




import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()





def        __UpperCAmelCase						(							lowerCamelCase_ :     Dict  ,							lowerCamelCase_ :     List[Any]  ,							lowerCamelCase_ :     Optional[int]  ,							lowerCamelCase_ :     str="attention"  )       ->  List[Any]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Any					  =      params[F'{prefix}/layers_{i}/{layer_name}/key/kernel']
			SCREAMING_SNAKE_CASE_   : str					  =      params[F'{prefix}/layers_{i}/{layer_name}/out/kernel']
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      params[F'{prefix}/layers_{i}/{layer_name}/query/kernel']
			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      params[F'{prefix}/layers_{i}/{layer_name}/value/kernel']
			return k, o, q, v





def        __UpperCAmelCase						(							lowerCamelCase_ :     Optional[int]  ,							lowerCamelCase_ :     List[str]  ,							lowerCamelCase_ :     Any  ,							lowerCamelCase_ :     Tuple=False  )       ->  Dict:







			"""simple docstring"""





			if split_mlp_wi:
						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      params[F'{prefix}/layers_{i}/mlp/wi_0/kernel']
						SCREAMING_SNAKE_CASE_   : int					  =      params[F'{prefix}/layers_{i}/mlp/wi_1/kernel']
						SCREAMING_SNAKE_CASE_   : Any					  =      (wi_a, wi_a)
			else:
						SCREAMING_SNAKE_CASE_   : int					  =      params[F'{prefix}/layers_{i}/mlp/wi/kernel']

			SCREAMING_SNAKE_CASE_   : str					  =      params[F'{prefix}/layers_{i}/mlp/wo/kernel']
			return wi, wo





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







			"""simple docstring"""





			return params[F'{prefix}/layers_{i}/{layer_name}/scale']





def        __UpperCAmelCase						(							lowerCamelCase_ :     dict  ,							*, lowerCamelCase_ :     int  ,							lowerCamelCase_ :     bool  )       ->  str:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      traverse_util.flatten_dict(variables['target']  )
			SCREAMING_SNAKE_CASE_   : str					  =      {'/'.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
			SCREAMING_SNAKE_CASE_   : Tuple					  =      'encoder/layers_0/mlp/wi_0/kernel' in old
			print('Split MLP:'  ,							lowerCamelCase_  )

			SCREAMING_SNAKE_CASE_   : int					  =      collections.OrderedDict()

			# Shared embeddings.
			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      old['token_embedder/embedding']

			# Encoder.
			for i in range(lowerCamelCase_  ):
						# Block i, layer 0 (Self Attention).
						SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      tax_layer_norm_lookup(lowerCamelCase_  ,							lowerCamelCase_  ,							'encoder'  ,							'pre_attention_layer_norm'  )
						SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Tuple					  =      tax_attention_lookup(lowerCamelCase_  ,							lowerCamelCase_  ,							'encoder'  ,							'attention'  )
						SCREAMING_SNAKE_CASE_   : List[str]					  =      layer_norm
						SCREAMING_SNAKE_CASE_   : List[Any]					  =      k.T
						SCREAMING_SNAKE_CASE_   : Any					  =      o.T
						SCREAMING_SNAKE_CASE_   : List[str]					  =      q.T
						SCREAMING_SNAKE_CASE_   : List[Any]					  =      v.T

						# Block i, layer 1 (MLP).
						SCREAMING_SNAKE_CASE_   : Dict					  =      tax_layer_norm_lookup(lowerCamelCase_  ,							lowerCamelCase_  ,							'encoder'  ,							'pre_mlp_layer_norm'  )
						SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      tax_mlp_lookup(lowerCamelCase_  ,							lowerCamelCase_  ,							'encoder'  ,							lowerCamelCase_  )
						SCREAMING_SNAKE_CASE_   : Optional[int]					  =      layer_norm
						if split_mlp_wi:
									SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      wi[0].T
									SCREAMING_SNAKE_CASE_   : str					  =      wi[1].T
						else:
									SCREAMING_SNAKE_CASE_   : List[str]					  =      wi.T
						SCREAMING_SNAKE_CASE_   : Tuple					  =      wo.T

			SCREAMING_SNAKE_CASE_   : str					  =      old[
			    'encoder/relpos_bias/rel_embedding'
			].T
			SCREAMING_SNAKE_CASE_   : Any					  =      old['encoder/encoder_norm/scale']

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

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

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

						SCREAMING_SNAKE_CASE_   : Tuple					  =      old['decoder/decoder_norm/scale']
						SCREAMING_SNAKE_CASE_   : Optional[int]					  =      old[
						    'decoder/relpos_bias/rel_embedding'
						].T

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

			return new





def        __UpperCAmelCase						(							lowerCamelCase_ :     Optional[int]  ,							lowerCamelCase_ :     bool  )       ->  Any:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      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:
						SCREAMING_SNAKE_CASE_   : Optional[int]					  =      state_dict['shared.weight']

			if not is_encoder_only:
						if "decoder.embed_tokens.weight" not in state_dict:
									SCREAMING_SNAKE_CASE_   : str					  =      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.'  )
									SCREAMING_SNAKE_CASE_   : Dict					  =      state_dict['shared.weight']

			return state_dict





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







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : List[str]					  =      checkpoints.load_tax_checkpoint(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Dict					  =      convert_tax_to_pytorch(lowerCamelCase_  ,							num_layers=config.num_layers  ,							is_encoder_only=lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : int					  =      make_state_dict(lowerCamelCase_  ,							lowerCamelCase_  )
			model.load_state_dict(lowerCamelCase_  ,							strict=lowerCamelCase_  )





def        __UpperCAmelCase						(							lowerCamelCase_ :     str  ,							lowerCamelCase_ :     List[Any]  ,							lowerCamelCase_ :     List[str]  ,							lowerCamelCase_ :     bool = False  )       ->  Optional[int]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : List[Any]					  =      TaConfig.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:
						SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      TaEncoderModel(lowerCamelCase_  )
			else:
						SCREAMING_SNAKE_CASE_   : Tuple					  =      TaForConditionalGeneration(lowerCamelCase_  )

			# Load weights from tf checkpoint
			load_tax_weights_in_ta(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__":
						UpperCamelCase__ :   Union[str, Any]						=							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
						)
						UpperCamelCase__ :   Dict						=							parser.parse_args()
						convert_tax_checkpoint_to_pytorch(
						    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
						)









import warnings

from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor


UpperCamelCase__ :   str						=							logging.get_logger(__name__)







class 				lowerCAmelCase_       (					lowerCamelCase_							):






				def __init__(			self      ,*snake_case__      ,**snake_case__ ):
							warnings.warn(
							    'The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
							    ' Please use PoolFormerImageProcessor instead.'      ,snake_case__      ,)
							super().__init__(*snake_case__      ,**snake_case__ )



