






"""simple docstring"""






import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)



def 					_A			(__a							) ->       Optional[int]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Tuple    						=		checkpoints.load_tax_checkpoint(__a							)
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		flatten_dict(__a							)
    return flax_params



def 					_A			(__a							) ->       Optional[int]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Dict    						=		{}

    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		{
        '''token_embedder''': '''embeddings''',
        '''encoder_norm''': '''layernorm''',
        '''kernel''': '''weight''',
        '''.out''': '''.output''',
        '''scale''': '''weight''',
        '''embedders_0.pos_embedding''': '''row_embedder.weight''',
        '''embedders_1.pos_embedding''': '''column_embedder.weight''',
    }

    SCREAMING_SNAKE_CASE_      :  Any    						=		{
        '''query''': '''attention.query''',
        '''key''': '''attention.key''',
        '''value''': '''attention.value''',
        '''output.dense''': '''output''',
        '''encoder_decoder_attention.o''': '''encoder_decoder_attention.attention.o''',
        '''pre_self_attention_layer_norm''': '''self_attention.layer_norm''',
        '''pre_cross_attention_layer_norm''': '''encoder_decoder_attention.layer_norm''',
        '''mlp.''': '''mlp.DenseReluDense.''',
        '''pre_mlp_layer_norm''': '''mlp.layer_norm''',
        '''self_attention.o''': '''self_attention.attention.o''',
        '''decoder.embeddings.embedding''': '''decoder.embed_tokens.weight''',
        '''decoder.relpos_bias.rel_embedding''': '''decoder.layer.0.self_attention.attention.relative_attention_bias.weight''',
        '''decoder.decoder_norm.weight''': '''decoder.final_layer_norm.weight''',
        '''decoder.logits_dense.weight''': '''decoder.lm_head.weight''',
    }

    for key in flax_dict.keys():
        if "target" in key:
            # remove the first prefix from the key
            SCREAMING_SNAKE_CASE_      :  str    						=		'''.'''.join(key[1:]							)

            # rename the key
            for old, new in CONVERSION_MAPPING.items():
                SCREAMING_SNAKE_CASE_      :  str    						=		new_key.replace(__a    ,				__a							)

            if "decoder" in new_key:
                for old, new in DECODER_CONVERSION_MAPPING.items():
                    SCREAMING_SNAKE_CASE_      :  Any    						=		new_key.replace(__a    ,				__a							)

            if "layers" in new_key and "decoder" not in new_key:
                # use regex to replace the layer number
                SCREAMING_SNAKE_CASE_      :  int    						=		re.sub(R'''layers_(\d+)'''    ,				R'''layer.\1'''    ,				__a							)
                SCREAMING_SNAKE_CASE_      :  Tuple    						=		new_key.replace('''encoder'''    ,				'''encoder.encoder'''							)

            elif "layers" in new_key and "decoder" in new_key:
                # use regex to replace the layer number
                SCREAMING_SNAKE_CASE_      :  Any    						=		re.sub(R'''layers_(\d+)'''    ,				R'''layer.\1'''    ,				__a							)

            SCREAMING_SNAKE_CASE_      :  str    						=		flax_dict[key]

    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		{}
    # convert converted_dict into torch format
    for key in converted_dict.keys():
        if ("embed_tokens" not in key) and ("embedder" not in key):
            SCREAMING_SNAKE_CASE_      :  List[Any]    						=		torch.from_numpy(converted_dict[key].T							)
        else:
            SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		torch.from_numpy(converted_dict[key]							)

    return converted_torch_dict



def 					_A			(__a    ,				__a    ,				__a=False    ,				__a=False							) ->       Dict:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		get_flax_param(__a							)

    if not use_large:
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		PixaStructVisionConfig()
        SCREAMING_SNAKE_CASE_      :  int    						=		PixaStructTextConfig()
    else:
        SCREAMING_SNAKE_CASE_      :  Dict    						=		PixaStructVisionConfig(
            hidden_size=15_36    ,				d_ff=39_68    ,				num_attention_heads=24    ,				num_hidden_layers=18							)
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		PixaStructTextConfig(hidden_size=15_36    ,				d_ff=39_68    ,				num_heads=24    ,				num_layers=18							)
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		PixaStructConfig(
        vision_config=encoder_config.to_dict()    ,				text_config=decoder_config.to_dict()    ,				is_vqa=__a							)

    SCREAMING_SNAKE_CASE_      :  Dict    						=		PixaStructForConditionalGeneration(__a							)

    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		rename_and_convert_flax_params(__a							)
    model.load_state_dict(__a							)

    SCREAMING_SNAKE_CASE_      :  Tuple    						=		AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''							)
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		PixaStructImageProcessor()
    SCREAMING_SNAKE_CASE_      :  str    						=		PixaStructProcessor(image_processor=__a    ,				tokenizer=__a							)

    if use_large:
        SCREAMING_SNAKE_CASE_      :  int    						=		40_96

    SCREAMING_SNAKE_CASE_      :  int    						=		True

    # mkdir if needed
    os.makedirs(__a    ,				exist_ok=__a							)

    model.save_pretrained(__a							)
    processor.save_pretrained(__a							)

    print('''Model saved in {}'''.format(__a							)							)


if __name__ == "__main__":
  UpperCAmelCase_   : Any   			= argparse.ArgumentParser()
  parser.add_argument("""--t5x_checkpoint_path""", default=None, type=str, help="""Path to the original T5x checkpoint.""")
  parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
  parser.add_argument("""--use_large""", action="""store_true""", help="""Use large model.""")
  parser.add_argument("""--is_vqa""", action="""store_true""", help="""Use large model.""")
  UpperCAmelCase_   : str   			= parser.parse_args()

  convert_pixastruct_original_pytorch_checkpoint_to_hf(
      args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
  )










