


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
	import PIL


_snake_case  :	Dict			      =       logging.get_logger(__name__)

class        a	(lowercase__   ):




							"""simple docstring"""


							__UpperCAmelCase					:							Dict  						=						["""pixel_values"""]

							def __init__(							self :						Optional[int]					,			lowerCamelCase :						bool = True					,			lowerCamelCase :						Dict[str, int] = None					,			lowerCamelCase :						int = 0.9					,			lowerCamelCase :						PILImageResampling = PILImageResampling.BICUBIC					,			lowerCamelCase :						bool = True					,			lowerCamelCase :						Dict[str, int] = None					,			lowerCamelCase :						Union[int, float] = 1 / 255					,			lowerCamelCase :						bool = True					,			lowerCamelCase :						bool = True					,			lowerCamelCase :						Optional[Union[float, List[float]]] = None					,			lowerCamelCase :						Optional[Union[float, List[float]]] = None					,			**lowerCamelCase :						Union[str, Any]					,			)			->					str:
														super().__init__(**__lowercase       )
														__snake_case	:    Optional[int]											=     size if size is not None else {"shortest_edge": 224}
														__snake_case	:    List[str]											=     get_size_dict(__lowercase					,			default_to_square=__lowercase       )
														__snake_case	:    Dict											=     crop_size if crop_size is not None else {"height": 224, "width": 224}
														__snake_case	:    List[str]											=     get_size_dict(__lowercase					,			param_name="crop_size"       )

														__snake_case	:    int											=     do_resize
														__snake_case	:    List[Any]											=     size
														__snake_case	:    Any											=     crop_pct
														__snake_case	:    List[str]											=     resample
														__snake_case	:    int											=     do_center_crop
														__snake_case	:    Dict											=     crop_size
														__snake_case	:    int											=     do_rescale
														__snake_case	:    List[str]											=     rescale_factor
														__snake_case	:    Optional[int]											=     do_normalize
														__snake_case	:    List[str]											=     image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
														__snake_case	:    Dict											=     image_std if image_std is not None else IMAGENET_DEFAULT_STD

							def 					__snake_case      (							self :						Dict					,			lowerCamelCase :						np.ndarray					,			lowerCamelCase :						Dict[str, int]					,			lowerCamelCase :						Optional[float] = None					,			lowerCamelCase :						PILImageResampling = PILImageResampling.BICUBIC					,			lowerCamelCase :						Optional[Union[str, ChannelDimension]] = None					,			**lowerCamelCase :						List[str]					,			)			->					Optional[Any]:
														__snake_case	:    Any											=     get_size_dict(__lowercase					,			default_to_square=__lowercase       )
														if "shortest_edge" not in size and ("height" not in size or "width" not in size):
																					raise ValueError(F'size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'       )
														if crop_pct is not None:
																					if "shortest_edge" in size:
																												__snake_case	:    Any											=     int(size["shortest_edge"] / crop_pct       )
																					elif "height" in size and "width" in size:
																												if size["height"] == size["width"]:
																																			__snake_case	:    Union[str, Any]											=     int(size["height"] / crop_pct       )
																												else:
																																			__snake_case	:    Union[str, Any]											=     (int(size["height"] / crop_pct       ), int(size["width"] / crop_pct       ))
																					else:
																												raise ValueError("Invalid size for resize: {}".format(__lowercase       )       )

																					__snake_case	:    List[str]											=     get_resize_output_image_size(__lowercase					,			size=__lowercase					,			default_to_square=__lowercase       )
														else:
																					if "shortest_edge" in size:
																												__snake_case	:    Tuple											=     get_resize_output_image_size(__lowercase					,			size=size["shortest_edge"]					,			default_to_square=__lowercase       )
																					elif "height" in size and "width" in size:
																												__snake_case	:    Dict											=     (size["height"], size["width"])
																					else:
																												raise ValueError("Invalid size for resize: {}".format(__lowercase       )       )

														return resize(__lowercase					,			size=__lowercase					,			resample=__lowercase					,			data_format=__lowercase					,			**__lowercase       )

