'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__SCREAMING_SNAKE_CASE :  List[str]  =						{
    """configuration_flaubert""": ["""FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FlaubertConfig""", """FlaubertOnnxConfig"""],
    """tokenization_flaubert""": ["""FlaubertTokenizer"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __SCREAMING_SNAKE_CASE :  Dict  =						[
      """FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """FlaubertForMultipleChoice""",
      """FlaubertForQuestionAnswering""",
      """FlaubertForQuestionAnsweringSimple""",
      """FlaubertForSequenceClassification""",
      """FlaubertForTokenClassification""",
      """FlaubertModel""",
      """FlaubertWithLMHeadModel""",
      """FlaubertPreTrainedModel""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __SCREAMING_SNAKE_CASE :  Optional[int]  =						[
      """TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFFlaubertForMultipleChoice""",
      """TFFlaubertForQuestionAnsweringSimple""",
      """TFFlaubertForSequenceClassification""",
      """TFFlaubertForTokenClassification""",
      """TFFlaubertModel""",
      """TFFlaubertPreTrainedModel""",
      """TFFlaubertWithLMHeadModel""",
  ]


if TYPE_CHECKING:
  from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
  from .tokenization_flaubert import FlaubertTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flaubert import (
        FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        FlaubertForMultipleChoice,
        FlaubertForQuestionAnswering,
        FlaubertForQuestionAnsweringSimple,
        FlaubertForSequenceClassification,
        FlaubertForTokenClassification,
        FlaubertModel,
        FlaubertPreTrainedModel,
        FlaubertWithLMHeadModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_flaubert import (
        TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFFlaubertForMultipleChoice,
        TFFlaubertForQuestionAnsweringSimple,
        TFFlaubertForSequenceClassification,
        TFFlaubertForTokenClassification,
        TFFlaubertModel,
        TFFlaubertPreTrainedModel,
        TFFlaubertWithLMHeadModel,
    )

else:
  import sys

  __SCREAMING_SNAKE_CASE :  Tuple  =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


'''simple docstring'''




import unittest

import requests

from transformers import MODEL_FOR_VISION_2_SEQ_MAPPING, TF_MODEL_FOR_VISION_2_SEQ_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
  from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
  __SCREAMING_SNAKE_CASE :  Any  =						False


if is_vision_available():
  from PIL import Image
else:

  class 			lowerCamelCase_			:
        '''simple docstring'''

        @staticmethod
        def   _A			( *A		:       str		,      **A		:       Optional[Any] ):
               pass

@is_pipeline_test
@require_vision
class 			lowerCamelCase_			(unittest.TestCase					):
      '''simple docstring'''

      __UpperCamelCase:						Union[str, Any]      			=		MODEL_FOR_VISION_2_SEQ_MAPPING
      __UpperCamelCase:						int      			=		TF_MODEL_FOR_VISION_2_SEQ_MAPPING




      def   _A			( self		:       Any		,      A		:       Any		,      A		:       str		,      A		:       Optional[int] ):
             _UpperCAmelCase  :						Dict							   =					pipeline("image-to-text"		,      model=A		,      tokenizer=A		,      image_processor=A )
             _UpperCAmelCase  :						Union[str, Any]							   =					[
                 Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" ),
                 "./tests/fixtures/tests_samples/COCO/000000039769.png",
             ]
             return pipe, examples




      def   _A			( self		:       Tuple		,      A		:       Union[str, Any]		,      A		:       Optional[Any] ):
             _UpperCAmelCase  :						List[str]							   =					pipe(A )
             self.assertEqual(
                 A		,      [
                     [{"generated_text": ANY(A )}],
                     [{"generated_text": ANY(A )}],
                 ]		,      )




      @require_tf
      def   _A			( self		:       Any ):
             _UpperCAmelCase  :						Any							   =					pipeline("image-to-text"		,      model="hf-internal-testing/tiny-random-vit-gpt2"		,      framework="tf" )
             _UpperCAmelCase  :						int							   =					"./tests/fixtures/tests_samples/COCO/000000039769.png"

             _UpperCAmelCase  :						List[str]							   =					pipe(A )
             self.assertEqual(
                 A		,      [
                     {
                         "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                     },
                 ]		,      )

             _UpperCAmelCase  :						Tuple							   =					pipe([image, image] )
             self.assertEqual(
                 A		,      [
                     [
                         {
                             "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                         }
                     ],
                     [
                         {
                             "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                         }
                     ],
                 ]		,      )

             _UpperCAmelCase  :						Dict							   =					pipe(A		,      max_new_tokens=1 )
             self.assertEqual(
                 A		,      [{"generated_text": "growth"}]		,      )




      @require_torch
      def   _A			( self		:       Union[str, Any] ):
             _UpperCAmelCase  :						List[str]							   =					pipeline("image-to-text"		,      model="hf-internal-testing/tiny-random-vit-gpt2" )
             _UpperCAmelCase  :						Optional[int]							   =					"./tests/fixtures/tests_samples/COCO/000000039769.png"

             _UpperCAmelCase  :						Optional[Any]							   =					pipe(A )
             self.assertEqual(
                 A		,      [
                     {
                         "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                     },
                 ]		,      )

             _UpperCAmelCase  :						str							   =					pipe([image, image] )
             self.assertEqual(
                 A		,      [
                     [
                         {
                             "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                         }
                     ],
                     [
                         {
                             "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                         }
                     ],
                 ]		,      )




