

import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM






def 					lowerCamelCase_     (							UpperCAmelCase_							:     List[Any]							,       UpperCAmelCase_							:     List[Any]							,       UpperCAmelCase_							:     Dict	)						->				int:




	'''simple docstring'''




	_UpperCamelCase   :		Tuple			=						AutoConfig.from_pretrained(UpperCAmelCase_	)
	_UpperCamelCase   :		Optional[int]			=						FlaxAutoModelForSeqaSeqLM.from_config(config=UpperCAmelCase_	)
	_UpperCamelCase   :		Any			=						checkpoints.load_tax_checkpoint(UpperCAmelCase_	)

	_UpperCamelCase   :		int			=						'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']

	if config.model_type == "t5":
		_UpperCamelCase   :		Optional[int]			=						'SelfAttention'
	if config.model_type == "longt5" and config.encoder_attention_type == "local":
		_UpperCamelCase   :		Any			=						'LocalSelfAttention'
	elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
		_UpperCamelCase   :		Any			=						'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   :		str			=						F'''layers_{str(UpperCAmelCase_	)}'''

		# Self-Attention
		_UpperCamelCase   :		Tuple			=						tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
		_UpperCamelCase   :		str			=						tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
		_UpperCamelCase   :		int			=						tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
		_UpperCamelCase   :		List[Any]			=						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   :		Optional[Any]			=						tax_model['target']['encoder'][layer_name]['attention']['T5LayerNorm_0']['scale']

		# Layer Normalization
		_UpperCamelCase   :		Union[str, Any]			=						tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']

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

		_UpperCamelCase   :		List[str]			=						tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']

		# Layer Normalization
		_UpperCamelCase   :		Optional[Any]			=						tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']

		# Assigning
		_UpperCamelCase   :		int			=						flax_model.params['encoder']['block'][str(UpperCAmelCase_	)]['layer']
		_UpperCamelCase   :		Any			=						tax_attention_key
		_UpperCamelCase   :		Optional[Any]			=						tax_attention_out
		_UpperCamelCase   :		Optional[Any]			=						tax_attention_query
		_UpperCamelCase   :		Optional[int]			=						tax_attention_value

		_UpperCamelCase   :		Optional[Any]			=						tax_attention_layer_norm

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

		if split_mlp_wi:
			_UpperCamelCase   :		Optional[int]			=						tax_mlp_wi_a
			_UpperCamelCase   :		Tuple			=						tax_mlp_wi_a
		else:
			_UpperCamelCase   :		List[Any]			=						tax_mlp_wi

		_UpperCamelCase   :		List[str]			=						tax_mlp_wo
		_UpperCamelCase   :		Optional[Any]			=						tax_mlp_layer_norm

		_UpperCamelCase   :		Any			=						flax_model_encoder_layer_block

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

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

	# Assigning
	_UpperCamelCase   :		Optional[int]			=						tax_model['target']['encoder']['encoder_norm']['scale']
	_UpperCamelCase   :		Any			=						tax_encoder_norm

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

		# Self-Attention
		_UpperCamelCase   :		Union[str, Any]			=						tax_model['target']['decoder'][layer_name]['self_attention']['key']['kernel']
		_UpperCamelCase   :		Dict			=						tax_model['target']['decoder'][layer_name]['self_attention']['out']['kernel']
		_UpperCamelCase   :		Optional[int]			=						tax_model['target']['decoder'][layer_name]['self_attention']['query']['kernel']
		_UpperCamelCase   :		Optional[Any]			=						tax_model['target']['decoder'][layer_name]['self_attention']['value']['kernel']

		# Layer Normalization
		_UpperCamelCase   :		List[str]			=						tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
		    'scale'
		]

		# Encoder-Decoder-Attention
		_UpperCamelCase   :		Any			=						tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']
		_UpperCamelCase   :		Optional[int]			=						tax_enc_dec_attention_module['key']['kernel']
		_UpperCamelCase   :		Tuple			=						tax_enc_dec_attention_module['out']['kernel']
		_UpperCamelCase   :		str			=						tax_enc_dec_attention_module['query']['kernel']
		_UpperCamelCase   :		List[str]			=						tax_enc_dec_attention_module['value']['kernel']

