


lowerCAmelCase	=							{
    'Pillow': 'Pillow<10.0.0',
    'accelerate': 'accelerate>=0.20.3',
    'av': 'av==9.2.0',
    'beautifulsoup4': 'beautifulsoup4',
    'black': 'black~=23.1',
    'codecarbon': 'codecarbon==1.2.0',
    'cookiecutter': 'cookiecutter==1.7.3',
    'dataclasses': 'dataclasses',
    'datasets': 'datasets!=2.5.0',
    'decord': 'decord==0.6.0',
    'deepspeed': 'deepspeed>=0.9.3',
    'diffusers': 'diffusers',
    'dill': 'dill<0.3.5',
    'evaluate': 'evaluate>=0.2.0',
    'fairscale': 'fairscale>0.3',
    'faiss-cpu': 'faiss-cpu',
    'fastapi': 'fastapi',
    'filelock': 'filelock',
    'flax': 'flax>=0.4.1,<=0.7.0',
    'ftfy': 'ftfy',
    'fugashi': 'fugashi>=1.0',
    'GitPython': 'GitPython<3.1.19',
    'hf-doc-builder': 'hf-doc-builder>=0.3.0',
    'huggingface-hub': 'huggingface-hub>=0.14.1,<1.0',
    'importlib_metadata': 'importlib_metadata',
    'ipadic': 'ipadic>=1.0.0,<2.0',
    'isort': 'isort>=5.5.4',
    'jax': 'jax>=0.2.8,!=0.3.2,<=0.4.13',
    'jaxlib': 'jaxlib>=0.1.65,<=0.4.13',
    'jieba': 'jieba',
    'kenlm': 'kenlm',
    'keras-nlp': 'keras-nlp>=0.3.1',
    'librosa': 'librosa',
    'nltk': 'nltk',
    'natten': 'natten>=0.14.6',
    'numpy': 'numpy>=1.17',
    'onnxconverter-common': 'onnxconverter-common',
    'onnxruntime-tools': 'onnxruntime-tools>=1.4.2',
    'onnxruntime': 'onnxruntime>=1.4.0',
    'opencv-python': 'opencv-python',
    'optuna': 'optuna',
    'optax': 'optax>=0.0.8,<=0.1.4',
    'packaging': 'packaging>=20.0',
    'parameterized': 'parameterized',
    'phonemizer': 'phonemizer',
    'protobuf': 'protobuf',
    'psutil': 'psutil',
    'pyyaml': 'pyyaml>=5.1',
    'pydantic': 'pydantic<2',
    'pytest': 'pytest>=7.2.0',
    'pytest-timeout': 'pytest-timeout',
    'pytest-xdist': 'pytest-xdist',
    'python': 'python>=3.8.0',
    'ray[tune]': 'ray[tune]',
    'regex': 'regex!=2019.12.17',
    'requests': 'requests',
    'rhoknp': 'rhoknp>=1.1.0,<1.3.1',
    'rjieba': 'rjieba',
    'rouge-score': 'rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1',
    'ruff': 'ruff>=0.0.241,<=0.0.259',
    'sacrebleu': 'sacrebleu>=1.4.12,<2.0.0',
    'sacremoses': 'sacremoses',
    'safetensors': 'safetensors>=0.3.1',
    'sagemaker': 'sagemaker>=2.31.0',
    'scikit-learn': 'scikit-learn',
    'sentencepiece': 'sentencepiece>=0.1.91,!=0.1.92',
    'sigopt': 'sigopt',
    'starlette': 'starlette',
    'sudachipy': 'sudachipy>=0.6.6',
    'sudachidict_core': 'sudachidict_core>=20220729',
    'tensorflow-cpu': 'tensorflow-cpu>=2.6,<2.14',
    'tensorflow': 'tensorflow>=2.6,<2.14',
    'tensorflow-text': 'tensorflow-text<2.14',
    'tf2onnx': 'tf2onnx',
    'timeout-decorator': 'timeout-decorator',
    'timm': 'timm',
    'tokenizers': 'tokenizers>=0.11.1,!=0.11.3,<0.14',
    'torch': 'torch>=1.9,!=1.12.0',
    'torchaudio': 'torchaudio',
    'torchvision': 'torchvision',
    'pyctcdecode': 'pyctcdecode>=0.4.0',
    'tqdm': 'tqdm>=4.27',
    'unidic': 'unidic>=1.0.2',
    'unidic_lite': 'unidic_lite>=1.0.7',
    'urllib3': 'urllib3<2.0.0',
    'uvicorn': 'uvicorn',
}






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					):


		"""simple docstring"""




		lowercase__						=				checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE					)
		lowercase__						=				flatten_dict(SCREAMING_SNAKE_CASE					)
		return flax_params
def       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				{}

		lowercase__						=				{
		    '''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''',
		}

		lowercase__						=				{
		    '''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
						lowercase__						=				'''.'''.join(key[1:]					)

						# rename the key
						for old, new in CONVERSION_MAPPING.items():
								lowercase__						=				new_key.replace(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)

						if "decoder" in new_key:
								for old, new in DECODER_CONVERSION_MAPPING.items():
										lowercase__						=				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
								lowercase__						=				re.sub(R'''layers_(\d+)'''						,    R'''layer.\1'''						,    SCREAMING_SNAKE_CASE					)
								lowercase__						=				new_key.replace('''encoder'''						,    '''encoder.encoder'''					)

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

						lowercase__						=				flax_dict[key]

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

		return converted_torch_dict
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=False						,    SCREAMING_SNAKE_CASE=False					):


		"""simple docstring"""




		lowercase__						=				get_flax_param(SCREAMING_SNAKE_CASE					)

		if not use_large:
				lowercase__						=				PixaStructVisionConfig()
				lowercase__						=				PixaStructTextConfig()
		else:
				lowercase__						=				PixaStructVisionConfig(
				    hidden_size=15_36						,    d_ff=39_68						,    num_attention_heads=24						,    num_hidden_layers=18					)
				lowercase__						=				PixaStructTextConfig(hidden_size=15_36						,    d_ff=39_68						,    num_heads=24						,    num_layers=18					)
		lowercase__						=				PixaStructConfig(
		    vision_config=encoder_config.to_dict()						,    text_config=decoder_config.to_dict()						,    is_vqa=SCREAMING_SNAKE_CASE					)

		lowercase__						=				PixaStructForConditionalGeneration(SCREAMING_SNAKE_CASE					)

		lowercase__						=				rename_and_convert_flax_params(SCREAMING_SNAKE_CASE					)
		model.load_state_dict(SCREAMING_SNAKE_CASE					)

		lowercase__						=				AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''					)
		lowercase__						=				PixaStructImageProcessor()
		lowercase__						=				PixaStructProcessor(image_processor=SCREAMING_SNAKE_CASE						,    tokenizer=SCREAMING_SNAKE_CASE					)

		if use_large:
				lowercase__						=				40_96

		lowercase__						=				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__":
			lowerCAmelCase	=							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.')
			lowerCAmelCase	=							parser.parse_args()

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


