pixel of the output video frames."""
+
+
+@dataclass_with_extra
+class ImageTextToVideoInput(BaseInferenceType):
+    """Inputs for Image Text To Video inference. Either inputs (image) or prompt (in parameters)
+    must be provided, or both.
+    """
+
+    inputs: str | None = None
+    """The input image data as a base64-encoded string. If no `parameters` are provided, you can
+    also provide the image data as a raw bytes payload. Either this or prompt must be
+    provided.
+    """
+    parameters: ImageTextToVideoParameters | None = None
+    """Additional inference parameters for Image Text To Video"""
+
+
+@dataclass_with_extra
+class ImageTextToVideoOutput(BaseInferenceType):
+    """Outputs of inference for the Image Text To Video task"""
+
+    video: Any
+    """The generated video returned as raw bytes in the payload."""

.cache/uv/archive-v0/4GZLBJvk803hh_euIbHls/huggingface_hub/inference/_generated/types/image_to_image.py

@@ -0,0 +1,60 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class ImageToImageTargetSize(BaseInferenceType):
+    """The size in pixels of the output image. This parameter is only supported by some
+    providers and for specific models. It will be ignored when unsupported.
+    """
+
+    height: int
+    width: int
+
+
+@dataclass_with_extra
+class ImageToImageParameters(BaseInferenceType):
+    """Additional inference parameters for Image To Image"""
+
+    guidance_scale: float | None = None
+    """For diffusion models. A higher guidance scale value encourages the model to generate
+    images closely linked to the text prompt at the expense of lower image quality.
+    """
+    negative_prompt: str | None = None
+    """One prompt to guide what NOT to include in image generation."""
+    num_inference_steps: int | None = None
+    """For diffusion models. The number of denoising steps. More denoising steps usually lead to
+    a higher quality image at the expense of slower inference.
+    """
+    prompt: str | None = None
+    """The text prompt to guide the image generation."""
+    target_size: ImageToImageTargetSize | None = None
+    """The size in pixels of the output image. This parameter is only supported by some
+    providers and for specific models. It will be ignored when unsupported.
+    """
+
+
+@dataclass_with_extra
+class ImageToImageInput(BaseInferenceType):
+    """Inputs for Image To Image inference"""
+
+    inputs: str
+    """The input image data as a base64-encoded string. If no `parameters` are provided, you can
+    also provide the image data as a raw bytes payload.
+    """
+    parameters: ImageToImageParameters | None = None
+    """Additional inference parameters for Image To Image"""
+
+
+@dataclass_with_extra
+class ImageToImageOutput(BaseInferenceType):
+    """Outputs of inference for the Image To Image task"""
+
+    image: Any
+    """The output image returned as raw bytes in the payload."""

.cache/uv/archive-v0/4GZLBJvk803hh_euIbHls/huggingface_hub/inference/_generated/types/image_to_video.py

@@ -0,0 +1,60 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class ImageToVideoTargetSize(BaseInferenceType):
+    """The size in pixel of the output video frames."""
+
+    height: int
+    width: int
+
+
+@dataclass_with_extra
+class ImageToVideoParameters(BaseInferenceType):
+    """Additional inference parameters for Image To Video"""
+
+    guidance_scale: float | None = None
+    """For diffusion models. A higher guidance scale value encourages the model to generate
+    videos closely linked to the text prompt at the expense of lower image quality.
+    """
+    negative_prompt: str | None = None
+    """One prompt to guide what NOT to include in video generation."""
+    num_frames: float | None = None
+    """The num_frames parameter determines how many video frames are generated."""
+    num_inference_steps: int | None = None
+    """The number of denoising steps. More denoising steps usually lead to a higher quality
+    video at the expense of slower inference.
+    """
+    prompt: str | None = None
+    """The text prompt to guide the video generation."""
+    seed: int | None = None
+    """Seed for the random number generator."""
+    target_size: ImageToVideoTargetSize | None = None
+    """The size in pixel of the output video frames."""
+
+
+@dataclass_with_extra
+class ImageToVideoInput(BaseInferenceType):
+    """Inputs for Image To Video inference"""
+
+    inputs: str
+    """The input image data as a base64-encoded string. If no `parameters` are provided, you can
+    also provide the image data as a raw bytes payload.
+    """
+    parameters: ImageToVideoParameters | None = None
+    """Additional inference parameters for Image To Video"""
+
+
+@dataclass_with_extra
+class ImageToVideoOutput(BaseInferenceType):
+    """Outputs of inference for the Image To Video task"""
+
+    video: Any
+    """The generated video returned as raw bytes in the payload."""

.cache/uv/archive-v0/4GZLBJvk803hh_euIbHls/huggingface_hub/inference/_generated/types/object_detection.py

@@ -0,0 +1,56 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class ObjectDetectionParameters(BaseInferenceType):
+    """Additional inference parameters for Object Detection"""
+
+    threshold: float | None = None
+    """The probability necessary to make a prediction."""
+
+
+@dataclass_with_extra
+class ObjectDetectionInput(BaseInferenceType):
+    """Inputs for Object Detection inference"""
+
+    inputs: str
+    """The input image data as a base64-encoded string. If no `parameters` are provided, you can
+    also provide the image data as a raw bytes payload.
+    """
+    parameters: ObjectDetectionParameters | None = None
+    """Additional inference parameters for Object Detection"""
+
+
+@dataclass_with_extra
+class ObjectDetectionBoundingBox(BaseInferenceType):
+    """The predicted bounding box. Coordinates are relative to the top left corner of the input
+    image.
+    """
+
+    xmax: int
+    """The x-coordinate of the bottom-right corner of the bounding box."""
+    xmin: int
+    """The x-coordinate of the top-left corner of the bounding box."""
+    ymax: int
+    """The y-coordinate of the bottom-right corner of the bounding box."""
+    ymin: int
+    """The y-coordinate of the top-left corner of the bounding box."""
+
+
+@dataclass_with_extra
+class ObjectDetectionOutputElement(BaseInferenceType):
+    """Outputs of inference for the Object Detection task"""
+
+    box: ObjectDetectionBoundingBox
+    """The predicted bounding box. Coordinates are relative to the top left corner of the input
+    image.
+    """
+    label: str
+    """The predicted label for the bounding box."""
+    score: float
+    """The associated score / probability."""

.cache/uv/archive-v0/4GZLBJvk803hh_euIbHls/huggingface_hub/inference/_generated/types/question_answering.py

@@ -0,0 +1,72 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class QuestionAnsweringInputData(BaseInferenceType):
+    """One (context, question) pair to answer"""
+
+    context: str
+    """The context to be used for answering the question"""
+    question: str
+    """The question to be answered"""
+
+
+@dataclass_with_extra
+class QuestionAnsweringParameters(BaseInferenceType):
+    """Additional inference parameters for Question Answering"""
+
+    align_to_words: bool | None = None
+    """Attempts to align the answer to real words. Improves quality on space separated
+    languages. Might hurt on non-space-separated languages (like Japanese or Chinese)
+    """
+    doc_stride: int | None = None
+    """If the context is too long to fit with the question for the model, it will be split in
+    several chunks with some overlap. This argument controls the size of that overlap.
+    """
+    handle_impossible_answer: bool | None = None
+    """Whether to accept impossible as an answer."""
+    max_answer_len: int | None = None
+    """The maximum length of predicted answers (e.g., only answers with a shorter length are
+    considered).
+    """
+    max_question_len: int | None = None
+    """The maximum length of the question after tokenization. It will be truncated if needed."""
+    max_seq_len: int | None = None
+    """The maximum length of the total sentence (context + question) in tokens of each chunk
+    passed to the model. The context will be split in several chunks (using docStride as
+    overlap) if needed.
+    """
+    top_k: int | None = None
+    """The number of answers to return (will be chosen by order of likelihood). Note that we
+    return less than topk answers if there are not enough options available within the
+    context.
+    """
+
+
+@dataclass_with_extra
+class QuestionAnsweringInput(BaseInferenceType):
+    """Inputs for Question Answering inference"""
+
+    inputs: QuestionAnsweringInputData
+    """One (context, question) pair to answer"""
+    parameters: QuestionAnsweringParameters | None = None
+    """Additional inference parameters for Question Answering"""
+
+
+@dataclass_with_extra
+class QuestionAnsweringOutputElement(BaseInferenceType):
+    """Outputs of inference for the Question Answering task"""
+
+    answer: str
+    """The answer to the question."""
+    end: int
+    """The character position in the input where the answer ends."""
+    score: float
+    """The probability associated to the answer."""
+    start: int
+    """The character position in the input where the answer begins."""

.cache/uv/archive-v0/4GZLBJvk803hh_euIbHls/huggingface_hub/inference/_generated/types/sentence_similarity.py

@@ -0,0 +1,27 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class SentenceSimilarityInputData(BaseInferenceType):
+    sentences: list[str]
+    """A list of strings which will be compared against the source_sentence."""
+    source_sentence: str
+    """The string that you wish to compare the other strings with. This can be a phrase,
+    sentence, or longer passage, depending on the model being used.
+    """
+
+
+@dataclass_with_extra
+class SentenceSimilarityInput(BaseInferenceType):
+    """Inputs for Sentence similarity inference"""
+
+    inputs: SentenceSimilarityInputData
+    parameters: dict[str, Any] | None = None
+    """Additional inference parameters for Sentence Similarity"""

.cache/uv/archive-v0/4GZLBJvk803hh_euIbHls/huggingface_hub/inference/_generated/types/summarization.py

@@ -0,0 +1,41 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any, Literal, Optional
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+SummarizationTruncationStrategy = Literal["do_not_truncate", "longest_first", "only_first", "only_second"]
+
+
+@dataclass_with_extra
+class SummarizationParameters(BaseInferenceType):
+    """Additional inference parameters for summarization."""
+
+    clean_up_tokenization_spaces: bool | None = None
+    """Whether to clean up the potential extra spaces in the text output."""
+    generate_parameters: dict[str, Any] | None = None
+    """Additional parametrization of the text generation algorithm."""
+    truncation: Optional["SummarizationTruncationStrategy"] = None
+    """The truncation strategy to use."""
+
+
+@dataclass_with_extra
+class SummarizationInput(BaseInferenceType):
+    """Inputs for Summarization inference"""
+
+    inputs: str
+    """The input text to summarize."""
+    parameters: SummarizationParameters | None = None
+    """Additional inference parameters for summarization."""
+
+
+@dataclass_with_extra
+class SummarizationOutput(BaseInferenceType):
+    """Outputs of inference for the Summarization task"""
+
+    summary_text: str
+    """The summarized text."""

.cache/uv/archive-v0/4GZLBJvk803hh_euIbHls/huggingface_hub/inference/_generated/types/table_question_answering.py

@@ -0,0 +1,62 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Literal, Optional
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class TableQuestionAnsweringInputData(BaseInferenceType):
+    """One (table, question) pair to answer"""
+
+    question: str
+    """The question to be answered about the table"""
+    table: dict[str, list[str]]
+    """The table to serve as context for the questions"""
+
+
+Padding = Literal["do_not_pad", "longest", "max_length"]
+
+
+@dataclass_with_extra
+class TableQuestionAnsweringParameters(BaseInferenceType):
+    """Additional inference parameters for Table Question Answering"""
+
+    padding: Optional["Padding"] = None
+    """Activates and controls padding."""
+    sequential: bool | None = None
+    """Whether to do inference sequentially or as a batch. Batching is faster, but models like
+    SQA require the inference to be done sequentially to extract relations within sequences,
+    given their conversational nature.
+    """
+    truncation: bool | None = None
+    """Activates and controls truncation."""
+
+
+@dataclass_with_extra
+class TableQuestionAnsweringInput(BaseInferenceType):
+    """Inputs for Table Question Answering inference"""
+
+    inputs: TableQuestionAnsweringInputData
+    """One (table, question) pair to answer"""
+    parameters: TableQuestionAnsweringParameters | None = None
+    """Additional inference parameters for Table Question Answering"""
+
+
+@dataclass_with_extra
+class TableQuestionAnsweringOutputElement(BaseInferenceType):
+    """Outputs of inference for the Table Question Answering task"""
+
+    answer: str
+    """The answer of the question given the table. If there is an aggregator, the answer will be
+    preceded by `AGGREGATOR >`.
+    """
+    cells: list[str]
+    """list of strings made up of the answer cell values."""
+    coordinates: list[list[int]]
+    """Coordinates of the cells of the answers."""
+    aggregator: str | None = None
+    """If the model has an aggregator, this returns the aggregator."""

.cache/uv/archive-v0/4GZLBJvk803hh_euIbHls/huggingface_hub/inference/_generated/types/text2text_generation.py

@@ -0,0 +1,42 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any, Literal, Optional
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+Text2TextGenerationTruncationStrategy = Literal["do_not_truncate", "longest_first", "only_first", "only_second"]
+
+
+@dataclass_with_extra
+class Text2TextGenerationParameters(BaseInferenceType):
+    """Additional inference parameters for Text2text Generation"""
+
+    clean_up_tokenization_spaces: bool | None = None
+    """Whether to clean up the potential extra spaces in the text output."""
+    generate_parameters: dict[str, Any] | None = None
+    """Additional parametrization of the text generation algorithm"""
+    truncation: Optional["Text2TextGenerationTruncationStrategy"] = None
+    """The truncation strategy to use"""
+
+
+@dataclass_with_extra
+class Text2TextGenerationInput(BaseInferenceType):
+    """Inputs for Text2text Generation inference"""
+
+    inputs: str
+    """The input text data"""
+    parameters: Text2TextGenerationParameters | None = None
+    """Additional inference parameters for Text2text Generation"""
+
+
+@dataclass_with_extra
+class Text2TextGenerationOutput(BaseInferenceType):
+    """Outputs of inference for the Text2text Generation task"""
+
+    generated_text: Any
+    text2_text_generation_output_generated_text: str | None = None
+    """The generated text."""

.cache/uv/archive-v0/4GZLBJvk803hh_euIbHls/huggingface_hub/inference/_generated/types/text_generation.py

@@ -0,0 +1,168 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any, Literal
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+TypeEnum = Literal["json", "regex", "json_schema"]
+
+
+@dataclass_with_extra
+class TextGenerationInputGrammarType(BaseInferenceType):
+    type: "TypeEnum"
+    value: Any
+    """A string that represents a [JSON Schema](https://json-schema.org/).
+    JSON Schema is a declarative language that allows to annotate JSON documents
+    with types and descriptions.
+    """
+
+
+@dataclass_with_extra
+class TextGenerationInputGenerateParameters(BaseInferenceType):
+    adapter_id: str | None = None
+    """Lora adapter id"""
+    best_of: int | None = None
+    """Generate best_of sequences and return the one if the highest token logprobs."""
+    decoder_input_details: bool | None = None
+    """Whether to return decoder input token logprobs and ids."""
+    details: bool | None = None
+    """Whether to return generation details."""
+    do_sample: bool | None = None
+    """Activate logits sampling."""
+    frequency_penalty: float | None = None
+    """The parameter for frequency penalty. 1.0 means no penalty
+    Penalize new tokens based on their existing frequency in the text so far,
+    decreasing the model's likelihood to repeat the same line verbatim.
+    """
+    grammar: TextGenerationInputGrammarType | None = None
+    max_new_tokens: int | None = None
+    """Maximum number of tokens to generate."""
+    repetition_penalty: float | None = None
+    """The parameter for repetition penalty. 1.0 means no penalty.
+    See [this paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
+    """
+    return_full_text: bool | None = None
+    """Whether to prepend the prompt to the generated text"""
+    seed: int | None = None
+    """Random sampling seed."""
+    stop: list[str] | None = None
+    """Stop generating tokens if a member of `stop` is generated."""
+    temperature: float | None = None
+    """The value used to module the logits distribution."""
+    top_k: int | None = None
+    """The number of highest probability vocabulary tokens to keep for top-k-filtering."""
+    top_n_tokens: int | None = None
+    """The number of highest probability vocabulary tokens to keep for top-n-filtering."""
+    top_p: float | None = None
+    """Top-p value for nucleus sampling."""
+    truncate: int | None = None
+    """Truncate inputs tokens to the given size."""
+    typical_p: float | None = None
+    """Typical Decoding mass
+    See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666)
+    for more information.
+    """
+    watermark: bool | None = None
+    """Watermarking with [A Watermark for Large Language
+    Models](https://arxiv.org/abs/2301.10226).
+    """
+
+
+@dataclass_with_extra
+class TextGenerationInput(BaseInferenceType):
+    """Text Generation Input.
+    Auto-generated from TGI specs.
+    For more details, check out
+    https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-tgi-import.ts.
+    """
+
+    inputs: str
+    parameters: TextGenerationInputGenerateParameters | None = None
+    stream: bool | None = None
+
+
+TextGenerationOutputFinishReason = Literal["length", "eos_token", "stop_sequence"]
+
+
+@dataclass_with_extra
+class TextGenerationOutputPrefillToken(BaseInferenceType):
+    id: int
+    logprob: float
+    text: str
+
+
+@dataclass_with_extra
+class TextGenerationOutputToken(BaseInferenceType):
+    id: int
+    logprob: float
+    special: bool
+    text: str
+
+
+@dataclass_with_extra
+class TextGenerationOutputBestOfSequence(BaseInferenceType):
+    finish_reason: "TextGenerationOutputFinishReason"
+    generated_text: str
+    generated_tokens: int
+    prefill: list[TextGenerationOutputPrefillToken]
+    tokens: list[TextGenerationOutputToken]
+    seed: int | None = None
+    top_tokens: list[list[TextGenerationOutputToken]] | None = None
+
+
+@dataclass_with_extra
+class TextGenerationOutputDetails(BaseInferenceType):
+    finish_reason: "TextGenerationOutputFinishReason"
+    generated_tokens: int
+    prefill: list[TextGenerationOutputPrefillToken]
+    tokens: list[TextGenerationOutputToken]
+    best_of_sequences: list[TextGenerationOutputBestOfSequence] | None = None
+    seed: int | None = None
+    top_tokens: list[list[TextGenerationOutputToken]] | None = None
+
+
+@dataclass_with_extra
+class TextGenerationOutput(BaseInferenceType):
+    """Text Generation Output.
+    Auto-generated from TGI specs.
+    For more details, check out
+    https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-tgi-import.ts.
+    """
+
+    generated_text: str
+    details: TextGenerationOutputDetails | None = None
+
+
+@dataclass_with_extra
+class TextGenerationStreamOutputStreamDetails(BaseInferenceType):
+    finish_reason: "TextGenerationOutputFinishReason"
+    generated_tokens: int
+    input_length: int
+    seed: int | None = None
+
+
+@dataclass_with_extra
+class TextGenerationStreamOutputToken(BaseInferenceType):
+    id: int
+    logprob: float
+    special: bool
+    text: str
+
+
+@dataclass_with_extra
+class TextGenerationStreamOutput(BaseInferenceType):
+    """Text Generation Stream Output.
+    Auto-generated from TGI specs.
+    For more details, check out
+    https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-tgi-import.ts.
+    """
+
+    index: int
+    token: TextGenerationStreamOutputToken
+    details: TextGenerationStreamOutputStreamDetails | None = None
+    generated_text: str | None = None
+    top_tokens: list[TextGenerationStreamOutputToken] | None = None

