




import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import TimesformerConfig, TimesformerForVideoClassification, VideoMAEImageProcessor


def   A		(__A  :       Any			)  -> Optional[Any]:







     """simple docstring"""


     UpperCAmelCase_		=				TimesformerConfig()

     if "large" in model_name:
          UpperCAmelCase_		=				96

     if "hr" in model_name:
          UpperCAmelCase_		=				16
          UpperCAmelCase_		=				448

     UpperCAmelCase_		=				'''huggingface/label-files'''
     if "k400" in model_name:
          UpperCAmelCase_		=				400
          UpperCAmelCase_		=				'''kinetics400-id2label.json'''
     elif "k600" in model_name:
          UpperCAmelCase_		=				600
          UpperCAmelCase_		=				'''kinetics600-id2label.json'''
     elif "ssv2" in model_name:
          UpperCAmelCase_		=				174
          UpperCAmelCase_		=				'''something-something-v2-id2label.json'''
     else:
          raise ValueError('''Model name should either contain \'k400\', \'k600\' or \'ssv2\'.'''			)
     UpperCAmelCase_		=				json.load(open(hf_hub_download(__A  ,							__A  ,							repo_type='''dataset'''			)  ,							'''r'''			)			)
     UpperCAmelCase_		=				{int(__A			): v for k, v in idalabel.items()}
     UpperCAmelCase_		=				idalabel
     UpperCAmelCase_		=				{v: k for k, v in idalabel.items()}

     return config


def   A		(__A  :       str			)  -> Any:







     """simple docstring"""


     if "encoder." in name:
          UpperCAmelCase_		=				name.replace('''encoder.'''  ,							''''''			)
     if "cls_token" in name:
          UpperCAmelCase_		=				name.replace('''cls_token'''  ,							'''timesformer.embeddings.cls_token'''			)
     if "pos_embed" in name:
          UpperCAmelCase_		=				name.replace('''pos_embed'''  ,							'''timesformer.embeddings.position_embeddings'''			)
     if "time_embed" in name:
          UpperCAmelCase_		=				name.replace('''time_embed'''  ,							'''timesformer.embeddings.time_embeddings'''			)
     if "patch_embed.proj" in name:
          UpperCAmelCase_		=				name.replace('''patch_embed.proj'''  ,							'''timesformer.embeddings.patch_embeddings.projection'''			)
     if "patch_embed.norm" in name:
          UpperCAmelCase_		=				name.replace('''patch_embed.norm'''  ,							'''timesformer.embeddings.norm'''			)
     if "blocks" in name:
          UpperCAmelCase_		=				name.replace('''blocks'''  ,							'''timesformer.encoder.layer'''			)
     if "attn.proj" in name:
          UpperCAmelCase_		=				name.replace('''attn.proj'''  ,							'''attention.output.dense'''			)
     if "attn" in name and "bias" not in name and "temporal" not in name:
          UpperCAmelCase_		=				name.replace('''attn'''  ,							'''attention.self'''			)
     if "attn" in name and "temporal" not in name:
          UpperCAmelCase_		=				name.replace('''attn'''  ,							'''attention.attention'''			)
     if "temporal_norm1" in name:
          UpperCAmelCase_		=				name.replace('''temporal_norm1'''  ,							'''temporal_layernorm'''			)
     if "temporal_attn.proj" in name:
          UpperCAmelCase_		=				name.replace('''temporal_attn'''  ,							'''temporal_attention.output.dense'''			)
     if "temporal_fc" in name:
          UpperCAmelCase_		=				name.replace('''temporal_fc'''  ,							'''temporal_dense'''			)
     if "norm1" in name and "temporal" not in name:
          UpperCAmelCase_		=				name.replace('''norm1'''  ,							'''layernorm_before'''			)
     if "norm2" in name:
          UpperCAmelCase_		=				name.replace('''norm2'''  ,							'''layernorm_after'''			)
     if "mlp.fc1" in name:
          UpperCAmelCase_		=				name.replace('''mlp.fc1'''  ,							'''intermediate.dense'''			)
     if "mlp.fc2" in name:
          UpperCAmelCase_		=				name.replace('''mlp.fc2'''  ,							'''output.dense'''			)
     if "norm.weight" in name and "fc" not in name and "temporal" not in name:
          UpperCAmelCase_		=				name.replace('''norm.weight'''  ,							'''timesformer.layernorm.weight'''			)
     if "norm.bias" in name and "fc" not in name and "temporal" not in name:
          UpperCAmelCase_		=				name.replace('''norm.bias'''  ,							'''timesformer.layernorm.bias'''			)
     if "head" in name:
          UpperCAmelCase_		=				name.replace('''head'''  ,							'''classifier'''			)

