
"""simple docstring"""







import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		Tuple		    =   AutoConfig.from_pretrained(_UpperCamelCase	)
	__lowerCAmelCase    :		int		    =   FlaxAutoModelForSeqaSeqLM.from_config(config=_UpperCamelCase	)
	__lowerCAmelCase    :		Optional[Any]		    =   checkpoints.load_tax_checkpoint(_UpperCamelCase	)

	__lowerCAmelCase    :		Any		    =   'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']

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

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

		# Layer Normalization
		__lowerCAmelCase    :		Optional[Any]		    =   tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']

		if split_mlp_wi:
			__lowerCAmelCase    :		Dict		    =   tax_model['target']['encoder'][layer_name]['mlp']['wi_0']['kernel']
			__lowerCAmelCase    :		Tuple		    =   tax_model['target']['encoder'][layer_name]['mlp']['wi_1']['kernel']
		else:
			__lowerCAmelCase    :		Any		    =   tax_model['target']['encoder'][layer_name]['mlp']['wi']['kernel']

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

		# Layer Normalization
		__lowerCAmelCase    :		Optional[int]		    =   tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']

		# Assigning
		__lowerCAmelCase    :		Tuple		    =   flax_model.params['encoder']['block'][str(_UpperCamelCase	)]['layer']
		__lowerCAmelCase    :		Union[str, Any]		    =   tax_attention_key
		__lowerCAmelCase    :		int		    =   tax_attention_out
		__lowerCAmelCase    :		List[str]		    =   tax_attention_query
		__lowerCAmelCase    :		List[Any]		    =   tax_attention_value

		__lowerCAmelCase    :		List[Any]		    =   tax_attention_layer_norm

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

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

		__lowerCAmelCase    :		Tuple		    =   tax_mlp_wo
		__lowerCAmelCase    :		str		    =   tax_mlp_layer_norm

		__lowerCAmelCase    :		Tuple		    =   flax_model_encoder_layer_block

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

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

	# Assigning
	__lowerCAmelCase    :		int		    =   tax_model['target']['encoder']['encoder_norm']['scale']
	__lowerCAmelCase    :		Tuple		    =   tax_encoder_norm

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

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

		# Layer Normalization
		__lowerCAmelCase    :		int		    =   tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
		    'scale'
		]

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

		# Layer Normalization
		__lowerCAmelCase    :		str		    =   tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']

		# MLP
		if split_mlp_wi:
			__lowerCAmelCase    :		int		    =   tax_model['target']['decoder'][layer_name]['mlp']['wi_0']['kernel']
			__lowerCAmelCase    :		Dict		    =   tax_model['target']['decoder'][layer_name]['mlp']['wi_1']['kernel']
		else:
			__lowerCAmelCase    :		Optional[int]		    =   tax_model['target']['decoder'][layer_name]['mlp']['wi']['kernel']

		__lowerCAmelCase    :		str		    =   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    :		Any		    =   flax_model.params['decoder']['block'][str(_UpperCamelCase	)]['layer']
		__lowerCAmelCase    :		Any		    =   tax_attention_key
		__lowerCAmelCase    :		List[str]		    =   tax_attention_out
		__lowerCAmelCase    :		List[Any]		    =   tax_attention_query
		__lowerCAmelCase    :		List[Any]		    =   tax_attention_value

		__lowerCAmelCase    :		Any		    =   tax_pre_attention_layer_norm

		__lowerCAmelCase    :		Any		    =   tax_enc_dec_attention_key
		__lowerCAmelCase    :		Optional[Any]		    =   tax_enc_dec_attention_out
		__lowerCAmelCase    :		str		    =   tax_enc_dec_attention_query
		__lowerCAmelCase    :		Tuple		    =   tax_enc_dec_attention_value