.cache/uv/archive-v0/4GZLBJvk803hh_euIbHls/huggingface_hub/inference/_generated/types/visual_question_answering.py

@@ -0,0 +1,49 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class VisualQuestionAnsweringInputData(BaseInferenceType):
+    """One (image, question) pair to answer"""
+
+    image: Any
+    """The image."""
+    question: str
+    """The question to answer based on the image."""
+
+
+@dataclass_with_extra
+class VisualQuestionAnsweringParameters(BaseInferenceType):
+    """Additional inference parameters for Visual Question Answering"""
+
+    top_k: int | None = None
+    """The number of answers to return (will be chosen by order of likelihood). Note that we
+    return less than topk answers if there are not enough options available within the
+    context.
+    """
+
+
+@dataclass_with_extra
+class VisualQuestionAnsweringInput(BaseInferenceType):
+    """Inputs for Visual Question Answering inference"""
+
+    inputs: VisualQuestionAnsweringInputData
+    """One (image, question) pair to answer"""
+    parameters: VisualQuestionAnsweringParameters | None = None
+    """Additional inference parameters for Visual Question Answering"""
+
+
+@dataclass_with_extra
+class VisualQuestionAnsweringOutputElement(BaseInferenceType):
+    """Outputs of inference for the Visual Question Answering task"""
+
+    score: float
+    """The associated score / probability"""
+    answer: str | None = None
+    """The answer to the question"""

