




import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM





def        __UpperCAmelCase						(							lowerCamelCase_ :     Any  ,							lowerCamelCase_ :     Union[str, Any]  ,							lowerCamelCase_ :     List[str]  )       ->  Dict:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : List[Any]					  =      AutoConfig.from_pretrained(_lowercase  )
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      FlaxAutoModelForSeqaSeqLM.from_config(config=_lowercase  )
			SCREAMING_SNAKE_CASE_   : int					  =      checkpoints.load_tax_checkpoint(_lowercase  )

			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      "wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

			if config.model_type == "t5":
						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      "SelfAttention"
			if config.model_type == "longt5" and config.encoder_attention_type == "local":
						SCREAMING_SNAKE_CASE_   : Dict					  =      "LocalSelfAttention"
			elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
						SCREAMING_SNAKE_CASE_   : str					  =      "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  ):
						SCREAMING_SNAKE_CASE_   : int					  =      F'layers_{str(_lowercase  )}'

						# Self-Attention
						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
						SCREAMING_SNAKE_CASE_   : Tuple					  =      tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
						SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
						SCREAMING_SNAKE_CASE_   : 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":
									SCREAMING_SNAKE_CASE_   : List[str]					  =      tax_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"]

						# Layer Normalization
						SCREAMING_SNAKE_CASE_   : List[Any]					  =      tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

						if split_mlp_wi:
									SCREAMING_SNAKE_CASE_   : List[Any]					  =      tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
									SCREAMING_SNAKE_CASE_   : Any					  =      tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
						else:
									SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

						SCREAMING_SNAKE_CASE_   : Dict					  =      tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

						# Layer Normalization
						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

						# Assigning
						SCREAMING_SNAKE_CASE_   : int					  =      flax_model.params["encoder"]["block"][str(_lowercase  )]["layer"]
						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      tax_attention_key
						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      tax_attention_out
						SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      tax_attention_query
						SCREAMING_SNAKE_CASE_   : List[str]					  =      tax_attention_value

						SCREAMING_SNAKE_CASE_   : Dict					  =      tax_attention_layer_norm

						# Global input layer norm
						if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
									SCREAMING_SNAKE_CASE_   : List[str]					  =      tax_global_layer_norm

						if split_mlp_wi:
									SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      tax_mlp_wi_a
									SCREAMING_SNAKE_CASE_   : int					  =      tax_mlp_wi_a
						else:
									SCREAMING_SNAKE_CASE_   : Dict					  =      tax_mlp_wi

						SCREAMING_SNAKE_CASE_   : int					  =      tax_mlp_wo
						SCREAMING_SNAKE_CASE_   : Tuple					  =      tax_mlp_layer_norm

						SCREAMING_SNAKE_CASE_   : Tuple					  =      flax_model_encoder_layer_block

			# Only for layer 0:
			SCREAMING_SNAKE_CASE_   : Any					  =      tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
			SCREAMING_SNAKE_CASE_   : int					  =      tax_encoder_rel_embedding

			# Side/global relative position_bias + layer norm
			if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
						SCREAMING_SNAKE_CASE_   : str					  =      tax_model["target"]["encoder"]["side_relpos_bias"]["rel_embedding"].T
						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      tax_encoder_global_rel_embedding

			# Assigning
			SCREAMING_SNAKE_CASE_   : int					  =      tax_model["target"]["encoder"]["encoder_norm"]["scale"]
			SCREAMING_SNAKE_CASE_   : Tuple					  =      tax_encoder_norm

			# Decoder
			for layer_index in range(config.num_layers  ):
						SCREAMING_SNAKE_CASE_   : Any					  =      F'layers_{str(_lowercase  )}'

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

						# Layer Normalization
						SCREAMING_SNAKE_CASE_   : str					  =      tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
						    "scale"
						]

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

						# Layer Normalization
						SCREAMING_SNAKE_CASE_   : str					  =      tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

						# MLP
						if split_mlp_wi:
									SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
									SCREAMING_SNAKE_CASE_   : List[Any]					  =      tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
						else:
									SCREAMING_SNAKE_CASE_   : Dict					  =      tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

