

import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






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

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

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

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

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

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

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

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

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

														snake_case_     							=       tax_attention_layer_norm

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

														if split_mlp_wi:
																					snake_case_     							=       tax_mlp_wi_a
																					snake_case_     							=       tax_mlp_wi_a
														else:
																					snake_case_     							=       tax_mlp_wi

														snake_case_     							=       tax_mlp_wo
														snake_case_     							=       tax_mlp_layer_norm

														snake_case_     							=       flax_model_encoder_layer_block

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

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

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

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

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

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

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

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

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

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

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

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

														snake_case_     							=       tax_pre_attention_layer_norm

														snake_case_     							=       tax_enc_dec_attention_key
														snake_case_     							=       tax_enc_dec_attention_out
														snake_case_     							=       tax_enc_dec_attention_query
														snake_case_     							=       tax_enc_dec_attention_value

														snake_case_     							=       tax_cross_layer_norm

														if split_mlp_wi:
																					snake_case_     							=       tax_mlp_wi_a
																					snake_case_     							=       tax_mlp_wi_a
														else:
																					snake_case_     							=       tax_mlp_wi

														snake_case_     							=       tax_mlp_wo

														snake_case_     							=       txa_mlp_layer_norm

														snake_case_     							=       flax_model_decoder_layer_block

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

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

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

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

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


if __name__ == "__main__":
					_UpperCAmelCase						:  List[str]									=			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						:  Optional[int]									=			parser.parse_args()
					convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)







import argparse


_UpperCAmelCase						:  Union[str, Any]									=			"""docs/source/_static/js/custom.js"""





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






							with open(UpperCamelCase__  ,				encoding='utf-8'  ,				newline='\n' ) as f:
														snake_case_     							=       f.readlines()
							snake_case_     							=       0

							# First let's put the right version
							while not lines[index].startswith('const stableVersion =' ):
														index += 1
							snake_case_     							=       F'''const stableVersion = "v{version}"\n'''

							# Then update the dictionary
							while not lines[index].startswith('const versionMapping = {' ):
														index += 1

							# We go until the end
							while not lines[index].startswith('}' ):
														index += 1
							# We add the new version at the end
							lines[index - 1] += F'''    "v{version}": "v{version}",\n'''

							with open(UpperCamelCase__  ,				'w'  ,				encoding='utf-8'  ,				newline='\n' ) as f:
														f.writelines(UpperCamelCase__ )


if __name__ == "__main__":
					_UpperCAmelCase						:  int									=			argparse.ArgumentParser()
					parser.add_argument("""--version""", help="""Release version.""")
					_UpperCAmelCase						:  str									=			parser.parse_args()
					update_custom_js(args.version)




