


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      (    __UpperCamelCase			):
     a_     :  Tuple				      =     TimesformerConfig()

     if "large" in model_name:
          a_     :  Optional[int]				      =     9_6

     if "hr" in model_name:
          a_     :  Tuple				      =     1_6
          a_     :  List[str]				      =     4_4_8

     a_     :  Tuple				      =     """huggingface/label-files"""
     if "k400" in model_name:
          a_     :  str				      =     4_0_0
          a_     :  List[Any]				      =     """kinetics400-id2label.json"""
     elif "k600" in model_name:
          a_     :  Any				      =     6_0_0
          a_     :  Any				      =     """kinetics600-id2label.json"""
     elif "ssv2" in model_name:
          a_     :  Union[str, Any]				      =     1_7_4
          a_     :  Optional[int]				      =     """something-something-v2-id2label.json"""
     else:
          raise ValueError("""Model name should either contain 'k400', 'k600' or 'ssv2'."""			)
     a_     :  str				      =     json.load(open(hf_hub_download(__UpperCamelCase	,	__UpperCamelCase	,	repo_type="""dataset"""			)	,	"""r"""			)			)
     a_     :  List[str]				      =     {int(__UpperCamelCase			): v for k, v in idalabel.items()}
     a_     :  Dict				      =     idalabel
     a_     :  Tuple				      =     {v: k for k, v in idalabel.items()}

     return config



def 			_a      (    __UpperCamelCase			):
     if "encoder." in name:
          a_     :  Tuple				      =     name.replace("""encoder."""	,	""""""			)
     if "cls_token" in name:
          a_     :  Tuple				      =     name.replace("""cls_token"""	,	"""timesformer.embeddings.cls_token"""			)
     if "pos_embed" in name:
          a_     :  Dict				      =     name.replace("""pos_embed"""	,	"""timesformer.embeddings.position_embeddings"""			)
     if "time_embed" in name:
          a_     :  Optional[Any]				      =     name.replace("""time_embed"""	,	"""timesformer.embeddings.time_embeddings"""			)
     if "patch_embed.proj" in name:
          a_     :  List[Any]				      =     name.replace("""patch_embed.proj"""	,	"""timesformer.embeddings.patch_embeddings.projection"""			)
     if "patch_embed.norm" in name:
          a_     :  str				      =     name.replace("""patch_embed.norm"""	,	"""timesformer.embeddings.norm"""			)
     if "blocks" in name:
          a_     :  List[Any]				      =     name.replace("""blocks"""	,	"""timesformer.encoder.layer"""			)
     if "attn.proj" in name:
          a_     :  Tuple				      =     name.replace("""attn.proj"""	,	"""attention.output.dense"""			)
     if "attn" in name and "bias" not in name and "temporal" not in name:
          a_     :  Optional[int]				      =     name.replace("""attn"""	,	"""attention.self"""			)
     if "attn" in name and "temporal" not in name:
          a_     :  List[str]				      =     name.replace("""attn"""	,	"""attention.attention"""			)
     if "temporal_norm1" in name:
          a_     :  List[Any]				      =     name.replace("""temporal_norm1"""	,	"""temporal_layernorm"""			)
     if "temporal_attn.proj" in name:
          a_     :  Optional[int]				      =     name.replace("""temporal_attn"""	,	"""temporal_attention.output.dense"""			)
     if "temporal_fc" in name:
          a_     :  Dict				      =     name.replace("""temporal_fc"""	,	"""temporal_dense"""			)
     if "norm1" in name and "temporal" not in name:
          a_     :  Optional[int]				      =     name.replace("""norm1"""	,	"""layernorm_before"""			)
     if "norm2" in name:
          a_     :  Optional[Any]				      =     name.replace("""norm2"""	,	"""layernorm_after"""			)
     if "mlp.fc1" in name:
          a_     :  Any				      =     name.replace("""mlp.fc1"""	,	"""intermediate.dense"""			)
     if "mlp.fc2" in name:
          a_     :  List[Any]				      =     name.replace("""mlp.fc2"""	,	"""output.dense"""			)
     if "norm.weight" in name and "fc" not in name and "temporal" not in name:
          a_     :  Tuple				      =     name.replace("""norm.weight"""	,	"""timesformer.layernorm.weight"""			)
     if "norm.bias" in name and "fc" not in name and "temporal" not in name:
          a_     :  Tuple				      =     name.replace("""norm.bias"""	,	"""timesformer.layernorm.bias"""			)
     if "head" in name:
          a_     :  Tuple				      =     name.replace("""head"""	,	"""classifier"""			)