.cache/uv/archive-v0/5itABfUpr52efEvNSNHEE/tqdm-4.67.3.dist-info/METADATA

@@ -0,0 +1,1595 @@
+Metadata-Version: 2.4
+Name: tqdm
+Version: 4.67.3
+Summary: Fast, Extensible Progress Meter
+Maintainer-email: tqdm developers <devs@tqdm.ml>
+License: MPL-2.0 AND MIT
+Project-URL: homepage, https://tqdm.github.io
+Project-URL: repository, https://github.com/tqdm/tqdm
+Project-URL: changelog, https://tqdm.github.io/releases
+Project-URL: wiki, https://github.com/tqdm/tqdm/wiki
+Keywords: progressbar,progressmeter,progress,bar,meter,rate,eta,console,terminal,time
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Environment :: Console
+Classifier: Environment :: MacOS X
+Classifier: Environment :: Other Environment
+Classifier: Environment :: Win32 (MS Windows)
+Classifier: Environment :: X11 Applications
+Classifier: Framework :: IPython
+Classifier: Framework :: Jupyter
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Education
+Classifier: Intended Audience :: End Users/Desktop
+Classifier: Intended Audience :: Other Audience
+Classifier: Intended Audience :: System Administrators
+Classifier: Operating System :: MacOS
+Classifier: Operating System :: MacOS :: MacOS X
+Classifier: Operating System :: Microsoft
+Classifier: Operating System :: Microsoft :: MS-DOS
+Classifier: Operating System :: Microsoft :: Windows
+Classifier: Operating System :: POSIX
+Classifier: Operating System :: POSIX :: BSD
+Classifier: Operating System :: POSIX :: BSD :: FreeBSD
+Classifier: Operating System :: POSIX :: Linux
+Classifier: Operating System :: POSIX :: SunOS/Solaris
+Classifier: Operating System :: Unix
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: 3.13
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Programming Language :: Python :: Implementation
+Classifier: Programming Language :: Python :: Implementation :: IronPython
+Classifier: Programming Language :: Python :: Implementation :: PyPy
+Classifier: Programming Language :: Unix Shell
+Classifier: Topic :: Desktop Environment
+Classifier: Topic :: Education :: Computer Aided Instruction (CAI)
+Classifier: Topic :: Education :: Testing
+Classifier: Topic :: Office/Business
+Classifier: Topic :: Other/Nonlisted Topic
+Classifier: Topic :: Software Development :: Build Tools
+Classifier: Topic :: Software Development :: Libraries
+Classifier: Topic :: Software Development :: Libraries :: Python Modules
+Classifier: Topic :: Software Development :: Pre-processors
+Classifier: Topic :: Software Development :: User Interfaces
+Classifier: Topic :: System :: Installation/Setup
+Classifier: Topic :: System :: Logging
+Classifier: Topic :: System :: Monitoring
+Classifier: Topic :: System :: Shells
+Classifier: Topic :: Terminals
+Classifier: Topic :: Utilities
+Requires-Python: >=3.7
+Description-Content-Type: text/x-rst
+License-File: LICENCE
+Requires-Dist: colorama; platform_system == "Windows"
+Requires-Dist: importlib_metadata; python_version < "3.8"
+Provides-Extra: dev
+Requires-Dist: pytest>=6; extra == "dev"
+Requires-Dist: pytest-cov; extra == "dev"
+Requires-Dist: pytest-timeout; extra == "dev"
+Requires-Dist: pytest-asyncio>=0.24; extra == "dev"
+Requires-Dist: nbval; extra == "dev"
+Provides-Extra: discord
+Requires-Dist: requests; extra == "discord"
+Provides-Extra: slack
+Requires-Dist: slack-sdk; extra == "slack"
+Provides-Extra: telegram
+Requires-Dist: requests; extra == "telegram"
+Provides-Extra: notebook
+Requires-Dist: ipywidgets>=6; extra == "notebook"
+Dynamic: license-file
+
+|Logo|
+
+tqdm
+====
+
+|Py-Versions| |Versions| |Conda-Forge-Status| |Docker| |Snapcraft|
+
+|Build-Status| |Coverage-Status| |Branch-Coverage-Status| |Codacy-Grade| |Libraries-Rank| |PyPI-Downloads|
+
+|LICENCE| |OpenHub-Status| |binder-demo| |awesome-python|
+
+``tqdm`` derives from the Arabic word *taqaddum* (تقدّم) which can mean "progress,"
+and is an abbreviation for "I love you so much" in Spanish (*te quiero demasiado*).
+
+Instantly make your loops show a smart progress meter - just wrap any
+iterable with ``tqdm(iterable)``, and you're done!
+
+.. code:: python
+
+    from tqdm import tqdm
+    for i in tqdm(range(10000)):
+        ...
+
+``76%|████████████████████████        | 7568/10000 [00:33<00:10, 229.00it/s]``
+
+``trange(N)`` can be also used as a convenient shortcut for
+``tqdm(range(N))``.
+
+|Screenshot|
+    |Video| |Slides| |Merch|
+
+It can also be executed as a module with pipes:
+
+.. code:: sh
+
+    $ seq 9999999 | tqdm --bytes | wc -l
+    75.2MB [00:00, 217MB/s]
+    9999999
+
+    $ tar -zcf - docs/ | tqdm --bytes --total `du -sb docs/ | cut -f1` \
+        > backup.tgz
+     32%|██████████▍                      | 8.89G/27.9G [00:42<01:31, 223MB/s]
+
+Overhead is low -- about 60ns per iteration (80ns with ``tqdm.gui``), and is
+unit tested against performance regression.
+By comparison, the well-established
+`ProgressBar <https://github.com/niltonvolpato/python-progressbar>`__ has
+an 800ns/iter overhead.
+
+In addition to its low overhead, ``tqdm`` uses smart algorithms to predict
+the remaining time and to skip unnecessary iteration displays, which allows
+for a negligible overhead in most cases.
+
+``tqdm`` works on any platform
+(Linux, Windows, Mac, FreeBSD, NetBSD, Solaris/SunOS),
+in any console or in a GUI, and is also friendly with IPython/Jupyter notebooks.
+
+``tqdm`` does not require any dependencies (not even ``curses``!), just
+Python and an environment supporting ``carriage return \r`` and
+``line feed \n`` control characters.
+
+------------------------------------------
+
+.. contents:: Table of contents
+   :backlinks: top
+   :local:
+
+
+Installation
+------------
+
+Latest PyPI stable release
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+|Versions| |PyPI-Downloads| |Libraries-Dependents|
+
+.. code:: sh
+
+    pip install tqdm
+
+Latest development release on GitHub
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+|GitHub-Status| |GitHub-Stars| |GitHub-Commits| |GitHub-Forks| |GitHub-Updated|
+
+Pull and install pre-release ``devel`` branch:
+
+.. code:: sh
+
+    pip install "git+https://github.com/tqdm/tqdm.git@devel#egg=tqdm"
+
+Latest Conda release
+~~~~~~~~~~~~~~~~~~~~
+
+|Conda-Forge-Status|
+
+.. code:: sh
+
+    conda install -c conda-forge tqdm
+
+Latest Snapcraft release
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+|Snapcraft|
+
+There are 3 channels to choose from:
+
+.. code:: sh
+
+    snap install tqdm  # implies --stable, i.e. latest tagged release
+    snap install tqdm  --candidate  # master branch
+    snap install tqdm  --edge  # devel branch
+
+Note that ``snap`` binaries are purely for CLI use (not ``import``-able), and
+automatically set up ``bash`` tab-completion.
+
+Latest Docker release
+~~~~~~~~~~~~~~~~~~~~~
+
+|Docker|
+
+.. code:: sh
+
+    docker pull tqdm/tqdm
+    docker run -i --rm tqdm/tqdm --help
+
+Other
+~~~~~
+
+There are other (unofficial) places where ``tqdm`` may be downloaded, particularly for CLI use:
+
+|Repology|
+
+.. |Repology| image:: https://repology.org/badge/tiny-repos/python:tqdm.svg
+   :target: https://repology.org/project/python:tqdm/versions
+
+Changelog
+---------
+
+The list of all changes is available either on GitHub's Releases:
+|GitHub-Status|, on the
+`wiki <https://github.com/tqdm/tqdm/wiki/Releases>`__, or on the
+`website <https://tqdm.github.io/releases>`__.
+
+
+Usage
+-----
+
+``tqdm`` is very versatile and can be used in a number of ways.
+The three main ones are given below.
+
+Iterable-based
+~~~~~~~~~~~~~~
+
+Wrap ``tqdm()`` around any iterable:
+
+.. code:: python
+
+    from tqdm import tqdm
+    from time import sleep
+
+    text = ""
+    for char in tqdm(["a", "b", "c", "d"]):
+        sleep(0.25)
+        text = text + char
+
+``trange(i)`` is a special optimised instance of ``tqdm(range(i))``:
+
+.. code:: python
+
+    from tqdm import trange
+
+    for i in trange(100):
+        sleep(0.01)
+
+Instantiation outside of the loop allows for manual control over ``tqdm()``:
+
+.. code:: python
+
+    pbar = tqdm(["a", "b", "c", "d"])
+    for char in pbar:
+        sleep(0.25)
+        pbar.set_description("Processing %s" % char)
+
+Manual
+~~~~~~
+
+Manual control of ``tqdm()`` updates using a ``with`` statement:
+
+.. code:: python
+
+    with tqdm(total=100) as pbar:
+        for i in range(10):
+            sleep(0.1)
+            pbar.update(10)
+
+If the optional variable ``total`` (or an iterable with ``len()``) is
+provided, predictive stats are displayed.
+
+``with`` is also optional (you can just assign ``tqdm()`` to a variable,
+but in this case don't forget to ``del`` or ``close()`` at the end:
+
+.. code:: python
+
+    pbar = tqdm(total=100)
+    for i in range(10):
+        sleep(0.1)
+        pbar.update(10)
+    pbar.close()
+
+Module
+~~~~~~
+
+Perhaps the most wonderful use of ``tqdm`` is in a script or on the command
+line. Simply inserting ``tqdm`` (or ``python -m tqdm``) between pipes will pass
+through all ``stdin`` to ``stdout`` while printing progress to ``stderr``.
+
+The example below demonstrate counting the number of lines in all Python files
+in the current directory, with timing information included.
+
+.. code:: sh
+
+    $ time find . -name '*.py' -type f -exec cat \{} \; | wc -l
+    857365
+
+    real    0m3.458s
+    user    0m0.274s
+    sys     0m3.325s
+
+    $ time find . -name '*.py' -type f -exec cat \{} \; | tqdm | wc -l
+    857366it [00:03, 246471.31it/s]
+    857365
+
+    real    0m3.585s
+    user    0m0.862s
+    sys     0m3.358s
+
+Note that the usual arguments for ``tqdm`` can also be specified.
+
+.. code:: sh
+
+    $ find . -name '*.py' -type f -exec cat \{} \; |
+        tqdm --unit loc --unit_scale --total 857366 >> /dev/null
+    100%|█████████████████████████████████| 857K/857K [00:04<00:00, 246Kloc/s]
+
+Backing up a large directory?
+
+.. code:: sh
+
+    $ tar -zcf - docs/ | tqdm --bytes --total `du -sb docs/ | cut -f1` \
+      > backup.tgz
+     44%|██████████████▊                   | 153M/352M [00:14<00:18, 11.0MB/s]
+
+This can be beautified further:
+
+.. code:: sh
+
+    $ BYTES=$(du -sb docs/ | cut -f1)
+    $ tar -cf - docs/ \
+      | tqdm --bytes --total "$BYTES" --desc Processing | gzip \
+      | tqdm --bytes --total "$BYTES" --desc Compressed --position 1 \
+      > ~/backup.tgz
+    Processing: 100%|██████████████████████| 352M/352M [00:14<00:00, 30.2MB/s]
+    Compressed:  42%|█████████▎            | 148M/352M [00:14<00:19, 10.9MB/s]
+
+Or done on a file level using 7-zip:
+
+.. code:: sh
+
+    $ 7z a -bd -r backup.7z docs/ | grep Compressing \
+      | tqdm --total $(find docs/ -type f | wc -l) --unit files \
+      | grep -v Compressing
+    100%|██████████████████████████▉| 15327/15327 [01:00<00:00, 712.96files/s]
+
+Pre-existing CLI programs already outputting basic progress information will
+benefit from ``tqdm``'s ``--update`` and ``--update_to`` flags:
+
+.. code:: sh
+
+    $ seq 3 0.1 5 | tqdm --total 5 --update_to --null
+    100%|████████████████████████████████████| 5.0/5 [00:00<00:00, 9673.21it/s]
+    $ seq 10 | tqdm --update --null  # 1 + 2 + ... + 10 = 55 iterations
+    55it [00:00, 90006.52it/s]
+
+FAQ and Known Issues
+--------------------
+
+|GitHub-Issues|
+
+The most common issues relate to excessive output on multiple lines, instead
+of a neat one-line progress bar.
+
+- Consoles in general: require support for carriage return (``CR``, ``\r``).
+
+  * Some cloud logging consoles which don't support ``\r`` properly
+    (`cloudwatch <https://github.com/tqdm/tqdm/issues/966>`__,
+    `K8s <https://github.com/tqdm/tqdm/issues/1319>`__) may benefit from
+    ``export TQDM_POSITION=-1``.
+
+- Nested progress bars:
+
+  * Consoles in general: require support for moving cursors up to the
+    previous line. For example,
+    `IDLE <https://github.com/tqdm/tqdm/issues/191#issuecomment-230168030>`__,
+    `ConEmu <https://github.com/tqdm/tqdm/issues/254>`__ and
+    `PyCharm <https://github.com/tqdm/tqdm/issues/203>`__ (also
+    `here <https://github.com/tqdm/tqdm/issues/208>`__,
+    `here <https://github.com/tqdm/tqdm/issues/307>`__, and
+    `here <https://github.com/tqdm/tqdm/issues/454#issuecomment-335416815>`__)
+    lack full support.
+  * Windows: additionally may require the Python module ``colorama``
+    to ensure nested bars stay within their respective lines.
+
+- Unicode:
+
+  * Environments which report that they support unicode will have solid smooth
+    progressbars. The fallback is an ``ascii``-only bar.
+  * Windows consoles often only partially support unicode and thus
+    `often require explicit ascii=True <https://github.com/tqdm/tqdm/issues/454#issuecomment-335416815>`__
+    (also `here <https://github.com/tqdm/tqdm/issues/499>`__). This is due to
+    either normal-width unicode characters being incorrectly displayed as
+    "wide", or some unicode characters not rendering.
+
+- Wrapping generators:
+
+  * Generator wrapper functions tend to hide the length of iterables.
+    ``tqdm`` does not.
+  * Replace ``tqdm(enumerate(...))`` with ``enumerate(tqdm(...))`` or
+    ``tqdm(enumerate(x), total=len(x), ...)``.
+    The same applies to ``numpy.ndenumerate``.
+  * Replace ``tqdm(zip(a, b))`` with ``zip(tqdm(a), b)`` or even
+    ``zip(tqdm(a), tqdm(b))``.
+  * The same applies to ``itertools``.
+  * Some useful convenience functions can be found under ``tqdm.contrib``.
+
+- `No intermediate output in docker-compose <https://github.com/tqdm/tqdm/issues/771>`__:
+  use ``docker-compose run`` instead of ``docker-compose up`` and ``tty: true``.
+
+- Overriding defaults via environment variables:
+  e.g. in CI/cloud jobs, ``export TQDM_MININTERVAL=5`` to avoid log spam.
+  This override logic is handled by the ``tqdm.utils.envwrap`` decorator
+  (useful independent of ``tqdm``).
+
+If you come across any other difficulties, browse and file |GitHub-Issues|.
+
+Documentation
+-------------
+
+|Py-Versions| |README-Hits| (Since 19 May 2016)
+
+.. code:: python
+
+    class tqdm():
+      """
+      Decorate an iterable object, returning an iterator which acts exactly
+      like the original iterable, but prints a dynamically updating
+      progressbar every time a value is requested.
+      """
+
+      @envwrap("TQDM_")  # override defaults via env vars
+      def __init__(self, iterable=None, desc=None, total=None, leave=True,
+                   file=None, ncols=None, mininterval=0.1,
+                   maxinterval=10.0, miniters=None, ascii=None, disable=False,
+                   unit='it', unit_scale=False, dynamic_ncols=False,
+                   smoothing=0.3, bar_format=None, initial=0, position=None,
+                   postfix=None, unit_divisor=1000, write_bytes=False,
+                   lock_args=None, nrows=None, colour=None, delay=0):
+
+Parameters
+~~~~~~~~~~
+
+* iterable  : iterable, optional  
+    Iterable to decorate with a progressbar.
+    Leave blank to manually manage the updates.
+* desc  : str, optional  
+    Prefix for the progressbar.
+* total  : int or float, optional  
+    The number of expected iterations. If unspecified,
+    len(iterable) is used if possible. If float("inf") or as a last
+    resort, only basic progress statistics are displayed
+    (no ETA, no progressbar).
+    If ``gui`` is True and this parameter needs subsequent updating,
+    specify an initial arbitrary large positive number,
+    e.g. 9e9.
+* leave  : bool, optional  
+    If [default: True], keeps all traces of the progressbar
+    upon termination of iteration.
+    If ``None``, will leave only if ``position`` is ``0``.
+* file  : ``io.TextIOWrapper`` or ``io.StringIO``, optional  
+    Specifies where to output the progress messages
+    (default: sys.stderr). Uses ``file.write(str)`` and ``file.flush()``
+    methods.  For encoding, see ``write_bytes``.
+* ncols  : int, optional  
+    The width of the entire output message. If specified,
+    dynamically resizes the progressbar to stay within this bound.
+    If unspecified, attempts to use environment width. The
+    fallback is a meter width of 10 and no limit for the counter and
+    statistics. If 0, will not print any meter (only stats).
+* mininterval  : float, optional  
+    Minimum progress display update interval [default: 0.1] seconds.
+* maxinterval  : float, optional  
+    Maximum progress display update interval [default: 10] seconds.
+    Automatically adjusts ``miniters`` to correspond to ``mininterval``
+    after long display update lag. Only works if ``dynamic_miniters``
+    or monitor thread is enabled.
+* miniters  : int or float, optional  
+    Minimum progress display update interval, in iterations.
+    If 0 and ``dynamic_miniters``, will automatically adjust to equal
+    ``mininterval`` (more CPU efficient, good for tight loops).
+    If > 0, will skip display of specified number of iterations.
+    Tweak this and ``mininterval`` to get very efficient loops.
+    If your progress is erratic with both fast and slow iterations
+    (network, skipping items, etc) you should set miniters=1.
+* ascii  : bool or str, optional  
+    If unspecified or False, use unicode (smooth blocks) to fill
+    the meter. The fallback is to use ASCII characters " 123456789#".
+* disable  : bool, optional  
+    Whether to disable the entire progressbar wrapper
+    [default: False]. If set to None, disable on non-TTY.
+* unit  : str, optional  
+    String that will be used to define the unit of each iteration
+    [default: it].
+* unit_scale  : bool or int or float, optional  
+    If 1 or True, the number of iterations will be reduced/scaled
+    automatically and a metric prefix following the
+    International System of Units standard will be added
+    (kilo, mega, etc.) [default: False]. If any other non-zero
+    number, will scale ``total`` and ``n``.
+* dynamic_ncols  : bool, optional  
+    If set, constantly alters ``ncols`` and ``nrows`` to the
+    environment (allowing for window resizes) [default: False].
+* smoothing  : float, optional  
+    Exponential moving average smoothing factor for speed estimates
+    (ignored in GUI mode). Ranges from 0 (average speed) to 1
+    (current/instantaneous speed) [default: 0.3].
+* bar_format  : str, optional  
+    Specify a custom bar string formatting. May impact performance.
+    [default: '{l_bar}{bar}{r_bar}'], where
+    l_bar='{desc}: {percentage:3.0f}%|' and
+    r_bar='| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, '
+    '{rate_fmt}{postfix}]'
+    Possible vars: l_bar, bar, r_bar, n, n_fmt, total, total_fmt,
+    percentage, elapsed, elapsed_s, ncols, nrows, desc, unit,
+    rate, rate_fmt, rate_noinv, rate_noinv_fmt,
+    rate_inv, rate_inv_fmt, postfix, unit_divisor,
+    remaining, remaining_s, eta.
+    Note that a trailing ": " is automatically removed after {desc}
+    if the latter is empty.
+* initial  : int or float, optional  
+    The initial counter value. Useful when restarting a progress
+    bar [default: 0]. If using float, consider specifying ``{n:.3f}``
+    or similar in ``bar_format``, or specifying ``unit_scale``.
+* position  : int, optional  
+    Specify the line offset to print this bar (starting from 0)
+    Automatic if unspecified.
+    Useful to manage multiple bars at once (eg, from threads).
+* postfix  : dict or ``*``, optional  
+    Specify additional stats to display at the end of the bar.
+    Calls ``set_postfix(**postfix)`` if possible (dict).
+* unit_divisor  : float, optional  
