

'''simple docstring'''



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase					:			Any       =	logging.get_logger(__name__)

UpperCAmelCase					:			Union[str, Any]       =	torch.device('cpu')







def   a__			(    ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = """http://images.cocodataset.org/val2017/000000039769.jpg"""
     __SCREAMING_SNAKE_CASE      = Image.open(requests.get(a__ ,       stream=a__		).raw		)
     return im







def   a__			(    a__		):




     """simple docstring"""


     if swiftformer_name == "swiftformer_xs":
          return torch.tensor([-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0, 8.8_6_8_5E-0_1, 2.4_3_6_0E-0_1]		)

     elif swiftformer_name == "swiftformer_s":
          return torch.tensor([3.9_6_3_6E-0_1, 2.3_4_7_8E-0_1, -1.6_9_6_3E0_0, -1.7_3_8_1E0_0, -8.6_3_3_7E-0_1]		)

     elif swiftformer_name == "swiftformer_l1":
          return torch.tensor([-4.2_7_6_8E-0_1, -4.7_4_2_9E-0_1, -1.0_8_9_7E0_0, -1.0_2_4_8E0_0, 3.5_5_2_3E-0_2]		)

     elif swiftformer_name == "swiftformer_l3":
          return torch.tensor([-2.5_3_3_0E-0_1, 2.4_2_1_1E-0_1, -6.0_1_8_5E-0_1, -8.2_7_8_9E-0_1, -6.0_4_4_6E-0_2]		)







def   a__			(    a__ ,       a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = dct.pop(a__		)
     __SCREAMING_SNAKE_CASE      = val







def   a__			(    a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = []
     for k in state_dict.keys():
          __SCREAMING_SNAKE_CASE      = k
          if ".pwconv" in k:
               __SCREAMING_SNAKE_CASE      = k_new.replace(""".pwconv""" ,       """.point_wise_conv"""		)
          if ".dwconv" in k:
               __SCREAMING_SNAKE_CASE      = k_new.replace(""".dwconv""" ,       """.depth_wise_conv"""		)
          if ".Proj." in k:
               __SCREAMING_SNAKE_CASE      = k_new.replace(""".Proj.""" ,       """.proj."""		)
          if "patch_embed" in k_new:
               __SCREAMING_SNAKE_CASE      = k_new.replace("""patch_embed""" ,       """swiftformer.patch_embed.patch_embedding"""		)
          if "network" in k_new:
               __SCREAMING_SNAKE_CASE      = k_new.split("""."""		)
               if ls[2].isdigit():
                    __SCREAMING_SNAKE_CASE      = """swiftformer.encoder.network.""" + ls[1] + """.blocks.""" + ls[2] + """.""" + """.""".join(ls[3:]		)
               else:
                    __SCREAMING_SNAKE_CASE      = k_new.replace("""network""" ,       """swiftformer.encoder.network"""		)
          rename_keys.append((k, k_new)		)
     return rename_keys







@torch.no_grad()
def   a__			(    a__ ,       a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = SwiftFormerConfig()

     # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
     __SCREAMING_SNAKE_CASE      = 10_00
     __SCREAMING_SNAKE_CASE      = """huggingface/label-files"""
     __SCREAMING_SNAKE_CASE      = """imagenet-1k-id2label.json"""
     __SCREAMING_SNAKE_CASE      = json.load(open(hf_hub_download(a__ ,       a__ ,       repo_type="""dataset"""		) ,       """r"""		)		)
     __SCREAMING_SNAKE_CASE      = {int(a__		): v for k, v in idalabel.items()}
     __SCREAMING_SNAKE_CASE      = idalabel
     __SCREAMING_SNAKE_CASE      = {v: k for k, v in idalabel.items()}

     # size of the architecture
     if swiftformer_name == "swiftformer_xs":
          __SCREAMING_SNAKE_CASE      = [3, 3, 6, 4]
          __SCREAMING_SNAKE_CASE      = [48, 56, 1_12, 2_20]

     elif swiftformer_name == "swiftformer_s":
          __SCREAMING_SNAKE_CASE      = [3, 3, 9, 6]
          __SCREAMING_SNAKE_CASE      = [48, 64, 1_68, 2_24]

     elif swiftformer_name == "swiftformer_l1":
          __SCREAMING_SNAKE_CASE      = [4, 3, 10, 5]
          __SCREAMING_SNAKE_CASE      = [48, 96, 1_92, 3_84]

     elif swiftformer_name == "swiftformer_l3":
          __SCREAMING_SNAKE_CASE      = [4, 4, 12, 6]
          __SCREAMING_SNAKE_CASE      = [64, 1_28, 3_20, 5_12]

