from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


_A		:			Any	      =logging.get_logger(__name__)

_A		:			int	      ={
    '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json''',
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}




class _lowerCAmelCase (  __UpperCAmelCase				):
      UpperCAmelCase_  :	Any     =					"blenderbot"
      UpperCAmelCase_  :	List[str]     =					["past_key_values"]
      UpperCAmelCase_  :	Union[str, Any]     =					{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







      def __init__(     self     ,						lowercase=8008     ,						lowercase=128     ,						lowercase=2     ,						lowercase=1_0240     ,						lowercase=32     ,						lowercase=24     ,						lowercase=1_0240     ,						lowercase=32     ,						lowercase=0.0     ,						lowercase=0.0     ,						lowercase=True     ,						lowercase=True     ,						lowercase="gelu"     ,						lowercase=2560     ,						lowercase=0.1     ,						lowercase=0.0     ,						lowercase=0.0     ,						lowercase=0.02     ,						lowercase=1     ,						lowercase=False     ,						lowercase=0     ,						lowercase=1     ,						lowercase=2     ,						lowercase=3     ,						lowercase=2     ,						**lowercase     ,						):
        lowercase							:       List[Any]											=      vocab_size
        lowercase							:       Union[str, Any]											=      max_position_embeddings
        lowercase							:       Optional[int]											=      d_model
        lowercase							:       Any											=      encoder_ffn_dim
        lowercase							:       List[Any]											=      encoder_layers
        lowercase							:       Tuple											=      encoder_attention_heads
        lowercase							:       Optional[int]											=      decoder_ffn_dim
        lowercase							:       Any											=      decoder_layers
        lowercase							:       Any											=      decoder_attention_heads
        lowercase							:       List[str]											=      dropout
        lowercase							:       Dict											=      attention_dropout
        lowercase							:       str											=      activation_dropout
        lowercase							:       Optional[int]											=      activation_function
        lowercase							:       Any											=      init_std
        lowercase							:       List[str]											=      encoder_layerdrop
        lowercase							:       List[str]											=      decoder_layerdrop
        lowercase							:       List[Any]											=      use_cache
        lowercase							:       Union[str, Any]											=      encoder_layers
        lowercase							:       Dict											=      scale_embedding  # scale factor will be sqrt(d_model) if True

        super().__init__(
            pad_token_id=lowercase_     ,						bos_token_id=lowercase_     ,						eos_token_id=lowercase_     ,						is_encoder_decoder=lowercase_     ,						decoder_start_token_id=lowercase_     ,						encoder_no_repeat_ngram_size=lowercase_     ,						forced_eos_token_id=lowercase_     ,						**lowercase_     ,						)





class _lowerCAmelCase (  __UpperCAmelCase				):







      @property
      def     lowerCamelCase__			(     self    ):

        if self.task in ["default", "seq2seq-lm"]:
          lowercase							:       Optional[int]											=      OrderedDict(
              [
                  ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                  ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
              ]    )
          if self.use_past:
            lowercase							:       str											=      {0: '''batch'''}
            lowercase							:       str											=      {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
          else:
            lowercase							:       List[str]											=      {0: '''batch''', 1: '''decoder_sequence'''}
            lowercase							:       str											=      {0: '''batch''', 1: '''decoder_sequence'''}
          if self.use_past:
            self.fill_with_past_key_values_(lowercase_     ,						direction='''inputs'''    )
        elif self.task == "causal-lm":
          lowercase							:       Dict											=      OrderedDict(
              [
                  ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                  ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
              ]    )
          if self.use_past:
            lowercase							:       List[str]											=      self.num_layers
            for i in range(lowercase_    ):
              lowercase							:       str											=      {0: '''batch''', 2: '''past_sequence + sequence'''}
              lowercase							:       List[Any]											=      {0: '''batch''', 2: '''past_sequence + sequence'''}
        else:
          lowercase							:       Optional[Any]											=      OrderedDict(
              [
                  ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                  ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                  ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
                  ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
              ]    )

        return common_inputs







      @property
      # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
      def     lowerCamelCase__			(     self    ):

        if self.task in ["default", "seq2seq-lm"]:
          lowercase							:       Dict											=      super().outputs
        else:
          lowercase							:       str											=      super(lowercase_     ,						self    ).outputs
          if self.use_past:
            lowercase							:       Optional[int]											=      self.num_layers
            for i in range(lowercase_    ):
              lowercase							:       Any											=      {0: '''batch''', 2: '''past_sequence + sequence'''}
              lowercase							:       int											=      {0: '''batch''', 2: '''past_sequence + sequence'''}
        return common_outputs







