



'''simple docstring'''

import argparse
import os
import re

import packaging.version


a							   =				'examples/'
a							   =				{
    'examples': (re.compile(r'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
    'init': (re.compile(r'^__version__\s+=\s+"([^"]+)"\s*$', re.MULTILINE), '__version__ = "VERSION"\n'),
    'setup': (re.compile(r'^(\s*)version\s*=\s*"[^"]+",', re.MULTILINE), r'\1version="VERSION",'),
    'doc': (re.compile(r'^(\s*)release\s*=\s*"[^"]+"$', re.MULTILINE), 'release = "VERSION"\n'),
}
a							   =				{
    'init': 'src/transformers/__init__.py',
    'setup': 'setup.py',
}
a							   =				'README.md'







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					List[Any]:





			"""simple docstring"""







			with open(__UpperCAmelCase				,							'r'				,							encoding='utf-8'				,							newline='\n'					) as f:
						snake_case:    Optional[int]      							=f.read()
			snake_case							,  snake_case:    int      							=REPLACE_PATTERNS[pattern]
			snake_case:    int      							=replace.replace('VERSION'				,							__UpperCAmelCase					)
			snake_case:    List[Any]      							=re_pattern.sub(__UpperCAmelCase				,							__UpperCAmelCase					)
			with open(__UpperCAmelCase				,							'w'				,							encoding='utf-8'				,							newline='\n'					) as f:
						f.write(__UpperCAmelCase					)







def a_     (    __UpperCAmelCase					)				->					List[str]:





			"""simple docstring"""







			for folder, directories, fnames in os.walk(__UpperCAmelCase					):
						# Removing some of the folders with non-actively maintained examples from the walk
						if "research_projects" in directories:
									directories.remove('research_projects'					)
						if "legacy" in directories:
									directories.remove('legacy'					)
						for fname in fnames:
									if fname.endswith('.py'					):
												update_version_in_file(os.path.join(__UpperCAmelCase				,							__UpperCAmelCase					)				,							__UpperCAmelCase				,							pattern='examples'					)







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase=False					)				->					Union[str, Any]:





			"""simple docstring"""







			for pattern, fname in REPLACE_FILES.items():
						update_version_in_file(__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)
			if not patch:
						update_version_in_examples(__UpperCAmelCase					)







def a_     (    )				->					Optional[Any]:





			"""simple docstring"""







			snake_case:    List[str]      							='🤗 Transformers currently provides the following architectures'
			snake_case:    Dict      							='1. Want to contribute a new model?'
			with open(__UpperCAmelCase				,							'r'				,							encoding='utf-8'				,							newline='\n'					) as f:
						snake_case:    Any      							=f.readlines()

			# Find the start of the list.
			snake_case:    Optional[int]      							=0
			while not lines[start_index].startswith(_start_prompt					):
						start_index += 1
			start_index += 1

			snake_case:    Any      							=start_index
			# Update the lines in the model list.
			while not lines[index].startswith(_end_prompt					):
						if lines[index].startswith('1.'					):
									snake_case:    Tuple      							=lines[index].replace(
									    'https://huggingface.co/docs/transformers/main/model_doc'				,							'https://huggingface.co/docs/transformers/model_doc'				,							)
						index += 1

			with open(__UpperCAmelCase				,							'w'				,							encoding='utf-8'				,							newline='\n'					) as f:
						f.writelines(__UpperCAmelCase					)







def a_     (    )				->					str:





			"""simple docstring"""







			with open(REPLACE_FILES['init']				,							'r'					) as f:
						snake_case:    List[Any]      							=f.read()
			snake_case:    Dict      							=REPLACE_PATTERNS['init'][0].search(__UpperCAmelCase					).groups()[0]
			return packaging.version.parse(__UpperCAmelCase					)







def a_     (    __UpperCAmelCase=False					)				->					Tuple:





			"""simple docstring"""







			snake_case:    List[str]      							=get_version()
			if patch and default_version.is_devrelease:
						raise ValueError('Can\'t create a patch version from the dev branch, checkout a released version!'					)
			if default_version.is_devrelease:
						snake_case:    Union[str, Any]      							=default_version.base_version
			elif patch:
						snake_case:    Optional[Any]      							=f'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
			else:
						snake_case:    Tuple      							=f'''{default_version.major}.{default_version.minor + 1}.0'''