		# Layer Normalization
		_UpperCamelCase   :		Optional[int]			=						tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']

		# MLP
		if split_mlp_wi:
			_UpperCamelCase   :		str			=						tax_model['target']['decoder'][layer_name]['mlp']['wi_0']['kernel']
			_UpperCamelCase   :		int			=						tax_model['target']['decoder'][layer_name]['mlp']['wi_1']['kernel']
		else:
			_UpperCamelCase   :		Tuple			=						tax_model['target']['decoder'][layer_name]['mlp']['wi']['kernel']

		_UpperCamelCase   :		Optional[int]			=						tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']

		# Layer Normalization
		_UpperCamelCase   :		List[Any]			=						tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']

		# Assigning
		_UpperCamelCase   :		List[str]			=						flax_model.params['decoder']['block'][str(UpperCAmelCase_	)]['layer']
		_UpperCamelCase   :		List[Any]			=						tax_attention_key
		_UpperCamelCase   :		Optional[int]			=						tax_attention_out
		_UpperCamelCase   :		Dict			=						tax_attention_query
		_UpperCamelCase   :		Dict			=						tax_attention_value

		_UpperCamelCase   :		Dict			=						tax_pre_attention_layer_norm

		_UpperCamelCase   :		Optional[Any]			=						tax_enc_dec_attention_key
		_UpperCamelCase   :		List[Any]			=						tax_enc_dec_attention_out
		_UpperCamelCase   :		int			=						tax_enc_dec_attention_query
		_UpperCamelCase   :		int			=						tax_enc_dec_attention_value

		_UpperCamelCase   :		Any			=						tax_cross_layer_norm

		if split_mlp_wi:
			_UpperCamelCase   :		int			=						tax_mlp_wi_a
			_UpperCamelCase   :		int			=						tax_mlp_wi_a
		else:
			_UpperCamelCase   :		Any			=						tax_mlp_wi

		_UpperCamelCase   :		Tuple			=						tax_mlp_wo

		_UpperCamelCase   :		List[Any]			=						txa_mlp_layer_norm

		_UpperCamelCase   :		str			=						flax_model_decoder_layer_block

	# Decoder Normalization
	_UpperCamelCase   :		Optional[Any]			=						tax_model['target']['decoder']['decoder_norm']['scale']
	_UpperCamelCase   :		Dict			=						txa_decoder_norm

	# Only for layer 0:
	_UpperCamelCase   :		Optional[int]			=						tax_model['target']['decoder']['relpos_bias']['rel_embedding'].T
	_UpperCamelCase   :		str			=						tax_decoder_rel_embedding

	# Token Embeddings
	_UpperCamelCase   :		List[str]			=						tax_model['target']['token_embedder']['embedding']
	_UpperCamelCase   :		Optional[int]			=						txa_token_embeddings

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

	flax_model.save_pretrained(UpperCAmelCase_	)
	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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase__								=		{
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__								=		[
							    """CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """CanineForMultipleChoice""",
							    """CanineForQuestionAnswering""",
							    """CanineForSequenceClassification""",
							    """CanineForTokenClassification""",
							    """CanineLayer""",
							    """CanineModel""",
							    """CaninePreTrainedModel""",
							    """load_tf_weights_in_canine""",
							]


if TYPE_CHECKING:
							from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
							from .tokenization_canine import CanineTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_canine import (
														    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    CanineForMultipleChoice,
														    CanineForQuestionAnswering,
														    CanineForSequenceClassification,
														    CanineForTokenClassification,
														    CanineLayer,
														    CanineModel,
														    CaninePreTrainedModel,
														    load_tf_weights_in_canine,
														)


else:
							import sys

							lowerCAmelCase__								=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




