

import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
       from PIL import Image
else:



       class 					__snake_case     :

         @staticmethod
         def   lowerCAmelCase__   (    *_A  ,						**_A):
             pass



@is_pipeline_test
@require_torch_or_tf
@require_vision
class 					__snake_case     (			unittest.TestCase				):
  __lowerCAmelCase   :		Optional[int]         =     MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
  __lowerCAmelCase   :		str         =     TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

  def   lowerCAmelCase__   (    self  ,						_A  ,						_A  ,						_A):
      SCREAMING_SNAKE_CASE_													=  ImageClassificationPipeline(model=_lowercase  ,						image_processor=_lowercase  ,						top_k=2)
      SCREAMING_SNAKE_CASE_													=  [
          Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'),
          'http://images.cocodataset.org/val2017/000000039769.jpg',
      ]
      return image_classifier, examples

  def   lowerCAmelCase__   (    self  ,						_A  ,						_A):
      SCREAMING_SNAKE_CASE_													=  image_classifier('./tests/fixtures/tests_samples/COCO/000000039769.png')

      self.assertEqual(
          _lowercase  ,						[
              {'score': ANY(_lowercase), 'label': ANY(_lowercase)},
              {'score': ANY(_lowercase), 'label': ANY(_lowercase)},
          ]  ,						)

      import datasets

      SCREAMING_SNAKE_CASE_													=  datasets.load_dataset('hf-internal-testing/fixtures_image_utils'  ,						'image'  ,						split='test')

      # Accepts URL + PIL.Image + lists
      SCREAMING_SNAKE_CASE_													=  image_classifier(
          [
              Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'),
              'http://images.cocodataset.org/val2017/000000039769.jpg',
              # RGBA
              dataset[0]['file'],
              # LA
              dataset[1]['file'],
              # L
              dataset[2]['file'],
          ])
      self.assertEqual(
          _lowercase  ,						[
              [
                  {'score': ANY(_lowercase), 'label': ANY(_lowercase)},
                  {'score': ANY(_lowercase), 'label': ANY(_lowercase)},
              ],
              [
                  {'score': ANY(_lowercase), 'label': ANY(_lowercase)},
                  {'score': ANY(_lowercase), 'label': ANY(_lowercase)},
              ],
              [
                  {'score': ANY(_lowercase), 'label': ANY(_lowercase)},
                  {'score': ANY(_lowercase), 'label': ANY(_lowercase)},
              ],
              [
                  {'score': ANY(_lowercase), 'label': ANY(_lowercase)},
                  {'score': ANY(_lowercase), 'label': ANY(_lowercase)},
              ],
              [
                  {'score': ANY(_lowercase), 'label': ANY(_lowercase)},
                  {'score': ANY(_lowercase), 'label': ANY(_lowercase)},
              ],
          ]  ,						)

  @require_torch
  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  'hf-internal-testing/tiny-random-vit'
      SCREAMING_SNAKE_CASE_													=  pipeline('image-classification'  ,						model=_lowercase)

      SCREAMING_SNAKE_CASE_													=  image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg')
      self.assertEqual(
          nested_simplify(_lowercase  ,						decimals=4)  ,						[{'label': 'LABEL_1', 'score': 0.5_7_4}, {'label': 'LABEL_0', 'score': 0.4_2_6}]  ,						)

      SCREAMING_SNAKE_CASE_													=  image_classifier(
          [
              'http://images.cocodataset.org/val2017/000000039769.jpg',
              'http://images.cocodataset.org/val2017/000000039769.jpg',
          ]  ,						top_k=2  ,						)
      self.assertEqual(
          nested_simplify(_lowercase  ,						decimals=4)  ,						[
              [{'label': 'LABEL_1', 'score': 0.5_7_4}, {'label': 'LABEL_0', 'score': 0.4_2_6}],
              [{'label': 'LABEL_1', 'score': 0.5_7_4}, {'label': 'LABEL_0', 'score': 0.4_2_6}],
          ]  ,						)