+    [default: 1000], ignored unless ``unit_scale`` is True.
+* write_bytes  : bool, optional  
+    Whether to write bytes. If (default: False) will write unicode.
+* lock_args  : tuple, optional  
+    Passed to ``refresh`` for intermediate output
+    (initialisation, iterating, and updating).
+* nrows  : int, optional  
+    The screen height. If specified, hides nested bars outside this
+    bound. If unspecified, attempts to use environment height.
+    The fallback is 20.
+* colour  : str, optional  
+    Bar colour (e.g. 'green', '#00ff00').
+* delay  : float, optional  
+    Don't display until [default: 0] seconds have elapsed.
+
+Extra CLI Options
+~~~~~~~~~~~~~~~~~
+
+* delim  : chr, optional  
+    Delimiting character [default: '\n']. Use '\0' for null.
+    N.B.: on Windows systems, Python converts '\n' to '\r\n'.
+* buf_size  : int, optional  
+    String buffer size in bytes [default: 256]
+    used when ``delim`` is specified.
+* bytes  : bool, optional  
+    If true, will count bytes, ignore ``delim``, and default
+    ``unit_scale`` to True, ``unit_divisor`` to 1024, and ``unit`` to 'B'.
+* tee  : bool, optional  
+    If true, passes ``stdin`` to both ``stderr`` and ``stdout``.
+* update  : bool, optional  
+    If true, will treat input as newly elapsed iterations,
+    i.e. numbers to pass to ``update()``. Note that this is slow
+    (~2e5 it/s) since every input must be decoded as a number.
+* update_to  : bool, optional  
+    If true, will treat input as total elapsed iterations,
+    i.e. numbers to assign to ``self.n``. Note that this is slow
+    (~2e5 it/s) since every input must be decoded as a number.
+* null  : bool, optional  
+    If true, will discard input (no stdout).
+* manpath  : str, optional  
+    Directory in which to install tqdm man pages.
+* comppath  : str, optional  
+    Directory in which to place tqdm completion.
+* log  : str, optional  
+    CRITICAL|FATAL|ERROR|WARN(ING)|[default: 'INFO']|DEBUG|NOTSET.
+
+Returns
+~~~~~~~
+
+* out  : decorated iterator.  
+
+.. code:: python
+
+    class tqdm():
+      def update(self, n=1):
+          """
+          Manually update the progress bar, useful for streams
+          such as reading files.
+          E.g.:
+          >>> t = tqdm(total=filesize) # Initialise
+          >>> for current_buffer in stream:
+          ...    ...
+          ...    t.update(len(current_buffer))
+          >>> t.close()
+          The last line is highly recommended, but possibly not necessary if
+          ``t.update()`` will be called in such a way that ``filesize`` will be
+          exactly reached and printed.
+
+          Parameters
+          ----------
+          n  : int or float, optional
+              Increment to add to the internal counter of iterations
+              [default: 1]. If using float, consider specifying ``{n:.3f}``
+              or similar in ``bar_format``, or specifying ``unit_scale``.
+
+          Returns
+          -------
+          out  : bool or None
+              True if a ``display()`` was triggered.
+          """
+
+      def close(self):
+          """Cleanup and (if leave=False) close the progressbar."""
+
+      def clear(self, nomove=False):
+          """Clear current bar display."""
+
+      def refresh(self):
+          """
+          Force refresh the display of this bar.
+
+          Parameters
+          ----------
+          nolock  : bool, optional
+              If ``True``, does not lock.
+              If [default: ``False``]: calls ``acquire()`` on internal lock.
+          lock_args  : tuple, optional
+              Passed to internal lock's ``acquire()``.
+              If specified, will only ``display()`` if ``acquire()`` returns ``True``.
+          """
+
+      def unpause(self):
+          """Restart tqdm timer from last print time."""
+
+      def reset(self, total=None):
+          """
+          Resets to 0 iterations for repeated use.
+
+          Consider combining with ``leave=True``.
+
+          Parameters
+          ----------
+          total  : int or float, optional. Total to use for the new bar.
+          """
+
+      def set_description(self, desc=None, refresh=True):
+          """
+          Set/modify description of the progress bar.
+
+          Parameters
+          ----------
+          desc  : str, optional
+          refresh  : bool, optional
+              Forces refresh [default: True].
+          """
+
+      def set_postfix(self, ordered_dict=None, refresh=True, **tqdm_kwargs):
+          """
+          Set/modify postfix (additional stats)
+          with automatic formatting based on datatype.
+
+          Parameters
+          ----------
+          ordered_dict  : dict or OrderedDict, optional
+          refresh  : bool, optional
+              Forces refresh [default: True].
+          kwargs  : dict, optional
+          """
+
+      @classmethod
+      def write(cls, s, file=sys.stdout, end="\n"):
+          """Print a message via tqdm (without overlap with bars)."""
+
+      @property
+      def format_dict(self):
+          """Public API for read-only member access."""
+
+      def display(self, msg=None, pos=None):
+          """
+          Use ``self.sp`` to display ``msg`` in the specified ``pos``.
+
+          Consider overloading this function when inheriting to use e.g.:
+          ``self.some_frontend(**self.format_dict)`` instead of ``self.sp``.
+
+          Parameters
+          ----------
+          msg  : str, optional. What to display (default: ``repr(self)``).
+          pos  : int, optional. Position to ``moveto``
+            (default: ``abs(self.pos)``).
+          """
+
+      @classmethod
+      @contextmanager
+      def wrapattr(cls, stream, method, total=None, bytes=True, **tqdm_kwargs):
+          """
+          stream  : file-like object.
+          method  : str, "read" or "write". The result of ``read()`` and
+              the first argument of ``write()`` should have a ``len()``.
+
+          >>> with tqdm.wrapattr(file_obj, "read", total=file_obj.size) as fobj:
+          ...     while True:
+          ...         chunk = fobj.read(chunk_size)
+          ...         if not chunk:
+          ...             break
+          """
+
+      @classmethod
+      def pandas(cls, *targs, **tqdm_kwargs):
+          """Registers the current `tqdm` class with `pandas`."""
+
+    def trange(*args, **tqdm_kwargs):
+        """Shortcut for `tqdm(range(*args), **tqdm_kwargs)`."""
+
+Convenience Functions
+~~~~~~~~~~~~~~~~~~~~~
+
+.. code:: python
+
+    def tqdm.contrib.tenumerate(iterable, start=0, total=None,
+                                tqdm_class=tqdm.auto.tqdm, **tqdm_kwargs):
+        """Equivalent of `numpy.ndenumerate` or builtin `enumerate`."""
+
+    def tqdm.contrib.tzip(iter1, *iter2plus, **tqdm_kwargs):
+        """Equivalent of builtin `zip`."""
+
+    def tqdm.contrib.tmap(function, *sequences, **tqdm_kwargs):
+        """Equivalent of builtin `map`."""
+
+Submodules
+~~~~~~~~~~
+
+.. code:: python
+
+    class tqdm.notebook.tqdm(tqdm.tqdm):
+        """IPython/Jupyter Notebook widget."""
+
+    class tqdm.auto.tqdm(tqdm.tqdm):
+        """Automatically chooses beween `tqdm.notebook` and `tqdm.tqdm`."""
+
+    class tqdm.asyncio.tqdm(tqdm.tqdm):
+      """Asynchronous version."""
+      @classmethod
+      def as_completed(cls, fs, *, loop=None, timeout=None, total=None,
+                       **tqdm_kwargs):
+          """Wrapper for `asyncio.as_completed`."""
+
+    class tqdm.gui.tqdm(tqdm.tqdm):
+        """Matplotlib GUI version."""
+
+    class tqdm.tk.tqdm(tqdm.tqdm):
+        """Tkinter GUI version."""
+
+    class tqdm.rich.tqdm(tqdm.tqdm):
+        """`rich.progress` version."""
+
+    class tqdm.keras.TqdmCallback(keras.callbacks.Callback):
+        """Keras callback for epoch and batch progress."""
+
+    class tqdm.dask.TqdmCallback(dask.callbacks.Callback):
+        """Dask callback for task progress."""
+
+
+``contrib``
++++++++++++
+
+The ``tqdm.contrib`` package also contains experimental modules:
+
+- ``tqdm.contrib.itertools``: Thin wrappers around ``itertools``
+- ``tqdm.contrib.concurrent``: Thin wrappers around ``concurrent.futures``
+- ``tqdm.contrib.slack``: Posts to `Slack <https://slack.com>`__ bots
+- ``tqdm.contrib.discord``: Posts to `Discord <https://discord.com>`__ bots
+- ``tqdm.contrib.telegram``: Posts to `Telegram <https://telegram.org>`__ bots
+- ``tqdm.contrib.bells``: Automagically enables all optional features
+
+  * ``auto``, ``pandas``, ``slack``, ``discord``, ``telegram``
+
+Examples and Advanced Usage
+---------------------------
+
+- See the `examples <https://github.com/tqdm/tqdm/tree/master/examples>`__
+  folder;
+- import the module and run ``help()``;
+- consult the `wiki <https://github.com/tqdm/tqdm/wiki>`__;
+
+  * this has an
+    `excellent article <https://github.com/tqdm/tqdm/wiki/How-to-make-a-great-Progress-Bar>`__
+    on how to make a **great** progressbar;
+
+- check out the `slides from PyData London <https://tqdm.github.io/PyData2019/slides.html>`__, or
+- run the |binder-demo|.
+
+Description and additional stats
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Custom information can be displayed and updated dynamically on ``tqdm`` bars
+with the ``desc`` and ``postfix`` arguments:
+
+.. code:: python
+
+    from tqdm import tqdm, trange
+    from random import random, randint
+    from time import sleep
+
+    with trange(10) as t:
+        for i in t:
+            # Description will be displayed on the left
+            t.set_description('GEN %i' % i)
+            # Postfix will be displayed on the right,
+            # formatted automatically based on argument's datatype
+            t.set_postfix(loss=random(), gen=randint(1,999), str='h',
+                          lst=[1, 2])
+            sleep(0.1)
+
+    with tqdm(total=10, bar_format="{postfix[0]} {postfix[1][value]:>8.2g}",
+              postfix=["Batch", {"value": 0}]) as t:
+        for i in range(10):
+            sleep(0.1)
+            t.postfix[1]["value"] = i / 2
+            t.update()
+
+Points to remember when using ``{postfix[...]}`` in the ``bar_format`` string:
+
+- ``postfix`` also needs to be passed as an initial argument in a compatible
+  format, and
+- ``postfix`` will be auto-converted to a string if it is a ``dict``-like
+  object. To prevent this behaviour, insert an extra item into the dictionary
+  where the key is not a string.
+
+Additional ``bar_format`` parameters may also be defined by overriding
+``format_dict``, and the bar itself may be modified using ``ascii``:
+
+.. code:: python
+
+    from tqdm import tqdm
+    class TqdmExtraFormat(tqdm):
+        """Provides a `total_time` format parameter"""
+        @property
+        def format_dict(self):
+            d = super().format_dict
+            total_time = d["elapsed"] * (d["total"] or 0) / max(d["n"], 1)
+            d.update(total_time=self.format_interval(total_time) + " in total")
+            return d
+
+    for i in TqdmExtraFormat(
+          range(9), ascii=" .oO0",
+          bar_format="{total_time}: {percentage:.0f}%|{bar}{r_bar}"):
+        if i == 4:
+            break
+
+.. code::
+
+    00:00 in total: 44%|0000.     | 4/9 [00:00<00:00, 962.93it/s]
+
+Note that ``{bar}`` also supports a format specifier ``[width][type]``.
+
+- ``width``
+
+  * unspecified (default): automatic to fill ``ncols``
+  * ``int >= 0``: fixed width overriding ``ncols`` logic
+  * ``int < 0``: subtract from the automatic default
+
+- ``type``
+
+  * ``a``: ascii (``ascii=True`` override)
+  * ``u``: unicode (``ascii=False`` override)
+  * ``b``: blank (``ascii="  "`` override)
+
+This means a fixed bar with right-justified text may be created by using:
+``bar_format="{l_bar}{bar:10}|{bar:-10b}right-justified"``
+
+Nested progress bars
+~~~~~~~~~~~~~~~~~~~~
+
+``tqdm`` supports nested progress bars. Here's an example:
+
+.. code:: python
+
+    from tqdm.auto import trange
+    from time import sleep
+
+    for i in trange(4, desc='1st loop'):
+        for j in trange(5, desc='2nd loop'):
+            for k in trange(50, desc='3rd loop', leave=False):
+                sleep(0.01)
+
+For manual control over positioning (e.g. for multi-processing use),
+you may specify ``position=n`` where ``n=0`` for the outermost bar,
+``n=1`` for the next, and so on.
+However, it's best to check if ``tqdm`` can work without manual ``position``
+first.
+
+.. code:: python
+
+    from time import sleep
+    from tqdm import trange, tqdm
+    from multiprocessing import Pool, RLock, freeze_support
+
+    L = list(range(9))
+
+    def progresser(n):
+        interval = 0.001 / (n + 2)
+        total = 5000
+        text = f"#{n}, est. {interval * total:<04.2}s"
+        for _ in trange(total, desc=text, position=n):
+            sleep(interval)
+
+    if __name__ == '__main__':
+        freeze_support()  # for Windows support
+        tqdm.set_lock(RLock())  # for managing output contention
+        p = Pool(initializer=tqdm.set_lock, initargs=(tqdm.get_lock(),))
+        p.map(progresser, L)
+
+Note that in Python 3, ``tqdm.write`` is thread-safe:
+
+.. code:: python
+
+    from time import sleep
+    from tqdm import tqdm, trange
+    from concurrent.futures import ThreadPoolExecutor
+
+    L = list(range(9))
+
+    def progresser(n):
+        interval = 0.001 / (n + 2)
+        total = 5000
+        text = f"#{n}, est. {interval * total:<04.2}s"
+        for _ in trange(total, desc=text):
+            sleep(interval)
+        if n == 6:
+            tqdm.write("n == 6 completed.")
+            tqdm.write("`tqdm.write()` is thread-safe in py3!")
+
+    if __name__ == '__main__':
+        with ThreadPoolExecutor() as p:
+            p.map(progresser, L)
+
+Hooks and callbacks
+~~~~~~~~~~~~~~~~~~~
+
+``tqdm`` can easily support callbacks/hooks and manual updates.
+Here's an example with ``urllib``:
+
+**``urllib.urlretrieve`` documentation**
+
+    | [...]
+    | If present, the hook function will be called once
+    | on establishment of the network connection and once after each block read
+    | thereafter. The hook will be passed three arguments; a count of blocks
+    | transferred so far, a block size in bytes, and the total size of the file.
+    | [...]
+
+.. code:: python
+
+    import urllib, os
+    from tqdm import tqdm
+    urllib = getattr(urllib, 'request', urllib)
+
+    class TqdmUpTo(tqdm):
+        """Provides `update_to(n)` which uses `tqdm.update(delta_n)`."""
+        def update_to(self, b=1, bsize=1, tsize=None):
+            """
+            b  : int, optional
+                Number of blocks transferred so far [default: 1].
+            bsize  : int, optional
+                Size of each block (in tqdm units) [default: 1].
+            tsize  : int, optional
+                Total size (in tqdm units). If [default: None] remains unchanged.
+            """
+            if tsize is not None:
+                self.total = tsize
+            return self.update(b * bsize - self.n)  # also sets self.n = b * bsize
+
+    eg_link = "https://caspersci.uk.to/matryoshka.zip"
+    with TqdmUpTo(unit='B', unit_scale=True, unit_divisor=1024, miniters=1,
+                  desc=eg_link.split('/')[-1]) as t:  # all optional kwargs
+        urllib.urlretrieve(eg_link, filename=os.devnull,
+                           reporthook=t.update_to, data=None)
+        t.total = t.n
+
+Inspired by `twine#242 <https://github.com/pypa/twine/pull/242>`__.
+Functional alternative in
+`examples/tqdm_wget.py <https://github.com/tqdm/tqdm/blob/master/examples/tqdm_wget.py>`__.
+
+It is recommend to use ``miniters=1`` whenever there is potentially
+large differences in iteration speed (e.g. downloading a file over
+a patchy connection).
+
+**Wrapping read/write methods**
+
+To measure throughput through a file-like object's ``read`` or ``write``
+methods, use ``CallbackIOWrapper``:
+
+.. code:: python
+
+    from tqdm.auto import tqdm
+    from tqdm.utils import CallbackIOWrapper
+
+    with tqdm(total=file_obj.size,
+              unit='B', unit_scale=True, unit_divisor=1024) as t:
+        fobj = CallbackIOWrapper(t.update, file_obj, "read")
+        while True:
+            chunk = fobj.read(chunk_size)
+            if not chunk:
+                break
+        t.reset()
+        # ... continue to use `t` for something else
+
+Alternatively, use the even simpler ``wrapattr`` convenience function,
+which would condense both the ``urllib`` and ``CallbackIOWrapper`` examples
+down to:
+
+.. code:: python
+
+    import urllib, os
+    from tqdm import tqdm
+
+    eg_link = "https://caspersci.uk.to/matryoshka.zip"
+    response = getattr(urllib, 'request', urllib).urlopen(eg_link)
+    with tqdm.wrapattr(open(os.devnull, "wb"), "write",
+                       miniters=1, desc=eg_link.split('/')[-1],
+                       total=getattr(response, 'length', None)) as fout:
+        for chunk in response:
+            fout.write(chunk)
+
+The ``requests`` equivalent is nearly identical:
+
+.. code:: python
+
+    import requests, os
+    from tqdm import tqdm
+
+    eg_link = "https://caspersci.uk.to/matryoshka.zip"
+    response = requests.get(eg_link, stream=True)
+    with tqdm.wrapattr(open(os.devnull, "wb"), "write",
+                       miniters=1, desc=eg_link.split('/')[-1],
+                       total=int(response.headers.get('content-length', 0))) as fout:
+        for chunk in response.iter_content(chunk_size=4096):
+            fout.write(chunk)
+
+**Custom callback**
+
+``tqdm`` is known for intelligently skipping unnecessary displays. To make a
+custom callback take advantage of this, simply use the return value of
+``update()``. This is set to ``True`` if a ``display()`` was triggered.
+
+.. code:: python
+
+    from tqdm.auto import tqdm as std_tqdm
+
+    def external_callback(*args, **kwargs):
+        ...
+
+    class TqdmExt(std_tqdm):
+        def update(self, n=1):
+            displayed = super().update(n)
+            if displayed:
+                external_callback(**self.format_dict)
+            return displayed
+
+``asyncio``
+~~~~~~~~~~~
+
+Note that ``break`` isn't currently caught by asynchronous iterators.
+This means that ``tqdm`` cannot clean up after itself in this case:
+
+.. code:: python
+
+    from tqdm.asyncio import tqdm
+
+    async for i in tqdm(range(9)):
+        if i == 2:
+            break
+
+Instead, either call ``pbar.close()`` manually or use the context manager syntax:
+
+.. code:: python
+
+    from tqdm.asyncio import tqdm
+
+    with tqdm(range(9)) as pbar:
+        async for i in pbar:
+            if i == 2:
+                break
+
+Pandas Integration
+~~~~~~~~~~~~~~~~~~
+
+Due to popular demand we've added support for ``pandas`` -- here's an example
+for ``DataFrame.progress_apply`` and ``DataFrameGroupBy.progress_apply``:
+
+.. code:: python
+
+    import pandas as pd
+    import numpy as np
+    from tqdm import tqdm
+
+    df = pd.DataFrame(np.random.randint(0, 100, (100000, 6)))
+
+    # Register `pandas.progress_apply` and `pandas.Series.map_apply` with `tqdm`
+    # (can use `tqdm.gui.tqdm`, `tqdm.notebook.tqdm`, optional kwargs, etc.)
+    tqdm.pandas(desc="my bar!")
+
+    # Now you can use `progress_apply` instead of `apply`
+    # and `progress_map` instead of `map`
+    df.progress_apply(lambda x: x**2)
+    # can also groupby:
+    # df.groupby(0).progress_apply(lambda x: x**2)
+
+In case you're interested in how this works (and how to modify it for your
