



"""simple docstring"""
import json
import os
from pathlib import Path

import pytest

from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename


A:   Optional[Any]								=		"http://www.mocksite.com/file1.txt"
A:   Union[str, Any]								=		"\"text\": [\"foo\", \"foo\"]"
A:   List[Any]								=		"6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8"



class       SCREAMING_SNAKE_CASE__  :
   __lowerCAmelCase  :   Dict						=						200
   __lowerCAmelCase  :   Optional[int]						=						{'Content-Length': '100'}
   __lowerCAmelCase  :   Dict						=						{}

   def 	SCREAMING_SNAKE_CASE     (   self      ,     **_SCREAMING_SNAKE_CASE						)  ->      Union[str, Any]:





    '''simple docstring'''




    return [bytes(_SCREAMING_SNAKE_CASE      ,     """utf-8"""						)]

def       _snake_case						(						*UpperCamelCase						:		Optional[int]			,		**UpperCamelCase						:		Dict						):
 return MockResponse()

@pytest.mark.parametrize("""urls_type"""			,		[str, list, dict]						)
def       _snake_case						(						UpperCamelCase						:		List[str]			,		UpperCamelCase						:		Dict			,		UpperCamelCase						:		int						):
 import requests

 monkeypatch.setattr(UpperCamelCase			,		"""request"""			,		UpperCamelCase						)

 UpperCAmelCase	:					int	=	URL
 if issubclass(UpperCamelCase			,		UpperCamelCase						):
  UpperCAmelCase	:					Tuple	=	url
 elif issubclass(UpperCamelCase			,		UpperCamelCase						):
  UpperCAmelCase	:					Any	=	[url]
 elif issubclass(UpperCamelCase			,		UpperCamelCase						):
  UpperCAmelCase	:					Any	=	{"""train""": url}
 UpperCAmelCase	:					Tuple	=	"""dummy"""
 UpperCAmelCase	:					Dict	=	"""downloads"""
 UpperCAmelCase	:					Optional[Any]	=	tmp_path
 UpperCAmelCase	:					Optional[int]	=	DownloadConfig(
     cache_dir=os.path.join(UpperCamelCase			,		UpperCamelCase						)			,		use_etag=UpperCamelCase			,		)
 UpperCAmelCase	:					str	=	DownloadManager(dataset_name=UpperCamelCase			,		download_config=UpperCamelCase						)
 UpperCAmelCase	:					Optional[Any]	=	dl_manager.download(UpperCamelCase						)
 UpperCAmelCase	:					List[str]	=	urls
 for downloaded_paths in [downloaded_paths]:
  if isinstance(UpperCamelCase			,		UpperCamelCase						):
   UpperCAmelCase	:					int	=	[downloaded_paths]
   UpperCAmelCase	:					Dict	=	[urls]
  elif isinstance(UpperCamelCase			,		UpperCamelCase						):
   assert "train" in downloaded_paths.keys()
   UpperCAmelCase	:					Tuple	=	downloaded_paths.values()
   UpperCAmelCase	:					Optional[Any]	=	urls.values()
  assert downloaded_paths
  for downloaded_path, input_url in zip(UpperCamelCase			,		UpperCamelCase						):
   assert downloaded_path == dl_manager.downloaded_paths[input_url]
   UpperCAmelCase	:					int	=	Path(UpperCamelCase						)
   UpperCAmelCase	:					List[str]	=	downloaded_path.parts
   assert parts[-1] == HASH
   assert parts[-2] == cache_subdir
   assert downloaded_path.exists()
   UpperCAmelCase	:					Optional[Any]	=	downloaded_path.read_text()
   assert content == CONTENT
   UpperCAmelCase	:					Dict	=	downloaded_path.with_suffix(""".json"""						)
   assert metadata_downloaded_path.exists()
   UpperCAmelCase	:					int	=	json.loads(metadata_downloaded_path.read_text()						)
   assert metadata_content == {"url": URL, "etag": None}

