


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	(							SCREAMING_SNAKE_CASE_      :		Optional[Any]       ):






       """simple docstring"""





       UpperCamelCase    :	Union[str, Any]   	=		checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE_       )
       UpperCamelCase    :	Optional[int]   	=		flatten_dict(SCREAMING_SNAKE_CASE_       )
       return flax_params


def      a	(							SCREAMING_SNAKE_CASE_      :		List[str]       ):






       """simple docstring"""





       UpperCamelCase    :	Optional[int]   	=		{}

       UpperCamelCase    :	int   	=		{
           '''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''',
       }

       UpperCamelCase    :	int   	=		{
           '''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
                     UpperCamelCase    :	Any   	=		'''.'''.join(key[1:]       )

                     # rename the key
                     for old, new in CONVERSION_MAPPING.items():
                            UpperCamelCase    :	Optional[int]   	=		new_key.replace(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )

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

                     if "layers" in new_key and "decoder" not in new_key:
                            # use regex to replace the layer number
                            UpperCamelCase    :	Union[str, Any]   	=		re.sub(R'''layers_(\d+)'''							,			R'''layer.\1'''							,			SCREAMING_SNAKE_CASE_       )
                            UpperCamelCase    :	Union[str, Any]   	=		new_key.replace('''encoder'''							,			'''encoder.encoder'''       )

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

                     UpperCamelCase    :	Optional[int]   	=		flax_dict[key]

       UpperCamelCase    :	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):
                     UpperCamelCase    :	Union[str, Any]   	=		torch.from_numpy(converted_dict[key].T       )
              else:
                     UpperCamelCase    :	Optional[Any]   	=		torch.from_numpy(converted_dict[key]       )

       return converted_torch_dict


def      a	(							SCREAMING_SNAKE_CASE_      :		Optional[Any]							,			SCREAMING_SNAKE_CASE_      :		Any							,			SCREAMING_SNAKE_CASE_      :		List[str]=False							,			SCREAMING_SNAKE_CASE_      :		List[Any]=False       ):






       """simple docstring"""





       UpperCamelCase    :	Dict   	=		get_flax_param(SCREAMING_SNAKE_CASE_       )

       if not use_large:
              UpperCamelCase    :	List[str]   	=		PixaStructVisionConfig()
              UpperCamelCase    :	str   	=		PixaStructTextConfig()
       else:
              UpperCamelCase    :	List[Any]   	=		PixaStructVisionConfig(
                  hidden_size=1_5_3_6							,			d_ff=3_9_6_8							,			num_attention_heads=2_4							,			num_hidden_layers=1_8       )
              UpperCamelCase    :	str   	=		PixaStructTextConfig(hidden_size=1_5_3_6							,			d_ff=3_9_6_8							,			num_heads=2_4							,			num_layers=1_8       )
       UpperCamelCase    :	List[str]   	=		PixaStructConfig(
           vision_config=encoder_config.to_dict()							,			text_config=decoder_config.to_dict()							,			is_vqa=SCREAMING_SNAKE_CASE_       )

       UpperCamelCase    :	Tuple   	=		PixaStructForConditionalGeneration(SCREAMING_SNAKE_CASE_       )

       UpperCamelCase    :	int   	=		rename_and_convert_flax_params(SCREAMING_SNAKE_CASE_       )
       model.load_state_dict(SCREAMING_SNAKE_CASE_       )

       UpperCamelCase    :	Tuple   	=		AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''       )
       UpperCamelCase    :	Optional[Any]   	=		PixaStructImageProcessor()
       UpperCamelCase    :	Tuple   	=		PixaStructProcessor(image_processor=SCREAMING_SNAKE_CASE_							,			tokenizer=SCREAMING_SNAKE_CASE_       )

       if use_large:
              UpperCamelCase    :	Dict   	=		4_0_9_6

       UpperCamelCase    :	Union[str, Any]   	=		True

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

       model.save_pretrained(SCREAMING_SNAKE_CASE_       )
       processor.save_pretrained(SCREAMING_SNAKE_CASE_       )

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


if __name__ == "__main__":
  __UpperCAmelCase : int 			=				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 : Optional[int] 			=				parser.parse_args()

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





import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


__UpperCAmelCase : Dict 			=				False



class    UpperCAmelCase_	(							unittest.TestCase):
       '''simple docstring'''
       pass




@slow
@require_torch_gpu
class    UpperCAmelCase_	(							unittest.TestCase):
       '''simple docstring'''






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Dict   	=		VersatileDiffusionImageVariationPipeline.from_pretrained('''shi-labs/versatile-diffusion'''						)
              pipe.to(__SCREAMING_SNAKE_CASE						)
              pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Union[str, Any]   	=		load_image(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''						)
              UpperCamelCase    :	str   	=		torch.manual_seed(0						)
              UpperCamelCase    :	Union[str, Any]   	=		pipe(
                  image=__SCREAMING_SNAKE_CASE ,					generator=__SCREAMING_SNAKE_CASE ,					guidance_scale=7.5 ,					num_inference_steps=50 ,					output_type='''numpy''' ,					).images

              UpperCamelCase    :	List[Any]   	=		image[0, 253:256, 253:256, -1]

              assert image.shape == (1, 512, 512, 3)
              UpperCamelCase    :	Dict   	=		np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]						)

              assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2

