
"""simple docstring"""


from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
      import tensorflow as tf


SCREAMING_SNAKE_CASE			  =  logging.get_logger(__name__)



@dataclass
class 	__a	(   _lowerCAmelCase						):
       UpperCamelCase_		:       str				=      [
           '''no_inference''',
           '''no_cuda''',
           '''no_tpu''',
           '''no_speed''',
           '''no_memory''',
           '''no_env_print''',
           '''no_multi_process''',
       ]






       def __init__(	self			:				List[Any]							,   **UpperCAmelCase_			:				Union[str, Any]      )->	Optional[Any]:







              """simple docstring"""



              for deprecated_arg in self.deprecated_args:
                     if deprecated_arg in kwargs:
                            UpperCamelCase       			=		deprecated_arg[3:]
                            UpperCamelCase       			=		not kwargs.pop(lowercase__      )
                            logger.warning(
                                f"{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"
                                f" {positive_arg}={kwargs[positive_arg]}"      )
              UpperCamelCase       			=		kwargs.pop("tpu_name"							,   self.tpu_name      )
              UpperCamelCase       			=		kwargs.pop("device_idx"							,   self.device_idx      )
              UpperCamelCase       			=		kwargs.pop("eager_mode"							,   self.eager_mode      )
              UpperCamelCase       			=		kwargs.pop("use_xla"							,   self.use_xla      )
              super().__init__(**lowercase__      )

       UpperCamelCase_		:       int				=      field(
           default=_lowerCAmelCase     ,  metadata={'''help''': '''Name of TPU'''}     ,  )
       UpperCamelCase_		:       int				=      field(
           default=0     ,  metadata={'''help''': '''CPU / GPU device index. Defaults to 0.'''}     ,  )
       UpperCamelCase_		:       List[Any]				=      field(default=_lowerCAmelCase     ,  metadata={'''help''': '''Benchmark models in eager model.'''}						)
       UpperCamelCase_		:       Optional[Any]				=      field(
           default=_lowerCAmelCase     ,  metadata={
               '''help''': '''Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'''
           }     ,  )






       @cached_property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	List[str]:







              """simple docstring"""
              requires_backends(self							,   ["tf"]      )
              UpperCamelCase       			=		None
              if self.tpu:
                     try:
                            if self.tpu_name:
                                   UpperCamelCase       			=		tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name      )
                            else:
                                   UpperCamelCase       			=		tf.distribute.cluster_resolver.TPUClusterResolver()
                     except ValueError:
                            UpperCamelCase       			=		None
              return tpu






       @cached_property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	List[Any]:







              """simple docstring"""
              requires_backends(self							,   ["tf"]      )
              if self.is_tpu:
                     tf.config.experimental_connect_to_cluster(self._setup_tpu      )
                     tf.tpu.experimental.initialize_tpu_system(self._setup_tpu      )

                     UpperCamelCase       			=		tf.distribute.TPUStrategy(self._setup_tpu      )
              else:
                     # currently no multi gpu is allowed
                     if self.is_gpu:
                            # TODO: Currently only single GPU is supported
                            tf.config.set_visible_devices(self.gpu_list[self.device_idx]							,   "GPU"      )
                            UpperCamelCase       			=		tf.distribute.OneDeviceStrategy(device=f"/gpu:{self.device_idx}"      )
                     else:
                            tf.config.set_visible_devices([]							,   "GPU"      )  # disable GPU
                            UpperCamelCase       			=		tf.distribute.OneDeviceStrategy(device=f"/cpu:{self.device_idx}"      )

              return strategy






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]      )->	List[str]:







              """simple docstring"""
              requires_backends(self							,   ["tf"]      )
              return self._setup_tpu is not None






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict      )->	int:







              """simple docstring"""
              requires_backends(self							,   ["tf"]      )
              return self._setup_strategy






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]      )->	str:







              """simple docstring"""
              requires_backends(self							,   ["tf"]      )
              return tf.config.list_physical_devices("GPU"      )






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any      )->	Optional[Any]:







              """simple docstring"""
              requires_backends(self							,   ["tf"]      )
              if self.cuda:
                     return len(self.gpu_list      )
              return 0






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	Optional[Any]:







              """simple docstring"""
              return self.n_gpu > 0








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      A__					(			__A  :						List[str]     ,				__A  :						Optional[Any]     ,				__A  :						Union[str, Any]     ,				__A  :						int="attention"			)					->str:
      __A    =params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
      __A    =params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
      __A    =params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
      __A    =params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
      return k, o, q, v





def      A__					(			__A  :						Optional[int]     ,				__A  :						List[str]     ,				__A  :						Any     ,				__A  :						Tuple=False			)					->Any:
      if split_mlp_wi:
            __A    =params[F'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
            __A    =params[F'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
            __A    =(wi_a, wi_a)
      else:
            __A    =params[F'''{prefix}/layers_{i}/mlp/wi/kernel''']

