





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 BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_  							=				logging.get_logger(__name__)




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Optional[int]	,     __UpperCAmelCase:    str=False	,     __UpperCAmelCase:    Dict=False   )   ->			List[str]:
    UpperCamelCase__							:       Dict      =   '''backbone.''' if is_semantic else ''''''

    UpperCamelCase__							:       Union[str, Any]      =   []
    for i in range(config.num_hidden_layers   ):
        # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
        rename_keys.append((f"{prefix}blocks.{i}.norm1.weight", f"beit.encoder.layer.{i}.layernorm_before.weight")   )
        rename_keys.append((f"{prefix}blocks.{i}.norm1.bias", f"beit.encoder.layer.{i}.layernorm_before.bias")   )
        rename_keys.append(
            (f"{prefix}blocks.{i}.attn.proj.weight", f"beit.encoder.layer.{i}.attention.output.dense.weight")   )
        rename_keys.append(
            (f"{prefix}blocks.{i}.attn.proj.bias", f"beit.encoder.layer.{i}.attention.output.dense.bias")   )
        rename_keys.append((f"{prefix}blocks.{i}.norm2.weight", f"beit.encoder.layer.{i}.layernorm_after.weight")   )
        rename_keys.append((f"{prefix}blocks.{i}.norm2.bias", f"beit.encoder.layer.{i}.layernorm_after.bias")   )
        rename_keys.append((f"{prefix}blocks.{i}.mlp.fc1.weight", f"beit.encoder.layer.{i}.intermediate.dense.weight")   )
        rename_keys.append((f"{prefix}blocks.{i}.mlp.fc1.bias", f"beit.encoder.layer.{i}.intermediate.dense.bias")   )
        rename_keys.append((f"{prefix}blocks.{i}.mlp.fc2.weight", f"beit.encoder.layer.{i}.output.dense.weight")   )
        rename_keys.append((f"{prefix}blocks.{i}.mlp.fc2.bias", f"beit.encoder.layer.{i}.output.dense.bias")   )

    # projection layer + position embeddings
    rename_keys.extend(
        [
            (f"{prefix}cls_token", '''beit.embeddings.cls_token'''),
            (f"{prefix}patch_embed.proj.weight", '''beit.embeddings.patch_embeddings.projection.weight'''),
            (f"{prefix}patch_embed.proj.bias", '''beit.embeddings.patch_embeddings.projection.bias'''),
            (f"{prefix}pos_embed", '''beit.embeddings.position_embeddings'''),
        ]   )

    if has_lm_head:
        # mask token + layernorm
        rename_keys.extend(
            [
                ('''mask_token''', '''beit.embeddings.mask_token'''),
                ('''norm.weight''', '''layernorm.weight'''),
                ('''norm.bias''', '''layernorm.bias'''),
            ]   )
    else:
        # layernorm + classification head
        rename_keys.extend(
            [
                ('''fc_norm.weight''', '''beit.pooler.layernorm.weight'''),
                ('''fc_norm.bias''', '''beit.pooler.layernorm.bias'''),
                ('''head.weight''', '''classifier.weight'''),
                ('''head.bias''', '''classifier.bias'''),
            ]   )

    return rename_keys




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Union[str, Any]	,     __UpperCAmelCase:    Optional[int]	,     __UpperCAmelCase:    Dict=False	,     __UpperCAmelCase:    List[str]=False   )   ->			int:
    for i in range(config.num_hidden_layers   ):
        UpperCamelCase__							:       Optional[int]      =   '''backbone.''' if is_semantic else ''''''
        # queries, keys and values
        UpperCamelCase__							:       Tuple      =   state_dict.pop(f"{prefix}blocks.{i}.attn.qkv.weight"   )
        UpperCamelCase__							:       List[str]      =   state_dict.pop(f"{prefix}blocks.{i}.attn.q_bias"   )
        UpperCamelCase__							:       Union[str, Any]      =   state_dict.pop(f"{prefix}blocks.{i}.attn.v_bias"   )

        UpperCamelCase__							:       int      =   in_proj_weight[
            : config.hidden_size, :
        ]
        UpperCamelCase__							:       List[Any]      =   q_bias
        UpperCamelCase__							:       Optional[int]      =   in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        UpperCamelCase__							:       Optional[int]      =   in_proj_weight[
            -config.hidden_size :, :
        ]
        UpperCamelCase__							:       Union[str, Any]      =   v_bias

        # gamma_1 and gamma_2
        # we call them lambda because otherwise they are renamed when using .from_pretrained
        UpperCamelCase__							:       Any      =   state_dict.pop(f"{prefix}blocks.{i}.gamma_1"   )
        UpperCamelCase__							:       Optional[Any]      =   state_dict.pop(f"{prefix}blocks.{i}.gamma_2"   )