							def 					__snake_case      (							self :						Any					,			lowerCamelCase :						np.ndarray					,			lowerCamelCase :						Dict[str, int]					,			lowerCamelCase :						Optional[Union[str, ChannelDimension]] = None					,			**lowerCamelCase :						Any					,			)			->					int:
														__snake_case	:    Optional[Any]											=     get_size_dict(__lowercase       )
														if "height" not in size or "width" not in size:
																					raise ValueError(F'size must contain \'height\' and \'width\' as keys. Got {size.keys()}'       )
														return center_crop(__lowercase					,			size=(size["height"], size["width"])					,			data_format=__lowercase					,			**__lowercase       )

							def 					__snake_case      (							self :						Optional[int]					,			lowerCamelCase :						np.ndarray					,			lowerCamelCase :						Union[int, float]					,			lowerCamelCase :						Optional[Union[str, ChannelDimension]] = None					,			**lowerCamelCase :						List[str]					,			)			->					Any:
														return rescale(__lowercase					,			scale=__lowercase					,			data_format=__lowercase					,			**__lowercase       )

							def 					__snake_case      (							self :						str					,			lowerCamelCase :						np.ndarray					,			lowerCamelCase :						Union[float, List[float]]					,			lowerCamelCase :						Union[float, List[float]]					,			lowerCamelCase :						Optional[Union[str, ChannelDimension]] = None					,			**lowerCamelCase :						Tuple					,			)			->					List[Any]:
														return normalize(__lowercase					,			mean=__lowercase					,			std=__lowercase					,			data_format=__lowercase					,			**__lowercase       )






							def 					__snake_case      (							self :						List[str]					,			lowerCamelCase :						ImageInput					,			lowerCamelCase :						bool = None					,			lowerCamelCase :						Dict[str, int] = None					,			lowerCamelCase :						int = None					,			lowerCamelCase :						PILImageResampling = None					,			lowerCamelCase :						bool = None					,			lowerCamelCase :						Dict[str, int] = None					,			lowerCamelCase :						bool = None					,			lowerCamelCase :						float = None					,			lowerCamelCase :						bool = None					,			lowerCamelCase :						Optional[Union[float, List[float]]] = None					,			lowerCamelCase :						Optional[Union[float, List[float]]] = None					,			lowerCamelCase :						Optional[Union[str, TensorType]] = None					,			lowerCamelCase :						ChannelDimension = ChannelDimension.FIRST					,			**lowerCamelCase :						Tuple					,			)			->					Dict:
														__snake_case	:    Union[str, Any]											=     do_resize if do_resize is not None else self.do_resize
														__snake_case	:    Any											=     crop_pct if crop_pct is not None else self.crop_pct
														__snake_case	:    List[str]											=     resample if resample is not None else self.resample
														__snake_case	:    Union[str, Any]											=     do_center_crop if do_center_crop is not None else self.do_center_crop
														__snake_case	:    Any											=     do_rescale if do_rescale is not None else self.do_rescale
														__snake_case	:    Tuple											=     rescale_factor if rescale_factor is not None else self.rescale_factor
														__snake_case	:    str											=     do_normalize if do_normalize is not None else self.do_normalize
														__snake_case	:    Any											=     image_mean if image_mean is not None else self.image_mean
														__snake_case	:    Dict											=     image_std if image_std is not None else self.image_std

														__snake_case	:    Tuple											=     size if size is not None else self.size
														__snake_case	:    List[str]											=     get_size_dict(__lowercase					,			default_to_square=__lowercase       )
														__snake_case	:    Tuple											=     crop_size if crop_size is not None else self.crop_size
														__snake_case	:    List[str]											=     get_size_dict(__lowercase					,			param_name="crop_size"       )

														__snake_case	:    Union[str, Any]											=     make_list_of_images(__lowercase       )

														if not valid_images(__lowercase       ):
																					raise ValueError(
																					    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
																					    "torch.Tensor, tf.Tensor or jax.ndarray."       )

														if do_resize and size is None or resample is None:
																					raise ValueError("Size and resample must be specified if do_resize is True."       )

														if do_center_crop and crop_pct is None:
																					raise ValueError("Crop_pct must be specified if do_center_crop is True."       )

														if do_rescale and rescale_factor is None:
																					raise ValueError("Rescale factor must be specified if do_rescale is True."       )

														if do_normalize and (image_mean is None or image_std is None):
																					raise ValueError("Image mean and std must be specified if do_normalize is True."       )

