'''simple docstring'''




import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()

def 			UpperCamelCase_							(      _UpperCAmelCase						:   Union[str, Any]    ,			_UpperCAmelCase						:   int    ,			_UpperCAmelCase						:   int    ,			_UpperCAmelCase						:   str    ,			_UpperCAmelCase						:   Tuple					)					->					Optional[Any]:


       """simple docstring"""







       _UpperCAmelCase  :						List[str]							   =					BertConfig.from_pretrained(
           "bert-large-cased"    ,			vocab_size=_UpperCAmelCase    ,			max_position_embeddings=512    ,			is_decoder=_UpperCAmelCase    ,			add_cross_attention=_UpperCAmelCase    ,			)
       _UpperCAmelCase  :						Union[str, Any]							   =					bert_config.to_dict()
       del bert_config_dict["type_vocab_size"]
       _UpperCAmelCase  :						List[Any]							   =					BertGenerationConfig(**_UpperCAmelCase					)
       if is_encoder:
              _UpperCAmelCase  :						Optional[int]							   =					BertGenerationEncoder(_UpperCAmelCase					)
       else:
              _UpperCAmelCase  :						List[Any]							   =					BertGenerationDecoder(_UpperCAmelCase					)
       print(F"""Building PyTorch model from configuration: {config}"""					)

       # Load weights from tf checkpoint
       load_tf_weights_in_bert_generation(
           _UpperCAmelCase    ,			_UpperCAmelCase    ,			model_class="bert"    ,			is_encoder_named_decoder=_UpperCAmelCase    ,			is_encoder=_UpperCAmelCase    ,			)

       # Save pytorch-model
       print(F"""Save PyTorch model and config to {pytorch_dump_path}"""					)
       model.save_pretrained(_UpperCAmelCase					)


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE :  str  =						argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--tf_hub_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  parser.add_argument(
      """--is_encoder_named_decoder""",
      action="""store_true""",
      help="""If decoder has to be renamed to encoder in PyTorch model.""",
  )
  parser.add_argument("""--is_encoder""", action="""store_true""", help="""If model is an encoder.""")
  parser.add_argument("""--vocab_size""", default=50_358, type=int, help="""Vocab size of model""")
  __SCREAMING_SNAKE_CASE :  List[Any]  =						parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.tf_hub_path,
      args.pytorch_dump_path,
      args.is_encoder_named_decoder,
      args.vocab_size,
      is_encoder=args.is_encoder,
  )


'''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 "}],
                 ]		,      )

