




import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM







def 		_A(       UpperCamelCase__							:			Optional[int]				,       UpperCamelCase__							:			Optional[Any]				,       UpperCamelCase__							:			List[str]				)    -> Union[str, Any]:
							'''simple docstring'''


							__lowercase      =    AutoConfig.from_pretrained(UpperCamelCase__				)
							__lowercase      =    FlaxAutoModelForSeqaSeqLM.from_config(config=UpperCamelCase__				)
							__lowercase      =    checkpoints.load_tax_checkpoint(UpperCamelCase__				)

							__lowercase      =    '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

							if config.model_type == "t5":
														__lowercase      =    '''SelfAttention'''
							if config.model_type == "longt5" and config.encoder_attention_type == "local":
														__lowercase      =    '''LocalSelfAttention'''
							elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
														__lowercase      =    '''TransientGlobalSelfAttention'''
							else:
														raise ValueError(
														    '''Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'''
														    ''' attribute with a value from [\'local\', \'transient-global].'''				)

							# Encoder
							for layer_index in range(config.num_layers				):
														__lowercase      =    F'layers_{str(UpperCamelCase__				)}'

														# Self-Attention
														__lowercase      =    tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
														__lowercase      =    tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
														__lowercase      =    tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
														__lowercase      =    tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

														# Global input layer norm
														if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
																					__lowercase      =    tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

														# Layer Normalization
														__lowercase      =    tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

														if split_mlp_wi:
																					__lowercase      =    tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
																					__lowercase      =    tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
														else:
																					__lowercase      =    tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

														__lowercase      =    tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

														# Layer Normalization
														__lowercase      =    tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

														# Assigning
														__lowercase      =    flax_model.params['''encoder''']['''block'''][str(UpperCamelCase__				)]['''layer''']
														__lowercase      =    tax_attention_key
														__lowercase      =    tax_attention_out
														__lowercase      =    tax_attention_query
														__lowercase      =    tax_attention_value

														__lowercase      =    tax_attention_layer_norm

														# Global input layer norm
														if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
																					__lowercase      =    tax_global_layer_norm

														if split_mlp_wi:
																					__lowercase      =    tax_mlp_wi_a
																					__lowercase      =    tax_mlp_wi_a
														else:
																					__lowercase      =    tax_mlp_wi

														__lowercase      =    tax_mlp_wo
														__lowercase      =    tax_mlp_layer_norm

														__lowercase      =    flax_model_encoder_layer_block

							# Only for layer 0:
							__lowercase      =    tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
							__lowercase      =    tax_encoder_rel_embedding

							# Side/global relative position_bias + layer norm
							if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
														__lowercase      =    tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
														__lowercase      =    tax_encoder_global_rel_embedding

							# Assigning
							__lowercase      =    tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
							__lowercase      =    tax_encoder_norm

							# Decoder
							for layer_index in range(config.num_layers				):
														__lowercase      =    F'layers_{str(UpperCamelCase__				)}'

														# Self-Attention
														__lowercase      =    tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
														__lowercase      =    tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
														__lowercase      =    tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
														__lowercase      =    tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

														# Layer Normalization
														__lowercase      =    tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
														    '''scale'''
														]

														# Encoder-Decoder-Attention
														__lowercase      =    tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
														__lowercase      =    tax_enc_dec_attention_module['''key''']['''kernel''']
														__lowercase      =    tax_enc_dec_attention_module['''out''']['''kernel''']
														__lowercase      =    tax_enc_dec_attention_module['''query''']['''kernel''']
														__lowercase      =    tax_enc_dec_attention_module['''value''']['''kernel''']

														# Layer Normalization
														__lowercase      =    tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

														# MLP
														if split_mlp_wi:
																					__lowercase      =    tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
																					__lowercase      =    tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
														else:
																					__lowercase      =    tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

														__lowercase      =    tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

														# Layer Normalization
														__lowercase      =    tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

														# Assigning
														__lowercase      =    flax_model.params['''decoder''']['''block'''][str(UpperCamelCase__				)]['''layer''']
														__lowercase      =    tax_attention_key
														__lowercase      =    tax_attention_out
														__lowercase      =    tax_attention_query
														__lowercase      =    tax_attention_value

														__lowercase      =    tax_pre_attention_layer_norm

														__lowercase      =    tax_enc_dec_attention_key
														__lowercase      =    tax_enc_dec_attention_out
														__lowercase      =    tax_enc_dec_attention_query
														__lowercase      =    tax_enc_dec_attention_value

														__lowercase      =    tax_cross_layer_norm

														if split_mlp_wi:
																					__lowercase      =    tax_mlp_wi_a
																					__lowercase      =    tax_mlp_wi_a
														else:
																					__lowercase      =    tax_mlp_wi

														__lowercase      =    tax_mlp_wo

														__lowercase      =    txa_mlp_layer_norm

														__lowercase      =    flax_model_decoder_layer_block

							# Decoder Normalization
							__lowercase      =    tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
							__lowercase      =    txa_decoder_norm

							# Only for layer 0:
							__lowercase      =    tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
							__lowercase      =    tax_decoder_rel_embedding

							# Token Embeddings
							__lowercase      =    tax_model['''target''']['''token_embedder''']['''embedding''']
							__lowercase      =    txa_token_embeddings

							# LM Head (only in v1.1 and LongT5 checkpoints)
							if "logits_dense" in tax_model["target"]["decoder"]:
														__lowercase      =    tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

							flax_model.save_pretrained(UpperCamelCase__				)
							print('''T5X Model was sucessfully converted!'''				)


if __name__ == "__main__":
			UpperCAmelCase__				     = argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the T5X checkpoint."
			)
			parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of LongT5/T5 model.")
			parser.add_argument(
			    "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
			)
			UpperCAmelCase__				     = parser.parse_args()
			convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)












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__							:			Tuple				)    -> Tuple:
							'''simple docstring'''


							__lowercase      =    checkpoints.load_tax_checkpoint(UpperCamelCase__				)
							__lowercase      =    flatten_dict(UpperCamelCase__				)
							return flax_params







def 		_A(       UpperCamelCase__							:			str				)    -> Dict:
							'''simple docstring'''


							__lowercase      =    {}

							__lowercase      =    {
							    '''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''',
							}

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

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

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

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

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

																					__lowercase      =    flax_dict[key]

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

							return converted_torch_dict







def 		_A(       UpperCamelCase__							:			Union[str, Any]				,       UpperCamelCase__							:			int				,       UpperCamelCase__							:			List[str]=False				,       UpperCamelCase__							:			List[str]=False				)    -> Any:
							'''simple docstring'''


							__lowercase      =    get_flax_param(UpperCamelCase__				)

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

							__lowercase      =    PixaStructForConditionalGeneration(UpperCamelCase__				)

							__lowercase      =    rename_and_convert_flax_params(UpperCamelCase__				)
							model.load_state_dict(UpperCamelCase__				)

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

							if use_large:
														__lowercase      =    4096

							__lowercase      =    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__":
			UpperCAmelCase__				     = 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.")
			UpperCAmelCase__				     = parser.parse_args()

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