     return name



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase			):
     for key in orig_state_dict.copy().keys():
          a_     :  List[Any]				      =     orig_state_dict.pop(__UpperCamelCase			)

          if key.startswith("""model."""			):
               a_     :  Dict				      =     key.replace("""model."""	,	""""""			)

          if "qkv" in key:
               a_     :  Tuple				      =     key.split("""."""			)
               a_     :  int				      =     int(key_split[1]			)
               a_     :  int				      =     """timesformer.encoder.layer."""
               if "temporal" in key:
                    a_     :  Tuple				      =     """.temporal_attention.attention.qkv."""
               else:
                    a_     :  List[str]				      =     """.attention.attention.qkv."""
               if "weight" in key:
                    a_     :  Optional[Any]				      =     val
               else:
                    a_     :  Optional[int]				      =     val
          else:
               a_     :  List[Any]				      =     val

     return orig_state_dict



def 			_a      (    ):
     a_     :  int				      =     hf_hub_download(
         repo_id="""hf-internal-testing/spaghetti-video"""	,	filename="""eating_spaghetti.npy"""	,	repo_type="""dataset"""			)
     a_     :  List[str]				      =     np.load(__UpperCamelCase			)
     return list(__UpperCamelCase			)



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     a_     :  List[Any]				      =     get_timesformer_config(__UpperCamelCase			)

     a_     :  Tuple				      =     TimesformerForVideoClassification(__UpperCamelCase			)

     # download original checkpoint, hosted on Google Drive
     a_     :  Dict				      =     """pytorch_model.bin"""
     gdown.cached_download(__UpperCamelCase	,	__UpperCamelCase	,	quiet=__UpperCamelCase			)
     a_     :  Dict				      =     torch.load(__UpperCamelCase	,	map_location="""cpu"""			)
     if "model" in files:
          a_     :  List[str]				      =     files["""model"""]
     elif "module" in files:
          a_     :  Optional[Any]				      =     files["""module"""]
     else:
          a_     :  str				      =     files["""model_state"""]
     a_     :  Tuple				      =     convert_state_dict(__UpperCamelCase	,	__UpperCamelCase			)

     model.load_state_dict(__UpperCamelCase			)
     model.eval()

     # verify model on basic input
     a_     :  Optional[int]				      =     VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]	,	image_std=[0.5, 0.5, 0.5]			)
     a_     :  List[Any]				      =     prepare_video()
     a_     :  Union[str, Any]				      =     image_processor(video[:8]	,	return_tensors="""pt"""			)

     a_     :  Optional[Any]				      =     model(**__UpperCamelCase			)
     a_     :  Optional[Any]				      =     outputs.logits