     # load state_dict of original model, remove and rename some keys
     if original_ckpt:
          if original_ckpt.startswith("""https"""		):
               __SCREAMING_SNAKE_CASE      = torch.hub.load_state_dict_from_url(a__ ,       map_location="""cpu""" ,       check_hash=a__		)
          else:
               __SCREAMING_SNAKE_CASE      = torch.load(a__ ,       map_location="""cpu"""		)
     __SCREAMING_SNAKE_CASE      = checkpoint

     __SCREAMING_SNAKE_CASE      = create_rename_keys(a__		)
     for rename_key_src, rename_key_dest in rename_keys:
          rename_key(a__ ,       a__ ,       a__		)

     # load HuggingFace model
     __SCREAMING_SNAKE_CASE      = SwiftFormerForImageClassification(a__		).eval()
     hf_model.load_state_dict(a__		)

     # prepare test inputs
     __SCREAMING_SNAKE_CASE      = prepare_img()
     __SCREAMING_SNAKE_CASE      = ViTImageProcessor.from_pretrained("""preprocessor_config"""		)
     __SCREAMING_SNAKE_CASE      = processor(images=a__ ,       return_tensors="""pt"""		)

     # compare outputs from both models
     __SCREAMING_SNAKE_CASE      = get_expected_output(a__		)
     __SCREAMING_SNAKE_CASE      = hf_model(inputs["""pixel_values"""]		).logits

     assert hf_logits.shape == torch.Size([1, 10_00]		)
     assert torch.allclose(hf_logits[0, 0:5] ,       a__ ,       atol=1E-3		)

     Path(a__		).mkdir(exist_ok=a__		)
     print(F'Saving model {swiftformer_name} to {pytorch_dump_folder_path}'		)
     hf_model.save_pretrained(a__		)


if __name__ == "__main__":
  UpperCAmelCase					:			int       =	argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--swiftformer_name',
      default='swiftformer_xs',
      choices=['swiftformer_xs', 'swiftformer_s', 'swiftformer_l1', 'swiftformer_l3'],
      type=str,
      help='Name of the SwiftFormer model you\'d like to convert.',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path',
      default='./converted_outputs/',
      type=str,
      help='Path to the output PyTorch model directory.',
  )
  parser.add_argument('--original_ckpt', default=None, type=str, help='Path to the original model checkpoint.')

  UpperCAmelCase					:			Any       =	parser.parse_args()
  convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)









'''simple docstring'''



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					:			str       =	logging.get_logger(__name__)

UpperCAmelCase					:			Optional[Any]       =	{
    'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json',
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = "blenderbot-small"
 lowerCAmelCase__       = ["past_key_values"]
 lowerCAmelCase__       = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
 def __init__(						self   :			List[Any]   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=50_265   ,				__SCREAMING_SNAKE_CASE   :			Tuple=512   ,				__SCREAMING_SNAKE_CASE   :			Tuple=8   ,				__SCREAMING_SNAKE_CASE   :			str=2_048   ,				__SCREAMING_SNAKE_CASE   :			Tuple=16   ,				__SCREAMING_SNAKE_CASE   :			Dict=8   ,				__SCREAMING_SNAKE_CASE   :			Dict=2_048   ,				__SCREAMING_SNAKE_CASE   :			str=16   ,				__SCREAMING_SNAKE_CASE   :			Tuple=0.0   ,				__SCREAMING_SNAKE_CASE   :			List[str]=0.0   ,				__SCREAMING_SNAKE_CASE   :			Any=True   ,				__SCREAMING_SNAKE_CASE   :			Any=True   ,				__SCREAMING_SNAKE_CASE   :			List[Any]="gelu"   ,				__SCREAMING_SNAKE_CASE   :			Tuple=512   ,				__SCREAMING_SNAKE_CASE   :			List[str]=0.1   ,				__SCREAMING_SNAKE_CASE   :			List[str]=0.0   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]=0.0   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=0.02   ,				__SCREAMING_SNAKE_CASE   :			Any=1   ,				__SCREAMING_SNAKE_CASE   :			Any=False   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]=0   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=1   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]=2   ,				__SCREAMING_SNAKE_CASE   :			Dict=2   ,				**__SCREAMING_SNAKE_CASE   :			Any   ,				)    ->					Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = vocab_size
      __SCREAMING_SNAKE_CASE      = max_position_embeddings
      __SCREAMING_SNAKE_CASE      = d_model
      __SCREAMING_SNAKE_CASE      = encoder_ffn_dim
      __SCREAMING_SNAKE_CASE      = encoder_layers
      __SCREAMING_SNAKE_CASE      = encoder_attention_heads
      __SCREAMING_SNAKE_CASE      = decoder_ffn_dim
      __SCREAMING_SNAKE_CASE      = decoder_layers
      __SCREAMING_SNAKE_CASE      = decoder_attention_heads
      __SCREAMING_SNAKE_CASE      = dropout
      __SCREAMING_SNAKE_CASE      = attention_dropout
      __SCREAMING_SNAKE_CASE      = activation_dropout
      __SCREAMING_SNAKE_CASE      = activation_function
      __SCREAMING_SNAKE_CASE      = init_std
      __SCREAMING_SNAKE_CASE      = encoder_layerdrop
      __SCREAMING_SNAKE_CASE      = decoder_layerdrop
      __SCREAMING_SNAKE_CASE      = use_cache
      __SCREAMING_SNAKE_CASE      = encoder_layers
      __SCREAMING_SNAKE_CASE      = scale_embedding  # scale factor will be sqrt(d_model) if True