		__lowerCAmelCase    :		Optional[Any]		    =   tax_cross_layer_norm

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

		__lowerCAmelCase    :		Union[str, Any]		    =   tax_mlp_wo

		__lowerCAmelCase    :		int		    =   txa_mlp_layer_norm

		__lowerCAmelCase    :		List[str]		    =   flax_model_decoder_layer_block

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

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

	# Token Embeddings
	__lowerCAmelCase    :		Tuple		    =   tax_model['target']['token_embedder']['embedding']
	__lowerCAmelCase    :		Dict		    =   txa_token_embeddings

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

"""simple docstring"""







from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 				A__ (			_lowerCamelCase):
		A_		: Any            =			['image_processor', 'tokenizer']
		A_		: Optional[Any]            =			'BlipImageProcessor'
		A_		: int            =			'AutoTokenizer'

		def __init__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Union[str, Any]		    =   False
			super().__init__(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		List[Any]		    =   self.image_processor

		def __call__(   self ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = True ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = 0 ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = True ,	_SCREAMING_SNAKE_CASE = None ,	**_SCREAMING_SNAKE_CASE ,	):
			if images is None and text is None:
				raise ValueError('You have to specify either images or text.'				)

			# Get only text
			if images is None:
				__lowerCAmelCase    :		Optional[int]		    =   self.tokenizer
				__lowerCAmelCase    :		List[str]		    =   self.tokenizer(
				    text=_SCREAMING_SNAKE_CASE ,	add_special_tokens=_SCREAMING_SNAKE_CASE ,	padding=_SCREAMING_SNAKE_CASE ,	truncation=_SCREAMING_SNAKE_CASE ,	max_length=_SCREAMING_SNAKE_CASE ,	stride=_SCREAMING_SNAKE_CASE ,	pad_to_multiple_of=_SCREAMING_SNAKE_CASE ,	return_attention_mask=_SCREAMING_SNAKE_CASE ,	return_overflowing_tokens=_SCREAMING_SNAKE_CASE ,	return_special_tokens_mask=_SCREAMING_SNAKE_CASE ,	return_offsets_mapping=_SCREAMING_SNAKE_CASE ,	return_token_type_ids=_SCREAMING_SNAKE_CASE ,	return_length=_SCREAMING_SNAKE_CASE ,	verbose=_SCREAMING_SNAKE_CASE ,	return_tensors=_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE ,	)
				return text_encoding

			# add pixel_values
			__lowerCAmelCase    :		List[Any]		    =   self.image_processor(_SCREAMING_SNAKE_CASE ,	return_tensors=_SCREAMING_SNAKE_CASE				)

			if text is not None:
				__lowerCAmelCase    :		Optional[int]		    =   self.tokenizer(
				    text=_SCREAMING_SNAKE_CASE ,	add_special_tokens=_SCREAMING_SNAKE_CASE ,	padding=_SCREAMING_SNAKE_CASE ,	truncation=_SCREAMING_SNAKE_CASE ,	max_length=_SCREAMING_SNAKE_CASE ,	stride=_SCREAMING_SNAKE_CASE ,	pad_to_multiple_of=_SCREAMING_SNAKE_CASE ,	return_attention_mask=_SCREAMING_SNAKE_CASE ,	return_overflowing_tokens=_SCREAMING_SNAKE_CASE ,	return_special_tokens_mask=_SCREAMING_SNAKE_CASE ,	return_offsets_mapping=_SCREAMING_SNAKE_CASE ,	return_token_type_ids=_SCREAMING_SNAKE_CASE ,	return_length=_SCREAMING_SNAKE_CASE ,	verbose=_SCREAMING_SNAKE_CASE ,	return_tensors=_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE ,	)
			else:
				__lowerCAmelCase    :		int		    =   None

			if text_encoding is not None:
				encoding_image_processor.update(_SCREAMING_SNAKE_CASE				)

			return encoding_image_processor

		def 							__lowerCamelCase							(   self ,	*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
			return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
			return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)







		@property
		# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		int		    =   self.tokenizer.model_input_names
			__lowerCAmelCase    :		Union[str, Any]		    =   self.image_processor.model_input_names
			return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)