



'''simple docstring'''

__lowerCAmelCase		     =  '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

__lowerCAmelCase		     =  [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
__lowerCAmelCase		     =  {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}









'''simple docstring'''

import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )     -> Dict:
		return params[f"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_="attention"    )     -> int:
		_a	: int								=				np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :]    )
		_a	: Dict								=				k_tmp.reshape(k_tmp.shape[0]		,     k_tmp.shape[1] * k_tmp.shape[2]    )
		_a	: Any								=				np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :]    )
		_a	: int								=				o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]		,     o_tmp.shape[2]    )
		_a	: List[str]								=				np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :]    )
		_a	: Dict								=				q_tmp.reshape(q_tmp.shape[0]		,     q_tmp.shape[1] * q_tmp.shape[2]    )
		_a	: List[str]								=				np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :]    )
		_a	: int								=				v_tmp.reshape(v_tmp.shape[0]		,     v_tmp.shape[1] * v_tmp.shape[2]    )
		return k, o, q, v



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_=False    )     -> Optional[Any]:
		if split_mlp_wi:
				_a	: Dict								=				params[f"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
				_a	: Dict								=				params[f"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
				_a	: Optional[int]								=				(wi_a, wi_a)
		else:
				_a	: Tuple								=				params[f"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

		_a	: Tuple								=				params[f"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
		return wi, wo



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )     -> Any:
		return params[f"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]



def 	__lowerCamelCase	( lowerCAmelCase_		,     *, lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_ = False    )     -> Any:
		_a	: Dict								=				traverse_util.flatten_dict(variables['target']    )
		_a	: Tuple								=				{'/'.join(lowerCAmelCase_    ): v for k, v in old.items()}

		# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
		_a	: int								=				'encoder/encoder/mlp/wi_0/kernel' in old
		print('Split MLP:'		,     lowerCAmelCase_    )

		_a	: str								=				collections.OrderedDict()

		# Shared embeddings.
		_a	: List[str]								=				old['token_embedder/embedding']

		# Encoder.
		for i in range(lowerCAmelCase_    ):
				# Block i, layer 0 (Self Attention).
				_a	: Any								=				tax_layer_norm_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'encoder'		,     'pre_attention_layer_norm'    )
				_a			,   _a			,   _a			,   _a	: List[str]								=				tax_attention_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'encoder'		,     'attention'    )
				_a	: str								=				layer_norm
				_a	: Union[str, Any]								=				k.T
				_a	: Dict								=				o.T
				_a	: int								=				q.T
				_a	: List[str]								=				v.T

				# Block i, layer 1 (MLP).
				_a	: Optional[int]								=				tax_layer_norm_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'encoder'		,     'pre_mlp_layer_norm'    )
				_a			,   _a	: Any								=				tax_mlp_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'encoder'		,     lowerCAmelCase_    )
				_a	: Optional[Any]								=				layer_norm
				if split_mlp_wi:
						_a	: Tuple								=				wi[0].T
						_a	: List[str]								=				wi[1].T
				else:
						_a	: Union[str, Any]								=				wi.T
				_a	: Any								=				wo.T
				if scalable_attention:
						# convert the rel_embedding of each layer
						_a	: Any								=				tax_relpos_bias_lookup(
						    lowerCAmelCase_		,     lowerCAmelCase_		,     'encoder'    ).T

		_a	: Optional[Any]								=				old['encoder/encoder_norm/scale']

		if not scalable_attention:
				_a	: Union[str, Any]								=				tax_relpos_bias_lookup(
				    lowerCAmelCase_		,     0		,     'encoder'    ).T
				_a	: Any								=				tax_relpos_bias_lookup(
				    lowerCAmelCase_		,     0		,     'decoder'    ).T

		if not is_encoder_only:
				# Decoder.
				for i in range(lowerCAmelCase_    ):
						# Block i, layer 0 (Self Attention).
						_a	: Dict								=				tax_layer_norm_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'decoder'		,     'pre_self_attention_layer_norm'    )
						_a			,   _a			,   _a			,   _a	: Union[str, Any]								=				tax_attention_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'decoder'		,     'self_attention'    )
						_a	: str								=				layer_norm
						_a	: List[Any]								=				k.T
						_a	: Union[str, Any]								=				o.T
						_a	: int								=				q.T
						_a	: List[Any]								=				v.T