+own callbacks), see the
+`examples <https://github.com/tqdm/tqdm/tree/master/examples>`__
+folder or import the module and run ``help()``.
+
+Keras Integration
+~~~~~~~~~~~~~~~~~
+
+A ``keras`` callback is also available:
+
+.. code:: python
+
+    from tqdm.keras import TqdmCallback
+
+    ...
+
+    model.fit(..., verbose=0, callbacks=[TqdmCallback()])
+
+Dask Integration
+~~~~~~~~~~~~~~~~
+
+A ``dask`` callback is also available:
+
+.. code:: python
+
+    from tqdm.dask import TqdmCallback
+
+    with TqdmCallback(desc="compute"):
+        ...
+        arr.compute()
+
+    # or use callback globally
+    cb = TqdmCallback(desc="global")
+    cb.register()
+    arr.compute()
+
+IPython/Jupyter Integration
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+IPython/Jupyter is supported via the ``tqdm.notebook`` submodule:
+
+.. code:: python
+
+    from tqdm.notebook import trange, tqdm
+    from time import sleep
+
+    for i in trange(3, desc='1st loop'):
+        for j in tqdm(range(100), desc='2nd loop'):
+            sleep(0.01)
+
+In addition to ``tqdm`` features, the submodule provides a native Jupyter
+widget (compatible with IPython v1-v4 and Jupyter), fully working nested bars
+and colour hints (blue: normal, green: completed, red: error/interrupt,
+light blue: no ETA); as demonstrated below.
+
+|Screenshot-Jupyter1|
+|Screenshot-Jupyter2|
+|Screenshot-Jupyter3|
+
+The ``notebook`` version supports percentage or pixels for overall width
+(e.g.: ``ncols='100%'`` or ``ncols='480px'``).
+
+It is also possible to let ``tqdm`` automatically choose between
+console or notebook versions by using the ``autonotebook`` submodule:
+
+.. code:: python
+
+    from tqdm.autonotebook import tqdm
+    tqdm.pandas()
+
+Note that this will issue a ``TqdmExperimentalWarning`` if run in a notebook
+since it is not meant to be possible to distinguish between ``jupyter notebook``
+and ``jupyter console``. Use ``auto`` instead of ``autonotebook`` to suppress
+this warning.
+
+Note that notebooks will display the bar in the cell where it was created.
+This may be a different cell from the one where it is used.
+If this is not desired, either
+
+- delay the creation of the bar to the cell where it must be displayed, or
+- create the bar with ``display=False``, and in a later cell call
+  ``display(bar.container)``:
+
+.. code:: python
+
+    from tqdm.notebook import tqdm
+    pbar = tqdm(..., display=False)
+
+.. code:: python
+
+    # different cell
+    display(pbar.container)
+
+The ``keras`` callback has a ``display()`` method which can be used likewise:
+
+.. code:: python
+
+    from tqdm.keras import TqdmCallback
+    cbk = TqdmCallback(display=False)
+
+.. code:: python
+
+    # different cell
+    cbk.display()
+    model.fit(..., verbose=0, callbacks=[cbk])
+
+Another possibility is to have a single bar (near the top of the notebook)
+which is constantly re-used (using ``reset()`` rather than ``close()``).
+For this reason, the notebook version (unlike the CLI version) does not
+automatically call ``close()`` upon ``Exception``.
+
+.. code:: python
+
+    from tqdm.notebook import tqdm
+    pbar = tqdm()
+
+.. code:: python
+
+    # different cell
+    iterable = range(100)
+    pbar.reset(total=len(iterable))  # initialise with new `total`
+    for i in iterable:
+        pbar.update()
+    pbar.refresh()  # force print final status but don't `close()`
+
+Custom Integration
+~~~~~~~~~~~~~~~~~~
+
+To change the default arguments (such as making ``dynamic_ncols=True``),
+simply use built-in Python magic:
+
+.. code:: python
+
+    from functools import partial
+    from tqdm import tqdm as std_tqdm
+    tqdm = partial(std_tqdm, dynamic_ncols=True)
+
+For further customisation,
+``tqdm`` may be inherited from to create custom callbacks (as with the
+``TqdmUpTo`` example `above <#hooks-and-callbacks>`__) or for custom frontends
+(e.g. GUIs such as notebook or plotting packages). In the latter case:
+
+1. ``def __init__()`` to call ``super().__init__(..., gui=True)`` to disable
+   terminal ``status_printer`` creation.
+2. Redefine: ``close()``, ``clear()``, ``display()``.
+
+Consider overloading ``display()`` to use e.g.
+``self.frontend(**self.format_dict)`` instead of ``self.sp(repr(self))``.
+
+Some submodule examples of inheritance:
+
+- `tqdm/notebook.py <https://github.com/tqdm/tqdm/blob/master/tqdm/notebook.py>`__
+- `tqdm/gui.py <https://github.com/tqdm/tqdm/blob/master/tqdm/gui.py>`__
+- `tqdm/tk.py <https://github.com/tqdm/tqdm/blob/master/tqdm/tk.py>`__
+- `tqdm/contrib/slack.py <https://github.com/tqdm/tqdm/blob/master/tqdm/contrib/slack.py>`__
+- `tqdm/contrib/discord.py <https://github.com/tqdm/tqdm/blob/master/tqdm/contrib/discord.py>`__
+- `tqdm/contrib/telegram.py <https://github.com/tqdm/tqdm/blob/master/tqdm/contrib/telegram.py>`__
+
+Dynamic Monitor/Meter
+~~~~~~~~~~~~~~~~~~~~~
+
+You can use a ``tqdm`` as a meter which is not monotonically increasing.
+This could be because ``n`` decreases (e.g. a CPU usage monitor) or ``total``
+changes.
+
+One example would be recursively searching for files. The ``total`` is the
+number of objects found so far, while ``n`` is the number of those objects which
+are files (rather than folders):
+
+.. code:: python
+
+    from tqdm import tqdm
+    import os.path
+
+    def find_files_recursively(path, show_progress=True):
+        files = []
+        # total=1 assumes `path` is a file
+        t = tqdm(total=1, unit="file", disable=not show_progress)
+        if not os.path.exists(path):
+            raise IOError("Cannot find:" + path)
+
+        def append_found_file(f):
+            files.append(f)
+            t.update()
+
+        def list_found_dir(path):
+            """returns os.listdir(path) assuming os.path.isdir(path)"""
+            listing = os.listdir(path)
+            # subtract 1 since a "file" we found was actually this directory
+            t.total += len(listing) - 1
+            # fancy way to give info without forcing a refresh
+            t.set_postfix(dir=path[-10:], refresh=False)
+            t.update(0)  # may trigger a refresh
+            return listing
+
+        def recursively_search(path):
+            if os.path.isdir(path):
+                for f in list_found_dir(path):
+                    recursively_search(os.path.join(path, f))
+            else:
+                append_found_file(path)
+
+        recursively_search(path)
+        t.set_postfix(dir=path)
+        t.close()
+        return files
+
+Using ``update(0)`` is a handy way to let ``tqdm`` decide when to trigger a
+display refresh to avoid console spamming.
+
+Writing messages
+~~~~~~~~~~~~~~~~
+
+This is a work in progress (see
+`#737 <https://github.com/tqdm/tqdm/issues/737>`__).
+
+Since ``tqdm`` uses a simple printing mechanism to display progress bars,
+you should not write any message in the terminal using ``print()`` while
+a progressbar is open.
+
+To write messages in the terminal without any collision with ``tqdm`` bar
+display, a ``.write()`` method is provided:
+
+.. code:: python
+
+    from tqdm.auto import tqdm, trange
+    from time import sleep
+
+    bar = trange(10)
+    for i in bar:
+        # Print using tqdm class method .write()
+        sleep(0.1)
+        if not (i % 3):
+            tqdm.write("Done task %i" % i)
+        # Can also use bar.write()
+
+By default, this will print to standard output ``sys.stdout``. but you can
+specify any file-like object using the ``file`` argument. For example, this
+can be used to redirect the messages writing to a log file or class.
+
+Redirecting writing
+~~~~~~~~~~~~~~~~~~~
+
+If using a library that can print messages to the console, editing the library
+by  replacing ``print()`` with ``tqdm.write()`` may not be desirable.
+In that case, redirecting ``sys.stdout`` to ``tqdm.write()`` is an option.
+
+To redirect ``sys.stdout``, create a file-like class that will write
+any input string to ``tqdm.write()``, and supply the arguments
+``file=sys.stdout, dynamic_ncols=True``.
+
+A reusable canonical example is given below:
+
+.. code:: python
+
+    from time import sleep
+    import contextlib
+    import sys
+    from tqdm import tqdm
+    from tqdm.contrib import DummyTqdmFile
+
+
+    @contextlib.contextmanager
+    def std_out_err_redirect_tqdm():
+        orig_out_err = sys.stdout, sys.stderr
+        try:
+            sys.stdout, sys.stderr = map(DummyTqdmFile, orig_out_err)
+            yield orig_out_err[0]
+        # Relay exceptions
+        except Exception as exc:
+            raise exc
+        # Always restore sys.stdout/err if necessary
+        finally:
+            sys.stdout, sys.stderr = orig_out_err
+
+    def some_fun(i):
+        print("Fee, fi, fo,".split()[i])
+
+    # Redirect stdout to tqdm.write() (don't forget the `as save_stdout`)
+    with std_out_err_redirect_tqdm() as orig_stdout:
+        # tqdm needs the original stdout
+        # and dynamic_ncols=True to autodetect console width
+        for i in tqdm(range(3), file=orig_stdout, dynamic_ncols=True):
+            sleep(.5)
+            some_fun(i)
+
+    # After the `with`, printing is restored
+    print("Done!")
+
+Redirecting ``logging``
+~~~~~~~~~~~~~~~~~~~~~~~
+
+Similar to ``sys.stdout``/``sys.stderr`` as detailed above, console ``logging``
+may also be redirected to ``tqdm.write()``.
+
+Warning: if also redirecting ``sys.stdout``/``sys.stderr``, make sure to
+redirect ``logging`` first if needed.
+
+Helper methods are available in ``tqdm.contrib.logging``. For example:
+
+.. code:: python
+
+    import logging
+    from tqdm import trange
+    from tqdm.contrib.logging import logging_redirect_tqdm
+
+    LOG = logging.getLogger(__name__)
+
+    if __name__ == '__main__':
+        logging.basicConfig(level=logging.INFO)
+        with logging_redirect_tqdm():
+            for i in trange(9):
+                if i == 4:
+                    LOG.info("console logging redirected to `tqdm.write()`")
+        # logging restored
+
+Monitoring thread, intervals and miniters
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+``tqdm`` implements a few tricks to increase efficiency and reduce overhead.
+
+- Avoid unnecessary frequent bar refreshing: ``mininterval`` defines how long
+  to wait between each refresh. ``tqdm`` always gets updated in the background,
+  but it will display only every ``mininterval``.
+- Reduce number of calls to check system clock/time.
+- ``mininterval`` is more intuitive to configure than ``miniters``.
+  A clever adjustment system ``dynamic_miniters`` will automatically adjust
+  ``miniters`` to the amount of iterations that fit into time ``mininterval``.
+  Essentially, ``tqdm`` will check if it's time to print without actually
+  checking time. This behaviour can be still be bypassed by manually setting
+  ``miniters``.
+
+However, consider a case with a combination of fast and slow iterations.
+After a few fast iterations, ``dynamic_miniters`` will set ``miniters`` to a
+large number. When iteration rate subsequently slows, ``miniters`` will
+remain large and thus reduce display update frequency. To address this:
+
+- ``maxinterval`` defines the maximum time between display refreshes.
+  A concurrent monitoring thread checks for overdue updates and forces one
+  where necessary.
+
+The monitoring thread should not have a noticeable overhead, and guarantees
+updates at least every 10 seconds by default.
+This value can be directly changed by setting the ``monitor_interval`` of
+any ``tqdm`` instance (i.e. ``t = tqdm.tqdm(...); t.monitor_interval = 2``).
+The monitor thread may be disabled application-wide by setting
+``tqdm.tqdm.monitor_interval = 0`` before instantiation of any ``tqdm`` bar.
+
+
+Merch
+-----
+
+You can buy `tqdm branded merch <https://tqdm.github.io/merch>`__ now!
+
+Contributions
+-------------
+
+|GitHub-Commits| |GitHub-Issues| |GitHub-PRs| |OpenHub-Status| |GitHub-Contributions| |CII Best Practices|
+
+All source code is hosted on `GitHub <https://github.com/tqdm/tqdm>`__.
+Contributions are welcome.
+
+See the
+`CONTRIBUTING <https://github.com/tqdm/tqdm/blob/master/CONTRIBUTING.md>`__
+file for more information.
+
+Developers who have made significant contributions, ranked by *SLoC*
+(surviving lines of code,
+`git fame <https://github.com/casperdcl/git-fame>`__ ``-wMC --excl '\.(png|gif|jpg)$'``),
+are:
+
+==================== ======================================================== ==== ================================
+Name                 ID                                                       SLoC Notes
+==================== ======================================================== ==== ================================
+Casper da Costa-Luis `casperdcl <https://github.com/casperdcl>`__             ~80% primary maintainer |Gift-Casper|
+Stephen Larroque     `lrq3000 <https://github.com/lrq3000>`__                 ~9%  team member
+Martin Zugnoni       `martinzugnoni <https://github.com/martinzugnoni>`__     ~3%
+Daniel Ecer          `de-code <https://github.com/de-code>`__                 ~2%
+Richard Sheridan     `richardsheridan <https://github.com/richardsheridan>`__ ~1%
+Guangshuo Chen       `chengs <https://github.com/chengs>`__                   ~1%
+Helio Machado        `0x2b3bfa0 <https://github.com/0x2b3bfa0>`__             ~1%
+Kyle Altendorf       `altendky <https://github.com/altendky>`__               <1%
+Noam Yorav-Raphael   `noamraph <https://github.com/noamraph>`__               <1%  original author
+Matthew Stevens      `mjstevens777 <https://github.com/mjstevens777>`__       <1%
+Hadrien Mary         `hadim <https://github.com/hadim>`__                     <1%  team member
+Mikhail Korobov      `kmike <https://github.com/kmike>`__                     <1%  team member
+==================== ======================================================== ==== ================================
+
+Ports to Other Languages
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+A list is available on
+`this wiki page <https://github.com/tqdm/tqdm/wiki/tqdm-ports>`__.
+
+
+LICENCE
+-------
+
+Open Source (OSI approved): |LICENCE|
+
+Citation information: |DOI|
+
+|README-Hits| (Since 19 May 2016)
+
+.. |Logo| image:: https://tqdm.github.io/img/logo.gif
+.. |Screenshot| image:: https://tqdm.github.io/img/tqdm.gif
+.. |Video| image:: https://tqdm.github.io/img/video.jpg
+   :target: https://tqdm.github.io/video
+.. |Slides| image:: https://tqdm.github.io/img/slides.jpg
+   :target: https://tqdm.github.io/PyData2019/slides.html
+.. |Merch| image:: https://tqdm.github.io/img/merch.jpg
+   :target: https://tqdm.github.io/merch
+.. |Build-Status| image:: https://img.shields.io/github/actions/workflow/status/tqdm/tqdm/test.yml?branch=master&label=tqdm&logo=GitHub
+   :target: https://github.com/tqdm/tqdm/actions/workflows/test.yml
+.. |Coverage-Status| image:: https://img.shields.io/coveralls/github/tqdm/tqdm/master?logo=coveralls
+   :target: https://coveralls.io/github/tqdm/tqdm
+.. |Branch-Coverage-Status| image:: https://codecov.io/gh/tqdm/tqdm/branch/master/graph/badge.svg
+   :target: https://codecov.io/gh/tqdm/tqdm
+.. |Codacy-Grade| image:: https://app.codacy.com/project/badge/Grade/3f965571598f44549c7818f29cdcf177
+   :target: https://www.codacy.com/gh/tqdm/tqdm/dashboard
+.. |CII Best Practices| image:: https://bestpractices.coreinfrastructure.org/projects/3264/badge
+   :target: https://bestpractices.coreinfrastructure.org/projects/3264
+.. |GitHub-Status| image:: https://img.shields.io/github/tag/tqdm/tqdm.svg?maxAge=86400&logo=github&logoColor=white
+   :target: https://github.com/tqdm/tqdm/releases
+.. |GitHub-Forks| image:: https://img.shields.io/github/forks/tqdm/tqdm.svg?logo=github&logoColor=white
+   :target: https://github.com/tqdm/tqdm/network
+.. |GitHub-Stars| image:: https://img.shields.io/github/stars/tqdm/tqdm.svg?logo=github&logoColor=white
+   :target: https://github.com/tqdm/tqdm/stargazers
+.. |GitHub-Commits| image:: https://img.shields.io/github/commit-activity/y/tqdm/tqdm.svg?logo=git&logoColor=white
+   :target: https://github.com/tqdm/tqdm/graphs/commit-activity
+.. |GitHub-Issues| image:: https://img.shields.io/github/issues-closed/tqdm/tqdm.svg?logo=github&logoColor=white
+   :target: https://github.com/tqdm/tqdm/issues?q=
+.. |GitHub-PRs| image:: https://img.shields.io/github/issues-pr-closed/tqdm/tqdm.svg?logo=github&logoColor=white
+   :target: https://github.com/tqdm/tqdm/pulls
+.. |GitHub-Contributions| image:: https://img.shields.io/github/contributors/tqdm/tqdm.svg?logo=github&logoColor=white
+   :target: https://github.com/tqdm/tqdm/graphs/contributors
+.. |GitHub-Updated| image:: https://img.shields.io/github/last-commit/tqdm/tqdm/master.svg?logo=github&logoColor=white&label=pushed
+   :target: https://github.com/tqdm/tqdm/pulse
+.. |Gift-Casper| image:: https://img.shields.io/badge/dynamic/json.svg?color=ff69b4&label=gifts%20received&prefix=%C2%A3&query=%24..sum&url=https%3A%2F%2Fcaspersci.uk.to%2Fgifts.json
+   :target: https://cdcl.ml/sponsor
+.. |Versions| image:: https://img.shields.io/pypi/v/tqdm.svg
+   :target: https://tqdm.github.io/releases
+.. |PyPI-Downloads| image:: https://img.shields.io/pypi/dm/tqdm.svg?label=pypi%20downloads&logo=PyPI&logoColor=white
+   :target: https://pepy.tech/project/tqdm
+.. |Py-Versions| image:: https://img.shields.io/pypi/pyversions/tqdm.svg?logo=python&logoColor=white
+   :target: https://pypi.org/project/tqdm
+.. |Conda-Forge-Status| image:: https://img.shields.io/conda/v/conda-forge/tqdm.svg?label=conda-forge&logo=conda-forge
+   :target: https://anaconda.org/conda-forge/tqdm
+.. |Snapcraft| image:: https://img.shields.io/badge/snap-install-82BEA0.svg?logo=snapcraft
+   :target: https://snapcraft.io/tqdm
+.. |Docker| image:: https://img.shields.io/badge/docker-pull-blue.svg?logo=docker&logoColor=white
+   :target: https://hub.docker.com/r/tqdm/tqdm
+.. |Libraries-Rank| image:: https://img.shields.io/librariesio/sourcerank/pypi/tqdm.svg?logo=koding&logoColor=white
+   :target: https://libraries.io/pypi/tqdm
+.. |Libraries-Dependents| image:: https://img.shields.io/librariesio/dependent-repos/pypi/tqdm.svg?logo=koding&logoColor=white
+    :target: https://github.com/tqdm/tqdm/network/dependents
+.. |OpenHub-Status| image:: https://www.openhub.net/p/tqdm/widgets/project_thin_badge?format=gif
+   :target: https://www.openhub.net/p/tqdm?ref=Thin+badge
+.. |awesome-python| image:: https://awesome.re/mentioned-badge.svg
+   :target: https://github.com/vinta/awesome-python
+.. |LICENCE| image:: https://img.shields.io/pypi/l/tqdm.svg
+   :target: https://raw.githubusercontent.com/tqdm/tqdm/master/LICENCE
+.. |DOI| image:: https://img.shields.io/badge/DOI-10.5281/zenodo.595120-blue.svg
+   :target: https://doi.org/10.5281/zenodo.595120
+.. |binder-demo| image:: https://mybinder.org/badge_logo.svg
+   :target: https://mybinder.org/v2/gh/tqdm/tqdm/master?filepath=DEMO.ipynb
+.. |Screenshot-Jupyter1| image:: https://tqdm.github.io/img/jupyter-1.gif
+.. |Screenshot-Jupyter2| image:: https://tqdm.github.io/img/jupyter-2.gif
+.. |Screenshot-Jupyter3| image:: https://tqdm.github.io/img/jupyter-3.gif
+.. |README-Hits| image:: https://cgi.cdcl.ml/hits?q=tqdm&style=social&r=https://github.com/tqdm/tqdm&l=https://tqdm.github.io/img/favicon.png&f=https://tqdm.github.io/img/logo.gif
+   :target: https://cgi.cdcl.ml/hits?q=tqdm&a=plot&r=https://github.com/tqdm/tqdm&l=https://tqdm.github.io/img/favicon.png&f=https://tqdm.github.io/img/logo.gif&style=social