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







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 @property
 def  UpperCAmelCase__						(						self   :			int     )    ->					Mapping[str, Mapping[int, str]]:


      """simple docstring"""




      if self.task in ["default", "seq2seq-lm"]:
           __SCREAMING_SNAKE_CASE      = OrderedDict(
               [
                   ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                   ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
               ]     )

           if self.use_past:
                __SCREAMING_SNAKE_CASE      = {0: """batch"""}
                __SCREAMING_SNAKE_CASE      = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
           else:
                __SCREAMING_SNAKE_CASE      = {0: """batch""", 1: """decoder_sequence"""}
                __SCREAMING_SNAKE_CASE      = {0: """batch""", 1: """decoder_sequence"""}

           if self.use_past:
                self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE   ,				direction="""inputs"""     )
      elif self.task == "causal-lm":
           # TODO: figure this case out.
           __SCREAMING_SNAKE_CASE      = OrderedDict(
               [
                   ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                   ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
               ]     )
           if self.use_past:
                __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = self.num_layers
                for i in range(__SCREAMING_SNAKE_CASE     ):
                     __SCREAMING_SNAKE_CASE      = {0: """batch""", 2: """past_sequence + sequence"""}
                     __SCREAMING_SNAKE_CASE      = {0: """batch""", 2: """past_sequence + sequence"""}
      else:
           __SCREAMING_SNAKE_CASE      = 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  UpperCAmelCase__						(						self   :			Dict     )    ->					Mapping[str, Mapping[int, str]]:


      """simple docstring"""




      if self.task in ["default", "seq2seq-lm"]:
           __SCREAMING_SNAKE_CASE      = super().outputs
      else:
           __SCREAMING_SNAKE_CASE      = super(__SCREAMING_SNAKE_CASE   ,				self     ).outputs
           if self.use_past:
                __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = self.num_layers
                for i in range(__SCREAMING_SNAKE_CASE     ):
                     __SCREAMING_SNAKE_CASE      = {0: """batch""", 2: """past_sequence + sequence"""}
                     __SCREAMING_SNAKE_CASE      = {0: """batch""", 2: """past_sequence + sequence"""}
      return common_outputs
 def  UpperCAmelCase__						(						self   :			int   ,				__SCREAMING_SNAKE_CASE   :			PreTrainedTokenizer   ,				__SCREAMING_SNAKE_CASE   :			int = -1   ,				__SCREAMING_SNAKE_CASE   :			int = -1   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			Optional[TensorType] = None   ,				)    ->					Mapping[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          __SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )

      # Generate decoder inputs
      __SCREAMING_SNAKE_CASE      = seq_length if not self.use_past else 1
      __SCREAMING_SNAKE_CASE      = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          __SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = {f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
      __SCREAMING_SNAKE_CASE      = dict(**__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )

      if self.use_past:
           if not is_torch_available():
                raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""     )
           else:
                import torch
           __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = common_inputs["""input_ids"""].shape
           __SCREAMING_SNAKE_CASE      = common_inputs["""decoder_input_ids"""].shape[1]
           __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = self.num_attention_heads
           __SCREAMING_SNAKE_CASE      = (
               batch,
               num_encoder_attention_heads,
               encoder_seq_length,
               self._config.hidden_size // num_encoder_attention_heads,
           )
           __SCREAMING_SNAKE_CASE      = decoder_seq_length + 3
           __SCREAMING_SNAKE_CASE      = (
               batch,
               num_decoder_attention_heads,
               decoder_past_length,
               self._config.hidden_size // num_decoder_attention_heads,
           )

           __SCREAMING_SNAKE_CASE      = torch.cat(
               [common_inputs["""decoder_attention_mask"""], torch.ones(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )]   ,				dim=1     )