						# Block i, layer 1 (Cross Attention).
						_a	: List[Any]								=				tax_layer_norm_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'decoder'		,     'pre_cross_attention_layer_norm'    )
						_a			,   _a			,   _a			,   _a	: Optional[int]								=				tax_attention_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'decoder'		,     'encoder_decoder_attention'    )
						_a	: str								=				layer_norm
						_a	: Union[str, Any]								=				k.T
						_a	: Union[str, Any]								=				o.T
						_a	: Any								=				q.T
						_a	: str								=				v.T

						# Block i, layer 2 (MLP).
						_a	: str								=				tax_layer_norm_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'decoder'		,     'pre_mlp_layer_norm'    )
						_a			,   _a	: int								=				tax_mlp_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'decoder'		,     lowerCAmelCase_    )
						_a	: List[str]								=				layer_norm
						if split_mlp_wi:
								_a	: List[str]								=				wi[0].T
								_a	: Union[str, Any]								=				wi[1].T
						else:
								_a	: Optional[Any]								=				wi.T
						_a	: List[str]								=				wo.T

						if scalable_attention:
								# convert the rel_embedding of each layer
								_a	: int								=				tax_relpos_bias_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'decoder'    ).T

				_a	: List[Any]								=				old['decoder/decoder_norm/scale']

				# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
				if "decoder/logits_dense/kernel" in old:
						_a	: List[Any]								=				old['decoder/logits_dense/kernel'].T

		return new



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_    )     -> str:
		_a	: List[str]								=				collections.OrderedDict([(k, torch.from_numpy(v.copy()    )) for (k, v) in converted_params.items()]    )

		# Add what is missing.
		if "encoder.embed_tokens.weight" not in state_dict:
				_a	: Tuple								=				state_dict['shared.weight']

		if not is_encoder_only:
				if "decoder.embed_tokens.weight" not in state_dict:
						_a	: Union[str, Any]								=				state_dict['shared.weight']

				if "lm_head.weight" not in state_dict:  # For old 1.0 models.
						print('Using shared word embeddings as lm_head.'    )
						_a	: Optional[Any]								=				state_dict['shared.weight']

		return state_dict



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )     -> int:
		_a	: Any								=				checkpoints.load_tax_checkpoint(lowerCAmelCase_    )
		_a	: str								=				convert_tax_to_pytorch(
		    lowerCAmelCase_		,     num_layers=config.num_layers		,     is_encoder_only=lowerCAmelCase_		,     scalable_attention=lowerCAmelCase_    )
		_a	: Dict								=				make_state_dict(lowerCAmelCase_		,     lowerCAmelCase_    )
		model.load_state_dict(lowerCAmelCase_		,     strict=lowerCAmelCase_    )



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_ = False		,     lowerCAmelCase_ = False		,     )     -> str:
		_a	: int								=				MTaConfig.from_json_file(lowerCAmelCase_    )
		print(f"""Building PyTorch model from configuration: {config}"""    )
		# Non-v1.1 checkpoints could also use T5Model, but this works for all.
		# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
		if is_encoder_only:
				_a	: Any								=				UMTaEncoderModel(lowerCAmelCase_    )
		else:
				_a	: Optional[int]								=				UMTaForConditionalGeneration(lowerCAmelCase_    )

		# Load weights from tf checkpoint
		load_tax_weights_in_ta(lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )

		# Save pytorch-model
		print(f"""Save PyTorch model to {pytorch_dump_path}"""    )
		model.save_pretrained(lowerCAmelCase_    )

		# Verify that we can load the checkpoint.
		model.from_pretrained(lowerCAmelCase_    )
		print('Done'    )


if __name__ == "__main__":
							__lowerCAmelCase		     =  argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
							# Required parameters
							parser.add_argument(
							    '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
							)
							parser.add_argument(
							    '''--config_file''',
							    default=None,
							    type=str,
							    required=True,
							    help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
							)
							parser.add_argument(
							    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
							)
							parser.add_argument(
							    '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
							)
							parser.add_argument(
							    '''--scalable_attention''',
							    action='''store_true''',
							    help='''Whether the model uses scaled attention (umt5 model)''',
							    default=False,
							)
							__lowerCAmelCase		     =  parser.parse_args()
							convert_tax_checkpoint_to_pytorch(
							    args.tax_checkpoint_path,
							    args.config_file,
							    args.pytorch_dump_path,
							    args.is_encoder_only,
							    args.scalable_attention,
							)




