'''simple docstring'''




import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig

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


       """simple docstring"""







       _UpperCAmelCase  :						Optional[int]							   =					TaConfig.from_pretrained(_UpperCAmelCase					)
       _UpperCAmelCase  :						Union[str, Any]							   =					FlaxTaForConditionalGeneration(config=_UpperCAmelCase					)
       _UpperCAmelCase  :						Optional[Any]							   =					checkpoints.load_tax_checkpoint(_UpperCAmelCase					)

       _UpperCAmelCase  :						Union[str, Any]							   =					"wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

       # Encoder
       for layer_index in range(config.num_layers					):
              _UpperCAmelCase  :						Union[str, Any]							   =					F"""layers_{str(_UpperCAmelCase					)}"""

              # Self-Attention
              _UpperCAmelCase  :						Optional[int]							   =					tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
              _UpperCAmelCase  :						Dict							   =					tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
              _UpperCAmelCase  :						int							   =					tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
              _UpperCAmelCase  :						Union[str, Any]							   =					tax_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"]

              # Layer Normalization
              _UpperCAmelCase  :						int							   =					tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

              if split_mlp_wi:
                     _UpperCAmelCase  :						Optional[int]							   =					tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
                     _UpperCAmelCase  :						Optional[Any]							   =					tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
              else:
                     _UpperCAmelCase  :						Optional[int]							   =					tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

              _UpperCAmelCase  :						str							   =					tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

              # Layer Normalization
              _UpperCAmelCase  :						Tuple							   =					tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

              # Assigning
              _UpperCAmelCase  :						List[Any]							   =					tax_attention_key
              _UpperCAmelCase  :						Optional[Any]							   =					tax_attention_out
              _UpperCAmelCase  :						List[str]							   =					tax_attention_query
              _UpperCAmelCase  :						str							   =					tax_attention_value

              _UpperCAmelCase  :						int							   =					tax_attention_layer_norm

              if split_mlp_wi:
                     _UpperCAmelCase  :						Optional[int]							   =					tax_mlp_wi_a
                     _UpperCAmelCase  :						Tuple							   =					tax_mlp_wi_a
              else:
                     _UpperCAmelCase  :						Optional[Any]							   =					tax_mlp_wi

              _UpperCAmelCase  :						List[Any]							   =					tax_mlp_wo
              _UpperCAmelCase  :						str							   =					tax_mlp_layer_norm

       # Only for layer 0:
       _UpperCAmelCase  :						int							   =					tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
       _UpperCAmelCase  :						Any							   =					tax_encoder_rel_embedding

       # Assigning
       _UpperCAmelCase  :						Tuple							   =					tax_model["target"]["encoder"]["encoder_norm"]["scale"]
       _UpperCAmelCase  :						int							   =					tax_encoder_norm

       # Decoder
       for layer_index in range(config.num_decoder_layers					):
              _UpperCAmelCase  :						Any							   =					F"""layers_{str(_UpperCAmelCase					)}"""

              # Self-Attention
              _UpperCAmelCase  :						Tuple							   =					tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
              _UpperCAmelCase  :						Any							   =					tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
              _UpperCAmelCase  :						Dict							   =					tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
              _UpperCAmelCase  :						Union[str, Any]							   =					tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

              # Layer Normalization
              _UpperCAmelCase  :						Dict							   =					tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
                  "scale"
              ]

              # Encoder-Decoder-Attention
              _UpperCAmelCase  :						Union[str, Any]							   =					tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["key"][
                  "kernel"
              ]
              _UpperCAmelCase  :						Tuple							   =					tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["out"][
                  "kernel"
              ]
              _UpperCAmelCase  :						Tuple							   =					tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["query"][
                  "kernel"
              ]
              _UpperCAmelCase  :						Tuple							   =					tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["value"][
                  "kernel"
              ]

              # Layer Normalization
              _UpperCAmelCase  :						Tuple							   =					tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

              # MLP
              if split_mlp_wi:
                     _UpperCAmelCase  :						Optional[int]							   =					tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
                     _UpperCAmelCase  :						Optional[Any]							   =					tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
              else:
                     _UpperCAmelCase  :						List[Any]							   =					tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

              _UpperCAmelCase  :						List[Any]							   =					tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

              # Layer Normalization
              _UpperCAmelCase  :						List[str]							   =					tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

              # Assigning
              _UpperCAmelCase  :						Dict							   =					tax_attention_key
              _UpperCAmelCase  :						Dict							   =					tax_attention_out
              _UpperCAmelCase  :						Any							   =					tax_attention_query
              _UpperCAmelCase  :						Dict							   =					tax_attention_value

              _UpperCAmelCase  :						int							   =					tax_pre_attention_layer_norm

              _UpperCAmelCase  :						Any							   =					tax_enc_dec_attention_key
              _UpperCAmelCase  :						str							   =					tax_enc_dec_attention_out
              _UpperCAmelCase  :						Optional[Any]							   =					tax_enc_dec_attention_query
              _UpperCAmelCase  :						Dict							   =					tax_enc_dec_attention_value

              _UpperCAmelCase  :						Union[str, Any]							   =					tax_cross_layer_norm

              if split_mlp_wi:
                     _UpperCAmelCase  :						Dict							   =					tax_mlp_wi_a
                     _UpperCAmelCase  :						str							   =					tax_mlp_wi_a
              else:
                     _UpperCAmelCase  :						List[Any]							   =					tax_mlp_wi

              _UpperCAmelCase  :						Optional[Any]							   =					tax_mlp_wo

              _UpperCAmelCase  :						str							   =					txa_mlp_layer_norm

       # Decoder Normalization
       _UpperCAmelCase  :						Union[str, Any]							   =					tax_model["target"]["decoder"]["decoder_norm"]["scale"]
       _UpperCAmelCase  :						Union[str, Any]							   =					txa_decoder_norm

       # Only for layer 0:
       _UpperCAmelCase  :						str							   =					tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
       _UpperCAmelCase  :						Optional[Any]							   =					tax_decoder_rel_embedding

       # Token Embeddings
       _UpperCAmelCase  :						Union[str, Any]							   =					tax_model["target"]["token_embedder"]["embedding"]
       _UpperCAmelCase  :						Optional[Any]							   =					txa_token_embeddings

       # LM Head (only in v1.1 checkpoints)
       if "logits_dense" in tax_model["target"]["decoder"]:
              _UpperCAmelCase  :						Optional[int]							   =					tax_model["target"]["decoder"]["logits_dense"]["kernel"]

       flax_model.save_pretrained(_UpperCAmelCase					)
       print("T5X Model was sucessfully converted!"					)


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE :  int  =						argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the TX5 checkpoint."""
  )
  parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of T5 model.""")
  parser.add_argument(
      """--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
  )
  __SCREAMING_SNAKE_CASE :  int  =						parser.parse_args()
  convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)


'''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 "}],
                 ]		,      )