     a_     :  Optional[int]				      =     [
         # 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":
          a_     :  List[Any]				      =     torch.Size([1, 4_0_0]			)
          a_     :  Optional[int]				      =     torch.tensor([-0.30_16, -0.77_13, -0.42_05]			)
     elif model_name == "timesformer-base-finetuned-k600":
          a_     :  Optional[Any]				      =     torch.Size([1, 6_0_0]			)
          a_     :  Dict				      =     torch.tensor([-0.72_67, -0.74_66, 3.24_04]			)
     elif model_name == "timesformer-base-finetuned-ssv2":
          a_     :  Optional[int]				      =     torch.Size([1, 1_7_4]			)
          a_     :  Optional[int]				      =     torch.tensor([-0.90_59, 0.64_33, -3.14_57]			)
     elif model_name == "timesformer-large-finetuned-k400":
          a_     :  Optional[int]				      =     torch.Size([1, 4_0_0]			)
          a_     :  Union[str, Any]				      =     torch.tensor([0, 0, 0]			)
     elif model_name == "timesformer-large-finetuned-k600":
          a_     :  Any				      =     torch.Size([1, 6_0_0]			)
          a_     :  Optional[Any]				      =     torch.tensor([0, 0, 0]			)
     elif model_name == "timesformer-large-finetuned-ssv2":
          a_     :  int				      =     torch.Size([1, 1_7_4]			)
          a_     :  Tuple				      =     torch.tensor([0, 0, 0]			)
     elif model_name == "timesformer-hr-finetuned-k400":
          a_     :  List[str]				      =     torch.Size([1, 4_0_0]			)
          a_     :  List[Any]				      =     torch.tensor([-0.96_17, -3.73_11, -3.77_08]			)
     elif model_name == "timesformer-hr-finetuned-k600":
          a_     :  Tuple				      =     torch.Size([1, 6_0_0]			)
          a_     :  Union[str, Any]				      =     torch.tensor([2.52_73, 0.71_27, 1.88_48]			)
     elif model_name == "timesformer-hr-finetuned-ssv2":
          a_     :  List[str]				      =     torch.Size([1, 1_7_4]			)
          a_     :  Tuple				      =     torch.tensor([-3.67_56, -0.75_13, 0.71_80]			)
     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]	,	__UpperCamelCase	,	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(__UpperCamelCase			)
          model.save_pretrained(__UpperCamelCase			)

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


if __name__ == "__main__":
       __lowerCamelCase   	=  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.'''
       )

       __lowerCamelCase   	=  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 ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


__SCREAMING_SNAKE_CASE     =	logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     =	{
    """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json""",
    # See all MBART models at https://huggingface.co/models?filter=mbart
}

class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''
						a__ =	"mbart"
						a__ =	["past_key_values"]
						a__ =	{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







						def __init__(						self   :      int						,						__lowerCamelCase   :      Optional[int]=5_02_65						,						__lowerCamelCase   :      str=10_24						,						__lowerCamelCase   :      str=12						,						__lowerCamelCase   :      Dict=40_96						,						__lowerCamelCase   :      str=16						,						__lowerCamelCase   :      int=12						,						__lowerCamelCase   :      Optional[Any]=40_96						,						__lowerCamelCase   :      Any=16						,						__lowerCamelCase   :      Optional[Any]=0.0						,						__lowerCamelCase   :      List[Any]=0.0						,						__lowerCamelCase   :      Dict=True						,						__lowerCamelCase   :      List[str]=True						,						__lowerCamelCase   :      Optional[int]="gelu"						,						__lowerCamelCase   :      List[Any]=10_24						,						__lowerCamelCase   :      Dict=0.1						,						__lowerCamelCase   :      Tuple=0.0						,						__lowerCamelCase   :      List[str]=0.0						,						__lowerCamelCase   :      Union[str, Any]=0.02						,						__lowerCamelCase   :      List[Any]=0.0						,						__lowerCamelCase   :      List[Any]=False						,						__lowerCamelCase   :      Optional[Any]=1						,						__lowerCamelCase   :      str=0						,						__lowerCamelCase   :      Optional[Any]=2						,						__lowerCamelCase   :      Dict=2						,						**__lowerCamelCase   :      int						,						)   ->					Union[str, Any]:
									A				:       Union[str, Any]														=    vocab_size
									A				:       List[str]														=    max_position_embeddings
									A				:       Optional[int]														=    d_model
									A				:       Any														=    encoder_ffn_dim
									A				:       Union[str, Any]														=    encoder_layers
									A				:       int														=    encoder_attention_heads
									A				:       Union[str, Any]														=    decoder_ffn_dim
									A				:       Any														=    decoder_layers
									A				:       Tuple														=    decoder_attention_heads
									A				:       Any														=    dropout
									A				:       Union[str, Any]														=    attention_dropout
									A				:       str														=    activation_dropout
									A				:       Dict														=    activation_function
									A				:       Optional[int]														=    init_std
									A				:       Optional[Any]														=    encoder_layerdrop
									A				:       Union[str, Any]														=    decoder_layerdrop
									A				:       Tuple														=    classifier_dropout
									A				:       Union[str, Any]														=    use_cache
									A				:       List[str]														=    encoder_layers
									A				:       List[str]														=    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						,						forced_eos_token_id=__lowerCamelCase						,						**__lowerCamelCase						,						)