						SCREAMING_SNAKE_CASE_   : Optional[int]					  =      tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

						# Layer Normalization
						SCREAMING_SNAKE_CASE_   : Optional[int]					  =      tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

						# Assigning
						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      flax_model.params["decoder"]["block"][str(_lowercase  )]["layer"]
						SCREAMING_SNAKE_CASE_   : int					  =      tax_attention_key
						SCREAMING_SNAKE_CASE_   : Any					  =      tax_attention_out
						SCREAMING_SNAKE_CASE_   : List[Any]					  =      tax_attention_query
						SCREAMING_SNAKE_CASE_   : int					  =      tax_attention_value

						SCREAMING_SNAKE_CASE_   : int					  =      tax_pre_attention_layer_norm

						SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      tax_enc_dec_attention_key
						SCREAMING_SNAKE_CASE_   : Optional[int]					  =      tax_enc_dec_attention_out
						SCREAMING_SNAKE_CASE_   : Dict					  =      tax_enc_dec_attention_query
						SCREAMING_SNAKE_CASE_   : Any					  =      tax_enc_dec_attention_value

						SCREAMING_SNAKE_CASE_   : List[Any]					  =      tax_cross_layer_norm

						if split_mlp_wi:
									SCREAMING_SNAKE_CASE_   : Any					  =      tax_mlp_wi_a
									SCREAMING_SNAKE_CASE_   : List[str]					  =      tax_mlp_wi_a
						else:
									SCREAMING_SNAKE_CASE_   : List[Any]					  =      tax_mlp_wi

						SCREAMING_SNAKE_CASE_   : List[str]					  =      tax_mlp_wo

						SCREAMING_SNAKE_CASE_   : str					  =      txa_mlp_layer_norm

						SCREAMING_SNAKE_CASE_   : List[str]					  =      flax_model_decoder_layer_block

			# Decoder Normalization
			SCREAMING_SNAKE_CASE_   : Tuple					  =      tax_model["target"]["decoder"]["decoder_norm"]["scale"]
			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      txa_decoder_norm

			# Only for layer 0:
			SCREAMING_SNAKE_CASE_   : Tuple					  =      tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
			SCREAMING_SNAKE_CASE_   : int					  =      tax_decoder_rel_embedding

			# Token Embeddings
			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      tax_model["target"]["token_embedder"]["embedding"]
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      txa_token_embeddings

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

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


if __name__ == "__main__":
						UpperCamelCase__ :   Union[str, Any]						=							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__ :   Any						=							parser.parse_args()
						convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)










"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


a		:					Optional[Any]	      =     {
    '''configuration_owlvit''': [
        '''OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''OwlViTConfig''',
        '''OwlViTOnnxConfig''',
        '''OwlViTTextConfig''',
        '''OwlViTVisionConfig''',
    ],
    '''processing_owlvit''': ['''OwlViTProcessor'''],
}


try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a		:					str	      =     ['''OwlViTFeatureExtractor''']
		a		:					List[Any]	      =     ['''OwlViTImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a		:					List[Any]	      =     [
		    '''OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''OwlViTModel''',
		    '''OwlViTPreTrainedModel''',
		    '''OwlViTTextModel''',
		    '''OwlViTVisionModel''',
		    '''OwlViTForObjectDetection''',
		]

if TYPE_CHECKING:
		from .configuration_owlvit import (
		    OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    OwlViTConfig,
		    OwlViTOnnxConfig,
		    OwlViTTextConfig,
		    OwlViTVisionConfig,
		)
		from .processing_owlvit import OwlViTProcessor

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_owlvit import OwlViTFeatureExtractor
				from .image_processing_owlvit import OwlViTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_owlvit import (
				    OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    OwlViTForObjectDetection,
				    OwlViTModel,
				    OwlViTPreTrainedModel,
				    OwlViTTextModel,
				    OwlViTVisionModel,
				)

else:
		import sys

		a		:					List[str]	      =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


