
import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
  import numpy as np
  import torch

  from . import (
      AlbertForPreTraining,
      BartForConditionalGeneration,
      BertForPreTraining,
      BertForQuestionAnswering,
      BertForSequenceClassification,
      CamembertForMaskedLM,
      CTRLLMHeadModel,
      DistilBertForMaskedLM,
      DistilBertForQuestionAnswering,
      DPRContextEncoder,
      DPRQuestionEncoder,
      DPRReader,
      ElectraForPreTraining,
      FlaubertWithLMHeadModel,
      GPTaLMHeadModel,
      LayoutLMForMaskedLM,
      LxmertForPreTraining,
      LxmertVisualFeatureEncoder,
      OpenAIGPTLMHeadModel,
      RobertaForMaskedLM,
      RobertaForSequenceClassification,
      TaForConditionalGeneration,
      TransfoXLLMHeadModel,
      XLMRobertaForMaskedLM,
      XLMWithLMHeadModel,
      XLNetLMHeadModel,
  )


logging.set_verbosity_info()

lowercase  :					Any						    =							{
    """bart""": (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """bert""": (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-large-uncased-whole-word-masking-finetuned-squad""": (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-large-cased-whole-word-masking-finetuned-squad""": (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-base-cased-finetuned-mrpc""": (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """dpr""": (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """gpt2""": (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlnet""": (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlm""": (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlm-roberta""": (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """transfo-xl""": (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """openai-gpt""": (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """roberta""": (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """layoutlm""": (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """roberta-large-mnli""": (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """camembert""": (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """flaubert""": (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """distilbert""": (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """distilbert-base-distilled-squad""": (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """lxmert""": (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """lxmert-visual-feature-encoder""": (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """ctrl""": (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """albert""": (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """t5""": (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """electra""": (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """wav2vec2""": (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=False					,	SCREAMING_SNAKE_CASE__=True				) ->    str:
       if model_type not in MODEL_CLASSES:
              raise ValueError(f"Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()				)}."				)

       lowercase	,			lowercase	,			lowercase	,			lowercase							:     Union[str, Any]       	=      MODEL_CLASSES[model_type]

       # Initialise TF model
       if config_file in aws_config_map:
              lowercase							:     List[Any]       	=      cached_file(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	force_download=not use_cached_models				)
       lowercase							:     List[str]       	=      config_class.from_json_file(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Optional[Any]       	=      True
       lowercase							:     Optional[int]       	=      True
       print(f"Building TensorFlow model from configuration: {config}"				)
       lowercase							:     Dict       	=      model_class(SCREAMING_SNAKE_CASE__				)

       # Load weights from tf checkpoint
       if pytorch_checkpoint_path in aws_config_map.keys():
              lowercase							:     List[str]       	=      cached_file(
                  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	force_download=not use_cached_models				)
       # Load PyTorch checkpoint in tf2 model:
       lowercase							:     Any       	=      load_pytorch_checkpoint_in_tfa_model(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

       if compare_with_pt_model:
              lowercase							:     int       	=      tf_model(tf_model.dummy_inputs					,	training=SCREAMING_SNAKE_CASE__				)  # build the network

              lowercase							:     List[str]       	=      torch.load(SCREAMING_SNAKE_CASE__					,	map_location="""cpu"""				)
              lowercase							:     Union[str, Any]       	=      pt_model_class.from_pretrained(
                  pretrained_model_name_or_path=SCREAMING_SNAKE_CASE__					,	config=SCREAMING_SNAKE_CASE__					,	state_dict=SCREAMING_SNAKE_CASE__				)

              with torch.no_grad():
                     lowercase							:     str       	=      pt_model(**pt_model.dummy_inputs				)

              lowercase							:     Optional[int]       	=      pto[0].numpy()
              lowercase							:     Union[str, Any]       	=      tfo[0].numpy()
              lowercase							:     int       	=      np.amax(np.abs(np_pt - np_tf				)				)
              print(f"Max absolute difference between models outputs {diff}"				)
              assert diff <= 2e-2, f"Error, model absolute difference is >2e-2: {diff}"

