

import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM



def    _lowercase			(	a__						:			Any							,  a__						:			Union[str, Any]							,  a__						:			Tuple					)							->					Union[str, Any]:





	"""simple docstring"""


	_UpperCamelCase			=   AutoConfig.from_pretrained(snake_case__					)
	_UpperCamelCase			=   FlaxAutoModelForSeqaSeqLM.from_config(config=snake_case__					)
	_UpperCamelCase			=   checkpoints.load_tax_checkpoint(snake_case__					)

	_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(snake_case__					)}'''

		# 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(snake_case__					)]["""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(snake_case__					)}'''

		# 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(snake_case__					)]["""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(snake_case__					)
	print("T5X Model was sucessfully converted!"					)


if __name__ == "__main__":
	__lowerCAmelCase			     =       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			     =       parser.parse_args()
	convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)








from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__					:     Optional[int]       =     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__					:     List[str]       =     {
    'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/config.json',
    'umberto-commoncrawl-cased-v1': (
        'https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'
    ),
    'umberto-wikipedia-uncased-v1': (
        'https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'
    ),
}


class 		_SCREAMING_SNAKE_CASE     (						A     ):
     __SCREAMING_SNAKE_CASE     					=    '''camembert'''


     def __init__(  self							,					A_=3_05_22							,					A_=7_68							,					A_=12							,					A_=12							,					A_=30_72							,					A_="gelu"							,					A_=0.1							,					A_=0.1							,					A_=5_12							,					A_=2							,					A_=0.0_2							,					A_=1e-12							,					A_=1							,					A_=0							,					A_=2							,					A_="absolute"							,					A_=True							,					A_=None							,					**A_							,					):
       super().__init__(pad_token_id=A_							,					bos_token_id=A_							,					eos_token_id=A_							,					**A_    )

       _UpperCAmelCase		:							Optional[Any]							=     vocab_size
       _UpperCAmelCase		:							int							=     hidden_size
       _UpperCAmelCase		:							Any							=     num_hidden_layers
       _UpperCAmelCase		:							Optional[Any]							=     num_attention_heads
       _UpperCAmelCase		:							Dict							=     hidden_act
       _UpperCAmelCase		:							str							=     intermediate_size
       _UpperCAmelCase		:							str							=     hidden_dropout_prob
       _UpperCAmelCase		:							List[Any]							=     attention_probs_dropout_prob
       _UpperCAmelCase		:							List[Any]							=     max_position_embeddings
       _UpperCAmelCase		:							Union[str, Any]							=     type_vocab_size
       _UpperCAmelCase		:							List[Any]							=     initializer_range
       _UpperCAmelCase		:							Optional[Any]							=     layer_norm_eps
       _UpperCAmelCase		:							int							=     position_embedding_type
       _UpperCAmelCase		:							Tuple							=     use_cache
       _UpperCAmelCase		:							Any							=     classifier_dropout


class 		_SCREAMING_SNAKE_CASE     (						A     ):


     @property
     def        __snake_case(  self    ):
       if self.task == "multiple-choice":
         _UpperCAmelCase		:							Any							=     {0: """batch""", 1: """choice""", 2: """sequence"""}
       else:
         _UpperCAmelCase		:							List[Any]							=     {0: """batch""", 1: """sequence"""}
       return OrderedDict(
           [
               ("""input_ids""", dynamic_axis),
               ("""attention_mask""", dynamic_axis),
           ]    )





