




'''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__   (				a							):
						__snake_case						 =   checkpoints.load_tax_checkpoint(a							)
						__snake_case						 =   flatten_dict(a							)
						return flax_params
def 						lowerCamelCase__   (				a							):
						__snake_case						 =   {}

						__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',
						}

						__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
																		__snake_case						 =   '.'.join(key[1:]							)

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

																		if "decoder" in new_key:
																								for old, new in DECODER_CONVERSION_MAPPING.items():
																														__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
																								__snake_case						 =   re.sub(r'layers_(\d+)'    ,	r'layer.\1'    ,	a							)
																								__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
																								__snake_case						 =   re.sub(r'layers_(\d+)'    ,	r'layer.\1'    ,	a							)

																		__snake_case						 =   flax_dict[key]

						__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):
																		__snake_case						 =   torch.from_numpy(converted_dict[key].T							)
												else:
																		__snake_case						 =   torch.from_numpy(converted_dict[key]							)

						return converted_torch_dict
def 						lowerCamelCase__   (				a    ,	a    ,	a=False    ,	a=False							):
						__snake_case						 =   get_flax_param(a							)

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

						__snake_case						 =   PixaStructForConditionalGeneration(a							)

						__snake_case						 =   rename_and_convert_flax_params(a							)
						model.load_state_dict(a							)

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

						if use_large:
												__snake_case						 =   4096

						__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__":
	_lowercase  =			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.""")
	_lowercase  =			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 warnings

from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor


_lowercase  =			logging.get_logger(__name__)
class 						a_				(			UpperCAmelCase__						):




	def __init__(       self     :  Tuple       ,		*__lowerCAmelCase     :  List[str]       ,		**__lowerCAmelCase     :  List[str]      ):
							warnings.warn(
							    'The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
							    ' Please use VideoMAEImageProcessor instead.'       ,		__lowerCAmelCase       ,		)
							super().__init__(*__lowerCAmelCase       ,		**__lowerCAmelCase      )