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+Wheel-Version: 1.0
+Generator: setuptools (80.10.2)
+Root-Is-Purelib: true
+Tag: py3-none-any
+

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+[console_scripts]
+tqdm = tqdm.cli:main

.cache/uv/archive-v0/5itABfUpr52efEvNSNHEE/tqdm-4.67.3.dist-info/licenses/LICENCE

@@ -0,0 +1,49 @@
+`tqdm` is a product of collaborative work.
+Unless otherwise stated, all authors (see commit logs) retain copyright
+for their respective work, and release the work under the MIT licence
+(text below).
+
+Exceptions or notable authors are listed below
+in reverse chronological order:
+
+* files: *
+  MPL-2.0 2015-2026 (c) Casper da Costa-Luis
+  [casperdcl](https://github.com/casperdcl).
+* files: tqdm/_tqdm.py
+  MIT 2016 (c) [PR #96] on behalf of Google Inc.
+* files: tqdm/_tqdm.py README.rst .gitignore
+  MIT 2013 (c) Noam Yorav-Raphael, original author.
+
+[PR #96]: https://github.com/tqdm/tqdm/pull/96
+
+
+Mozilla Public Licence (MPL) v. 2.0 - Exhibit A
+-----------------------------------------------
+
+This Source Code Form is subject to the terms of the
+Mozilla Public License, v. 2.0.
+If a copy of the MPL was not distributed with this project,
+You can obtain one at https://mozilla.org/MPL/2.0/.
+
+
+MIT License (MIT)
+-----------------
+
+Copyright (c) 2013 noamraph
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of
+this software and associated documentation files (the "Software"), to deal in
+the Software without restriction, including without limitation the rights to
+use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+the Software, and to permit persons to whom the Software is furnished to do so,
+subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

.cache/uv/archive-v0/5itABfUpr52efEvNSNHEE/tqdm-4.67.3.dist-info/top_level.txt

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+tqdm

.cache/uv/archive-v0/5itABfUpr52efEvNSNHEE/tqdm/__init__.py

@@ -0,0 +1,38 @@
+from ._monitor import TMonitor, TqdmSynchronisationWarning
+from ._tqdm_pandas import tqdm_pandas
+from .cli import main  # TODO: remove in v5.0.0
+from .gui import tqdm as tqdm_gui  # TODO: remove in v5.0.0
+from .gui import trange as tgrange  # TODO: remove in v5.0.0
+from .std import (
+    TqdmDeprecationWarning, TqdmExperimentalWarning, TqdmKeyError, TqdmMonitorWarning,
+    TqdmTypeError, TqdmWarning, tqdm, trange)
+from .version import __version__
+
+__all__ = ['tqdm', 'tqdm_gui', 'trange', 'tgrange', 'tqdm_pandas',
+           'tqdm_notebook', 'tnrange', 'main', 'TMonitor',
+           'TqdmTypeError', 'TqdmKeyError',
+           'TqdmWarning', 'TqdmDeprecationWarning',
+           'TqdmExperimentalWarning',
+           'TqdmMonitorWarning', 'TqdmSynchronisationWarning',
+           '__version__']
+
+
+def tqdm_notebook(*args, **kwargs):  # pragma: no cover
+    """See tqdm.notebook.tqdm for full documentation"""
+    from warnings import warn
+
+    from .notebook import tqdm as _tqdm_notebook
+    warn("This function will be removed in tqdm==5.0.0\n"
+         "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`",
+         TqdmDeprecationWarning, stacklevel=2)
+    return _tqdm_notebook(*args, **kwargs)
+
+
+def tnrange(*args, **kwargs):  # pragma: no cover
+    """Shortcut for `tqdm.notebook.tqdm(range(*args), **kwargs)`."""
+    from warnings import warn
+
+    from .notebook import trange as _tnrange
+    warn("Please use `tqdm.notebook.trange` instead of `tqdm.tnrange`",
+         TqdmDeprecationWarning, stacklevel=2)
+    return _tnrange(*args, **kwargs)

.cache/uv/archive-v0/5itABfUpr52efEvNSNHEE/tqdm/__main__.py

@@ -0,0 +1,3 @@
+from .cli import main
+
+main()

.cache/uv/archive-v0/5itABfUpr52efEvNSNHEE/tqdm/_main.py

@@ -0,0 +1,9 @@
+from warnings import warn
+
+from .cli import *  # NOQA
+from .cli import __all__  # NOQA
+from .std import TqdmDeprecationWarning
+
+warn("This function will be removed in tqdm==5.0.0\n"
+     "Please use `tqdm.cli.*` instead of `tqdm._main.*`",
+     TqdmDeprecationWarning, stacklevel=2)

.cache/uv/archive-v0/5itABfUpr52efEvNSNHEE/tqdm/_monitor.py

@@ -0,0 +1,95 @@
+import atexit
+from threading import Event, Thread, current_thread
+from time import time
+from warnings import warn
+
+__all__ = ["TMonitor", "TqdmSynchronisationWarning"]
+
+
+class TqdmSynchronisationWarning(RuntimeWarning):
+    """tqdm multi-thread/-process errors which may cause incorrect nesting
+    but otherwise no adverse effects"""
+    pass
+
+
+class TMonitor(Thread):
+    """
+    Monitoring thread for tqdm bars.
+    Monitors if tqdm bars are taking too much time to display
+    and readjusts miniters automatically if necessary.
+
+    Parameters
+    ----------
+    tqdm_cls  : class
+        tqdm class to use (can be core tqdm or a submodule).
+    sleep_interval  : float
+        Time to sleep between monitoring checks.
+    """
+    _test = {}  # internal vars for unit testing
+
+    def __init__(self, tqdm_cls, sleep_interval):
+        Thread.__init__(self)
+        self.daemon = True  # kill thread when main killed (KeyboardInterrupt)
+        self.woken = 0  # last time woken up, to sync with monitor
+        self.tqdm_cls = tqdm_cls
+        self.sleep_interval = sleep_interval
+        self._time = self._test.get("time", time)
+        self.was_killed = self._test.get("Event", Event)()
+        atexit.register(self.exit)
+        self.start()
+
+    def exit(self):
+        self.was_killed.set()
+        if self is not current_thread():
+            self.join()
+        return self.report()
+
+    def get_instances(self):
+        # returns a copy of started `tqdm_cls` instances
+        return [i for i in self.tqdm_cls._instances.copy()
+                # Avoid race by checking that the instance started
+                if hasattr(i, 'start_t')]
+
+    def run(self):
+        cur_t = self._time()
+        while True:
+            # After processing and before sleeping, notify that we woke
+            # Need to be done just before sleeping
+            self.woken = cur_t
+            # Sleep some time...
+            self.was_killed.wait(self.sleep_interval)
+            # Quit if killed
+            if self.was_killed.is_set():
+                return
+            # Then monitor!
+            # Acquire lock (to access _instances)
+            with self.tqdm_cls.get_lock():
+                cur_t = self._time()
+                # Check tqdm instances are waiting too long to print
+                instances = self.get_instances()
+                for instance in instances:
+                    # Check event in loop to reduce blocking time on exit
+                    if self.was_killed.is_set():
+                        return
+                    # Only if mininterval > 1 (else iterations are just slow)
+                    # and last refresh exceeded maxinterval
+                    if (
+                        instance.miniters > 1
+                        and (cur_t - instance.last_print_t) >= instance.maxinterval
+                    ):
+                        # force bypassing miniters on next iteration
+                        # (dynamic_miniters adjusts mininterval automatically)
+                        instance.miniters = 1
+                        # Refresh now! (works only for manual tqdm)
+                        instance.refresh(nolock=True)
+                    # Remove accidental long-lived strong reference
+                    del instance
+                if instances != self.get_instances():  # pragma: nocover
+                    warn("Set changed size during iteration" +
+                         " (see https://github.com/tqdm/tqdm/issues/481)",
+                         TqdmSynchronisationWarning, stacklevel=2)
+                # Remove accidental long-lived strong references
+                del instances
+
+    def report(self):
+        return not self.was_killed.is_set()

.cache/uv/archive-v0/5itABfUpr52efEvNSNHEE/tqdm/_tqdm.py

@@ -0,0 +1,9 @@
+from warnings import warn
+
+from .std import *  # NOQA
+from .std import __all__  # NOQA
+from .std import TqdmDeprecationWarning
+
+warn("This function will be removed in tqdm==5.0.0\n"
+     "Please use `tqdm.std.*` instead of `tqdm._tqdm.*`",
+     TqdmDeprecationWarning, stacklevel=2)

.cache/uv/archive-v0/5itABfUpr52efEvNSNHEE/tqdm/_tqdm_gui.py

@@ -0,0 +1,9 @@
+from warnings import warn
+
+from .gui import *  # NOQA
+from .gui import __all__  # NOQA
+from .std import TqdmDeprecationWarning
+
+warn("This function will be removed in tqdm==5.0.0\n"
+     "Please use `tqdm.gui.*` instead of `tqdm._tqdm_gui.*`",
+     TqdmDeprecationWarning, stacklevel=2)

.cache/uv/archive-v0/5itABfUpr52efEvNSNHEE/tqdm/_tqdm_notebook.py

@@ -0,0 +1,9 @@
+from warnings import warn
+
+from .notebook import *  # NOQA
+from .notebook import __all__  # NOQA
+from .std import TqdmDeprecationWarning
+
+warn("This function will be removed in tqdm==5.0.0\n"
+     "Please use `tqdm.notebook.*` instead of `tqdm._tqdm_notebook.*`",
+     TqdmDeprecationWarning, stacklevel=2)

.cache/uv/archive-v0/5itABfUpr52efEvNSNHEE/tqdm/_tqdm_pandas.py

@@ -0,0 +1,24 @@
+import sys
+
+__author__ = "github.com/casperdcl"
+__all__ = ['tqdm_pandas']
+
+
+def tqdm_pandas(tclass, **tqdm_kwargs):
+    """
+    Registers the given `tqdm` instance with
+    `pandas.core.groupby.DataFrameGroupBy.progress_apply`.
+    """
+    from tqdm import TqdmDeprecationWarning
+
+    if isinstance(tclass, type) or (getattr(tclass, '__name__', '').startswith(
+            'tqdm_')):  # delayed adapter case
+        TqdmDeprecationWarning(
+            "Please use `tqdm.pandas(...)` instead of `tqdm_pandas(tqdm, ...)`.",
+            fp_write=getattr(tqdm_kwargs.get('file', None), 'write', sys.stderr.write))
+        tclass.pandas(**tqdm_kwargs)
+    else:
+        TqdmDeprecationWarning(
+            "Please use `tqdm.pandas(...)` instead of `tqdm_pandas(tqdm(...))`.",
+            fp_write=getattr(tclass.fp, 'write', sys.stderr.write))
+        type(tclass).pandas(deprecated_t=tclass)

.cache/uv/archive-v0/5itABfUpr52efEvNSNHEE/tqdm/_utils.py

@@ -0,0 +1,11 @@
+from warnings import warn
+
+from .std import TqdmDeprecationWarning
+from .utils import (  # NOQA, pylint: disable=unused-import
+    CUR_OS, IS_NIX, IS_WIN, RE_ANSI, Comparable, FormatReplace, SimpleTextIOWrapper,
+    _environ_cols_wrapper, _is_ascii, _is_utf, _screen_shape_linux, _screen_shape_tput,
+    _screen_shape_windows, _screen_shape_wrapper, _supports_unicode, _term_move_up, colorama)
+
+warn("This function will be removed in tqdm==5.0.0\n"
+     "Please use `tqdm.utils.*` instead of `tqdm._utils.*`",
+     TqdmDeprecationWarning, stacklevel=2)

.cache/uv/archive-v0/5itABfUpr52efEvNSNHEE/tqdm/asyncio.py

@@ -0,0 +1,93 @@
+"""
+Asynchronous progressbar decorator for iterators.
+Includes a default `range` iterator printing to `stderr`.
+
+Usage:
+>>> from tqdm.asyncio import trange, tqdm
+>>> async for i in trange(10):
+...     ...
+"""
+import asyncio
+from sys import version_info
+
+from .std import tqdm as std_tqdm
+
+__author__ = {"github.com/": ["casperdcl"]}
+__all__ = ['tqdm_asyncio', 'tarange', 'tqdm', 'trange']
+
+
+class tqdm_asyncio(std_tqdm):
+    """
+    Asynchronous-friendly version of tqdm.
+    """
+    def __init__(self, iterable=None, *args, **kwargs):
+        super().__init__(iterable, *args, **kwargs)
+        self.iterable_awaitable = False
+        if iterable is not None:
+            if hasattr(iterable, "__anext__"):
+                self.iterable_next = iterable.__anext__
+                self.iterable_awaitable = True
+            elif hasattr(iterable, "__next__"):
+                self.iterable_next = iterable.__next__
+            else:
+                self.iterable_iterator = iter(iterable)
+                self.iterable_next = self.iterable_iterator.__next__
+
+    def __aiter__(self):
+        return self
+
+    async def __anext__(self):
+        try:
+            if self.iterable_awaitable:
+                res = await self.iterable_next()
+            else:
+                res = self.iterable_next()
+            self.update()
+            return res
+        except StopIteration:
+            self.close()
+            raise StopAsyncIteration
+        except BaseException:
+            self.close()
+            raise
+
+    def send(self, *args, **kwargs):
+        return self.iterable.send(*args, **kwargs)
+
+    @classmethod
+    def as_completed(cls, fs, *, loop=None, timeout=None, total=None, **tqdm_kwargs):
+        """
+        Wrapper for `asyncio.as_completed`.
+        """
+        if total is None:
+            total = len(fs)
+        kwargs = {}
+        if version_info[:2] < (3, 10):
+            kwargs['loop'] = loop
+        yield from cls(asyncio.as_completed(fs, timeout=timeout, **kwargs),
+                       total=total, **tqdm_kwargs)
+
+    @classmethod
+    async def gather(cls, *fs, loop=None, timeout=None, total=None, **tqdm_kwargs):
+        """
+        Wrapper for `asyncio.gather`.
+        """
+        async def wrap_awaitable(i, f):
+            return i, await f
+
+        ifs = [wrap_awaitable(i, f) for i, f in enumerate(fs)]
+        res = [await f for f in cls.as_completed(ifs, loop=loop, timeout=timeout,
+                                                 total=total, **tqdm_kwargs)]
+        return [i for _, i in sorted(res)]
+
+
+def tarange(*args, **kwargs):
+    """
+    A shortcut for `tqdm.asyncio.tqdm(range(*args), **kwargs)`.
+    """
+    return tqdm_asyncio(range(*args), **kwargs)
+
+
+# Aliases
+tqdm = tqdm_asyncio
+trange = tarange

.cache/uv/archive-v0/5itABfUpr52efEvNSNHEE/tqdm/auto.py

@@ -0,0 +1,40 @@
+"""
+Enables multiple commonly used features.
+
+Method resolution order:
+
+- `tqdm.autonotebook` without import warnings
+- `tqdm.asyncio`
+- `tqdm.std` base class
+
+Usage:
+>>> from tqdm.auto import trange, tqdm
+>>> for i in trange(10):
+...     ...
+"""
+import warnings
+
+from .std import TqdmExperimentalWarning
+
+with warnings.catch_warnings():
+    warnings.simplefilter("ignore", category=TqdmExperimentalWarning)
+    from .autonotebook import tqdm as notebook_tqdm
+
+from .asyncio import tqdm as asyncio_tqdm
+from .std import tqdm as std_tqdm
+
+if notebook_tqdm != std_tqdm:
+    class tqdm(notebook_tqdm, asyncio_tqdm):  # pylint: disable=inconsistent-mro
+        pass
+else:
+    tqdm = asyncio_tqdm
+
+
+def trange(*args, **kwargs):
+    """
+    A shortcut for `tqdm.auto.tqdm(range(*args), **kwargs)`.
+    """
+    return tqdm(range(*args), **kwargs)
+
+
+__all__ = ["tqdm", "trange"]

.cache/uv/archive-v0/5itABfUpr52efEvNSNHEE/tqdm/autonotebook.py

@@ -0,0 +1,29 @@
+"""
+Automatically choose between `tqdm.notebook` and `tqdm.std`.
+
+Usage:
+>>> from tqdm.autonotebook import trange, tqdm
+>>> for i in trange(10):
+...     ...
+"""
+import sys
+from warnings import warn
+
+try:
+    get_ipython = sys.modules['IPython'].get_ipython
+    if 'IPKernelApp' not in get_ipython().config:  # pragma: no cover
+        raise ImportError("console")
+    from .notebook import WARN_NOIPYW, IProgress
+    if IProgress is None:
+        from .std import TqdmWarning
+        warn(WARN_NOIPYW, TqdmWarning, stacklevel=2)
+        raise ImportError('ipywidgets')
+except Exception:
+    from .std import tqdm, trange
+else:  # pragma: no cover
+    from .notebook import tqdm, trange
+    from .std import TqdmExperimentalWarning
+    warn("Using `tqdm.autonotebook.tqdm` in notebook mode."
+         " Use `tqdm.tqdm` instead to force console mode"
+         " (e.g. in jupyter console)", TqdmExperimentalWarning, stacklevel=2)
+__all__ = ["tqdm", "trange"]