														# All transformations expect numpy arrays.
														__snake_case	:    Optional[Any]											=     [to_numpy_array(__lowercase       ) for image in images]

														if do_resize:
																					__snake_case	:    str											=     [self.resize(image=__lowercase					,			size=__lowercase					,			crop_pct=__lowercase					,			resample=__lowercase       ) for image in images]

														if do_center_crop:
																					__snake_case	:    str											=     [self.center_crop(image=__lowercase					,			size=__lowercase       ) for image in images]

														if do_rescale:
																					__snake_case	:    Tuple											=     [self.rescale(image=__lowercase					,			scale=__lowercase       ) for image in images]

														if do_normalize:
																					__snake_case	:    Optional[int]											=     [self.normalize(image=__lowercase					,			mean=__lowercase					,			std=__lowercase       ) for image in images]

														__snake_case	:    List[str]											=     [to_channel_dimension_format(__lowercase					,			__lowercase       ) for image in images]

														__snake_case	:    Tuple											=     {"pixel_values": images}
														return BatchFeature(data=__lowercase					,			tensor_type=__lowercase       )





import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
_snake_case  :	Dict			      =       {"LayoutLMv2Config", "LayoutLMv3Config"}

@is_pipeline_test
class        a	(unittest.TestCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							Union[str, Any]  						=						MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
							__UpperCAmelCase					:							Optional[int]  						=						TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

							if model_mapping is not None:
														__UpperCAmelCase					:							Optional[int]  						=						{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
							if tf_model_mapping is not None:
														__UpperCAmelCase					:							Any  						=						{
														    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
														}

							@require_torch
							def 					__snake_case      (							self :						str       )			->					str:
														__snake_case	:    Dict											=     pipeline(
														    task="text-classification"					,			model="hf-internal-testing/tiny-random-distilbert"					,			framework="pt"       )

														__snake_case	:    Optional[Any]											=     text_classifier("This is great !"       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "LABEL_0", "score": 0.5_04}]       )

														__snake_case	:    Any											=     text_classifier("This is great !"					,			top_k=2       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase       )					,			[{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}]       )

														__snake_case	:    int											=     text_classifier(["This is great !", "This is bad"]					,			top_k=2       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase       )					,			[
														        [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
														        [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
														    ]					,			)

														__snake_case	:    List[str]											=     text_classifier("This is great !"					,			top_k=1       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "LABEL_0", "score": 0.5_04}]       )

														# Legacy behavior
														__snake_case	:    Dict											=     text_classifier("This is great !"					,			return_all_scores=lowerCamelCase       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "LABEL_0", "score": 0.5_04}]       )

														__snake_case	:    Any											=     text_classifier("This is great !"					,			return_all_scores=lowerCamelCase       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase       )					,			[[{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}]]       )

														__snake_case	:    Tuple											=     text_classifier(["This is great !", "Something else"]					,			return_all_scores=lowerCamelCase       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase       )					,			[
														        [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
														        [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
														    ]					,			)

														__snake_case	:    Tuple											=     text_classifier(["This is great !", "Something else"]					,			return_all_scores=lowerCamelCase       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase       )					,			[
														        {"label": "LABEL_0", "score": 0.5_04},
														        {"label": "LABEL_0", "score": 0.5_04},
														    ]					,			)

							@require_torch
							def 					__snake_case      (							self :						Optional[int]       )			->					List[Any]:
														import torch

														__snake_case	:    Dict											=     pipeline(
														    task="text-classification"					,			model="hf-internal-testing/tiny-random-distilbert"					,			framework="pt"					,			device=torch.device("cpu"       )					,			)

														__snake_case	:    Optional[int]											=     text_classifier("This is great !"       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "LABEL_0", "score": 0.5_04}]       )

							@require_tf
							def 					__snake_case      (							self :						Any       )			->					Tuple:
														__snake_case	:    List[Any]											=     pipeline(
														    task="text-classification"					,			model="hf-internal-testing/tiny-random-distilbert"					,			framework="tf"       )

														__snake_case	:    str											=     text_classifier("This is great !"       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "LABEL_0", "score": 0.5_04}]       )