       # Save pytorch-model
       print(f"Save TensorFlow model to {tf_dump_path}"				)
       tf_model.save_weights(SCREAMING_SNAKE_CASE__					,	save_format="""h5"""				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=None					,	SCREAMING_SNAKE_CASE__=None					,	SCREAMING_SNAKE_CASE__=False					,	SCREAMING_SNAKE_CASE__=False					,	SCREAMING_SNAKE_CASE__=False					,	SCREAMING_SNAKE_CASE__=False					,	) ->    Optional[Any]:
       if args_model_type is None:
              lowercase							:     Optional[int]       	=      list(MODEL_CLASSES.keys()				)
       else:
              lowercase							:     int       	=      [args_model_type]

       for j, model_type in enumerate(SCREAMING_SNAKE_CASE__					,	start=1				):
              print("""=""" * 100				)
              print(f" Converting model type {j}/{len(SCREAMING_SNAKE_CASE__				)}: {model_type}"				)
              print("""=""" * 100				)
              if model_type not in MODEL_CLASSES:
                     raise ValueError(f"Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()				)}."				)

              lowercase	,			lowercase	,			lowercase	,			lowercase	,			lowercase							:     List[str]       	=      MODEL_CLASSES[model_type]

              if model_shortcut_names_or_path is None:
                     lowercase							:     Optional[Any]       	=      list(aws_model_maps.keys()				)
              if config_shortcut_names_or_path is None:
                     lowercase							:     Dict       	=      model_shortcut_names_or_path

              for i, (model_shortcut_name, config_shortcut_name) in enumerate(
                  zip(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)					,	start=1				):
                     print("""-""" * 100				)
                     if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
                            if not only_convert_finetuned_models:
                                   print(f"    Skipping finetuned checkpoint {model_shortcut_name}"				)
                                   continue
                            lowercase							:     Union[str, Any]       	=      model_shortcut_name
                     elif only_convert_finetuned_models:
                            print(f"    Skipping not finetuned checkpoint {model_shortcut_name}"				)
                            continue
                     print(
                         f"    Converting checkpoint {i}/{len(SCREAMING_SNAKE_CASE__				)}: {model_shortcut_name} - model_type {model_type}"				)
                     print("""-""" * 100				)

                     if config_shortcut_name in aws_config_map:
                            lowercase							:     Optional[int]       	=      cached_file(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	force_download=not use_cached_models				)
                     else:
                            lowercase							:     Optional[Any]       	=      config_shortcut_name

                     if model_shortcut_name in aws_model_maps:
                            lowercase							:     Dict       	=      cached_file(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	force_download=not use_cached_models				)
                     else:
                            lowercase							:     str       	=      model_shortcut_name

                     if os.path.isfile(SCREAMING_SNAKE_CASE__				):
                            lowercase							:     Tuple       	=      """converted_model"""

                     convert_pt_checkpoint_to_tf(
                         model_type=SCREAMING_SNAKE_CASE__					,	pytorch_checkpoint_path=SCREAMING_SNAKE_CASE__					,	config_file=SCREAMING_SNAKE_CASE__					,	tf_dump_path=os.path.join(SCREAMING_SNAKE_CASE__					,	model_shortcut_name + """-tf_model.h5"""				)					,	compare_with_pt_model=SCREAMING_SNAKE_CASE__					,	)
                     if remove_cached_files:
                            os.remove(SCREAMING_SNAKE_CASE__				)
                            os.remove(SCREAMING_SNAKE_CASE__				)


