

'''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       (				__lowerCAmelCase		:      Dict     ):

		"""simple docstring"""

		snake_case__						:       Optional[int]       						=  checkpoints.load_tax_checkpoint(a__     )
		snake_case__						:       Optional[Any]       						=  flatten_dict(a__     )
		return flax_params




def 	_a       (				__lowerCAmelCase		:      List[Any]     ):

		"""simple docstring"""

		snake_case__						:       str       						=  {}

		snake_case__						:       str       						=  {
		    '''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__						:       Dict       						=  {
		    '''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__						:       Tuple       						=  '''.'''.join(key[1:]     )

						# rename the key
						for old, new in CONVERSION_MAPPING.items():
								snake_case__						:       int       						=  new_key.replace(a__   ,							a__     )

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

						if "layers" in new_key and "decoder" not in new_key:
								# use regex to replace the layer number
								snake_case__						:       str       						=  re.sub(r'''layers_(\d+)'''   ,							r'''layer.\1'''   ,							a__     )
								snake_case__						:       str       						=  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[int]       						=  re.sub(r'''layers_(\d+)'''   ,							r'''layer.\1'''   ,							a__     )

						snake_case__						:       Tuple       						=  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__						:       Tuple       						=  torch.from_numpy(converted_dict[key].T     )
				else:
						snake_case__						:       Optional[int]       						=  torch.from_numpy(converted_dict[key]     )

		return converted_torch_dict




def 	_a       (				__lowerCAmelCase		:      Optional[int]   ,							__lowerCAmelCase		:      Any   ,							__lowerCAmelCase		:      Any=False   ,							__lowerCAmelCase		:      Any=False     ):

		"""simple docstring"""

		snake_case__						:       str       						=  get_flax_param(a__     )

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

		snake_case__						:       Any       						=  PixaStructForConditionalGeneration(a__     )

		snake_case__						:       str       						=  rename_and_convert_flax_params(a__     )
		model.load_state_dict(a__     )

		snake_case__						:       str       						=  AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''     )
		snake_case__						:       Optional[Any]       						=  PixaStructImageProcessor()
		snake_case__						:       Optional[Any]       						=  PixaStructProcessor(image_processor=a__   ,							tokenizer=a__     )

		if use_large:
				snake_case__						:       Dict       						=  40_96

		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__":
					lowerCAmelCase__						:       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.""")
					lowerCAmelCase__						:       List[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 argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()




def 	_a       (				__lowerCAmelCase		:      Union[str, Any]   ,							__lowerCAmelCase		:      Any   ,							__lowerCAmelCase		:      Optional[int]   ,							__lowerCAmelCase		:      List[Any]     ):

		"""simple docstring"""

		snake_case__						:       Any       						=  BigBirdConfig.from_json_file(__lowerCAmelCase     )
		print(F"""Building PyTorch model from configuration: {config}"""     )

		if is_trivia_qa:
				snake_case__						:       str       						=  BigBirdForQuestionAnswering(__lowerCAmelCase     )
		else:
				snake_case__						:       Tuple       						=  BigBirdForPreTraining(__lowerCAmelCase     )

		# Load weights from tf checkpoint
		load_tf_weights_in_big_bird(__lowerCAmelCase   ,							__lowerCAmelCase   ,							is_trivia_qa=__lowerCAmelCase     )

		# Save pytorch-model
		print(F"""Save PyTorch model to {pytorch_dump_path}"""     )
		model.save_pretrained(__lowerCAmelCase     )


if __name__ == "__main__":
					lowerCAmelCase__						:       Union[str, Any]										=						argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
					)
					parser.add_argument(
					    """--big_bird_config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        """The config json file corresponding to the pre-trained BERT model. \n"""
					        """This specifies the model architecture."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					parser.add_argument(
					    """--is_trivia_qa""", action="""store_true""", help="""Whether to convert a model with a trivia_qa head."""
					)
					lowerCAmelCase__						:       Optional[int]										=						parser.parse_args()
					convert_tf_checkpoint_to_pytorch(
					    args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
					)