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						@property
						def       SCREAMING_SNAKE_CASE__		(						self   :      Any			)   ->					Mapping[str, Mapping[int, str]]:
									if self.task in ["default", "seq2seq-lm"]:
												A				:       List[str]														=    OrderedDict(
												    [
												        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
												        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
												    ]			)

												if self.use_past:
															A				:       str														=    {0: "batch"}
															A				:       Optional[int]														=    {0: "batch", 1: "past_decoder_sequence + sequence"}
												else:
															A				:       Optional[int]														=    {0: "batch", 1: "decoder_sequence"}
															A				:       Union[str, Any]														=    {0: "batch", 1: "decoder_sequence"}

												if self.use_past:
															self.fill_with_past_key_values_(__lowerCamelCase						,						direction="inputs"			)
									elif self.task == "causal-lm":
												# TODO: figure this case out.
												A				:       Dict														=    OrderedDict(
												    [
												        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
												        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
												    ]			)
												if self.use_past:
															A							,			A				:       Union[str, Any]														=    self.num_layers
															for i in range(__lowerCamelCase			):
																		A				:       List[str]														=    {0: "batch", 2: "past_sequence + sequence"}
																		A				:       int														=    {0: "batch", 2: "past_sequence + sequence"}
									else:
												A				:       Optional[int]														=    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
						def       SCREAMING_SNAKE_CASE__		(						self   :      int			)   ->					Mapping[str, Mapping[int, str]]:
									if self.task in ["default", "seq2seq-lm"]:
												A				:       str														=    super().outputs
									else:
												A				:       int														=    super(__lowerCamelCase						,						self			).outputs
												if self.use_past:
															A							,			A				:       Optional[int]														=    self.num_layers
															for i in range(__lowerCamelCase			):
																		A				:       List[Any]														=    {0: "batch", 2: "past_sequence + sequence"}
																		A				:       Tuple														=    {0: "batch", 2: "past_sequence + sequence"}
									return common_outputs







						def       SCREAMING_SNAKE_CASE__		(						self   :      int						,						__lowerCamelCase   :      PreTrainedTokenizer						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      bool = False						,						__lowerCamelCase   :      Optional[TensorType] = None						,						)   ->					Mapping[str, Any]:
									A				:       Tuple														=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    __lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase			)

									# Generate decoder inputs
									A				:       Optional[int]														=    seq_length if not self.use_past else 1
									A				:       str														=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    __lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase			)
									A				:       Tuple														=    {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				:       List[Any]														=    common_inputs["input_ids"].shape
												A				:       Any														=    common_inputs["decoder_input_ids"].shape[1]
												A							,			A				:       Union[str, Any]														=    self.num_attention_heads
												A				:       Tuple														=    (
												    batch,
												    num_encoder_attention_heads,
												    encoder_seq_length,
												    self._config.hidden_size // num_encoder_attention_heads,
												)
												A				:       Dict														=    decoder_seq_length + 3
												A				:       Union[str, Any]														=    (
												    batch,
												    num_decoder_attention_heads,
												    decoder_past_length,
												    self._config.hidden_size // num_decoder_attention_heads,
												)

												A				:       int														=    torch.cat(
												    [common_inputs["decoder_attention_mask"], torch.ones(__lowerCamelCase						,						__lowerCamelCase			)]						,						dim=1			)