							@slow
							@require_torch
							def 					__snake_case      (							self :						int       )			->					int:
														__snake_case	:    Dict											=     pipeline("text-classification"       )

														__snake_case	:    Dict											=     text_classifier("This is great !"       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "POSITIVE", "score": 1.0}]       )
														__snake_case	:    int											=     text_classifier("This is bad !"       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "NEGATIVE", "score": 1.0}]       )
														__snake_case	:    str											=     text_classifier("Birds are a type of animal"       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "POSITIVE", "score": 0.9_88}]       )

							@slow
							@require_tf
							def 					__snake_case      (							self :						List[Any]       )			->					str:
														__snake_case	:    Optional[Any]											=     pipeline("text-classification"					,			framework="tf"       )

														__snake_case	:    Any											=     text_classifier("This is great !"       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "POSITIVE", "score": 1.0}]       )
														__snake_case	:    Optional[int]											=     text_classifier("This is bad !"       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "NEGATIVE", "score": 1.0}]       )
														__snake_case	:    Any											=     text_classifier("Birds are a type of animal"       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "POSITIVE", "score": 0.9_88}]       )

							def 					__snake_case      (							self :						str					,			lowerCamelCase :						Tuple					,			lowerCamelCase :						Any					,			lowerCamelCase :						Union[str, Any]       )			->					List[Any]:
														__snake_case	:    Union[str, Any]											=     TextClassificationPipeline(model=lowerCamelCase					,			tokenizer=lowerCamelCase       )
														return text_classifier, ["HuggingFace is in", "This is another test"]






							def 					__snake_case      (							self :						List[str]					,			lowerCamelCase :						Optional[Any]					,			lowerCamelCase :						Tuple       )			->					str:
														__snake_case	:    Tuple											=     text_classifier.model
														# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
														__snake_case	:    Any											=     "HuggingFace is in"
														__snake_case	:    Tuple											=     text_classifier(lowerCamelCase       )

														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": ANY(lowerCamelCase       ), "score": ANY(lowerCamelCase       )}]       )
														self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()       )

														__snake_case	:    Union[str, Any]											=     ["HuggingFace is in ", "Paris is in France"]
														__snake_case	:    Union[str, Any]											=     text_classifier(lowerCamelCase       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase       )					,			[{"label": ANY(lowerCamelCase       ), "score": ANY(lowerCamelCase       )}, {"label": ANY(lowerCamelCase       ), "score": ANY(lowerCamelCase       )}]					,			)
														self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()       )
														self.assertTrue(outputs[1]["label"] in model.config.idalabel.values()       )

														# Forcing to get all results with `top_k=None`
														# This is NOT the legacy format
														__snake_case	:    str											=     text_classifier(lowerCamelCase					,			top_k=lowerCamelCase       )
														__snake_case	:    int											=     len(model.config.idalabel.values()       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase       )					,			[[{"label": ANY(lowerCamelCase       ), "score": ANY(lowerCamelCase       )}] * N, [{"label": ANY(lowerCamelCase       ), "score": ANY(lowerCamelCase       )}] * N]					,			)

														__snake_case	:    Optional[int]											=     {"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
														__snake_case	:    Optional[Any]											=     text_classifier(lowerCamelCase       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase       )					,			{"label": ANY(lowerCamelCase       ), "score": ANY(lowerCamelCase       )}					,			)
														self.assertTrue(outputs["label"] in model.config.idalabel.values()       )

														# This might be used a text pair, but tokenizer + pipe interaction
														# makes it hard to understand that it's not using the pair properly
														# https://github.com/huggingface/transformers/issues/17305
														# We disabled this usage instead as it was outputting wrong outputs.
														__snake_case	:    Any											=     [["HuggingFace is in ", "Paris is in France"]]
														with self.assertRaises(lowerCamelCase       ):
																					text_classifier(lowerCamelCase       )

														# This used to be valid for doing text pairs
														# We're keeping it working because of backward compatibility
														__snake_case	:    List[Any]											=     text_classifier([[["HuggingFace is in ", "Paris is in France"]]]       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase       )					,			[{"label": ANY(lowerCamelCase       ), "score": ANY(lowerCamelCase       )}]					,			)
														self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()       )

