






'''simple docstring'''



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.
__UpperCamelCase      :       int      			=      {"""LayoutLMv2Config""", """LayoutLMv3Config"""}
@is_pipeline_test
class 							__SCREAMING_SNAKE_CASE       (							unittest.TestCase     ):
      __a  					=MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
      __a  					=TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

      if model_mapping is not None:
            __a  					={config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
      if tf_model_mapping is not None:
            __a  					={
                config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
            }




      @require_torch
      def  __UpperCamelCase							(  self      )							->str:



         '''simple docstring'''
         __a				     =    pipeline(
             task='text-classification' ,   model='hf-internal-testing/tiny-random-distilbert' ,   framework='pt'      )

         __a				     =    text_classifier('This is great !'      )
         self.assertEqual(nested_simplify(lowerCamelCase      ) ,   [{'label': 'LABEL_0', 'score': 0.504}]      )

         __a				     =    text_classifier('This is great !' ,   top_k=2      )
         self.assertEqual(
             nested_simplify(lowerCamelCase      ) ,   [{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}]      )

         __a				     =    text_classifier(['This is great !', 'This is bad'] ,   top_k=2      )
         self.assertEqual(
             nested_simplify(lowerCamelCase      ) ,   [
                 [{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}],
                 [{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}],
             ] ,   )

         __a				     =    text_classifier('This is great !' ,   top_k=1      )
         self.assertEqual(nested_simplify(lowerCamelCase      ) ,   [{'label': 'LABEL_0', 'score': 0.504}]      )

         # Legacy behavior
         __a				     =    text_classifier('This is great !' ,   return_all_scores=lowerCamelCase      )
         self.assertEqual(nested_simplify(lowerCamelCase      ) ,   [{'label': 'LABEL_0', 'score': 0.504}]      )

         __a				     =    text_classifier('This is great !' ,   return_all_scores=lowerCamelCase      )
         self.assertEqual(
             nested_simplify(lowerCamelCase      ) ,   [[{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}]]      )

         __a				     =    text_classifier(['This is great !', 'Something else'] ,   return_all_scores=lowerCamelCase      )
         self.assertEqual(
             nested_simplify(lowerCamelCase      ) ,   [
                 [{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}],
                 [{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}],
             ] ,   )

         __a				     =    text_classifier(['This is great !', 'Something else'] ,   return_all_scores=lowerCamelCase      )
         self.assertEqual(
             nested_simplify(lowerCamelCase      ) ,   [
                 {'label': 'LABEL_0', 'score': 0.504},
                 {'label': 'LABEL_0', 'score': 0.504},
             ] ,   )




      @require_torch
      def  __UpperCamelCase							(  self      )							->List[Any]:



         '''simple docstring'''
         import torch

         __a				     =    pipeline(
             task='text-classification' ,   model='hf-internal-testing/tiny-random-distilbert' ,   framework='pt' ,   device=torch.device('cpu'      ) ,   )

         __a				     =    text_classifier('This is great !'      )
         self.assertEqual(nested_simplify(lowerCamelCase      ) ,   [{'label': 'LABEL_0', 'score': 0.504}]      )




      @require_tf
      def  __UpperCamelCase							(  self      )							->str:



         '''simple docstring'''
         __a				     =    pipeline(
             task='text-classification' ,   model='hf-internal-testing/tiny-random-distilbert' ,   framework='tf'      )

         __a				     =    text_classifier('This is great !'      )
         self.assertEqual(nested_simplify(lowerCamelCase      ) ,   [{'label': 'LABEL_0', 'score': 0.504}]      )




      @slow
      @require_torch
      def  __UpperCamelCase							(  self      )							->List[str]:



         '''simple docstring'''
         __a				     =    pipeline('text-classification'      )

         __a				     =    text_classifier('This is great !'      )
         self.assertEqual(nested_simplify(lowerCamelCase      ) ,   [{'label': 'POSITIVE', 'score': 1.0}]      )
         __a				     =    text_classifier('This is bad !'      )
         self.assertEqual(nested_simplify(lowerCamelCase      ) ,   [{'label': 'NEGATIVE', 'score': 1.0}]      )
         __a				     =    text_classifier('Birds are a type of animal'      )
         self.assertEqual(nested_simplify(lowerCamelCase      ) ,   [{'label': 'POSITIVE', 'score': 0.988}]      )




      @slow
      @require_tf
      def  __UpperCamelCase							(  self      )							->Tuple:



         '''simple docstring'''
         __a				     =    pipeline('text-classification' ,   framework='tf'      )

         __a				     =    text_classifier('This is great !'      )
         self.assertEqual(nested_simplify(lowerCamelCase      ) ,   [{'label': 'POSITIVE', 'score': 1.0}]      )
         __a				     =    text_classifier('This is bad !'      )
         self.assertEqual(nested_simplify(lowerCamelCase      ) ,   [{'label': 'NEGATIVE', 'score': 1.0}]      )
         __a				     =    text_classifier('Birds are a type of animal'      )
         self.assertEqual(nested_simplify(lowerCamelCase      ) ,   [{'label': 'POSITIVE', 'score': 0.988}]      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->int:



         '''simple docstring'''
         __a				     =    TextClassificationPipeline(model=lowerCamelCase ,   tokenizer=lowerCamelCase      )
         return text_classifier, ["HuggingFace is in", "This is another test"]






      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase      )							->Any:



         '''simple docstring'''
         __a				     =    text_classifier.model
         # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
         __a				     =    'HuggingFace is in'
         __a				     =    text_classifier(lowerCamelCase      )

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

         __a				     =    ['HuggingFace is in ', 'Paris is in France']
         __a				     =    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
         __a				     =    text_classifier(lowerCamelCase ,   top_k=lowerCamelCase      )
         __a				     =    len(model.config.idalabel.values()      )
         self.assertEqual(
             nested_simplify(lowerCamelCase      ) ,   [[{'label': ANY(lowerCamelCase      ), 'score': ANY(lowerCamelCase      )}] * N, [{'label': ANY(lowerCamelCase      ), 'score': ANY(lowerCamelCase      )}] * N] ,   )

         __a				     =    {'text': 'HuggingFace is in ', 'text_pair': 'Paris is in France'}
         __a				     =    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.
         __a				     =    [['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
         __a				     =    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()      )







'''simple docstring'''



from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
 import PIL

__UpperCamelCase      :       Dict      			=      logging.get_logger(__name__)



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	str    )  ->			List[List[ImageInput]]:

   """simple docstring"""
   if isinstance(SCREAMING_SNAKE_CASE__,					(list, tuple)    ) and isinstance(videos[0],					(list, tuple)    ) and is_valid_image(videos[0][0]    ):
      return videos

   elif isinstance(SCREAMING_SNAKE_CASE__,					(list, tuple)    ) and is_valid_image(videos[0]    ):
      return [videos]

   elif is_valid_image(SCREAMING_SNAKE_CASE__    ):
      return [[videos]]

   raise ValueError(f"""Could not make batched video from {videos}"""    )



class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					=["pixel_values"]




      def __init__(  self ,   lowerCamelCase = True ,   lowerCamelCase = None ,   lowerCamelCase = PILImageResampling.BILINEAR ,   lowerCamelCase = True ,   lowerCamelCase = None ,   lowerCamelCase = True ,   lowerCamelCase = 1 / 255 ,   lowerCamelCase = True ,   lowerCamelCase = True ,   lowerCamelCase = None ,   lowerCamelCase = None ,   **lowerCamelCase ,   )							->None:



         '''simple docstring'''
         super().__init__(**lowerCamelCase      )
         __a				     =    size if size is not None else {'shortest_edge': 256}
         __a				     =    get_size_dict(lowerCamelCase ,   default_to_square=lowerCamelCase      )
         __a				     =    crop_size if crop_size is not None else {'height': 224, 'width': 224}
         __a				     =    get_size_dict(lowerCamelCase ,   param_name='crop_size'      )

         __a				     =    do_resize
         __a				     =    size
         __a				     =    do_center_crop
         __a				     =    crop_size
         __a				     =    resample
         __a				     =    do_rescale
         __a				     =    rescale_factor
         __a				     =    offset
         __a				     =    do_normalize
         __a				     =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         __a				     =    image_std if image_std is not None else IMAGENET_STANDARD_STD




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase = PILImageResampling.BILINEAR ,   lowerCamelCase = None ,   **lowerCamelCase ,   )							->np.ndarray:



