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 	__lowerCamelCase							(UpperCAmelCase__     :							int  ):
					SCREAMING_SNAKE_CASE    			=       checkpoints.load_tax_checkpoint(UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       flatten_dict(UpperCAmelCase__  )
					return flax_params





def 	__lowerCamelCase							(UpperCAmelCase__     :							Tuple  ):
					SCREAMING_SNAKE_CASE    			=       {}

					SCREAMING_SNAKE_CASE    			=       {
					    "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    			=       {
					    "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    			=       ".".join(key[1:]  )

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

															if "decoder" in new_key:
																				for old, new in DECODER_CONVERSION_MAPPING.items():
																									SCREAMING_SNAKE_CASE    			=       new_key.replace(UpperCAmelCase__      , UpperCAmelCase__  )

															if "layers" in new_key and "decoder" not in new_key:
																				# use regex to replace the layer number
																				SCREAMING_SNAKE_CASE    			=       re.sub(r"layers_(\d+)"      , r"layer.\1"      , UpperCAmelCase__  )
																				SCREAMING_SNAKE_CASE    			=       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    			=       re.sub(r"layers_(\d+)"      , r"layer.\1"      , UpperCAmelCase__  )

															SCREAMING_SNAKE_CASE    			=       flax_dict[key]

					SCREAMING_SNAKE_CASE    			=       {}
					# 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    			=       torch.from_numpy(converted_dict[key].T  )
										else:
															SCREAMING_SNAKE_CASE    			=       torch.from_numpy(converted_dict[key]  )

					return converted_torch_dict





def 	__lowerCamelCase							(UpperCAmelCase__     :							Optional[Any]      , UpperCAmelCase__     :							str      , UpperCAmelCase__     :							Dict=False      , UpperCAmelCase__     :							Any=False  ):
					SCREAMING_SNAKE_CASE    			=       get_flax_param(UpperCAmelCase__  )

					if not use_large:
										SCREAMING_SNAKE_CASE    			=       PixaStructVisionConfig()
										SCREAMING_SNAKE_CASE    			=       PixaStructTextConfig()
					else:
										SCREAMING_SNAKE_CASE    			=       PixaStructVisionConfig(
										    hidden_size=1_5_3_6      , d_ff=3_9_6_8      , num_attention_heads=2_4      , num_hidden_layers=1_8  )
										SCREAMING_SNAKE_CASE    			=       PixaStructTextConfig(hidden_size=1_5_3_6      , d_ff=3_9_6_8      , num_heads=2_4      , num_layers=1_8  )
					SCREAMING_SNAKE_CASE    			=       PixaStructConfig(
					    vision_config=encoder_config.to_dict()      , text_config=decoder_config.to_dict()      , is_vqa=UpperCAmelCase__  )

					SCREAMING_SNAKE_CASE    			=       PixaStructForConditionalGeneration(UpperCAmelCase__  )

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

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

					if use_large:
										SCREAMING_SNAKE_CASE    			=       4_0_9_6

					SCREAMING_SNAKE_CASE    			=       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__":
							_lowerCamelCase					:   Optional[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					:   int							=		parser.parse_args()

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





import functools
import logging
import os
import sys
import threading
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

import huggingface_hub.utils as hf_hub_utils
from tqdm import auto as tqdm_lib


_lowerCamelCase					:   str							=		threading.Lock()
_lowerCamelCase					:   Optional[logging.Handler]							=		None

_lowerCamelCase					:   Any							=		{
    '''debug''': logging.DEBUG,
    '''info''': logging.INFO,
    '''warning''': logging.WARNING,
    '''error''': logging.ERROR,
    '''critical''': logging.CRITICAL,
}

_lowerCamelCase					:   Union[str, Any]							=		logging.WARNING

_lowerCamelCase					:   List[Any]							=		True





def 	__lowerCamelCase							():
					SCREAMING_SNAKE_CASE    			=       os.getenv("TRANSFORMERS_VERBOSITY"      , UpperCAmelCase__  )
					if env_level_str:
										if env_level_str in log_levels:
															return log_levels[env_level_str]
										else:
															logging.getLogger().warning(
															    F"Unknown option TRANSFORMERS_VERBOSITY={env_level_str}, "
															    F"has to be one of: { ', '.join(log_levels.keys()  ) }"  )
					return _default_log_level





def 	__lowerCamelCase							():
					return __name__.split("."  )[0]





def 	__lowerCamelCase							():
					return logging.getLogger(_get_library_name()  )





def 	__lowerCamelCase							():
					global _default_handler

					with _lock:
										if _default_handler:
															# This library has already configured the library root logger.
															return
										SCREAMING_SNAKE_CASE    			=       logging.StreamHandler()  # Set sys.stderr as stream.
										SCREAMING_SNAKE_CASE    			=       sys.stderr.flush

										# Apply our default configuration to the library root logger.
										SCREAMING_SNAKE_CASE    			=       _get_library_root_logger()
										library_root_logger.addHandler(_default_handler  )
										library_root_logger.setLevel(_get_default_logging_level()  )
										SCREAMING_SNAKE_CASE    			=       False





def 	__lowerCamelCase							():
					global _default_handler

					with _lock:
										if not _default_handler:
															return

										SCREAMING_SNAKE_CASE    			=       _get_library_root_logger()
										library_root_logger.removeHandler(_default_handler  )
										library_root_logger.setLevel(logging.NOTSET  )
										SCREAMING_SNAKE_CASE    			=       None





def 	__lowerCamelCase							():
					return log_levels





def 	__lowerCamelCase							(UpperCAmelCase__     :							Optional[str] = None  ):

					if name is None:
										SCREAMING_SNAKE_CASE    			=       _get_library_name()