        UpperCamelCase__							:       Tuple      =   gamma_a
        UpperCamelCase__							:       Optional[int]      =   gamma_a




def 	lowerCAmelCase_					(						__UpperCAmelCase:    List[Any]	,     __UpperCAmelCase:    Union[str, Any]	,     __UpperCAmelCase:    Any   )   ->			Any:
    UpperCamelCase__							:       List[str]      =   dct.pop(__UpperCAmelCase   )
    UpperCamelCase__							:       Optional[int]      =   val




def 	lowerCAmelCase_					(						)   ->			Dict:
    UpperCamelCase__							:       int      =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    UpperCamelCase__							:       int      =   Image.open(requests.get(__UpperCAmelCase	,     stream=__UpperCAmelCase   ).raw   )
    return im




@torch.no_grad()
def 	lowerCAmelCase_					(						__UpperCAmelCase:    Union[str, Any]	,     __UpperCAmelCase:    str	,     __UpperCAmelCase:    Optional[int]=False   )   ->			int:
    UpperCamelCase__							:       Dict      =   False if '''rvlcdip''' in checkpoint_url else True
    UpperCamelCase__							:       Dict      =   BeitConfig(use_absolute_position_embeddings=__UpperCAmelCase	,     use_mask_token=__UpperCAmelCase   )

    # size of the architecture
    if "large" in checkpoint_url or "dit-l" in checkpoint_url:
        UpperCamelCase__							:       Any      =   1024
        UpperCamelCase__							:       Any      =   4096
        UpperCamelCase__							:       Optional[int]      =   24
        UpperCamelCase__							:       int      =   16

    # labels
    if "rvlcdip" in checkpoint_url:
        UpperCamelCase__							:       int      =   16
        UpperCamelCase__							:       Any      =   '''huggingface/label-files'''
        UpperCamelCase__							:       List[str]      =   '''rvlcdip-id2label.json'''
        UpperCamelCase__							:       Union[str, Any]      =   json.load(open(hf_hub_download(__UpperCAmelCase	,     __UpperCAmelCase	,     repo_type='''dataset'''   )	,     '''r'''   )   )
        UpperCamelCase__							:       List[Any]      =   {int(__UpperCAmelCase   ): v for k, v in idalabel.items()}
        UpperCamelCase__							:       Union[str, Any]      =   idalabel
        UpperCamelCase__							:       Dict      =   {v: k for k, v in idalabel.items()}

    # load state_dict of original model, remove and rename some keys
    UpperCamelCase__							:       str      =   torch.hub.load_state_dict_from_url(__UpperCAmelCase	,     map_location='''cpu'''   )['''model''']

    UpperCamelCase__							:       List[str]      =   create_rename_keys(__UpperCAmelCase	,     has_lm_head=__UpperCAmelCase   )
    for src, dest in rename_keys:
        rename_key(__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase   )
    read_in_q_k_v(__UpperCAmelCase	,     __UpperCAmelCase	,     has_lm_head=__UpperCAmelCase   )

    # load HuggingFace model
    UpperCamelCase__							:       Tuple      =   BeitForMaskedImageModeling(__UpperCAmelCase   ) if has_lm_head else BeitForImageClassification(__UpperCAmelCase   )
    model.eval()
    model.load_state_dict(__UpperCAmelCase   )

    # Check outputs on an image
    UpperCamelCase__							:       Any      =   BeitImageProcessor(
        size=config.image_size	,     resample=PILImageResampling.BILINEAR	,     do_center_crop=__UpperCAmelCase   )
    UpperCamelCase__							:       Optional[Any]      =   prepare_img()

    UpperCamelCase__							:       List[Any]      =   image_processor(images=__UpperCAmelCase	,     return_tensors='''pt'''   )
    UpperCamelCase__							:       Optional[Any]      =   encoding['''pixel_values''']

    UpperCamelCase__							:       List[str]      =   model(__UpperCAmelCase   )
    UpperCamelCase__							:       List[Any]      =   outputs.logits

    # verify logits
    UpperCamelCase__							:       int      =   [1, 16] if '''rvlcdip''' in checkpoint_url else [1, 196, 8192]
    assert logits.shape == torch.Size(__UpperCAmelCase   ), "Shape of logits not as expected"

