
import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM



def 							lowerCamelCase_	( __UpperCamelCase			,    __UpperCamelCase			,    __UpperCamelCase  ):
				A_													=	AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE  )
				A_													=	FlaxAutoModelForSeqaSeqLM.from_config(config=_SCREAMING_SNAKE_CASE  )
				A_													=	checkpoints.load_tax_checkpoint(_SCREAMING_SNAKE_CASE  )

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

				if config.model_type == "t5":
								A_													=	'''SelfAttention'''
				if config.model_type == "longt5" and config.encoder_attention_type == "local":
								A_													=	'''LocalSelfAttention'''
				elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
								A_													=	'''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  ):
								A_													=	F"layers_{str(_SCREAMING_SNAKE_CASE  )}"

								# Self-Attention
								A_													=	tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
								A_													=	tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
								A_													=	tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
								A_													=	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":
												A_													=	tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

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

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

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

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

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

								A_													=	tax_attention_layer_norm

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

								if split_mlp_wi:
												A_													=	tax_mlp_wi_a
												A_													=	tax_mlp_wi_a
								else:
												A_													=	tax_mlp_wi

								A_													=	tax_mlp_wo
								A_													=	tax_mlp_layer_norm

								A_													=	flax_model_encoder_layer_block

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

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

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

				# Decoder
				for layer_index in range(config.num_layers  ):
								A_													=	F"layers_{str(_SCREAMING_SNAKE_CASE  )}"

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

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

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

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

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

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

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

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

								A_													=	tax_pre_attention_layer_norm

								A_													=	tax_enc_dec_attention_key
								A_													=	tax_enc_dec_attention_out
								A_													=	tax_enc_dec_attention_query
								A_													=	tax_enc_dec_attention_value

								A_													=	tax_cross_layer_norm

								if split_mlp_wi:
												A_													=	tax_mlp_wi_a
												A_													=	tax_mlp_wi_a
								else:
												A_													=	tax_mlp_wi

								A_													=	tax_mlp_wo

								A_													=	txa_mlp_layer_norm

								A_													=	flax_model_decoder_layer_block

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

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

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

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

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


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE :		Dict       			=							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."
						)
						SCREAMING_SNAKE_CASE :		Optional[Any]       			=							parser.parse_args()
						convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)



'''simple docstring'''





# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


UpperCAmelCase__  			=				subprocess.check_output('''git merge-base main HEAD'''.split()).decode('''utf-8''')
UpperCAmelCase__  			=				subprocess.check_output(F"""git diff --name-only {fork_point_sha}""".split()).decode('''utf-8''').split()

UpperCAmelCase__  			=				'''|'''.join(sys.argv[1:])
UpperCAmelCase__  			=				re.compile(rF"""^({joined_dirs}).*?\.py$""")

UpperCAmelCase__  			=				[x for x in modified_files if regex.match(x)]
print(''' '''.join(relevant_modified_files), end='''''')