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





def      A__					(			__A  :						int     ,				__A  :						Any     ,				__A  :						Any     ,				__A  :						Optional[Any]			)					->str:
      return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']





def      A__					(			__A  :						dict     ,				*, __A  :						int     ,				__A  :						bool			)					->Optional[Any]:
      __A    =traverse_util.flatten_dict(variables['''target''']			)
      __A    ={'''/'''.join(__A			): v for k, v in old.items()}

      # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
      __A    ='''encoder/layers_0/mlp/wi_0/kernel''' in old
      print('''Split MLP:'''     ,				__A			)

      __A    =collections.OrderedDict()

      # Shared embeddings.
      __A    =old['''token_embedder/embedding''']

      # Encoder.
      for i in range(__A			):
            # Block i, layer 0 (Self Attention).
            __A    =tax_layer_norm_lookup(__A     ,				__A     ,				'''encoder'''     ,				'''pre_attention_layer_norm'''			)
            __A    ,		__A    ,		__A    ,		__A    =tax_attention_lookup(__A     ,				__A     ,				'''encoder'''     ,				'''attention'''			)
            __A    =layer_norm
            __A    =k.T
            __A    =o.T
            __A    =q.T
            __A    =v.T

            # Block i, layer 1 (MLP).
            __A    =tax_layer_norm_lookup(__A     ,				__A     ,				'''encoder'''     ,				'''pre_mlp_layer_norm'''			)
            __A    ,		__A    =tax_mlp_lookup(__A     ,				__A     ,				'''encoder'''     ,				__A			)
            __A    =layer_norm
            if split_mlp_wi:
                  __A    =wi[0].T
                  __A    =wi[1].T
            else:
                  __A    =wi.T
            __A    =wo.T

      __A    =old[
          '''encoder/relpos_bias/rel_embedding'''
      ].T
      __A    =old['''encoder/encoder_norm/scale''']

      if not is_encoder_only:
            # Decoder.
            for i in range(__A			):
                  # Block i, layer 0 (Self Attention).
                  __A    =tax_layer_norm_lookup(__A     ,				__A     ,				'''decoder'''     ,				'''pre_self_attention_layer_norm'''			)
                  __A    ,		__A    ,		__A    ,		__A    =tax_attention_lookup(__A     ,				__A     ,				'''decoder'''     ,				'''self_attention'''			)
                  __A    =layer_norm
                  __A    =k.T
                  __A    =o.T
                  __A    =q.T
                  __A    =v.T

                  # Block i, layer 1 (Cross Attention).
                  __A    =tax_layer_norm_lookup(__A     ,				__A     ,				'''decoder'''     ,				'''pre_cross_attention_layer_norm'''			)
                  __A    ,		__A    ,		__A    ,		__A    =tax_attention_lookup(__A     ,				__A     ,				'''decoder'''     ,				'''encoder_decoder_attention'''			)
                  __A    =layer_norm
                  __A    =k.T
                  __A    =o.T
                  __A    =q.T
                  __A    =v.T

                  # Block i, layer 2 (MLP).
                  __A    =tax_layer_norm_lookup(__A     ,				__A     ,				'''decoder'''     ,				'''pre_mlp_layer_norm'''			)
                  __A    ,		__A    =tax_mlp_lookup(__A     ,				__A     ,				'''decoder'''     ,				__A			)
                  __A    =layer_norm
                  if split_mlp_wi:
                        __A    =wi[0].T
                        __A    =wi[1].T
                  else:
                        __A    =wi.T
                  __A    =wo.T

            __A    =old['''decoder/decoder_norm/scale''']
            __A    =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:
                  __A    =old['''decoder/logits_dense/kernel'''].T

      return new





def      A__					(			__A  :						Union[str, Any]     ,				__A  :						bool			)					->Any:
      __A    =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    =state_dict['''shared.weight''']

      if not is_encoder_only:
            if "decoder.embed_tokens.weight" not in state_dict:
                  __A    =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    =state_dict['''shared.weight''']

      return state_dict





def      A__					(			__A  :						str     ,				__A  :						Optional[int]     ,				__A  :						int     ,				__A  :						Optional[Any]			)					->Tuple:
      __A    =checkpoints.load_tax_checkpoint(__A			)
      __A    =convert_tax_to_pytorch(__A     ,				num_layers=config.num_layers     ,				is_encoder_only=__A			)
      __A    =make_state_dict(__A     ,				__A			)
      model.load_state_dict(__A     ,				strict=__A			)





def      A__					(			__A  :						List[str]     ,				__A  :						str     ,				__A  :						str     ,				__A  :						bool = False			)					->List[str]:
      __A    =TaConfig.from_json_file(__A			)
      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    =TaEncoderModel(__A			)
      else:
            __A    =TaForConditionalGeneration(__A			)

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

      # Save pytorch-model
      print(F'''Save PyTorch model to {pytorch_dump_path}'''			)
      model.save_pretrained(__A			)

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


if __name__ == "__main__":
   _lowerCamelCase      :					List[str]					    =      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
   )
   _lowerCamelCase      :					int					    =      parser.parse_args()
   convert_tax_checkpoint_to_pytorch(
       args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
   )