  @require_tf
  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  'hf-internal-testing/tiny-random-vit'
      SCREAMING_SNAKE_CASE_													=  pipeline('image-classification'  ,						model=_lowercase  ,						framework='tf')

      SCREAMING_SNAKE_CASE_													=  image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg')
      self.assertEqual(
          nested_simplify(_lowercase  ,						decimals=4)  ,						[{'label': 'LABEL_1', 'score': 0.5_7_4}, {'label': 'LABEL_0', 'score': 0.4_2_6}]  ,						)

      SCREAMING_SNAKE_CASE_													=  image_classifier(
          [
              'http://images.cocodataset.org/val2017/000000039769.jpg',
              'http://images.cocodataset.org/val2017/000000039769.jpg',
          ]  ,						top_k=2  ,						)
      self.assertEqual(
          nested_simplify(_lowercase  ,						decimals=4)  ,						[
              [{'label': 'LABEL_1', 'score': 0.5_7_4}, {'label': 'LABEL_0', 'score': 0.4_2_6}],
              [{'label': 'LABEL_1', 'score': 0.5_7_4}, {'label': 'LABEL_0', 'score': 0.4_2_6}],
          ]  ,						)

  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  PreTrainedTokenizer()

      # Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
      SCREAMING_SNAKE_CASE_													=  pipeline(
          'image-classification'  ,						model='hf-internal-testing/tiny-random-vit'  ,						tokenizer=_lowercase)

      self.assertIs(image_classifier.tokenizer  ,						_lowercase)


  @slow
  @require_torch
  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  pipeline('image-classification'  ,						model='deepmind/vision-perceiver-conv')
      SCREAMING_SNAKE_CASE_													=  image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg')
      self.assertEqual(
          nested_simplify(_lowercase  ,						decimals=4)  ,						[
              {'score': 0.4_3_8_5, 'label': 'tabby, tabby cat'},
              {'score': 0.3_2_1, 'label': 'tiger cat'},
              {'score': 0.0_5_0_2, 'label': 'Egyptian cat'},
              {'score': 0.0_1_3_7, 'label': 'crib, cot'},
              {'score': 0.0_0_7, 'label': 'radiator'},
          ]  ,						)

      SCREAMING_SNAKE_CASE_													=  pipeline('image-classification'  ,						model='deepmind/vision-perceiver-fourier')
      SCREAMING_SNAKE_CASE_													=  image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg')
      self.assertEqual(
          nested_simplify(_lowercase  ,						decimals=4)  ,						[
              {'score': 0.5_6_5_8, 'label': 'tabby, tabby cat'},
              {'score': 0.1_3_0_9, 'label': 'tiger cat'},
              {'score': 0.0_7_2_2, 'label': 'Egyptian cat'},
              {'score': 0.0_7_0_7, 'label': 'remote control, remote'},
              {'score': 0.0_0_8_2, 'label': 'computer keyboard, keypad'},
          ]  ,						)

      SCREAMING_SNAKE_CASE_													=  pipeline('image-classification'  ,						model='deepmind/vision-perceiver-learned')
      SCREAMING_SNAKE_CASE_													=  image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg')
      self.assertEqual(
          nested_simplify(_lowercase  ,						decimals=4)  ,						[
              {'score': 0.3_0_2_2, 'label': 'tabby, tabby cat'},
              {'score': 0.2_3_6_2, 'label': 'Egyptian cat'},
              {'score': 0.1_8_5_6, 'label': 'tiger cat'},
              {'score': 0.0_3_2_4, 'label': 'remote control, remote'},
              {'score': 0.0_0_9_6, 'label': 'quilt, comforter, comfort, puff'},
          ]  ,						)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


_snake_case				= {}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case				= ["""MBart50Tokenizer"""]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case				= ["""MBart50TokenizerFast"""]


if TYPE_CHECKING:
  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mbartaa import MBartaaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
  import sys

  _snake_case				= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






