


'''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       :      Optional[Any]					):




						'''simple docstring'''






						UpperCAmelCase					    =     checkpoints.load_tax_checkpoint(A					)
						UpperCAmelCase					    =     flatten_dict(A					)
						return flax_params


def 				lowerCamelCase__				(	A       :      Union[str, Any]					):




						'''simple docstring'''






						UpperCAmelCase					    =     {}

						UpperCAmelCase					    =     {
						    '''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''',
						}

						UpperCAmelCase					    =     {
						    '''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
																		UpperCAmelCase					    =     '''.'''.join(key[1:]					)

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

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

																		if "layers" in new_key and "decoder" not in new_key:
																								# use regex to replace the layer number
																								UpperCAmelCase					    =     re.sub(R'''layers_(\d+)'''				,       R'''layer.\1'''				,       A					)
																								UpperCAmelCase					    =     new_key.replace('''encoder'''				,       '''encoder.encoder'''					)

																		elif "layers" in new_key and "decoder" in new_key:
																								# use regex to replace the layer number
																								UpperCAmelCase					    =     re.sub(R'''layers_(\d+)'''				,       R'''layer.\1'''				,       A					)

																		UpperCAmelCase					    =     flax_dict[key]

						UpperCAmelCase					    =     {}
						# convert converted_dict into torch format
						for key in converted_dict.keys():
												if ("embed_tokens" not in key) and ("embedder" not in key):
																		UpperCAmelCase					    =     torch.from_numpy(converted_dict[key].T					)
												else:
																		UpperCAmelCase					    =     torch.from_numpy(converted_dict[key]					)

						return converted_torch_dict


def 				lowerCamelCase__				(	A       :      str				,       A       :      int				,       A       :      List[str]=False				,       A       :      str=False					):




						'''simple docstring'''






						UpperCAmelCase					    =     get_flax_param(A					)

						if not use_large:
												UpperCAmelCase					    =     PixaStructVisionConfig()
												UpperCAmelCase					    =     PixaStructTextConfig()
						else:
												UpperCAmelCase					    =     PixaStructVisionConfig(
												    hidden_size=15_36				,       d_ff=39_68				,       num_attention_heads=24				,       num_hidden_layers=18					)
												UpperCAmelCase					    =     PixaStructTextConfig(hidden_size=15_36				,       d_ff=39_68				,       num_heads=24				,       num_layers=18					)
						UpperCAmelCase					    =     PixaStructConfig(
						    vision_config=encoder_config.to_dict()				,       text_config=decoder_config.to_dict()				,       is_vqa=A					)

						UpperCAmelCase					    =     PixaStructForConditionalGeneration(A					)

						UpperCAmelCase					    =     rename_and_convert_flax_params(A					)
						model.load_state_dict(A					)

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

						if use_large:
												UpperCAmelCase					    =     40_96

						UpperCAmelCase					    =     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      :      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.""")
	_lowercase      :      Union[str, Any]            =					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 ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 	UpperCamelCase__(       lowerCAmelCase	):
				__magic_name__    :				List[Any]  		=	["image_processor", "tokenizer"]
				__magic_name__    :				Tuple  		=	"ViTImageProcessor"
				__magic_name__    :				int  		=	("CLIPTokenizer", "CLIPTokenizerFast")




				def __init__(		self      :			List[str]				,   lowerCAmelCase      :			Tuple=None				,   lowerCAmelCase      :			List[str]=None				,   **lowerCAmelCase      :			Optional[int]				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase					    =     None
										if "feature_extractor" in kwargs:
																warnings.warn(
																    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																    ''' instead.'''				,   lowerCAmelCase				,   )
																UpperCAmelCase					    =     kwargs.pop('''feature_extractor'''				)

										UpperCAmelCase					    =     image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
																raise ValueError('''You need to specify an `image_processor`.'''				)
										if tokenizer is None:
																raise ValueError('''You need to specify a `tokenizer`.'''				)

										super().__init__(lowerCAmelCase				,   lowerCAmelCase				)




				def __call__(		self      :			int				,   lowerCAmelCase      :			Dict=None				,   lowerCAmelCase      :			Optional[int]=None				,   lowerCAmelCase      :			Union[str, Any]=None				,   lowerCAmelCase      :			int=None				,   **lowerCAmelCase      :			Tuple				)->					Optional[int]:

										"""simple docstring"""




										if text is None and visual_prompt is None and images is None:
																raise ValueError('''You have to specify either text, visual prompt or images.'''				)

										if text is not None and visual_prompt is not None:
																raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''				)

										if text is not None:
																UpperCAmelCase					    =     self.tokenizer(lowerCAmelCase				,   return_tensors=lowerCAmelCase				,   **lowerCAmelCase				)

										if visual_prompt is not None:
																UpperCAmelCase					    =     self.image_processor(lowerCAmelCase				,   return_tensors=lowerCAmelCase				,   **lowerCAmelCase				)

										if images is not None:
																UpperCAmelCase					    =     self.image_processor(lowerCAmelCase				,   return_tensors=lowerCAmelCase				,   **lowerCAmelCase				)

										if visual_prompt is not None and images is not None:
																UpperCAmelCase					    =     {
																    '''pixel_values''': image_features.pixel_values,
																    '''conditional_pixel_values''': prompt_features.pixel_values,
																}
																return encoding
										elif text is not None and images is not None:
																UpperCAmelCase					    =     image_features.pixel_values
																return encoding
										elif text is not None:
																return encoding
										elif visual_prompt is not None:
																UpperCAmelCase					    =     {
																    '''conditional_pixel_values''': prompt_features.pixel_values,
																}
																return encoding
										else:
																return BatchEncoding(data=dict(**lowerCAmelCase				)				,   tensor_type=lowerCAmelCase				)




				def 					a__(		self      :			Optional[int]				,   *lowerCAmelCase      :			List[str]				,   **lowerCAmelCase      :			Dict				)->					Tuple:

										"""simple docstring"""




										return self.tokenizer.batch_decode(*lowerCAmelCase				,   **lowerCAmelCase				)




				def 					a__(		self      :			List[Any]				,   *lowerCAmelCase      :			str				,   **lowerCAmelCase      :			List[Any]				)->					Optional[Any]:

										"""simple docstring"""




										return self.tokenizer.decode(*lowerCAmelCase				,   **lowerCAmelCase				)




				@property
				def 					a__(		self      :			Any				)->					Optional[int]:

										"""simple docstring"""




										warnings.warn(
										    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''				,   lowerCAmelCase				,   )
										return self.image_processor_class





				@property
				def 					a__(		self      :			str				)->					List[Any]:

										"""simple docstring"""




										warnings.warn(
										    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''				,   lowerCAmelCase				,   )
										return self.image_processor