      @require_torch
      def   _A			( self		:       Union[str, Any] ):
             _UpperCAmelCase  :						Any							   =					pipeline("image-to-text"		,      model="hf-internal-testing/tiny-random-BlipForConditionalGeneration" )
             _UpperCAmelCase  :						Any							   =					"./tests/fixtures/tests_samples/COCO/000000039769.png"
             _UpperCAmelCase  :						int							   =					"a photo of"

             _UpperCAmelCase  :						str							   =					pipe(A		,      prompt=A )
             self.assertTrue(outputs[0]["generated_text"].startswith(A ) )




      @slow
      @require_torch
      def   _A			( self		:       List[Any] ):
             _UpperCAmelCase  :						Dict							   =					pipeline("image-to-text"		,      model="ydshieh/vit-gpt2-coco-en" )
             _UpperCAmelCase  :						int							   =					"./tests/fixtures/tests_samples/COCO/000000039769.png"

             _UpperCAmelCase  :						List[str]							   =					pipe(A )
             self.assertEqual(A		,      [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}] )

             _UpperCAmelCase  :						int							   =					pipe([image, image] )
             self.assertEqual(
                 A		,      [
                     [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
                     [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
                 ]		,      )




      @slow
      @require_torch
      def   _A			( self		:       Optional[Any] ):
             _UpperCAmelCase  :						Tuple							   =					pipeline("image-to-text"		,      model="Salesforce/blip-image-captioning-base" )
             _UpperCAmelCase  :						int							   =					"https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png"
             _UpperCAmelCase  :						Optional[int]							   =					Image.open(requests.get(A		,      stream=A ).raw )

             _UpperCAmelCase  :						Any							   =					pipe(A )
             self.assertEqual(A		,      [{"generated_text": "a pink pokemon pokemon with a blue shirt and a blue shirt"}] )




      @slow
      @require_torch
      def   _A			( self		:       List[Any] ):
             _UpperCAmelCase  :						List[str]							   =					pipeline("image-to-text"		,      model="microsoft/git-base-coco" )
             _UpperCAmelCase  :						Tuple							   =					"https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png"
             _UpperCAmelCase  :						Dict							   =					Image.open(requests.get(A		,      stream=A ).raw )

             _UpperCAmelCase  :						List[str]							   =					pipe(A )
             self.assertEqual(A		,      [{"generated_text": "a cartoon of a purple character."}] )




      @slow
      @require_torch
      def   _A			( self		:       int ):
             _UpperCAmelCase  :						Any							   =					pipeline("image-to-text"		,      model="Salesforce/blip-image-captioning-base" )
             _UpperCAmelCase  :						List[str]							   =					"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
             _UpperCAmelCase  :						int							   =					Image.open(requests.get(A		,      stream=A ).raw )

             _UpperCAmelCase  :						Dict							   =					"a photography of"

             _UpperCAmelCase  :						Optional[int]							   =					pipe(A		,      prompt=A )
             self.assertEqual(A		,      [{"generated_text": "a photography of a volcano"}] )

             with self.assertRaises(A ):
                    _UpperCAmelCase  :						List[Any]							   =					pipe([image, image]		,      prompt=[prompt, prompt] )




      @slow
      @require_torch
      def   _A			( self		:       Union[str, Any] ):
             _UpperCAmelCase  :						Optional[int]							   =					pipeline("image-to-text"		,      model="microsoft/git-base-coco" )
             _UpperCAmelCase  :						Optional[Any]							   =					"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
             _UpperCAmelCase  :						List[str]							   =					Image.open(requests.get(A		,      stream=A ).raw )

             _UpperCAmelCase  :						List[Any]							   =					"a photo of a"

             _UpperCAmelCase  :						Tuple							   =					pipe(A		,      prompt=A )
             self.assertEqual(A		,      [{"generated_text": "a photo of a tent with a tent and a tent in the background."}] )

             with self.assertRaises(A ):
                    _UpperCAmelCase  :						List[str]							   =					pipe([image, image]		,      prompt=[prompt, prompt] )




      @unittest.skipIf(
          not is_torch_greater_or_equal_than_1_11		,      reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`." )
      @slow
      @require_torch
      def   _A			( self		:       Union[str, Any] ):
             _UpperCAmelCase  :						str							   =					pipeline("image-to-text"		,      model="google/pix2struct-ai2d-base" )
             _UpperCAmelCase  :						List[Any]							   =					"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
             _UpperCAmelCase  :						List[Any]							   =					Image.open(requests.get(A		,      stream=A ).raw )

             _UpperCAmelCase  :						Optional[int]							   =					"What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"

             _UpperCAmelCase  :						str							   =					pipe(A		,      prompt=A )
             self.assertEqual(A		,      [{"generated_text": "ash cloud"}] )

             with self.assertRaises(A ):
                    _UpperCAmelCase  :						List[str]							   =					pipe([image, image]		,      prompt=[prompt, prompt] )





      @slow
      @require_tf
      def   _A			( self		:       List[Any] ):
             _UpperCAmelCase  :						str							   =					pipeline("image-to-text"		,      model="ydshieh/vit-gpt2-coco-en"		,      framework="tf" )
             _UpperCAmelCase  :						str							   =					"./tests/fixtures/tests_samples/COCO/000000039769.png"

             _UpperCAmelCase  :						Any							   =					pipe(A )
             self.assertEqual(A		,      [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}] )

             _UpperCAmelCase  :						Optional[Any]							   =					pipe([image, image] )
             self.assertEqual(
                 A		,      [
                     [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
                     [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
                 ]		,      )