			# Now let's ask nicely if that's the right one.
			snake_case:    int      							=input(f'''Which version are you releasing? [{default_version}]'''					)
			if len(__UpperCAmelCase					) == 0:
						snake_case:    List[Any]      							=default_version

			print(f'''Updating version to {version}.'''					)
			global_version_update(__UpperCAmelCase				,							patch=__UpperCAmelCase					)
			if not patch:
						print('Cleaning main README, don\'t forget to run `make fix-copies`.'					)
						clean_main_ref_in_model_list()







def a_     (    )				->					List[str]:





			"""simple docstring"""







			snake_case:    List[Any]      							=get_version()
			snake_case:    str      							=f'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
			snake_case:    str      							=current_version.base_version

			# Check with the user we got that right.
			snake_case:    Any      							=input(f'''Which version are we developing now? [{dev_version}]'''					)
			if len(__UpperCAmelCase					) == 0:
						snake_case:    Optional[Any]      							=dev_version

			print(f'''Updating version to {version}.'''					)
			global_version_update(__UpperCAmelCase					)
			print('Cleaning main README, don\'t forget to run `make fix-copies`.'					)
			clean_main_ref_in_model_list()


if __name__ == "__main__":
	a							   =				argparse.ArgumentParser()
	parser.add_argument('--post_release', action='store_true', help='Whether this is pre or post release.')
	parser.add_argument('--patch', action='store_true', help='Whether or not this is a patch release.')
	a							   =				parser.parse_args()
	if not args.post_release:
		pre_release_work(patch=args.patch)
	elif args.patch:
		print('Nothing to do after a patch :-)')
	else:
		post_release_work()









'''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_     (    __UpperCAmelCase					)				->					int:





			"""simple docstring"""







			snake_case:    List[str]      							=checkpoints.load_tax_checkpoint(__UpperCAmelCase					)
			snake_case:    str      							=flatten_dict(__UpperCAmelCase					)
			return flax_params







def a_     (    __UpperCAmelCase					)				->					Optional[Any]:





			"""simple docstring"""







			snake_case:    int      							={}

			snake_case:    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',
			}

			snake_case:    Union[str, 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
									snake_case:    Dict      							='.'.join(key[1:]					)

									# rename the key
									for old, new in CONVERSION_MAPPING.items():
												snake_case:    Union[str, Any]      							=new_key.replace(__UpperCAmelCase				,							__UpperCAmelCase					)

									if "decoder" in new_key:
												for old, new in DECODER_CONVERSION_MAPPING.items():
															snake_case:    Optional[Any]      							=new_key.replace(__UpperCAmelCase				,							__UpperCAmelCase					)

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

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

									snake_case:    Optional[int]      							=flax_dict[key]

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

			return converted_torch_dict







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase=False				,							__UpperCAmelCase=False					)				->					Tuple:





			"""simple docstring"""







			snake_case:    Any      							=get_flax_param(__UpperCAmelCase					)

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

			snake_case:    List[Any]      							=PixaStructForConditionalGeneration(__UpperCAmelCase					)

			snake_case:    str      							=rename_and_convert_flax_params(__UpperCAmelCase					)
			model.load_state_dict(__UpperCAmelCase					)

			snake_case:    List[Any]      							=AutoTokenizer.from_pretrained('ybelkada/test-pix2struct-tokenizer'					)
			snake_case:    Optional[Any]      							=PixaStructImageProcessor()
			snake_case:    Optional[int]      							=PixaStructProcessor(image_processor=__UpperCAmelCase				,							tokenizer=__UpperCAmelCase					)

			if use_large:
						snake_case:    Optional[Any]      							=40_96

			snake_case:    str      							=True

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

			model.save_pretrained(__UpperCAmelCase					)
			processor.save_pretrained(__UpperCAmelCase					)

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


if __name__ == "__main__":
	a							   =				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.')
	a							   =				parser.parse_args()

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