if __name__ == "__main__":
  lowercase  :					Dict						    =							argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--tf_dump_path""", default=None, type=str, required=True, help="""Path to the output Tensorflow dump file."""
  )
  parser.add_argument(
      """--model_type""",
      default=None,
      type=str,
      help=(
          F'''Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and '''
          """convert all the models from AWS."""
      ),
  )
  parser.add_argument(
      """--pytorch_checkpoint_path""",
      default=None,
      type=str,
      help=(
          """Path to the PyTorch checkpoint path or shortcut name to download from AWS. """
          """If not given, will download and convert all the checkpoints from AWS."""
      ),
  )
  parser.add_argument(
      """--config_file""",
      default=None,
      type=str,
      help=(
          """The config json file corresponding to the pre-trained model. \n"""
          """This specifies the model architecture. If not given and """
          """--pytorch_checkpoint_path is not given or is a shortcut name """
          """use the configuration associated to the shortcut name on the AWS"""
      ),
  )
  parser.add_argument(
      """--compare_with_pt_model""", action="""store_true""", help="""Compare Tensorflow and PyTorch model predictions."""
  )
  parser.add_argument(
      """--use_cached_models""",
      action="""store_true""",
      help="""Use cached models if possible instead of updating to latest checkpoint versions.""",
  )
  parser.add_argument(
      """--remove_cached_files""",
      action="""store_true""",
      help="""Remove pytorch models after conversion (save memory when converting in batches).""",
  )
  parser.add_argument("""--only_convert_finetuned_models""", action="""store_true""", help="""Only convert finetuned models.""")
  lowercase  :					Union[str, Any]						    =							parser.parse_args()

  # if args.pytorch_checkpoint_path is not None:
  #     convert_pt_checkpoint_to_tf(args.model_type.lower(),
  #                                 args.pytorch_checkpoint_path,
  #                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
  #                                 args.tf_dump_path,
  #                                 compare_with_pt_model=args.compare_with_pt_model,
  #                                 use_cached_models=args.use_cached_models)
  # else:
  convert_all_pt_checkpoints_to_tf(
      args.model_type.lower() if args.model_type is not None else None,
      args.tf_dump_path,
      model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
      if args.pytorch_checkpoint_path is not None
      else None,
      config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
      compare_with_pt_model=args.compare_with_pt_model,
      use_cached_models=args.use_cached_models,
      remove_cached_files=args.remove_cached_files,
      only_convert_finetuned_models=args.only_convert_finetuned_models,
  )


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

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


lowercase  :					List[str]						    =							logging.get_logger(__name__)

lowercase  :					Optional[Any]						    =							{
    """EleutherAI/gpt-neo-1.3B""": """https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json""",
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}

class __snake_case		(      lowerCAmelCase     ):
    _a		:						str=				"gpt_neo"
    _a		:						Optional[int]=				["past_key_values"]
    _a		:						Dict=				{"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
    def __init__(  self		,snake_case=50257		,snake_case=2048		,snake_case=2048		,snake_case=24		,snake_case=[[["global", "local"], 12]]		,snake_case=16		,snake_case=None		,snake_case=256		,snake_case="gelu_new"		,snake_case=0.0		,snake_case=0.0		,snake_case=0.0		,snake_case=0.1		,snake_case=1e-5		,snake_case=0.02		,snake_case=True		,snake_case=50256		,snake_case=50256		,**snake_case		,):



           '''simple docstring'''







           lowercase							:     int       	=      vocab_size
           lowercase							:     Union[str, Any]       	=      max_position_embeddings
           lowercase							:     Dict       	=      hidden_size
           lowercase							:     Union[str, Any]       	=      num_layers
           lowercase							:     Union[str, Any]       	=      num_heads
           lowercase							:     Optional[int]       	=      intermediate_size
           lowercase							:     List[str]       	=      window_size
           lowercase							:     Optional[int]       	=      activation_function
           lowercase							:     List[str]       	=      resid_dropout
           lowercase							:     int       	=      embed_dropout
           lowercase							:     Optional[int]       	=      attention_dropout
           lowercase							:     Tuple       	=      classifier_dropout
           lowercase							:     Optional[int]       	=      layer_norm_epsilon
           lowercase							:     Dict       	=      initializer_range
           lowercase							:     List[str]       	=      use_cache

           lowercase							:     Optional[int]       	=      bos_token_id
           lowercase							:     int       	=      eos_token_id

           lowercase							:     Union[str, Any]       	=      attention_types
           lowercase							:     Dict       	=      self.expand_attention_types_params(snake_case      )