           __SCREAMING_SNAKE_CASE      = []
           # If the number of encoder and decoder layers are present in the model configuration, both are considered
           __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = self.num_layers
           __SCREAMING_SNAKE_CASE      = min(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
           __SCREAMING_SNAKE_CASE      = max(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     ) - min_num_layers
           __SCREAMING_SNAKE_CASE      = """encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

           for _ in range(__SCREAMING_SNAKE_CASE     ):
                common_inputs["past_key_values"].append(
                    (
                        torch.zeros(__SCREAMING_SNAKE_CASE     ),
                        torch.zeros(__SCREAMING_SNAKE_CASE     ),
                        torch.zeros(__SCREAMING_SNAKE_CASE     ),
                        torch.zeros(__SCREAMING_SNAKE_CASE     ),
                    )     )
           # TODO: test this.
           __SCREAMING_SNAKE_CASE      = encoder_shape if remaining_side_name == """encoder""" else decoder_shape
           for _ in range(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     ):
                common_inputs["past_key_values"].append((torch.zeros(__SCREAMING_SNAKE_CASE     ), torch.zeros(__SCREAMING_SNAKE_CASE     ))     )
      return common_inputs
 def  UpperCAmelCase__						(						self   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			PreTrainedTokenizer   ,				__SCREAMING_SNAKE_CASE   :			int = -1   ,				__SCREAMING_SNAKE_CASE   :			int = -1   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			Optional[TensorType] = None   ,				)    ->					Mapping[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          __SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )

      if self.use_past:
           if not is_torch_available():
                raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""     )
           else:
                import torch
           __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = common_inputs["""input_ids"""].shape
           # Not using the same length for past_key_values
           __SCREAMING_SNAKE_CASE      = seqlen + 2
           __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = self.num_layers
           __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = self.num_attention_heads
           __SCREAMING_SNAKE_CASE      = (
               batch,
               num_encoder_attention_heads,
               past_key_values_length,
               self._config.hidden_size // num_encoder_attention_heads,
           )

           __SCREAMING_SNAKE_CASE      = common_inputs["""attention_mask"""].dtype
           __SCREAMING_SNAKE_CASE      = torch.cat(
               [common_inputs["""attention_mask"""], torch.ones(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				dtype=__SCREAMING_SNAKE_CASE     )]   ,				dim=1     )
           __SCREAMING_SNAKE_CASE      = [
               (torch.zeros(__SCREAMING_SNAKE_CASE     ), torch.zeros(__SCREAMING_SNAKE_CASE     )) for _ in range(__SCREAMING_SNAKE_CASE     )
           ]
      return common_inputs
 def  UpperCAmelCase__						(						self   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			PreTrainedTokenizer   ,				__SCREAMING_SNAKE_CASE   :			int = -1   ,				__SCREAMING_SNAKE_CASE   :			int = -1   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			Optional[TensorType] = None   ,				)    ->					Mapping[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = compute_effective_axis_dimension(
          __SCREAMING_SNAKE_CASE   ,				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
      __SCREAMING_SNAKE_CASE      = tokenizer.num_special_tokens_to_add(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = compute_effective_axis_dimension(
          __SCREAMING_SNAKE_CASE   ,				fixed_dimension=OnnxConfig.default_fixed_sequence   ,				num_token_to_add=__SCREAMING_SNAKE_CASE     )

      # Generate dummy inputs according to compute batch and sequence
      __SCREAMING_SNAKE_CASE      = [""" """.join([tokenizer.unk_token]     ) * seq_length] * batch_size
      __SCREAMING_SNAKE_CASE      = dict(tokenizer(__SCREAMING_SNAKE_CASE   ,				return_tensors=__SCREAMING_SNAKE_CASE     )     )
      return common_inputs
 def  UpperCAmelCase__						(						self   :			Any   ,				__SCREAMING_SNAKE_CASE   :			PreTrainedTokenizer   ,				__SCREAMING_SNAKE_CASE   :			int = -1   ,				__SCREAMING_SNAKE_CASE   :			int = -1   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			Optional[TensorType] = None   ,				)    ->					Mapping[str, Any]:


      """simple docstring"""




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

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

      return common_inputs


 def  UpperCAmelCase__						(						self   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			str   ,				__SCREAMING_SNAKE_CASE   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			List[str]   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]     )    ->					Optional[Any]:


      """simple docstring"""




      if self.task in ["default", "seq2seq-lm"]:
           __SCREAMING_SNAKE_CASE      = super()._flatten_past_key_values_(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
      else:
           __SCREAMING_SNAKE_CASE      = super(__SCREAMING_SNAKE_CASE   ,				self     )._flatten_past_key_values_(
               __SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )






