



'''simple docstring'''

import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
	from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
	import torch_xla.core.xla_model as xm







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





			"""simple docstring"""







			if is_torch_version('<'				,							'2.0.0'					) or not hasattr(__UpperCAmelCase				,							'_dynamo'					):
						return False
			return isinstance(__UpperCAmelCase				,							torch._dynamo.eval_frame.OptimizedModule					)







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





			"""simple docstring"""







			snake_case:    Any      							=(torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

			snake_case:    Union[str, Any]      							=is_compiled_module(__UpperCAmelCase					)
			if is_compiled:
						snake_case:    List[str]      							=model
						snake_case:    Tuple      							=model._orig_mod

			if is_deepspeed_available():
						options += (DeepSpeedEngine,)

			while isinstance(__UpperCAmelCase				,							__UpperCAmelCase					):
						snake_case:    str      							=model.module

			if not keep_fpaa_wrapper:
						snake_case:    int      							=getattr(__UpperCAmelCase				,							'forward'					)
						snake_case:    Optional[Any]      							=model.__dict__.pop('_original_forward'				,							__UpperCAmelCase					)
						if original_forward is not None:
									while hasattr(__UpperCAmelCase				,							'__wrapped__'					):
												snake_case:    Dict      							=forward.__wrapped__
												if forward == original_forward:
															break
									snake_case:    Union[str, Any]      							=forward
						if getattr(__UpperCAmelCase				,							'_converted_to_transformer_engine'				,							__UpperCAmelCase					):
									convert_model(__UpperCAmelCase				,							to_transformer_engine=__UpperCAmelCase					)

			if is_compiled:
						snake_case:    Optional[int]      							=model
						snake_case:    Optional[Any]      							=compiled_model

			return model







def a_     (    )				->					List[str]:





			"""simple docstring"""







			PartialState().wait_for_everyone()







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





			"""simple docstring"""







			if PartialState().distributed_type == DistributedType.TPU:
						xm.save(__UpperCAmelCase				,							__UpperCAmelCase					)
			elif PartialState().local_process_index == 0:
						torch.save(__UpperCAmelCase				,							__UpperCAmelCase					)







@contextmanager
def a_     (    **__UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			for key, value in kwargs.items():
						snake_case:    Tuple      							=str(__UpperCAmelCase					)

			yield

			for key in kwargs:
						if key.upper() in os.environ:
									del os.environ[key.upper()]







def a_     (    __UpperCAmelCase					)				->					Any:





			"""simple docstring"""







			if not hasattr(__UpperCAmelCase				,							'__qualname__'					) and not hasattr(__UpperCAmelCase				,							'__name__'					):
						snake_case:    List[Any]      							=getattr(__UpperCAmelCase				,							'__class__'				,							__UpperCAmelCase					)
			if hasattr(__UpperCAmelCase				,							'__qualname__'					):
						return obj.__qualname__
			if hasattr(__UpperCAmelCase				,							'__name__'					):
						return obj.__name__
			return str(__UpperCAmelCase					)







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





			"""simple docstring"""







			for key, value in source.items():
						if isinstance(__UpperCAmelCase				,							__UpperCAmelCase					):
									snake_case:    Optional[Any]      							=destination.setdefault(__UpperCAmelCase				,							{}					)
									merge_dicts(__UpperCAmelCase				,							__UpperCAmelCase					)
						else:
									snake_case:    int      							=value

			return destination







def a_     (    __UpperCAmelCase = None					)				->					bool:





			"""simple docstring"""







			if port is None:
						snake_case:    Optional[int]      							=2_95_00
			with socket.socket(socket.AF_INET				,							socket.SOCK_STREAM					) as s:
						return s.connect_ex(('localhost', port)					) == 0









'''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
	)