           if len(self.attention_layers      ) != self.num_layers:
                  raise ValueError(
                      """Configuration for convolutional module is incorrect. """
                      """It is required that `len(config.attention_layers)` == `config.num_layers` """
                      f"but is `len(config.attention_layers) = {len(self.attention_layers      )}`, "
                      f"`config.num_layers = {self.num_layers}`. "
                      """`config.attention_layers` is prepared using `config.attention_types`. """
                      """Please verify the value of `config.attention_types` argument."""      )

           super().__init__(bos_token_id=snake_case		,eos_token_id=snake_case		,**snake_case      )
    @staticmethod
    def       _SCREAMING_SNAKE_CASE       (  snake_case      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      []
           for item in attention_types:
                  for _ in range(item[1]      ):
                         attentions.extend(item[0]      )
           return attentions







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Tuple:
       import torch

       lowercase							:     Tuple       	=      input.size()
       lowercase							:     int       	=      len(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Union[str, Any]       	=      shape[dimension]

       lowercase							:     int       	=      torch.arange(0					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     Optional[Any]       	=      torch.div(sizedim - size					,	SCREAMING_SNAKE_CASE__					,	rounding_mode="""floor"""				) + 1
       lowercase							:     Dict       	=      torch.arange(SCREAMING_SNAKE_CASE__				) + low_indices[:min_length][:, None]

       lowercase							:     Union[str, Any]       	=      [slice(SCREAMING_SNAKE_CASE__				)] * rank
       lowercase							:     Optional[Any]       	=      indices
       lowercase							:     List[str]       	=      input[s]

       lowercase							:     Optional[int]       	=      list(range(0					,	rank + 1				)				)
       perm.append(perm.pop(dimension + 1				)				)

       return sliced.permute(SCREAMING_SNAKE_CASE__				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Union[str, Any]:
       import torch

       lowercase							:     Union[str, Any]       	=      torch.arange(1					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     Optional[Any]       	=      torch.remainder(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     List[Any]       	=      remainders == 0
       lowercase							:     Optional[int]       	=      candidates[divisor_indices]
       lowercase							:     List[Any]       	=      torch.max(SCREAMING_SNAKE_CASE__				)
       return largest_divisor, torch.div(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	rounding_mode="""floor"""				)




class __snake_case		(      lowerCAmelCase     ):
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Dict       	=      OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}      )
           if self.use_past:
                  self.fill_with_past_key_values_(snake_case		,direction="""inputs"""      )
                  lowercase							:     Dict       	=      {0: """batch""", 1: """past_sequence + sequence"""}
           else:
                  lowercase							:     List[str]       	=      {0: """batch""", 1: """sequence"""}

           return common_inputs
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return self._config.num_heads
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = -1		,snake_case = -1		,snake_case = False		,snake_case = None		,):



           '''simple docstring'''







           lowercase							:     Any       	=      super(snake_case		,self      ).generate_dummy_inputs(
               snake_case		,batch_size=snake_case		,seq_length=snake_case		,is_pair=snake_case		,framework=snake_case      )

           # We need to order the input in the way they appears in the forward()
           lowercase							:     List[str]       	=      OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}      )

           # Need to add the past_keys
           if self.use_past:
                  if not is_torch_available():
                         raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""      )
                  else:
                         import torch

                         lowercase	,			lowercase							:     List[Any]       	=      common_inputs["""input_ids"""].shape
                         # Not using the same length for past_key_values
                         lowercase							:     Optional[int]       	=      seqlen + 2
                         lowercase							:     int       	=      (
                             batch,
                             self.num_attention_heads,
                             past_key_values_length,
                             self._config.hidden_size // self.num_attention_heads,
                         )
                         lowercase							:     Optional[Any]       	=      [
                             (torch.zeros(snake_case      ), torch.zeros(snake_case      )) for _ in range(self.num_layers      )
                         ]

           lowercase							:     Optional[Any]       	=      common_inputs["""attention_mask"""]
           if self.use_past:
                  lowercase							:     Any       	=      ordered_inputs["""attention_mask"""].dtype
                  lowercase							:     Union[str, Any]       	=      torch.cat(
                      [ordered_inputs["""attention_mask"""], torch.ones(snake_case		,snake_case		,dtype=snake_case      )]		,dim=1      )

           return ordered_inputs
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return 13