     return name


def   A		(__A  :       int  ,							__A  :       List[Any]			)  -> Tuple:







     """simple docstring"""


     for key in orig_state_dict.copy().keys():
          UpperCAmelCase_		=				orig_state_dict.pop(__A			)

          if key.startswith('''model.'''			):
               UpperCAmelCase_		=				key.replace('''model.'''  ,							''''''			)

          if "qkv" in key:
               UpperCAmelCase_		=				key.split('''.'''			)
               UpperCAmelCase_		=				int(key_split[1]			)
               UpperCAmelCase_		=				'''timesformer.encoder.layer.'''
               if "temporal" in key:
                    UpperCAmelCase_		=				'''.temporal_attention.attention.qkv.'''
               else:
                    UpperCAmelCase_		=				'''.attention.attention.qkv.'''
               if "weight" in key:
                    UpperCAmelCase_		=				val
               else:
                    UpperCAmelCase_		=				val
          else:
               UpperCAmelCase_		=				val

     return orig_state_dict


def   A		()  -> Tuple:







     """simple docstring"""


     UpperCAmelCase_		=				hf_hub_download(
         repo_id='''hf-internal-testing/spaghetti-video'''  ,							filename='''eating_spaghetti.npy'''  ,							repo_type='''dataset'''			)
     UpperCAmelCase_		=				np.load(__A			)
     return list(__A			)


def   A		(__A  :       List[str]  ,							__A  :       Any  ,							__A  :       int  ,							__A  :       Dict			)  -> Tuple:







     """simple docstring"""


     UpperCAmelCase_		=				get_timesformer_config(__A			)

     UpperCAmelCase_		=				TimesformerForVideoClassification(__A			)

     # download original checkpoint, hosted on Google Drive
     UpperCAmelCase_		=				'''pytorch_model.bin'''
     gdown.cached_download(__A  ,							__A  ,							quiet=__A			)
     UpperCAmelCase_		=				torch.load(__A  ,							map_location='''cpu'''			)
     if "model" in files:
          UpperCAmelCase_		=				files['''model''']
     elif "module" in files:
          UpperCAmelCase_		=				files['''module''']
     else:
          UpperCAmelCase_		=				files['''model_state''']
     UpperCAmelCase_		=				convert_state_dict(__A  ,							__A			)

     model.load_state_dict(__A			)
     model.eval()

     # verify model on basic input
     UpperCAmelCase_		=				VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]  ,							image_std=[0.5, 0.5, 0.5]			)
     UpperCAmelCase_		=				prepare_video()
     UpperCAmelCase_		=				image_processor(video[:8]  ,							return_tensors='''pt'''			)

     UpperCAmelCase_		=				model(**__A			)
     UpperCAmelCase_		=				outputs.logits

     UpperCAmelCase_		=				[
         # Kinetics-400 checkpoints (hr = high resolution input of 448px instead of 224px)
         '''timesformer-base-finetuned-k400''',
         '''timesformer-large-finetuned-k400''',
         '''timesformer-hr-finetuned-k400''',
         # Kinetics-600 checkpoints (hr = high resolution input of 448px instead of 224px)
         '''timesformer-base-finetuned-k600''',
         '''timesformer-large-finetuned-k600''',
         '''timesformer-hr-finetuned-k600''',
         # Something-Something-v2 checkpoints (hr = high resolution input of 448px instead of 224px)
         '''timesformer-base-finetuned-ssv2''',
         '''timesformer-large-finetuned-ssv2''',
         '''timesformer-hr-finetuned-ssv2''',
     ]

     # NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
     if model_name == "timesformer-base-finetuned-k400":
          UpperCAmelCase_		=				torch.Size([1, 400]			)
          UpperCAmelCase_		=				torch.tensor([-0.3_016, -0.7_713, -0.4_205]			)
     elif model_name == "timesformer-base-finetuned-k600":
          UpperCAmelCase_		=				torch.Size([1, 600]			)
          UpperCAmelCase_		=				torch.tensor([-0.7_267, -0.7_466, 3.2_404]			)
     elif model_name == "timesformer-base-finetuned-ssv2":
          UpperCAmelCase_		=				torch.Size([1, 174]			)
          UpperCAmelCase_		=				torch.tensor([-0.9_059, 0.6_433, -3.1_457]			)
     elif model_name == "timesformer-large-finetuned-k400":
          UpperCAmelCase_		=				torch.Size([1, 400]			)
          UpperCAmelCase_		=				torch.tensor([0, 0, 0]			)
     elif model_name == "timesformer-large-finetuned-k600":
          UpperCAmelCase_		=				torch.Size([1, 600]			)
          UpperCAmelCase_		=				torch.tensor([0, 0, 0]			)
     elif model_name == "timesformer-large-finetuned-ssv2":
          UpperCAmelCase_		=				torch.Size([1, 174]			)
          UpperCAmelCase_		=				torch.tensor([0, 0, 0]			)
     elif model_name == "timesformer-hr-finetuned-k400":
          UpperCAmelCase_		=				torch.Size([1, 400]			)
          UpperCAmelCase_		=				torch.tensor([-0.9_617, -3.7_311, -3.7_708]			)
     elif model_name == "timesformer-hr-finetuned-k600":
          UpperCAmelCase_		=				torch.Size([1, 600]			)
          UpperCAmelCase_		=				torch.tensor([2.5_273, 0.7_127, 1.8_848]			)
     elif model_name == "timesformer-hr-finetuned-ssv2":
          UpperCAmelCase_		=				torch.Size([1, 174]			)
          UpperCAmelCase_		=				torch.tensor([-3.6_756, -0.7_513, 0.7_180]			)
     else:
          raise ValueError(F"""Model name not supported. Should be one of {model_names}"""			)

     # verify logits
     assert logits.shape == expected_shape
     assert torch.allclose(logits[0, :3]  ,							__A  ,							atol=1E-4			)
     print('''Logits ok!'''			)

     if pytorch_dump_folder_path is not None:
          print(F"""Saving model and image processor to {pytorch_dump_folder_path}"""			)
          image_processor.save_pretrained(__A			)
          model.save_pretrained(__A			)

     if push_to_hub:
          print('''Pushing to the hub...'''			)
          model.push_to_hub(F"""fcakyon/{model_name}"""			)


if __name__ == "__main__":
    snake_case_					:					str       						=			argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--checkpoint_url",
        default="https://drive.google.com/u/1/uc?id=17yvuYp9L4mn-HpIcK5Zo6K3UoOy1kA5l&export=download",
        type=str,
        help=(
            "URL of the original PyTorch checkpoint (on Google Drive) you\'d like to convert. Should be a direct"
            " download link."
        ),
    )
    parser.add_argument(
        "--pytorch_dump_folder_path",
        default="",
        type=str,
        help="Path to the output PyTorch model directory.",
    )
    parser.add_argument("--model_name", default="timesformer-base-finetuned-k400", type=str, help="Name of the model.")
    parser.add_argument(
        "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
    )

    snake_case_					:					Dict       						=			parser.parse_args()
    convert_timesformer_checkpoint(
        args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
    )








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


UpperCAmelCase__						:	Any	     = logging.get_logger(__name__)