    Path(__UpperCAmelCase   ).mkdir(exist_ok=__UpperCAmelCase   )
    print(f"Saving model to {pytorch_dump_folder_path}"   )
    model.save_pretrained(__UpperCAmelCase   )
    print(f"Saving image processor to {pytorch_dump_folder_path}"   )
    image_processor.save_pretrained(__UpperCAmelCase   )

    if push_to_hub:
        if has_lm_head:
            UpperCamelCase__							:       Optional[Any]      =   '''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
        else:
            UpperCamelCase__							:       int      =   '''dit-base-finetuned-rvlcdip''' if '''dit-b''' in checkpoint_url else '''dit-large-finetuned-rvlcdip'''
        image_processor.push_to_hub(
            repo_path_or_name=Path(__UpperCAmelCase	,     __UpperCAmelCase   )	,     organization='''nielsr'''	,     commit_message='''Add image processor'''	,     use_temp_dir=__UpperCAmelCase	,     )
        model.push_to_hub(
            repo_path_or_name=Path(__UpperCAmelCase	,     __UpperCAmelCase   )	,     organization='''nielsr'''	,     commit_message='''Add model'''	,     use_temp_dir=__UpperCAmelCase	,     )


if __name__ == "__main__":
  UpperCAmelCase_  							=				argparse.ArgumentParser()

  parser.add_argument(
      '--checkpoint_url',
      default='https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth',
      type=str,
      help='URL to the original PyTorch checkpoint (.pth file).',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
  )
  parser.add_argument(
      '--push_to_hub',
      action='store_true',
  )
  UpperCAmelCase_  							=				parser.parse_args()
  convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, 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


UpperCAmelCase_  							=				logging.get_logger(__name__)

UpperCAmelCase_  							=				{
    'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
    # See all Marian models at https://huggingface.co/models?filter=marian
}






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							Optional[int]     	=	"marian"
   a		:							Optional[Any]     	=	["past_key_values"]
   a		:							Union[str, Any]     	=	{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




   def __init__(					self,	__magic_name__=58101,	__magic_name__=None,	__magic_name__=1024,	__magic_name__=12,	__magic_name__=4096,	__magic_name__=16,	__magic_name__=12,	__magic_name__=4096,	__magic_name__=16,	__magic_name__=0.0,	__magic_name__=0.0,	__magic_name__=True,	__magic_name__=True,	__magic_name__="gelu",	__magic_name__=1024,	__magic_name__=0.1,	__magic_name__=0.0,	__magic_name__=0.0,	__magic_name__=0.02,	__magic_name__=58100,	__magic_name__=False,	__magic_name__=58100,	__magic_name__=0,	__magic_name__=0,	__magic_name__=True,	**__magic_name__,	)				->   Tuple:






       """simple docstring"""
       UpperCamelCase__							:       Optional[int]      =   vocab_size
       UpperCamelCase__							:       Dict      =   decoder_vocab_size or vocab_size
       UpperCamelCase__							:       Any      =   max_position_embeddings
       UpperCamelCase__							:       Any      =   d_model
       UpperCamelCase__							:       Union[str, Any]      =   encoder_ffn_dim
       UpperCamelCase__							:       Optional[Any]      =   encoder_layers
       UpperCamelCase__							:       List[str]      =   encoder_attention_heads
       UpperCamelCase__							:       str      =   decoder_ffn_dim
       UpperCamelCase__							:       str      =   decoder_layers
       UpperCamelCase__							:       Optional[int]      =   decoder_attention_heads
       UpperCamelCase__							:       str      =   dropout
       UpperCamelCase__							:       Optional[Any]      =   attention_dropout
       UpperCamelCase__							:       List[Any]      =   activation_dropout
       UpperCamelCase__							:       int      =   activation_function
       UpperCamelCase__							:       Optional[int]      =   init_std
       UpperCamelCase__							:       Any      =   encoder_layerdrop
       UpperCamelCase__							:       Tuple      =   decoder_layerdrop
       UpperCamelCase__							:       Dict      =   use_cache
       UpperCamelCase__							:       str      =   encoder_layers
       UpperCamelCase__							:       str      =   scale_embedding  # scale factor will be sqrt(d_model) if True
       UpperCamelCase__							:       Union[str, Any]      =   share_encoder_decoder_embeddings
       super().__init__(
           pad_token_id=__magic_name__,	eos_token_id=__magic_name__,	is_encoder_decoder=__magic_name__,	decoder_start_token_id=__magic_name__,	forced_eos_token_id=__magic_name__,	**__magic_name__,	)






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   @property
   # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
   def    UpperCamelCase__						(					self							)				->   Mapping[str, Mapping[int, str]]:






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

           if self.use_past:
               UpperCamelCase__							:       str      =   {0: '''batch'''}
               UpperCamelCase__							:       List[str]      =   {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
           else:
               UpperCamelCase__							:       List[Any]      =   {0: '''batch''', 1: '''decoder_sequence'''}
               UpperCamelCase__							:       Optional[Any]      =   {0: '''batch''', 1: '''decoder_sequence'''}

           if self.use_past:
               self.fill_with_past_key_values_(__magic_name__,	direction='''inputs'''							)
       elif self.task == "causal-lm":
           # TODO: figure this case out.
           UpperCamelCase__							:       Any      =   OrderedDict(
               [
                   ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                   ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
               ]							)
           if self.use_past:
               UpperCamelCase__     ,UpperCamelCase__							:       List[str]      =   self.num_layers
               for i in range(__magic_name__							):
                   UpperCamelCase__							:       Tuple      =   {0: '''batch''', 2: '''past_sequence + sequence'''}
                   UpperCamelCase__							:       List[Any]      =   {0: '''batch''', 2: '''past_sequence + sequence'''}
       else:
           UpperCamelCase__							:       Optional[Any]      =   OrderedDict(
               [
                   ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                   ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                   ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
                   ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
               ]							)

       return common_inputs




   @property
   # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
   def    UpperCamelCase__						(					self							)				->   Mapping[str, Mapping[int, str]]:






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




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = -1,	__magic_name__ = -1,	__magic_name__ = False,	__magic_name__ = None,	)				->   Mapping[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   self._generate_dummy_inputs_for_encoder_and_decoder(
           __magic_name__,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							)

       # Generate decoder inputs
       UpperCamelCase__							:       Optional[Any]      =   seq_length if not self.use_past else 1
       UpperCamelCase__							:       List[str]      =   self._generate_dummy_inputs_for_encoder_and_decoder(
           __magic_name__,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							)
       UpperCamelCase__							:       Dict      =   {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
       UpperCamelCase__							:       Dict      =   dict(**__magic_name__,	**__magic_name__							)

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

           UpperCamelCase__							:       List[str]      =   torch.cat(
               [common_inputs['''decoder_attention_mask'''], torch.ones(__magic_name__,	__magic_name__							)],	dim=1							)

           UpperCamelCase__							:       Any      =   []
           # If the number of encoder and decoder layers are present in the model configuration, both are considered
           UpperCamelCase__     ,UpperCamelCase__							:       Optional[Any]      =   self.num_layers
           UpperCamelCase__							:       Union[str, Any]      =   min(__magic_name__,	__magic_name__							)
           UpperCamelCase__							:       List[Any]      =   max(__magic_name__,	__magic_name__							) - min_num_layers
           UpperCamelCase__							:       str      =   '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

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




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = -1,	__magic_name__ = -1,	__magic_name__ = False,	__magic_name__ = None,	)				->   Mapping[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:       int      =   self._generate_dummy_inputs_for_encoder_and_decoder(
           __magic_name__,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							)

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

           UpperCamelCase__							:       int      =   common_inputs['''attention_mask'''].dtype
           UpperCamelCase__							:       Union[str, Any]      =   torch.cat(
               [common_inputs['''attention_mask'''], torch.ones(__magic_name__,	__magic_name__,	dtype=__magic_name__							)],	dim=1							)
           UpperCamelCase__							:       Union[str, Any]      =   [
               (torch.zeros(__magic_name__							), torch.zeros(__magic_name__							)) for _ in range(__magic_name__							)
           ]
       return common_inputs




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = -1,	__magic_name__ = -1,	__magic_name__ = False,	__magic_name__ = None,	)				->   Mapping[str, Any]:






       """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
       UpperCamelCase__							:       Any      =   compute_effective_axis_dimension(
           __magic_name__,	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
       UpperCamelCase__							:       Optional[int]      =   tokenizer.num_special_tokens_to_add(__magic_name__							)
       UpperCamelCase__							:       Dict      =   compute_effective_axis_dimension(
           __magic_name__,	fixed_dimension=OnnxConfig.default_fixed_sequence,	num_token_to_add=__magic_name__							)

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




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = -1,	__magic_name__ = -1,	__magic_name__ = False,	__magic_name__ = None,	)				->   Mapping[str, Any]:






       """simple docstring"""
       if self.task in ["default", "seq2seq-lm"]:
           UpperCamelCase__							:       int      =   self._generate_dummy_inputs_for_default_and_seqaseq_lm(
               __magic_name__,	batch_size=__magic_name__,	seq_length=__magic_name__,	is_pair=__magic_name__,	framework=__magic_name__							)

       else:
           UpperCamelCase__							:       Optional[Any]      =   self._generate_dummy_inputs_for_causal_lm(
               __magic_name__,	batch_size=__magic_name__,	seq_length=__magic_name__,	is_pair=__magic_name__,	framework=__magic_name__							)

       return common_inputs




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							)				->   int:






       """simple docstring"""
       if self.task in ["default", "seq2seq-lm"]:
           UpperCamelCase__							:       List[str]      =   super()._flatten_past_key_values_(__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							)
       else:
           UpperCamelCase__							:       Dict      =   super(__magic_name__,	self							)._flatten_past_key_values_(
               __magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							)




   @property
   def    UpperCamelCase__						(					self							)				->   float:






       """simple docstring"""
       return 1E-4