.cache/uv/archive-v0/5itABfUpr52efEvNSNHEE/tqdm/cli.py

@@ -0,0 +1,324 @@
+"""
+Module version for monitoring CLI pipes (`... | python -m tqdm | ...`).
+"""
+import logging
+import re
+import sys
+from ast import literal_eval as numeric
+from textwrap import indent
+
+from .std import TqdmKeyError, TqdmTypeError, tqdm
+from .version import __version__
+
+__all__ = ["main"]
+log = logging.getLogger(__name__)
+
+
+def cast(val, typ):
+    log.debug((val, typ))
+    if " or " in typ:
+        for t in typ.split(" or "):
+            try:
+                return cast(val, t)
+            except TqdmTypeError:
+                pass
+        raise TqdmTypeError(f"{val} : {typ}")
+
+    # sys.stderr.write('\ndebug | `val:type`: `' + val + ':' + typ + '`.\n')
+    if typ == 'bool':
+        if (val == 'True') or (val == ''):
+            return True
+        if val == 'False':
+            return False
+        raise TqdmTypeError(val + ' : ' + typ)
+    if typ == 'chr':
+        if len(val) == 1:
+            return val.encode()
+        if re.match(r"^\\\w+$", val):
+            return eval(f'"{val}"').encode()
+        raise TqdmTypeError(f"{val} : {typ}")
+    if typ == 'str':
+        return val
+    if typ == 'int':
+        try:
+            return int(val)
+        except ValueError as exc:
+            raise TqdmTypeError(f"{val} : {typ}") from exc
+    if typ == 'float':
+        try:
+            return float(val)
+        except ValueError as exc:
+            raise TqdmTypeError(f"{val} : {typ}") from exc
+    raise TqdmTypeError(f"{val} : {typ}")
+
+
+def posix_pipe(fin, fout, delim=b'\\n', buf_size=256,
+               callback=lambda float: None, callback_len=True):
+    """
+    Params
+    ------
+    fin  : binary file with `read(buf_size : int)` method
+    fout  : binary file with `write` (and optionally `flush`) methods.
+    callback  : function(float), e.g.: `tqdm.update`
+    callback_len  : If (default: True) do `callback(len(buffer))`.
+      Otherwise, do `callback(data) for data in buffer.split(delim)`.
+    """
+    fp_write = fout.write
+
+    if not delim:
+        while True:
+            tmp = fin.read(buf_size)
+
+            # flush at EOF
+            if not tmp:
+                getattr(fout, 'flush', lambda: None)()
+                return
+
+            fp_write(tmp)
+            callback(len(tmp))
+        # return
+
+    buf = b''
+    len_delim = len(delim)
+    # n = 0
+    while True:
+        tmp = fin.read(buf_size)
+
+        # flush at EOF
+        if not tmp:
+            if buf:
+                fp_write(buf)
+                if callback_len:
+                    # n += 1 + buf.count(delim)
+                    callback(1 + buf.count(delim))
+                else:
+                    for i in buf.split(delim):
+                        callback(i)
+            getattr(fout, 'flush', lambda: None)()
+            return  # n
+
+        while True:
+            i = tmp.find(delim)
+            if i < 0:
+                buf += tmp
+                break
+            fp_write(buf + tmp[:i + len(delim)])
+            # n += 1
+            callback(1 if callback_len else (buf + tmp[:i]))
+            buf = b''
+            tmp = tmp[i + len_delim:]
+
+
+# ((opt, type), ... )
+RE_OPTS = re.compile(r'\n {4}(\S+)\s{2,}:\s*([^,]+)')
+# better split method assuming no positional args
+RE_SHLEX = re.compile(r'\s*(?<!\S)--?([^\s=]+)(\s+|=|$)')
+
+# TODO: add custom support for some of the following?
+UNSUPPORTED_OPTS = ('iterable', 'gui', 'out', 'file')
+
+# The 8 leading spaces are required for consistency
+CLI_EXTRA_DOC = r"""
+    Extra CLI Options
+    -----------------
+    name  : type, optional
+        TODO: find out why this is needed.
+    delim  : chr, optional
+        Delimiting character [default: '\n']. Use '\0' for null.
+        N.B.: on Windows systems, Python converts '\n' to '\r\n'.
+    buf_size  : int, optional
+        String buffer size in bytes [default: 256]
+        used when `delim` is specified.
+    bytes  : bool, optional
+        If true, will count bytes, ignore `delim`, and default
+        `unit_scale` to True, `unit_divisor` to 1024, and `unit` to 'B'.
+    tee  : bool, optional
+        If true, passes `stdin` to both `stderr` and `stdout`.
+    update  : bool, optional
+        If true, will treat input as newly elapsed iterations,
+        i.e. numbers to pass to `update()`. Note that this is slow
+        (~2e5 it/s) since every input must be decoded as a number.
+    update_to  : bool, optional
+        If true, will treat input as total elapsed iterations,
+        i.e. numbers to assign to `self.n`. Note that this is slow
+        (~2e5 it/s) since every input must be decoded as a number.
+    null  : bool, optional
+        If true, will discard input (no stdout).
+    manpath  : str, optional
+        Directory in which to install tqdm man pages.
+    comppath  : str, optional
+        Directory in which to place tqdm completion.
+    log  : str, optional
+        CRITICAL|FATAL|ERROR|WARN(ING)|[default: 'INFO']|DEBUG|NOTSET.
+"""
+
+
+def main(fp=sys.stderr, argv=None):
+    """
+    Parameters (internal use only)
+    ---------
+    fp  : file-like object for tqdm
+    argv  : list (default: sys.argv[1:])
+    """
+    if argv is None:
+        argv = sys.argv[1:]
+    try:
+        log_idx = argv.index('--log')
+    except ValueError:
+        for i in argv:
+            if i.startswith('--log='):
+                logLevel = i[len('--log='):]
+                break
+        else:
+            logLevel = 'INFO'
+    else:
+        # argv.pop(log_idx)
+        # logLevel = argv.pop(log_idx)
+        logLevel = argv[log_idx + 1]
+    logging.basicConfig(level=getattr(logging, logLevel),
+                        format="%(levelname)s:%(module)s:%(lineno)d:%(message)s")
+
+    # py<3.13 doesn't dedent docstrings
+    d = (tqdm.__doc__ if sys.version_info < (3, 13)
+         else indent(tqdm.__doc__, "    ")) + CLI_EXTRA_DOC
+
+    opt_types = dict(RE_OPTS.findall(d))
+    # opt_types['delim'] = 'chr'
+
+    for o in UNSUPPORTED_OPTS:
+        opt_types.pop(o)
+
+    log.debug(sorted(opt_types.items()))
+
+    # d = RE_OPTS.sub(r'  --\1=<\1>  : \2', d)
+    split = RE_OPTS.split(d)
+    opt_types_desc = zip(split[1::3], split[2::3], split[3::3])
+    d = ''.join(('\n  --{0}  : {2}{3}' if otd[1] == 'bool' else
+                 '\n  --{0}=<{1}>  : {2}{3}').format(
+                     otd[0].replace('_', '-'), otd[0], *otd[1:])
+                for otd in opt_types_desc if otd[0] not in UNSUPPORTED_OPTS)
+
+    help_short = "Usage:\n  tqdm [--help | options]\n"
+    d = help_short + """
+Options:
+  -h, --help     Print this help and exit.
+  -v, --version  Print version and exit.
+""" + d.strip('\n') + '\n'
+
+    # opts = docopt(d, version=__version__)
+    if any(v in argv for v in ('-v', '--version')):
+        sys.stdout.write(__version__ + '\n')
+        sys.exit(0)
+    elif any(v in argv for v in ('-h', '--help')):
+        sys.stdout.write(d + '\n')
+        sys.exit(0)
+    elif argv and argv[0][:2] != '--':
+        sys.stderr.write(f"Error:Unknown argument:{argv[0]}\n{help_short}")
+
+    argv = RE_SHLEX.split(' '.join(["tqdm"] + argv))
+    opts = dict(zip(argv[1::3], argv[3::3]))
+
+    log.debug(opts)
+    opts.pop('log', True)
+
+    tqdm_args = {'file': fp}
+    try:
+        for (o, v) in opts.items():
+            o = o.replace('-', '_')
+            try:
+                tqdm_args[o] = cast(v, opt_types[o])
+            except KeyError as e:
+                raise TqdmKeyError(str(e))
+        log.debug('args:' + str(tqdm_args))
+
+        delim_per_char = tqdm_args.pop('bytes', False)
+        update = tqdm_args.pop('update', False)
+        update_to = tqdm_args.pop('update_to', False)
+        if sum((delim_per_char, update, update_to)) > 1:
+            raise TqdmKeyError("Can only have one of --bytes --update --update_to")
+    except Exception:
+        fp.write("\nError:\n" + help_short)
+        stdin, stdout_write = sys.stdin, sys.stdout.write
+        for i in stdin:
+            stdout_write(i)
+        raise
+    else:
+        buf_size = tqdm_args.pop('buf_size', 256)
+        delim = tqdm_args.pop('delim', b'\\n')
+        tee = tqdm_args.pop('tee', False)
+        manpath = tqdm_args.pop('manpath', None)
+        comppath = tqdm_args.pop('comppath', None)
+        if tqdm_args.pop('null', False):
+            class stdout:
+                @staticmethod
+                def write(_):
+                    pass
+        else:
+            stdout = sys.stdout
+            stdout = getattr(stdout, 'buffer', stdout)
+        stdin = getattr(sys.stdin, 'buffer', sys.stdin)
+        if manpath or comppath:
+            try:  # py<3.9
+                import importlib_resources as resources
+            except ImportError:
+                from importlib import resources
+            from pathlib import Path
+
+            def cp(name, dst):
+                """copy resource `name` to `dst`"""
+                fi = resources.files('tqdm') / name
+                dst.write_bytes(fi.read_bytes())
+                log.info("written:%s", dst)
+            if manpath is not None:
+                cp('tqdm.1', Path(manpath) / 'tqdm.1')
+            if comppath is not None:
+                cp('completion.sh', Path(comppath) / 'tqdm_completion.sh')
+            sys.exit(0)
+        if tee:
+            stdout_write = stdout.write
+            fp_write = getattr(fp, 'buffer', fp).write
+
+            class stdout:  # pylint: disable=function-redefined
+                @staticmethod
+                def write(x):
+                    with tqdm.external_write_mode(file=fp):
+                        fp_write(x)
+                    stdout_write(x)
+        if delim_per_char:
+            tqdm_args.setdefault('unit', 'B')
+            tqdm_args.setdefault('unit_scale', True)
+            tqdm_args.setdefault('unit_divisor', 1024)
+            log.debug(tqdm_args)
+            with tqdm(**tqdm_args) as t:
+                posix_pipe(stdin, stdout, '', buf_size, t.update)
+        elif delim == b'\\n':
+            log.debug(tqdm_args)
+            write = stdout.write
+            if update or update_to:
+                with tqdm(**tqdm_args) as t:
+                    if update:
+                        def callback(i):
+                            t.update(numeric(i.decode()))
+                    else:  # update_to
+                        def callback(i):
+                            t.update(numeric(i.decode()) - t.n)
+                    for i in stdin:
+                        write(i)
+                        callback(i)
+            else:
+                for i in tqdm(stdin, **tqdm_args):
+                    write(i)
+        else:
+            log.debug(tqdm_args)
+            with tqdm(**tqdm_args) as t:
+                callback_len = False
+                if update:
+                    def callback(i):
+                        t.update(numeric(i.decode()))
+                elif update_to:
+                    def callback(i):
+                        t.update(numeric(i.decode()) - t.n)
+                else:
+                    callback = t.update
+                    callback_len = True
+                posix_pipe(stdin, stdout, delim, buf_size, callback, callback_len)

.cache/uv/archive-v0/5itABfUpr52efEvNSNHEE/tqdm/completion.sh

@@ -0,0 +1,19 @@
+#!/usr/bin/env bash
+_tqdm(){
+  local cur prv
+  cur="${COMP_WORDS[COMP_CWORD]}"
+  prv="${COMP_WORDS[COMP_CWORD - 1]}"
+
+  case ${prv} in
+  --bar_format|--buf_size|--colour|--comppath|--delay|--delim|--desc|--initial|--lock_args|--manpath|--maxinterval|--mininterval|--miniters|--ncols|--nrows|--position|--postfix|--smoothing|--total|--unit|--unit_divisor)
+    # await user input
+    ;;
+  "--log")
+    COMPREPLY=($(compgen -W       'CRITICAL FATAL ERROR WARN WARNING INFO DEBUG NOTSET' -- ${cur}))
+    ;;
+  *)
+    COMPREPLY=($(compgen -W '--ascii --bar_format --buf_size --bytes --colour --comppath --delay --delim --desc --disable --dynamic_ncols --help --initial --leave --lock_args --log --manpath --maxinterval --mininterval --miniters --ncols --nrows --null --position --postfix --smoothing --tee --total --unit --unit_divisor --unit_scale --update --update_to --version --write_bytes -h -v' -- ${cur}))
+    ;;
+  esac
+}
+complete -F _tqdm tqdm

.cache/uv/archive-v0/5itABfUpr52efEvNSNHEE/tqdm/contrib/__init__.py

@@ -0,0 +1,91 @@
+"""
+Thin wrappers around common functions.
+
+Subpackages contain potentially unstable extensions.
+"""
+from warnings import warn
+
+from ..auto import tqdm as tqdm_auto
+from ..std import TqdmDeprecationWarning, tqdm
+from ..utils import ObjectWrapper
+
+__author__ = {"github.com/": ["casperdcl"]}
+__all__ = ['tenumerate', 'tzip', 'tmap']
+
+
+class DummyTqdmFile(ObjectWrapper):
+    """Dummy file-like that will write to tqdm"""
+
+    def __init__(self, wrapped):
+        super().__init__(wrapped)
+        self._buf = []
+
+    def write(self, x, nolock=False):
+        nl = b"\n" if isinstance(x, bytes) else "\n"
+        pre, sep, post = x.rpartition(nl)
+        if sep:
+            blank = type(nl)()
+            tqdm.write(blank.join(self._buf + [pre, sep]),
+                       end=blank, file=self._wrapped, nolock=nolock)
+            self._buf = [post]
+        else:
+            self._buf.append(x)
+
+    def __del__(self):
+        if self._buf:
+            blank = type(self._buf[0])()
+            try:
+                tqdm.write(blank.join(self._buf), end=blank, file=self._wrapped)
+            except (OSError, ValueError):
+                pass
+
+
+def builtin_iterable(func):
+    """Returns `func`"""
+    warn("This function has no effect, and will be removed in tqdm==5.0.0",
+         TqdmDeprecationWarning, stacklevel=2)
+    return func
+
+
+def tenumerate(iterable, start=0, total=None, tqdm_class=tqdm_auto, **tqdm_kwargs):
+    """
+    Equivalent of `numpy.ndenumerate` or builtin `enumerate`.
+
+    Parameters
+    ----------
+    tqdm_class  : [default: tqdm.auto.tqdm].
+    """
+    try:
+        import numpy as np
+    except ImportError:
+        pass
+    else:
+        if isinstance(iterable, np.ndarray):
+            return tqdm_class(np.ndenumerate(iterable), total=total or iterable.size,
+                              **tqdm_kwargs)
+    return enumerate(tqdm_class(iterable, total=total, **tqdm_kwargs), start)
+
+
+def tzip(iter1, *iter2plus, **tqdm_kwargs):
+    """
+    Equivalent of builtin `zip`.
+
+    Parameters
+    ----------
+    tqdm_class  : [default: tqdm.auto.tqdm].
+    """
+    kwargs = tqdm_kwargs.copy()
+    tqdm_class = kwargs.pop("tqdm_class", tqdm_auto)
+    yield from zip(tqdm_class(iter1, **kwargs), *iter2plus)
+
+
+def tmap(function, *sequences, **tqdm_kwargs):
+    """
+    Equivalent of builtin `map`.
+
+    Parameters
+    ----------
+    tqdm_class  : [default: tqdm.auto.tqdm].
+    """
+    for i in tzip(*sequences, **tqdm_kwargs):
+        yield function(*i)