					_configure_library_root_logger()
					return logging.getLogger(UpperCAmelCase__  )





def 	__lowerCamelCase							():
					_configure_library_root_logger()
					return _get_library_root_logger().getEffectiveLevel()





def 	__lowerCamelCase							(UpperCAmelCase__     :							int  ):
					_configure_library_root_logger()
					_get_library_root_logger().setLevel(UpperCAmelCase__  )





def 	__lowerCamelCase							():
					return set_verbosity(UpperCAmelCase__  )





def 	__lowerCamelCase							():
					return set_verbosity(UpperCAmelCase__  )





def 	__lowerCamelCase							():
					return set_verbosity(UpperCAmelCase__  )





def 	__lowerCamelCase							():
					return set_verbosity(UpperCAmelCase__  )





def 	__lowerCamelCase							():
					_configure_library_root_logger()

					assert _default_handler is not None
					_get_library_root_logger().removeHandler(_default_handler  )





def 	__lowerCamelCase							():
					_configure_library_root_logger()

					assert _default_handler is not None
					_get_library_root_logger().addHandler(_default_handler  )





def 	__lowerCamelCase							(UpperCAmelCase__     :							logging.Handler  ):
					_configure_library_root_logger()

					assert handler is not None
					_get_library_root_logger().addHandler(UpperCAmelCase__  )





def 	__lowerCamelCase							(UpperCAmelCase__     :							logging.Handler  ):
					_configure_library_root_logger()

					assert handler is not None and handler not in _get_library_root_logger().handlers
					_get_library_root_logger().removeHandler(UpperCAmelCase__  )





def 	__lowerCamelCase							():
					_configure_library_root_logger()
					SCREAMING_SNAKE_CASE    			=       False





def 	__lowerCamelCase							():
					_configure_library_root_logger()
					SCREAMING_SNAKE_CASE    			=       True





def 	__lowerCamelCase							():
					SCREAMING_SNAKE_CASE    			=       _get_library_root_logger().handlers

					for handler in handlers:
										SCREAMING_SNAKE_CASE    			=       logging.Formatter("[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s"  )
										handler.setFormatter(UpperCAmelCase__  )





def 	__lowerCamelCase							():
					SCREAMING_SNAKE_CASE    			=       _get_library_root_logger().handlers

					for handler in handlers:
										handler.setFormatter(UpperCAmelCase__  )





def 	__lowerCamelCase							(self     :							str      , *UpperCAmelCase__     :							Union[str, Any]      , **UpperCAmelCase__     :							List[str]  ):
					SCREAMING_SNAKE_CASE    			=       os.getenv("TRANSFORMERS_NO_ADVISORY_WARNINGS"      , UpperCAmelCase__  )
					if no_advisory_warnings:
										return
					self.warning(*UpperCAmelCase__      , **UpperCAmelCase__  )


_lowerCamelCase					:   str							=		warning_advice





@functools.lru_cache(UpperCAmelCase__  )
def 	__lowerCamelCase							(self     :							List[str]      , *UpperCAmelCase__     :							Tuple      , **UpperCAmelCase__     :							int  ):
					self.warning(*UpperCAmelCase__      , **UpperCAmelCase__  )


_lowerCamelCase					:   Dict							=		warning_once


class 						lowercase    :




							def __init__(						self   :					List[Any]	,							*_UpperCamelCase   :					Union[str, Any]	,							**_UpperCamelCase   :					str      )					->				List[Any]:  # pylint: disable=unused-argument



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       args[0] if args else None




							def __iter__(						self   :					Optional[Any]      )					->				str:



												'''simple docstring'''

												return iter(self._iterator      )




							def __getattr__(						self   :					List[str]	,							_UpperCamelCase   :					Any      )					->				List[Any]:



												'''simple docstring'''

												def empty_fn(*_UpperCamelCase   :					List[str]	,							**_UpperCamelCase   :					Union[str, Any]      ):  # pylint: disable=unused-argument
																	return

												return empty_fn




							def __enter__(						self   :					Any      )					->				Optional[Any]:



												'''simple docstring'''

												return self





							def __exit__(						self   :					Optional[Any]	,							_UpperCamelCase   :					Tuple	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					List[str]      )					->				Union[str, Any]:



												'''simple docstring'''

												return



class 						lowercase    :




							def __call__(						self   :					Union[str, Any]	,							*_UpperCamelCase   :					Optional[Any]	,							**_UpperCamelCase   :					Tuple      )					->				Tuple:



												'''simple docstring'''

												if _tqdm_active:
																	return tqdm_lib.tqdm(*_UpperCamelCase	,							**_UpperCamelCase      )
												else:
																	return EmptyTqdm(*_UpperCamelCase	,							**_UpperCamelCase      )




							def 	__snake_case(						self   :					Dict	,							*_UpperCamelCase   :					Dict	,							**_UpperCamelCase   :					Union[str, Any]      )					->				Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       None
												if _tqdm_active:
																	return tqdm_lib.tqdm.set_lock(*_UpperCamelCase	,							**_UpperCamelCase      )





							def 	__snake_case(						self   :					Optional[int]      )					->				Union[str, Any]:



												'''simple docstring'''

												if _tqdm_active:
																	return tqdm_lib.tqdm.get_lock()



_lowerCamelCase					:   Union[str, Any]							=		_tqdm_cls()





def 	__lowerCamelCase							():
					global _tqdm_active
					return bool(_tqdm_active  )





def 	__lowerCamelCase							():
					global _tqdm_active
					SCREAMING_SNAKE_CASE    			=       True
					hf_hub_utils.enable_progress_bars()





def 	__lowerCamelCase							():
					global _tqdm_active
					SCREAMING_SNAKE_CASE    			=       False
					hf_hub_utils.disable_progress_bars()