      def     lowerCamelCase__			(     self     ,						lowercase     ,						lowercase = -1     ,						lowercase = -1     ,						lowercase = False     ,						lowercase = None     ,						):
        lowercase							:       Union[str, Any]											=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowercase_     ,						lowercase_     ,						lowercase_     ,						lowercase_     ,						lowercase_    )
        # Generate decoder inputs
        lowercase							:       List[Any]											=      seq_length if not self.use_past else 1
        lowercase							:       Tuple											=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowercase_     ,						lowercase_     ,						lowercase_     ,						lowercase_     ,						lowercase_    )
        lowercase							:       str											=      {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
        lowercase							:       Optional[Any]											=      dict(**lowercase_     ,						**lowercase_    )

        if self.use_past:
          if not is_torch_available():
            raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''    )
          else:
            import torch
          lowercase							:       str											=      common_inputs['''input_ids'''].shape
          lowercase							:       int											=      common_inputs['''decoder_input_ids'''].shape[1]
          lowercase							:       Optional[int]											=      self.num_attention_heads
          lowercase							:       Optional[int]											=      (
              batch,
              num_encoder_attention_heads,
              encoder_seq_length,
              self._config.hidden_size // num_encoder_attention_heads,
          )
          lowercase							:       List[str]											=      decoder_seq_length
          lowercase							:       List[str]											=      (
              batch,
              num_decoder_attention_heads,
              decoder_past_length,
              self._config.hidden_size // num_decoder_attention_heads,
          )
          lowercase							:       List[str]											=      torch.cat(
              [common_inputs['''decoder_attention_mask'''], torch.ones(lowercase_     ,						lowercase_    )]     ,						dim=1    )
          lowercase							:       Any											=      []
          lowercase							:       int											=      self.num_layers

          for _ in range(lowercase_    ):
            common_inputs["past_key_values"].append(
                (
                    torch.zeros(lowercase_    ),
                    torch.zeros(lowercase_    ),
                    torch.zeros(lowercase_    ),
                    torch.zeros(lowercase_    ),
                )    )
        return common_inputs







      def     lowerCamelCase__			(     self     ,						lowercase     ,						lowercase = -1     ,						lowercase = -1     ,						lowercase = False     ,						lowercase = None     ,						):
        lowercase							:       Optional[Any]											=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowercase_     ,						lowercase_     ,						lowercase_     ,						lowercase_     ,						lowercase_    )

        if self.use_past:
          if not is_torch_available():
            raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''    )
          else:
            import torch
          lowercase							:       Optional[int]											=      common_inputs['''input_ids'''].shape
          lowercase							:       List[Any]											=      seqlen
          lowercase							:       List[Any]											=      self.num_layers
          lowercase							:       str											=      self.num_attention_heads
          lowercase							:       List[str]											=      (
              batch,
              num_encoder_attention_heads,
              past_key_values_length,
              self._config.hidden_size // num_encoder_attention_heads,
          )
          lowercase							:       str											=      common_inputs['''attention_mask'''].dtype
          lowercase							:       List[str]											=      torch.cat(
              [common_inputs['''attention_mask'''], torch.ones(lowercase_     ,						lowercase_     ,						dtype=lowercase_    )]     ,						dim=1    )
          lowercase							:       Union[str, Any]											=      [
              (torch.zeros(lowercase_    ), torch.zeros(lowercase_    )) for _ in range(lowercase_    )
          ]
        return common_inputs







      def     lowerCamelCase__			(     self     ,						lowercase     ,						lowercase = -1     ,						lowercase = -1     ,						lowercase = False     ,						lowercase = None     ,						):
        lowercase							:       List[str]											=      compute_effective_axis_dimension(
            lowercase_     ,						fixed_dimension=OnnxConfig.default_fixed_batch     ,						num_token_to_add=0    )

        # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
        lowercase							:       int											=      tokenizer.num_special_tokens_to_add(lowercase_    )
        lowercase							:       Optional[Any]											=      compute_effective_axis_dimension(
            lowercase_     ,						fixed_dimension=OnnxConfig.default_fixed_sequence     ,						num_token_to_add=lowercase_    )