.cache/uv/archive-v0/5itABfUpr52efEvNSNHEE/tqdm/contrib/bells.py

@@ -0,0 +1,26 @@
+"""
+Even more features than `tqdm.auto` (all the bells & whistles):
+
+- `tqdm.auto`
+- `tqdm.tqdm.pandas`
+- `tqdm.contrib.telegram`
+    + uses `${TQDM_TELEGRAM_TOKEN}` and `${TQDM_TELEGRAM_CHAT_ID}`
+- `tqdm.contrib.discord`
+    + uses `${TQDM_DISCORD_TOKEN}` and `${TQDM_DISCORD_CHANNEL_ID}`
+"""
+__all__ = ['tqdm', 'trange']
+import warnings
+from os import getenv
+
+if getenv("TQDM_SLACK_TOKEN") and getenv("TQDM_SLACK_CHANNEL"):
+    from .slack import tqdm, trange
+elif getenv("TQDM_TELEGRAM_TOKEN") and getenv("TQDM_TELEGRAM_CHAT_ID"):
+    from .telegram import tqdm, trange
+elif getenv("TQDM_DISCORD_TOKEN") and getenv("TQDM_DISCORD_CHANNEL_ID"):
+    from .discord import tqdm, trange
+else:
+    from ..auto import tqdm, trange
+
+with warnings.catch_warnings():
+    warnings.simplefilter("ignore", category=FutureWarning)
+    tqdm.pandas()

.cache/uv/archive-v0/5itABfUpr52efEvNSNHEE/tqdm/contrib/concurrent.py

@@ -0,0 +1,105 @@
+"""
+Thin wrappers around `concurrent.futures`.
+"""
+from contextlib import contextmanager
+from operator import length_hint
+from os import cpu_count
+
+from ..auto import tqdm as tqdm_auto
+from ..std import TqdmWarning
+
+__author__ = {"github.com/": ["casperdcl"]}
+__all__ = ['thread_map', 'process_map']
+
+
+@contextmanager
+def ensure_lock(tqdm_class, lock_name=""):
+    """get (create if necessary) and then restore `tqdm_class`'s lock"""
+    old_lock = getattr(tqdm_class, '_lock', None)  # don't create a new lock
+    lock = old_lock or tqdm_class.get_lock()  # maybe create a new lock
+    lock = getattr(lock, lock_name, lock)  # maybe subtype
+    tqdm_class.set_lock(lock)
+    yield lock
+    if old_lock is None:
+        del tqdm_class._lock
+    else:
+        tqdm_class.set_lock(old_lock)
+
+
+def _executor_map(PoolExecutor, fn, *iterables, **tqdm_kwargs):
+    """
+    Implementation of `thread_map` and `process_map`.
+
+    Parameters
+    ----------
+    tqdm_class  : [default: tqdm.auto.tqdm].
+    max_workers  : [default: min(32, cpu_count() + 4)].
+    chunksize  : [default: 1].
+    lock_name  : [default: "":str].
+    """
+    kwargs = tqdm_kwargs.copy()
+    if "total" not in kwargs:
+        kwargs["total"] = length_hint(iterables[0])
+    tqdm_class = kwargs.pop("tqdm_class", tqdm_auto)
+    max_workers = kwargs.pop("max_workers", min(32, cpu_count() + 4))
+    chunksize = kwargs.pop("chunksize", 1)
+    lock_name = kwargs.pop("lock_name", "")
+    with ensure_lock(tqdm_class, lock_name=lock_name) as lk:
+        # share lock in case workers are already using `tqdm`
+        with PoolExecutor(max_workers=max_workers, initializer=tqdm_class.set_lock,
+                          initargs=(lk,)) as ex:
+            return list(tqdm_class(ex.map(fn, *iterables, chunksize=chunksize), **kwargs))
+
+
+def thread_map(fn, *iterables, **tqdm_kwargs):
+    """
+    Equivalent of `list(map(fn, *iterables))`
+    driven by `concurrent.futures.ThreadPoolExecutor`.
+
+    Parameters
+    ----------
+    tqdm_class  : optional
+        `tqdm` class to use for bars [default: tqdm.auto.tqdm].
+    max_workers  : int, optional
+        Maximum number of workers to spawn; passed to
+        `concurrent.futures.ThreadPoolExecutor.__init__`.
+        [default: max(32, cpu_count() + 4)].
+    """
+    from concurrent.futures import ThreadPoolExecutor
+    return _executor_map(ThreadPoolExecutor, fn, *iterables, **tqdm_kwargs)
+
+
+def process_map(fn, *iterables, **tqdm_kwargs):
+    """
+    Equivalent of `list(map(fn, *iterables))`
+    driven by `concurrent.futures.ProcessPoolExecutor`.
+
+    Parameters
+    ----------
+    tqdm_class  : optional
+        `tqdm` class to use for bars [default: tqdm.auto.tqdm].
+    max_workers  : int, optional
+        Maximum number of workers to spawn; passed to
+        `concurrent.futures.ProcessPoolExecutor.__init__`.
+        [default: min(32, cpu_count() + 4)].
+    chunksize  : int, optional
+        Size of chunks sent to worker processes; passed to
+        `concurrent.futures.ProcessPoolExecutor.map`. [default: 1].
+    lock_name  : str, optional
+        Member of `tqdm_class.get_lock()` to use [default: mp_lock].
+    """
+    from concurrent.futures import ProcessPoolExecutor
+    if iterables and "chunksize" not in tqdm_kwargs:
+        # default `chunksize=1` has poor performance for large iterables
+        # (most time spent dispatching items to workers).
+        longest_iterable_len = max(map(length_hint, iterables))
+        if longest_iterable_len > 1000:
+            from warnings import warn
+            warn("Iterable length %d > 1000 but `chunksize` is not set."
+                 " This may seriously degrade multiprocess performance."
+                 " Set `chunksize=1` or more." % longest_iterable_len,
+                 TqdmWarning, stacklevel=2)
+    if "lock_name" not in tqdm_kwargs:
+        tqdm_kwargs = tqdm_kwargs.copy()
+        tqdm_kwargs["lock_name"] = "mp_lock"
+    return _executor_map(ProcessPoolExecutor, fn, *iterables, **tqdm_kwargs)

.cache/uv/archive-v0/5itABfUpr52efEvNSNHEE/tqdm/contrib/discord.py

@@ -0,0 +1,156 @@
+"""
+Sends updates to a Discord bot.
+
+Usage:
+>>> from tqdm.contrib.discord import tqdm, trange
+>>> for i in trange(10, token='{token}', channel_id='{channel_id}'):
+...     ...
+
+![screenshot](https://tqdm.github.io/img/screenshot-discord.png)
+"""
+from os import getenv
+from warnings import warn
+
+from requests import Session
+from requests.utils import default_user_agent
+
+from ..auto import tqdm as tqdm_auto
+from ..std import TqdmWarning
+from ..version import __version__
+from .utils_worker import MonoWorker
+
+__author__ = {"github.com/": ["casperdcl", "guigoruiz1"]}
+__all__ = ['DiscordIO', 'tqdm_discord', 'tdrange', 'tqdm', 'trange']
+
+
+class DiscordIO(MonoWorker):
+    """Non-blocking file-like IO using a Discord Bot."""
+    API = "https://discord.com/api/v10"
+    UA = f"tqdm (https://tqdm.github.io, {__version__}) {default_user_agent()}"
+
+    def __init__(self, token, channel_id):
+        """Creates a new message in the given `channel_id`."""
+        super().__init__()
+        self.token = token
+        self.channel_id = channel_id
+        self.session = Session()
+        self.text = self.__class__.__name__
+        self.message_id
+
+    @property
+    def message_id(self):
+        if hasattr(self, '_message_id'):
+            return self._message_id
+        try:
+            res = self.session.post(
+                f'{self.API}/channels/{self.channel_id}/messages',
+                headers={'Authorization': f'Bot {self.token}', 'User-Agent': self.UA},
+                json={'content': f"`{self.text}`"}).json()
+        except Exception as e:
+            tqdm_auto.write(str(e))
+        else:
+            if res.get('error_code') == 429:
+                warn("Creation rate limit: try increasing `mininterval`.",
+                     TqdmWarning, stacklevel=2)
+            else:
+                self._message_id = res['id']
+                return self._message_id
+
+    def write(self, s):
+        """Replaces internal `message_id`'s text with `s`."""
+        if not s:
+            s = "..."
+        s = s.replace('\r', '').strip()
+        if s == self.text:
+            return  # avoid duplicate message Bot error
+        message_id = self.message_id
+        if message_id is None:
+            return
+        self.text = s
+        try:
+            future = self.submit(
+                self.session.patch,
+                f'{self.API}/channels/{self.channel_id}/messages/{message_id}',
+                headers={'Authorization': f'Bot {self.token}', 'User-Agent': self.UA},
+                json={'content': f"`{self.text}`"})
+        except Exception as e:
+            tqdm_auto.write(str(e))
+        else:
+            return future
+
+    def delete(self):
+        """Deletes internal `message_id`."""
+        try:
+            future = self.submit(
+                self.session.delete,
+                f'{self.API}/channels/{self.channel_id}/messages/{self.message_id}',
+                headers={'Authorization': f'Bot {self.token}', 'User-Agent': self.UA})
+        except Exception as e:
+            tqdm_auto.write(str(e))
+        else:
+            return future
+
+
+class tqdm_discord(tqdm_auto):
+    """
+    Standard `tqdm.auto.tqdm` but also sends updates to a Discord Bot.
+    May take a few seconds to create (`__init__`).
+
+    - create a discord bot (not public, no requirement of OAuth2 code
+      grant, only send message permissions) & invite it to a channel:
+      <https://discordpy.readthedocs.io/en/latest/discord.html>
+    - copy the bot `{token}` & `{channel_id}` and paste below
+
+    >>> from tqdm.contrib.discord import tqdm, trange
+    >>> for i in tqdm(iterable, token='{token}', channel_id='{channel_id}'):
+    ...     ...
+    """
+    def __init__(self, *args, **kwargs):
+        """
+        Parameters
+        ----------
+        token  : str, required. Discord bot token
+            [default: ${TQDM_DISCORD_TOKEN}].
+        channel_id  : int, required. Discord channel ID
+            [default: ${TQDM_DISCORD_CHANNEL_ID}].
+
+        See `tqdm.auto.tqdm.__init__` for other parameters.
+        """
+        if not kwargs.get('disable'):
+            kwargs = kwargs.copy()
+            self.dio = DiscordIO(
+                kwargs.pop('token', getenv('TQDM_DISCORD_TOKEN')),
+                kwargs.pop('channel_id', getenv('TQDM_DISCORD_CHANNEL_ID')))
+        super().__init__(*args, **kwargs)
+
+    def display(self, **kwargs):
+        super().display(**kwargs)
+        fmt = self.format_dict
+        if fmt.get('bar_format', None):
+            fmt['bar_format'] = fmt['bar_format'].replace(
+                '<bar/>', '{bar:10u}').replace('{bar}', '{bar:10u}')
+        else:
+            fmt['bar_format'] = '{l_bar}{bar:10u}{r_bar}'
+        self.dio.write(self.format_meter(**fmt))
+
+    def clear(self, *args, **kwargs):
+        super().clear(*args, **kwargs)
+        if not self.disable:
+            self.dio.write("")
+
+    def close(self):
+        if self.disable:
+            return
+        super().close()
+        if not (self.leave or (self.leave is None and self.pos == 0)):
+            self.dio.delete()
+
+
+def tdrange(*args, **kwargs):
+    """Shortcut for `tqdm.contrib.discord.tqdm(range(*args), **kwargs)`."""
+    return tqdm_discord(range(*args), **kwargs)
+
+
+# Aliases
+tqdm = tqdm_discord
+trange = tdrange

.cache/uv/archive-v0/5itABfUpr52efEvNSNHEE/tqdm/contrib/itertools.py

@@ -0,0 +1,35 @@
+"""
+Thin wrappers around `itertools`.
+"""
+import itertools
+
+from ..auto import tqdm as tqdm_auto
+
+__author__ = {"github.com/": ["casperdcl"]}
+__all__ = ['product']
+
+
+def product(*iterables, **tqdm_kwargs):
+    """
+    Equivalent of `itertools.product`.
+
+    Parameters
+    ----------
+    tqdm_class  : [default: tqdm.auto.tqdm].
+    """
+    kwargs = tqdm_kwargs.copy()
+    tqdm_class = kwargs.pop("tqdm_class", tqdm_auto)
+    try:
+        lens = list(map(len, iterables))
+    except TypeError:
+        total = None
+    else:
+        total = 1
+        for i in lens:
+            total *= i
+        kwargs.setdefault("total", total)
+    with tqdm_class(**kwargs) as t:
+        it = itertools.product(*iterables)
+        for i in it:
+            yield i
+            t.update()

.cache/uv/archive-v0/5itABfUpr52efEvNSNHEE/tqdm/contrib/logging.py

@@ -0,0 +1,126 @@
+"""
+Helper functionality for interoperability with stdlib `logging`.
+"""
+import logging
+import sys
+from contextlib import contextmanager
+
+try:
+    from typing import Iterator, List, Optional, Type  # noqa: F401
+except ImportError:
+    pass
+
+from ..std import tqdm as std_tqdm
+
+
+class _TqdmLoggingHandler(logging.StreamHandler):
+    def __init__(
+        self,
+        tqdm_class=std_tqdm  # type: Type[std_tqdm]
+    ):
+        super().__init__()
+        self.tqdm_class = tqdm_class
+
+    def emit(self, record):
+        try:
+            msg = self.format(record)
+            self.tqdm_class.write(msg, file=self.stream)
+            self.flush()
+        except (KeyboardInterrupt, SystemExit):
+            raise
+        except:  # noqa pylint: disable=bare-except
+            self.handleError(record)
+
+
+def _is_console_logging_handler(handler):
+    return (isinstance(handler, logging.StreamHandler)
+            and handler.stream in {sys.stdout, sys.stderr})
+
+
+def _get_first_found_console_logging_handler(handlers):
+    for handler in handlers:
+        if _is_console_logging_handler(handler):
+            return handler
+
+
+@contextmanager
+def logging_redirect_tqdm(
+    loggers=None,  # type: Optional[List[logging.Logger]],
+    tqdm_class=std_tqdm  # type: Type[std_tqdm]
+):
+    # type: (...) -> Iterator[None]
+    """
+    Context manager redirecting console logging to `tqdm.write()`, leaving
+    other logging handlers (e.g. log files) unaffected.
+
+    Parameters
+    ----------
+    loggers  : list, optional
+      Which handlers to redirect (default: [logging.root]).
+    tqdm_class  : optional
+
+    Example
+    -------
+    ```python
+    import logging
+    from tqdm import trange
+    from tqdm.contrib.logging import logging_redirect_tqdm
+
+    LOG = logging.getLogger(__name__)
+
+    if __name__ == '__main__':
+        logging.basicConfig(level=logging.INFO)
+        with logging_redirect_tqdm():
+            for i in trange(9):
+                if i == 4:
+                    LOG.info("console logging redirected to `tqdm.write()`")
+        # logging restored
+    ```
+    """
+    if loggers is None:
+        loggers = [logging.root]
+    original_handlers_list = [logger.handlers for logger in loggers]
+    try:
+        for logger in loggers:
+            tqdm_handler = _TqdmLoggingHandler(tqdm_class)
+            orig_handler = _get_first_found_console_logging_handler(logger.handlers)
+            if orig_handler is not None:
+                tqdm_handler.setFormatter(orig_handler.formatter)
+                tqdm_handler.stream = orig_handler.stream
+            logger.handlers = [
+                handler for handler in logger.handlers
+                if not _is_console_logging_handler(handler)] + [tqdm_handler]
+        yield
+    finally:
+        for logger, original_handlers in zip(loggers, original_handlers_list):
+            logger.handlers = original_handlers
+
+
+@contextmanager
+def tqdm_logging_redirect(
+    *args,
+    # loggers=None,  # type: Optional[List[logging.Logger]]
+    # tqdm=None,  # type: Optional[Type[tqdm.tqdm]]
+    **kwargs
+):
+    # type: (...) -> Iterator[None]
+    """
+    Convenience shortcut for:
+    ```python
+    with tqdm_class(*args, **tqdm_kwargs) as pbar:
+        with logging_redirect_tqdm(loggers=loggers, tqdm_class=tqdm_class):
+            yield pbar
+    ```
+
+    Parameters
+    ----------
+    tqdm_class  : optional, (default: tqdm.std.tqdm).
+    loggers  : optional, list.
+    **tqdm_kwargs  : passed to `tqdm_class`.
+    """
+    tqdm_kwargs = kwargs.copy()
+    loggers = tqdm_kwargs.pop('loggers', None)
+    tqdm_class = tqdm_kwargs.pop('tqdm_class', std_tqdm)
+    with tqdm_class(*args, **tqdm_kwargs) as pbar:
+        with logging_redirect_tqdm(loggers=loggers, tqdm_class=tqdm_class):
+            yield pbar