UpperCAmelCase__						:	Optional[Any]	     = {
    '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 UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''
   __UpperCamelCase						:       Optional[Any]												=       '''blenderbot'''
   __UpperCamelCase						:       Optional[int]												=       ['''past_key_values''']
   __UpperCamelCase						:       Union[str, Any]												=       {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}


   def __init__(   self	:				Any     ,     lowerCAmelCase_	:				List[str]=8_0_0_8     ,     lowerCAmelCase_	:				Any=1_2_8     ,     lowerCAmelCase_	:				List[str]=2     ,     lowerCAmelCase_	:				Dict=1_0_2_4_0     ,     lowerCAmelCase_	:				Dict=3_2     ,     lowerCAmelCase_	:				Union[str, Any]=2_4     ,     lowerCAmelCase_	:				Union[str, Any]=1_0_2_4_0     ,     lowerCAmelCase_	:				Union[str, Any]=3_2     ,     lowerCAmelCase_	:				Optional[Any]=0.0     ,     lowerCAmelCase_	:				Optional[int]=0.0     ,     lowerCAmelCase_	:				Optional[int]=True     ,     lowerCAmelCase_	:				Optional[Any]=True     ,     lowerCAmelCase_	:				Union[str, Any]="gelu"     ,     lowerCAmelCase_	:				Optional[int]=2_5_6_0     ,     lowerCAmelCase_	:				List[str]=0.1     ,     lowerCAmelCase_	:				Any=0.0     ,     lowerCAmelCase_	:				Union[str, Any]=0.0     ,     lowerCAmelCase_	:				Any=0.02     ,     lowerCAmelCase_	:				List[Any]=1     ,     lowerCAmelCase_	:				List[str]=False     ,     lowerCAmelCase_	:				str=0     ,     lowerCAmelCase_	:				str=1     ,     lowerCAmelCase_	:				List[Any]=2     ,     lowerCAmelCase_	:				List[str]=3     ,     lowerCAmelCase_	:				int=2     ,     **lowerCAmelCase_	:				Tuple     ,     ):

      """simple docstring"""
      _A:    Any						     =			vocab_size
      _A:    Optional[int]						     =			max_position_embeddings
      _A:    int						     =			d_model
      _A:    Optional[int]						     =			encoder_ffn_dim
      _A:    Optional[int]						     =			encoder_layers
      _A:    int						     =			encoder_attention_heads
      _A:    Optional[Any]						     =			decoder_ffn_dim
      _A:    Dict						     =			decoder_layers
      _A:    int						     =			decoder_attention_heads
      _A:    List[str]						     =			dropout
      _A:    Union[str, Any]						     =			attention_dropout
      _A:    Tuple						     =			activation_dropout
      _A:    Dict						     =			activation_function
      _A:    Optional[Any]						     =			init_std
      _A:    Optional[int]						     =			encoder_layerdrop
      _A:    Any						     =			decoder_layerdrop
      _A:    List[str]						     =			use_cache
      _A:    Dict						     =			encoder_layers
      _A:    int						     =			scale_embedding  # scale factor will be sqrt(d_model) if True

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






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''


   @property
   def 					__magic_name__							(   self	:				Any	):

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

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


   def 					__magic_name__							(   self	:				Tuple     ,     lowerCAmelCase_	:				PreTrainedTokenizer     ,     lowerCAmelCase_	:				int = -1     ,     lowerCAmelCase_	:				int = -1     ,     lowerCAmelCase_	:				bool = False     ,     lowerCAmelCase_	:				Optional[TensorType] = None     ,     ):

      """simple docstring"""
      _A:    Optional[Any]						     =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_	)
      # Generate decoder inputs
      _A:    int						     =			seq_length if not self.use_past else 1
      _A:    Any						     =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_	)
      _A:    Optional[int]						     =			{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
      _A:    Union[str, Any]						     =			dict(**lowerCAmelCase_     ,     **lowerCAmelCase_	)

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

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


   def 					__magic_name__							(   self	:				Union[str, Any]     ,     lowerCAmelCase_	:				PreTrainedTokenizer     ,     lowerCAmelCase_	:				int = -1     ,     lowerCAmelCase_	:				int = -1     ,     lowerCAmelCase_	:				bool = False     ,     lowerCAmelCase_	:				Optional[TensorType] = None     ,     ):

      """simple docstring"""
      _A:    Tuple						     =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_	)