"""simple docstring"""






import unittest

from diffusers.pipelines.pipeline_utils import is_safetensors_compatible




class 							lowerCAmelCase__				(			unittest.TestCase ):






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		[
              '''safety_checker/pytorch_model.bin''',
              '''safety_checker/model.safetensors''',
              '''vae/diffusion_pytorch_model.bin''',
              '''vae/diffusion_pytorch_model.safetensors''',
              '''text_encoder/pytorch_model.bin''',
              '''text_encoder/model.safetensors''',
              '''unet/diffusion_pytorch_model.bin''',
              '''unet/diffusion_pytorch_model.safetensors''',
          ]
          self.assertTrue(is_safetensors_compatible(lowercase_))
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		[
              '''unet/diffusion_pytorch_model.bin''',
              '''unet/diffusion_pytorch_model.safetensors''',
          ]
          self.assertTrue(is_safetensors_compatible(lowercase_))
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		[
              '''safety_checker/pytorch_model.bin''',
              '''safety_checker/model.safetensors''',
              '''vae/diffusion_pytorch_model.bin''',
              '''vae/diffusion_pytorch_model.safetensors''',
              '''text_encoder/pytorch_model.bin''',
              '''text_encoder/model.safetensors''',
              '''unet/diffusion_pytorch_model.bin''',
              # Removed: 'unet/diffusion_pytorch_model.safetensors',
          ]
          self.assertFalse(is_safetensors_compatible(lowercase_))
      def _SCREAMING_SNAKE_CASE (						self     :  int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		[
              '''text_encoder/pytorch_model.bin''',
              '''text_encoder/model.safetensors''',
          ]
          self.assertTrue(is_safetensors_compatible(lowercase_))
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		[
              '''safety_checker/pytorch_model.bin''',
              '''safety_checker/model.safetensors''',
              '''vae/diffusion_pytorch_model.bin''',
              '''vae/diffusion_pytorch_model.safetensors''',
              '''text_encoder/pytorch_model.bin''',
              # Removed: 'text_encoder/model.safetensors',
              '''unet/diffusion_pytorch_model.bin''',
              '''unet/diffusion_pytorch_model.safetensors''',
          ]
          self.assertFalse(is_safetensors_compatible(lowercase_))
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  str    						=		[
              '''safety_checker/pytorch_model.fp16.bin''',
              '''safety_checker/model.fp16.safetensors''',
              '''vae/diffusion_pytorch_model.fp16.bin''',
              '''vae/diffusion_pytorch_model.fp16.safetensors''',
              '''text_encoder/pytorch_model.fp16.bin''',
              '''text_encoder/model.fp16.safetensors''',
              '''unet/diffusion_pytorch_model.fp16.bin''',
              '''unet/diffusion_pytorch_model.fp16.safetensors''',
          ]
          SCREAMING_SNAKE_CASE_      :  Any    						=		'''fp16'''
          self.assertTrue(is_safetensors_compatible(lowercase_						,	variant=lowercase_))
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		[
              '''unet/diffusion_pytorch_model.fp16.bin''',
              '''unet/diffusion_pytorch_model.fp16.safetensors''',
          ]
          SCREAMING_SNAKE_CASE_      :  Dict    						=		'''fp16'''
          self.assertTrue(is_safetensors_compatible(lowercase_						,	variant=lowercase_))
      def _SCREAMING_SNAKE_CASE (						self     :  int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		[
              '''unet/diffusion_pytorch_model.bin''',
              '''unet/diffusion_pytorch_model.safetensors''',
          ]
          SCREAMING_SNAKE_CASE_      :  Any    						=		'''fp16'''
          self.assertTrue(is_safetensors_compatible(lowercase_						,	variant=lowercase_))
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		[
              '''safety_checker/pytorch_model.fp16.bin''',
              '''safety_checker/model.fp16.safetensors''',
              '''vae/diffusion_pytorch_model.fp16.bin''',
              '''vae/diffusion_pytorch_model.fp16.safetensors''',
              '''text_encoder/pytorch_model.fp16.bin''',
              '''text_encoder/model.fp16.safetensors''',
              '''unet/diffusion_pytorch_model.fp16.bin''',
              # Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
          ]
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		'''fp16'''
          self.assertFalse(is_safetensors_compatible(lowercase_						,	variant=lowercase_))
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		[
              '''text_encoder/pytorch_model.fp16.bin''',
              '''text_encoder/model.fp16.safetensors''',
          ]
          SCREAMING_SNAKE_CASE_      :  Any    						=		'''fp16'''
          self.assertTrue(is_safetensors_compatible(lowercase_						,	variant=lowercase_))
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		[
              '''text_encoder/pytorch_model.bin''',
              '''text_encoder/model.safetensors''',
          ]
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		'''fp16'''
          self.assertTrue(is_safetensors_compatible(lowercase_						,	variant=lowercase_))



      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		[
              '''safety_checker/pytorch_model.fp16.bin''',
              '''safety_checker/model.fp16.safetensors''',
              '''vae/diffusion_pytorch_model.fp16.bin''',
              '''vae/diffusion_pytorch_model.fp16.safetensors''',
              '''text_encoder/pytorch_model.fp16.bin''',
              # 'text_encoder/model.fp16.safetensors',
              '''unet/diffusion_pytorch_model.fp16.bin''',
              '''unet/diffusion_pytorch_model.fp16.safetensors''',
          ]
          SCREAMING_SNAKE_CASE_      :  str    						=		'''fp16'''
          self.assertFalse(is_safetensors_compatible(lowercase_						,	variant=lowercase_))