         '''simple docstring'''
         __a				     =    get_size_dict(lowerCamelCase ,   default_to_square=lowerCamelCase      )
         if "shortest_edge" in size:
            __a				     =    get_resize_output_image_size(lowerCamelCase ,   size['shortest_edge'] ,   default_to_square=lowerCamelCase      )
         elif "height" in size and "width" in size:
            __a				     =    (size['height'], size['width'])
         else:
            raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""      )
         return resize(lowerCamelCase ,   size=lowerCamelCase ,   resample=lowerCamelCase ,   data_format=lowerCamelCase ,   **lowerCamelCase      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase = None ,   **lowerCamelCase ,   )							->np.ndarray:



         '''simple docstring'''
         __a				     =    get_size_dict(lowerCamelCase      )
         if "height" not in size or "width" not in size:
            raise ValueError(F"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""      )
         return center_crop(lowerCamelCase ,   size=(size['height'], size['width']) ,   data_format=lowerCamelCase ,   **lowerCamelCase      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase = True ,   lowerCamelCase = None ,   **lowerCamelCase ,   )							->Optional[Any]:



         '''simple docstring'''
         __a				     =    image.astype(np.floataa      )
         if offset:
            __a				     =    image - (scale / 2)
         return rescale(lowerCamelCase ,   scale=lowerCamelCase ,   data_format=lowerCamelCase ,   **lowerCamelCase      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase = None ,   **lowerCamelCase ,   )							->np.ndarray:



         '''simple docstring'''
         return normalize(lowerCamelCase ,   mean=lowerCamelCase ,   std=lowerCamelCase ,   data_format=lowerCamelCase ,   **lowerCamelCase      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = ChannelDimension.FIRST ,   )							->np.ndarray:



         '''simple docstring'''
         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_size is None:
            raise ValueError('Crop size 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.'      )

         if offset and not do_rescale:
            raise ValueError('For offset, do_rescale must also be set to True.'      )

         # All transformations expect numpy arrays.
         __a				     =    to_numpy_array(lowerCamelCase      )

         if do_resize:
            __a				     =    self.resize(image=lowerCamelCase ,   size=lowerCamelCase ,   resample=lowerCamelCase      )

         if do_center_crop:
            __a				     =    self.center_crop(lowerCamelCase ,   size=lowerCamelCase      )

         if do_rescale:
            __a				     =    self.rescale(image=lowerCamelCase ,   scale=lowerCamelCase ,   offset=lowerCamelCase      )

         if do_normalize:
            __a				     =    self.normalize(image=lowerCamelCase ,   mean=lowerCamelCase ,   std=lowerCamelCase      )

         __a				     =    to_channel_dimension_format(lowerCamelCase ,   lowerCamelCase      )
         return image






      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = ChannelDimension.FIRST ,   **lowerCamelCase ,   )							->PIL.Image.Image:



         '''simple docstring'''
         __a				     =    do_resize if do_resize is not None else self.do_resize
         __a				     =    resample if resample is not None else self.resample
         __a				     =    do_center_crop if do_center_crop is not None else self.do_center_crop
         __a				     =    do_rescale if do_rescale is not None else self.do_rescale
         __a				     =    rescale_factor if rescale_factor is not None else self.rescale_factor
         __a				     =    offset if offset is not None else self.offset
         __a				     =    do_normalize if do_normalize is not None else self.do_normalize
         __a				     =    image_mean if image_mean is not None else self.image_mean
         __a				     =    image_std if image_std is not None else self.image_std

         __a				     =    size if size is not None else self.size
         __a				     =    get_size_dict(lowerCamelCase ,   default_to_square=lowerCamelCase      )
         __a				     =    crop_size if crop_size is not None else self.crop_size
         __a				     =    get_size_dict(lowerCamelCase ,   param_name='crop_size'      )

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

         __a				     =    make_batched(lowerCamelCase      )

         __a				     =    [
             [
                 self._preprocess_image(
                     image=lowerCamelCase ,   do_resize=lowerCamelCase ,   size=lowerCamelCase ,   resample=lowerCamelCase ,   do_center_crop=lowerCamelCase ,   crop_size=lowerCamelCase ,   do_rescale=lowerCamelCase ,   rescale_factor=lowerCamelCase ,   offset=lowerCamelCase ,   do_normalize=lowerCamelCase ,   image_mean=lowerCamelCase ,   image_std=lowerCamelCase ,   data_format=lowerCamelCase ,   )
                 for img in video
             ]
             for video in videos
         ]

         __a				     =    {'pixel_values': videos}
         return BatchFeature(data=lowerCamelCase ,   tensor_type=lowerCamelCase      )