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


   def 					__magic_name__							(   self	:				Any     ,     lowerCAmelCase_	:				PreTrainedTokenizer     ,     lowerCAmelCase_	:				int = -1     ,     lowerCAmelCase_	:				int = -1     ,     lowerCAmelCase_	:				bool = False     ,     lowerCAmelCase_	:				Optional[TensorType] = None     ,     ):

      """simple docstring"""
      # Copied from OnnxConfig.generate_dummy_inputs
      # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
      # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
      _A:    Optional[int]						     =			compute_effective_axis_dimension(
          lowerCAmelCase_     ,     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
      _A:    int						     =			tokenizer.num_special_tokens_to_add(lowerCAmelCase_	)
      _A:    Any						     =			compute_effective_axis_dimension(
          lowerCAmelCase_     ,     fixed_dimension=OnnxConfig.default_fixed_sequence     ,     num_token_to_add=lowerCAmelCase_	)

      # Generate dummy inputs according to compute batch and sequence
      _A:    Tuple						     =			[''' '''.join([tokenizer.unk_token]	) * seq_length] * batch_size
      _A:    Any						     =			dict(tokenizer(lowerCAmelCase_     ,     return_tensors=lowerCAmelCase_	)	)
      return common_inputs


   def 					__magic_name__							(   self	:				Dict     ,     lowerCAmelCase_	:				PreTrainedTokenizer     ,     lowerCAmelCase_	:				int = -1     ,     lowerCAmelCase_	:				int = -1     ,     lowerCAmelCase_	:				bool = False     ,     lowerCAmelCase_	:				Optional[TensorType] = None     ,     ):

      """simple docstring"""
      if self.task in ["default", "seq2seq-lm"]:
         _A:    Union[str, Any]						     =			self._generate_dummy_inputs_for_default_and_seqaseq_lm(
             lowerCAmelCase_     ,     batch_size=lowerCAmelCase_     ,     seq_length=lowerCAmelCase_     ,     is_pair=lowerCAmelCase_     ,     framework=lowerCAmelCase_	)

      elif self.task == "causal-lm":
         _A:    Optional[Any]						     =			self._generate_dummy_inputs_for_causal_lm(
             lowerCAmelCase_     ,     batch_size=lowerCAmelCase_     ,     seq_length=lowerCAmelCase_     ,     is_pair=lowerCAmelCase_     ,     framework=lowerCAmelCase_	)
      else:
         _A:    Tuple						     =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             lowerCAmelCase_     ,     batch_size=lowerCAmelCase_     ,     seq_length=lowerCAmelCase_     ,     is_pair=lowerCAmelCase_     ,     framework=lowerCAmelCase_	)

      return common_inputs


   def 					__magic_name__							(   self	:				int     ,     lowerCAmelCase_	:				Tuple     ,     lowerCAmelCase_	:				Any     ,     lowerCAmelCase_	:				str     ,     lowerCAmelCase_	:				List[str]	):

      """simple docstring"""
      if self.task in ["default", "seq2seq-lm"]:
         _A:    int						     =			super()._flatten_past_key_values_(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_	)
      else:
         _A:    Optional[int]						     =			super(lowerCAmelCase_     ,     self	)._flatten_past_key_values_(
             lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				Dict     ,     lowerCAmelCase_	:				Mapping[str, Mapping[int, str]]     ,     lowerCAmelCase_	:				str	):

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

      _A:    Tuple						     =			'''past_key_values''' if direction == '''inputs''' else '''present'''
      _A     ,    _A:    int						     =			self.num_layers

      _A:    int						     =			'''past_encoder_sequence'''
      _A:    int						     =			'''past_decoder_sequence''' if direction == '''inputs''' else '''past_decoder_sequence + sequence'''

      for i in range(lowerCAmelCase_	):
         _A:    Optional[int]						     =			{0: '''batch''', 2: decoder_sequence}
         _A:    Dict						     =			{0: '''batch''', 2: decoder_sequence}
         _A:    int						     =			{0: '''batch''', 2: encoder_sequence}
         _A:    Optional[int]						     =			{0: '''batch''', 2: encoder_sequence}



