



import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig


def     UpperCamelCase(						__UpperCamelCase							:	List[str]  ,__UpperCamelCase							:	str  ,__UpperCamelCase							:	Any				):
		lowerCAmelCase_    :  int             =				TaConfig.from_pretrained(__UpperCamelCase				)
		lowerCAmelCase_    :  Dict             =				FlaxTaForConditionalGeneration(config=__UpperCamelCase				)
		lowerCAmelCase_    :  Optional[Any]             =				checkpoints.load_tax_checkpoint(__UpperCamelCase				)

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

		# Encoder
		for layer_index in range(config.num_layers				):
				lowerCAmelCase_    :  int             =				f"""layers_{str(__UpperCamelCase				)}"""

				# Self-Attention
				lowerCAmelCase_    :  List[str]             =				tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
				lowerCAmelCase_    :  List[str]             =				tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
				lowerCAmelCase_    :  Dict             =				tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
				lowerCAmelCase_    :  Dict             =				tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

				# Layer Normalization
				lowerCAmelCase_    :  Dict             =				tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

				if split_mlp_wi:
						lowerCAmelCase_    :  Optional[Any]             =				tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
						lowerCAmelCase_    :  List[str]             =				tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
				else:
						lowerCAmelCase_    :  Tuple             =				tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

				lowerCAmelCase_    :  Any             =				tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

				# Layer Normalization
				lowerCAmelCase_    :  str             =				tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

				# Assigning
				lowerCAmelCase_    :  Optional[Any]             =				tax_attention_key
				lowerCAmelCase_    :  str             =				tax_attention_out
				lowerCAmelCase_    :  Optional[Any]             =				tax_attention_query
				lowerCAmelCase_    :  Dict             =				tax_attention_value

				lowerCAmelCase_    :  List[str]             =				tax_attention_layer_norm

				if split_mlp_wi:
						lowerCAmelCase_    :  Any             =				tax_mlp_wi_a
						lowerCAmelCase_    :  Any             =				tax_mlp_wi_a
				else:
						lowerCAmelCase_    :  Union[str, Any]             =				tax_mlp_wi

				lowerCAmelCase_    :  Any             =				tax_mlp_wo
				lowerCAmelCase_    :  Optional[int]             =				tax_mlp_layer_norm

		# Only for layer 0:
		lowerCAmelCase_    :  List[str]             =				tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
		lowerCAmelCase_    :  Dict             =				tax_encoder_rel_embedding

		# Assigning
		lowerCAmelCase_    :  Optional[Any]             =				tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
		lowerCAmelCase_    :  str             =				tax_encoder_norm

		# Decoder
		for layer_index in range(config.num_decoder_layers				):
				lowerCAmelCase_    :  Any             =				f"""layers_{str(__UpperCamelCase				)}"""

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

				# Layer Normalization
				lowerCAmelCase_    :  List[Any]             =				tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
				    '''scale'''
				]

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

				# Layer Normalization
				lowerCAmelCase_    :  Dict             =				tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

				# MLP
				if split_mlp_wi:
						lowerCAmelCase_    :  Union[str, Any]             =				tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
						lowerCAmelCase_    :  str             =				tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
				else:
						lowerCAmelCase_    :  Optional[int]             =				tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

				lowerCAmelCase_    :  Union[str, Any]             =				tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

				# Layer Normalization
				lowerCAmelCase_    :  Optional[int]             =				tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

				# Assigning
				lowerCAmelCase_    :  int             =				tax_attention_key
				lowerCAmelCase_    :  Tuple             =				tax_attention_out
				lowerCAmelCase_    :  List[str]             =				tax_attention_query
				lowerCAmelCase_    :  Tuple             =				tax_attention_value

				lowerCAmelCase_    :  Optional[int]             =				tax_pre_attention_layer_norm

				lowerCAmelCase_    :  List[str]             =				tax_enc_dec_attention_key
				lowerCAmelCase_    :  List[str]             =				tax_enc_dec_attention_out
				lowerCAmelCase_    :  Any             =				tax_enc_dec_attention_query
				lowerCAmelCase_    :  Optional[Any]             =				tax_enc_dec_attention_value

				lowerCAmelCase_    :  Optional[Any]             =				tax_cross_layer_norm

				if split_mlp_wi:
						lowerCAmelCase_    :  Optional[int]             =				tax_mlp_wi_a
						lowerCAmelCase_    :  Tuple             =				tax_mlp_wi_a
				else:
						lowerCAmelCase_    :  Optional[int]             =				tax_mlp_wi

				lowerCAmelCase_    :  int             =				tax_mlp_wo

				lowerCAmelCase_    :  Optional[Any]             =				txa_mlp_layer_norm

		# Decoder Normalization
		lowerCAmelCase_    :  Optional[int]             =				tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
		lowerCAmelCase_    :  Tuple             =				txa_decoder_norm

		# Only for layer 0:
		lowerCAmelCase_    :  Dict             =				tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
		lowerCAmelCase_    :  List[str]             =				tax_decoder_rel_embedding

		# Token Embeddings
		lowerCAmelCase_    :  int             =				tax_model['''target''']['''token_embedder''']['''embedding''']
		lowerCAmelCase_    :  Optional[Any]             =				txa_token_embeddings

		# LM Head (only in v1.1 checkpoints)
		if "logits_dense" in tax_model["target"]["decoder"]:
				lowerCAmelCase_    :  Optional[int]             =				tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

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


if __name__ == "__main__":
						A__						:						Dict          =      argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path the TX5 checkpoint.'''
						)
						parser.add_argument('''--config_name''', default=None, type=str, required=True, help='''Config name of T5 model.''')
						parser.add_argument(
						    '''--flax_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output FLAX model.'''
						)
						A__						:						Dict          =      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_tf_available,
    is_torch_available,
    is_vision_available,
)


A__						:						List[str]          =      {
    '''configuration_segformer''': ['''SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SegformerConfig''', '''SegformerOnnxConfig''']
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__						:						int          =      ['''SegformerFeatureExtractor''']
						A__						:						Dict          =      ['''SegformerImageProcessor''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__						:						Any          =      [
						    '''SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''SegformerDecodeHead''',
						    '''SegformerForImageClassification''',
						    '''SegformerForSemanticSegmentation''',
						    '''SegformerLayer''',
						    '''SegformerModel''',
						    '''SegformerPreTrainedModel''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__						:						List[Any]          =      [
						    '''TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFSegformerDecodeHead''',
						    '''TFSegformerForImageClassification''',
						    '''TFSegformerForSemanticSegmentation''',
						    '''TFSegformerModel''',
						    '''TFSegformerPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_segformer import SegformerFeatureExtractor
												from .image_processing_segformer import SegformerImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_segformer import (
												    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SegformerDecodeHead,
												    SegformerForImageClassification,
												    SegformerForSemanticSegmentation,
												    SegformerLayer,
												    SegformerModel,
												    SegformerPreTrainedModel,
												)
						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_segformer import (
												    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFSegformerDecodeHead,
												    TFSegformerForImageClassification,
												    TFSegformerForSemanticSegmentation,
												    TFSegformerModel,
												    TFSegformerPreTrainedModel,
												)

else:
						import sys

						A__						:						Dict          =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





