






'''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 				lowerCamelCase__				(_UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= checkpoints.load_tax_checkpoint(A_)
			SCREAMING_SNAKE_CASE						= flatten_dict(A_)
			return flax_params






def 				lowerCamelCase__				(_UpperCAmelCase):
			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(A_							,       A_)

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

									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'							,       A_)
												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'							,       A_)

									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							,       _UpperCAmelCase							,       _UpperCAmelCase=False							,       _UpperCAmelCase=False):
			SCREAMING_SNAKE_CASE						= get_flax_param(A_)

			if not use_large:
						SCREAMING_SNAKE_CASE						= PixaStructVisionConfig()
						SCREAMING_SNAKE_CASE						= PixaStructTextConfig()
			else:
						SCREAMING_SNAKE_CASE						= PixaStructVisionConfig(
						    hidden_size=1536							,       d_ff=3968							,       num_attention_heads=24							,       num_hidden_layers=18)
						SCREAMING_SNAKE_CASE						= PixaStructTextConfig(hidden_size=1536							,       d_ff=3968							,       num_heads=24							,       num_layers=18)
			SCREAMING_SNAKE_CASE						= PixaStructConfig(
			    vision_config=encoder_config.to_dict()							,       text_config=decoder_config.to_dict()							,       is_vqa=A_)

			SCREAMING_SNAKE_CASE						= PixaStructForConditionalGeneration(A_)

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

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

			if use_large:
						SCREAMING_SNAKE_CASE						= 4096

			SCREAMING_SNAKE_CASE						= 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__":
				a_		:						Union[str, Any]             =    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_		:						Optional[int]             =    parser.parse_args()

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












import unittest

from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script

@require_cpu
class 		_snake_case  (  unittest.TestCase					):






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						debug_launcher(test_script.main)







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						debug_launcher(test_ops.main)




