
'''simple docstring'''
import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM







def 		A_(     A						:		str		,     A						:		List[Any]		,     A						:		Optional[Any]):
							UpperCamelCase   			=     AutoConfig.from_pretrained(A)
							UpperCamelCase   			=     FlaxAutoModelForSeqaSeqLM.from_config(config=A)
							UpperCamelCase   			=     checkpoints.load_tax_checkpoint(A)

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

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

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

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

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

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

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

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

														UpperCamelCase   			=     tax_attention_layer_norm

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

														if split_mlp_wi:
																					UpperCamelCase   			=     tax_mlp_wi_a
																					UpperCamelCase   			=     tax_mlp_wi_a
														else:
																					UpperCamelCase   			=     tax_mlp_wi

														UpperCamelCase   			=     tax_mlp_wo
														UpperCamelCase   			=     tax_mlp_layer_norm

														UpperCamelCase   			=     flax_model_encoder_layer_block

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

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

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

							# Decoder
							for layer_index in range(config.num_layers):
														UpperCamelCase   			=     f'''layers_{str(A)}'''

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

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

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

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

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

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

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

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

														UpperCamelCase   			=     tax_pre_attention_layer_norm

														UpperCamelCase   			=     tax_enc_dec_attention_key
														UpperCamelCase   			=     tax_enc_dec_attention_out
														UpperCamelCase   			=     tax_enc_dec_attention_query
														UpperCamelCase   			=     tax_enc_dec_attention_value

														UpperCamelCase   			=     tax_cross_layer_norm

														if split_mlp_wi:
																					UpperCamelCase   			=     tax_mlp_wi_a
																					UpperCamelCase   			=     tax_mlp_wi_a
														else:
																					UpperCamelCase   			=     tax_mlp_wi

														UpperCamelCase   			=     tax_mlp_wo

														UpperCamelCase   			=     txa_mlp_layer_norm

														UpperCamelCase   			=     flax_model_decoder_layer_block

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

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

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

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

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


if __name__ == "__main__":
						lowerCAmelCase					:    Tuple           = 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.'
						)
						lowerCAmelCase					:    int           = parser.parse_args()
						convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)










from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
							from .features import FeatureType




@dataclass
class 		__A   :
						UpperCamelCase      =       42
						UpperCamelCase      =       None
						# Automatically constructed
						UpperCamelCase      =       "dict"
						UpperCamelCase      =       None
						UpperCamelCase      =       field(default="""Translation"""       ,    init=UpperCamelCase__       ,    repr=UpperCamelCase__      )

						def __call__(		self :Union[str, Any]			):
										'''simple docstring'''

										return pa.struct({lang: pa.string() for lang in sorted(self.languages			)}			)


						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										from .features import Value

										return {k: Value("""string"""			) for k in sorted(self.languages			)}




@dataclass
class 		__A   :
						UpperCamelCase      =       None
						UpperCamelCase      =       None
						UpperCamelCase      =       None
						# Automatically constructed
						UpperCamelCase      =       "dict"
						UpperCamelCase      =       None
						UpperCamelCase      =       field(default="""TranslationVariableLanguages"""       ,    init=UpperCamelCase__       ,    repr=UpperCamelCase__      )

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						List[Any]										=sorted(set(self.languages			)			) if self.languages else None
										__magic_name__						:						Optional[int]										=len(self.languages			) if self.languages else None

						def __call__(		self :List[str]			):
										'''simple docstring'''

										return pa.struct({"""language""": pa.list_(pa.string()			), """translation""": pa.list_(pa.string()			)}			)

						def      A__    (		self :str     ,			__snake_case :str			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=set(self.languages			)
										if self.languages and set(__snake_case			) - lang_set:
														raise ValueError(
														    f"Some languages in example ({', '.join(sorted(set(__snake_case			) - lang_set			)			)}) are not in valid set ({', '.join(__snake_case			)})."			)

										# Convert dictionary into tuples, splitting out cases where there are
										# multiple translations for a single language.
										__magic_name__						:						Any										=[]
										for lang, text in translation_dict.items():
														if isinstance(__snake_case     ,			__snake_case			):
																		translation_tuples.append((lang, text)			)
														else:
																		translation_tuples.extend([(lang, el) for el in text]			)

        # Ensure translations are in ascending order by language code.
										__magic_name__					,			__magic_name__						:						List[str]										=zip(*sorted(__snake_case			)			)

										return {"language": languages, "translation": translations}


						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										from .features import Sequence, Value

										return {
										    "language": Sequence(Value("""string"""			)			),
										    "translation": Sequence(Value("""string"""			)			),
										}