        # Generate dummy inputs according to compute batch and sequence
        lowercase							:       int											=      [''' '''.join([tokenizer.unk_token]    ) * seq_length] * batch_size
        lowercase							:       str											=      dict(tokenizer(lowercase_     ,						return_tensors=lowercase_    )    )
        return common_inputs







      def     lowerCamelCase__			(     self     ,						lowercase     ,						lowercase = -1     ,						lowercase = -1     ,						lowercase = False     ,						lowercase = None     ,						):

        if self.task in ["default", "seq2seq-lm"]:
          lowercase							:       Dict											=      self._generate_dummy_inputs_for_default_and_seqaseq_lm(
              lowercase_     ,						batch_size=lowercase_     ,						seq_length=lowercase_     ,						is_pair=lowercase_     ,						framework=lowercase_    )

        elif self.task == "causal-lm":
          lowercase							:       Any											=      self._generate_dummy_inputs_for_causal_lm(
              lowercase_     ,						batch_size=lowercase_     ,						seq_length=lowercase_     ,						is_pair=lowercase_     ,						framework=lowercase_    )
        else:
          lowercase							:       Dict											=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              lowercase_     ,						batch_size=lowercase_     ,						seq_length=lowercase_     ,						is_pair=lowercase_     ,						framework=lowercase_    )

        return common_inputs







      def     lowerCamelCase__			(     self     ,						lowercase     ,						lowercase     ,						lowercase     ,						lowercase    ):

        if self.task in ["default", "seq2seq-lm"]:
          lowercase							:       Union[str, Any]											=      super()._flatten_past_key_values_(lowercase_     ,						lowercase_     ,						lowercase_     ,						lowercase_    )
        else:
          lowercase							:       Optional[Any]											=      super(lowercase_     ,						self    )._flatten_past_key_values_(
              lowercase_     ,						lowercase_     ,						lowercase_     ,						lowercase_    )







      def     lowerCamelCase__			(     self     ,						lowercase     ,						lowercase    ):

        if direction not in ["inputs", "outputs"]:
          raise ValueError(F"""direction must either be \"inputs\" or \"outputs\", but {direction} was given"""    )

        lowercase							:       List[Any]											=      '''past_key_values''' if direction == '''inputs''' else '''present'''
        lowercase							:       str											=      self.num_layers

        lowercase							:       Optional[int]											=      '''past_encoder_sequence'''
        lowercase							:       Tuple											=      '''past_decoder_sequence''' if direction == '''inputs''' else '''past_decoder_sequence + sequence'''

        for i in range(lowercase_    ):
          lowercase							:       List[str]											=      {0: '''batch''', 2: decoder_sequence}
          lowercase							:       Tuple											=      {0: '''batch''', 2: decoder_sequence}
          lowercase							:       List[str]											=      {0: '''batch''', 2: encoder_sequence}
          lowercase							:       Optional[int]											=      {0: '''batch''', 2: encoder_sequence}
'''simple docstring'''
import argparse

import torch

from transformers import ElectraConfig, ElectraForMaskedLM, ElectraForPreTraining, load_tf_weights_in_electra
from transformers.utils import logging


logging.set_verbosity_info()







def 							__UpperCamelCase   (				__UpperCAmelCase     :				Optional[Any]   ,      __UpperCAmelCase     :				Tuple   ,      __UpperCAmelCase     :				Union[str, Any]   ,      __UpperCAmelCase     :				List[str] )       ->  List[str]:
  # Initialise PyTorch model
  lowercase							:       Any											=      ElectraConfig.from_json_file(__UpperCAmelCase )
  print(f"""Building PyTorch model from configuration: {config}""" )

  if discriminator_or_generator == "discriminator":
    lowercase							:       Union[str, Any]											=      ElectraForPreTraining(__UpperCAmelCase )
  elif discriminator_or_generator == "generator":
    lowercase							:       Tuple											=      ElectraForMaskedLM(__UpperCAmelCase )
  else:
    raise ValueError('''The discriminator_or_generator argument should be either \'discriminator\' or \'generator\'''' )

  # Load weights from tf checkpoint
  load_tf_weights_in_electra(
      __UpperCAmelCase   ,      __UpperCAmelCase   ,      __UpperCAmelCase   ,      discriminator_or_generator=__UpperCAmelCase )

  # Save pytorch-model
  print(f"""Save PyTorch model to {pytorch_dump_path}""" )
  torch.save(model.state_dict()   ,      __UpperCAmelCase )


if __name__ == "__main__":
 _A		:			str	      =argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
 )
 parser.add_argument(
     '''--config_file''',
     default=None,
     type=str,
     required=True,
     help='''The config json file corresponding to the pre-trained 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(
     '''--discriminator_or_generator''',
     default=None,
     type=str,
     required=True,
     help=(
         '''Whether to export the generator or the discriminator. Should be a string, either \'discriminator\' or '''
         '''\'generator\'.'''
     ),
 )
 _A		:			Any	      =parser.parse_args()
 convert_tf_checkpoint_to_pytorch(
     args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
 )