												A				:       Optional[int]														=    []
												# If the number of encoder and decoder layers are present in the model configuration, both are considered
												A							,			A				:       Dict														=    self.num_layers
												A				:       Dict														=    min(__lowerCamelCase						,						__lowerCamelCase			)
												A				:       Optional[Any]														=    max(__lowerCamelCase						,						__lowerCamelCase			) - min_num_layers
												A				:       Tuple														=    "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

												for _ in range(__lowerCamelCase			):
															common_inputs["past_key_values"].append(
															    (
															        torch.zeros(__lowerCamelCase			),
															        torch.zeros(__lowerCamelCase			),
															        torch.zeros(__lowerCamelCase			),
															        torch.zeros(__lowerCamelCase			),
															    )			)
												# TODO: test this.
												A				:       List[str]														=    encoder_shape if remaining_side_name == "encoder" else decoder_shape
												for _ in range(__lowerCamelCase						,						__lowerCamelCase			):
															common_inputs["past_key_values"].append((torch.zeros(__lowerCamelCase			), torch.zeros(__lowerCamelCase			))			)
									return common_inputs







						def       SCREAMING_SNAKE_CASE__		(						self   :      str						,						__lowerCamelCase   :      PreTrainedTokenizer						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      bool = False						,						__lowerCamelCase   :      Optional[TensorType] = None						,						)   ->					Mapping[str, Any]:
									A				:       List[Any]														=    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				:       List[Any]														=    common_inputs["input_ids"].shape
												# Not using the same length for past_key_values
												A				:       Any														=    seqlen + 2
												A							,			A				:       List[Any]														=    self.num_layers
												A							,			A				:       int														=    self.num_attention_heads
												A				:       int														=    (
												    batch,
												    num_encoder_attention_heads,
												    past_key_values_length,
												    self._config.hidden_size // num_encoder_attention_heads,
												)

												A				:       Tuple														=    common_inputs["attention_mask"].dtype
												A				:       Dict														=    torch.cat(
												    [common_inputs["attention_mask"], torch.ones(__lowerCamelCase						,						__lowerCamelCase						,						dtype=__lowerCamelCase			)]						,						dim=1			)
												A				:       Optional[Any]														=    [
												    (torch.zeros(__lowerCamelCase			), torch.zeros(__lowerCamelCase			)) for _ in range(__lowerCamelCase			)
												]
									return common_inputs







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]						,						__lowerCamelCase   :      PreTrainedTokenizer						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      bool = False						,						__lowerCamelCase   :      Optional[TensorType] = None						,						)   ->					Mapping[str, Any]:
									# 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				:       Any														=    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				:       Any														=    tokenizer.num_special_tokens_to_add(__lowerCamelCase			)
									A				:       Tuple														=    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				:       Dict														=    [" ".join([tokenizer.unk_token]			) * seq_length] * batch_size
									A				:       Tuple														=    dict(tokenizer(__lowerCamelCase						,						return_tensors=__lowerCamelCase			)			)
									return common_inputs







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]						,						__lowerCamelCase   :      PreTrainedTokenizer						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      bool = False						,						__lowerCamelCase   :      Optional[TensorType] = None						,						)   ->					Mapping[str, Any]:
									if self.task in ["default", "seq2seq-lm"]:
												A				:       List[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				:       Tuple														=    self._generate_dummy_inputs_for_causal_lm(
												    __lowerCamelCase						,						batch_size=__lowerCamelCase						,						seq_length=__lowerCamelCase						,						is_pair=__lowerCamelCase						,						framework=__lowerCamelCase			)
									else:
												A				:       List[Any]														=    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       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]						,						__lowerCamelCase   :      str						,						__lowerCamelCase   :      Union[str, Any]						,						__lowerCamelCase   :      Any						,						__lowerCamelCase   :      List[Any]			)   ->					Dict:
									if self.task in ["default", "seq2seq-lm"]:
												A				:       Any														=    super()._flatten_past_key_values_(__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase			)
									else:
												A				:       Optional[Any]														=    super(__lowerCamelCase						,						self			)._flatten_past_key_values_(
												    __lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase			)