@pytest.mark.parametrize("""paths_type"""			,		[str, list, dict]						)
def       _snake_case						(						UpperCamelCase						:		List[Any]			,		UpperCamelCase						:		Dict			,		UpperCamelCase						:		List[Any]						):
 UpperCAmelCase	:					Tuple	=	str(UpperCamelCase						)
 if issubclass(UpperCamelCase			,		UpperCamelCase						):
  UpperCAmelCase	:					Dict	=	filename
 elif issubclass(UpperCamelCase			,		UpperCamelCase						):
  UpperCAmelCase	:					Optional[int]	=	[filename]
 elif issubclass(UpperCamelCase			,		UpperCamelCase						):
  UpperCAmelCase	:					Dict	=	{"""train""": filename}
 UpperCAmelCase	:					str	=	"""dummy"""
 UpperCAmelCase	:					Tuple	=	xz_file.parent
 UpperCAmelCase	:					int	=	"""extracted"""
 UpperCAmelCase	:					Tuple	=	DownloadConfig(
     cache_dir=UpperCamelCase			,		use_etag=UpperCamelCase			,		)
 UpperCAmelCase	:					Dict	=	DownloadManager(dataset_name=UpperCamelCase			,		download_config=UpperCamelCase						)
 UpperCAmelCase	:					Tuple	=	dl_manager.extract(UpperCamelCase						)
 UpperCAmelCase	:					str	=	paths
 for extracted_paths in [extracted_paths]:
  if isinstance(UpperCamelCase			,		UpperCamelCase						):
   UpperCAmelCase	:					Optional[int]	=	[extracted_paths]
   UpperCAmelCase	:					int	=	[paths]
  elif isinstance(UpperCamelCase			,		UpperCamelCase						):
   assert "train" in extracted_paths.keys()
   UpperCAmelCase	:					Union[str, Any]	=	extracted_paths.values()
   UpperCAmelCase	:					Tuple	=	paths.values()
  assert extracted_paths
  for extracted_path, input_path in zip(UpperCamelCase			,		UpperCamelCase						):
   assert extracted_path == dl_manager.extracted_paths[input_path]
   UpperCAmelCase	:					Optional[Any]	=	Path(UpperCamelCase						)
   UpperCAmelCase	:					Optional[int]	=	extracted_path.parts
   assert parts[-1] == hash_url_to_filename(UpperCamelCase			,		etag=UpperCamelCase						)
   assert parts[-2] == extracted_subdir
   assert extracted_path.exists()
   UpperCAmelCase	:					int	=	extracted_path.read_text()
   UpperCAmelCase	:					Tuple	=	text_file.read_text()
   assert extracted_file_content == expected_file_content

def       _snake_case						(						UpperCamelCase						:		Dict			,		UpperCamelCase						:		Dict						):
 assert path.endswith(""".jsonl"""						)
 for num_items, line in enumerate(UpperCamelCase			,		start=1						):
  UpperCAmelCase	:					Optional[int]	=	json.loads(line.decode("""utf-8"""						)						)
  assert item.keys() == {"col_1", "col_2", "col_3"}
 assert num_items == 4

@pytest.mark.parametrize("""archive_jsonl"""			,		["""tar_jsonl_path""", """zip_jsonl_path"""]						)
def       _snake_case						(						UpperCamelCase						:		int			,		UpperCamelCase						:		Tuple						):
 UpperCAmelCase	:					Dict	=	request.getfixturevalue(UpperCamelCase						)
 UpperCAmelCase	:					int	=	DownloadManager()
 for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(UpperCamelCase						)			,		start=1						):
  _test_jsonl(UpperCamelCase			,		UpperCamelCase						)
 assert num_jsonl == 2

@pytest.mark.parametrize("""archive_nested_jsonl"""			,		["""tar_nested_jsonl_path""", """zip_nested_jsonl_path"""]						)
def       _snake_case						(						UpperCamelCase						:		Optional[Any]			,		UpperCamelCase						:		int						):
 UpperCAmelCase	:					Optional[int]	=	request.getfixturevalue(UpperCamelCase						)
 UpperCAmelCase	:					List[str]	=	DownloadManager()
 for num_tar, (path, file) in enumerate(dl_manager.iter_archive(UpperCamelCase						)			,		start=1						):
  for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(UpperCamelCase						)			,		start=1						):
   _test_jsonl(UpperCamelCase			,		UpperCamelCase						)
 assert num_tar == 1
 assert num_jsonl == 2

def       _snake_case						(						UpperCamelCase						:		str						):
 UpperCAmelCase	:					str	=	DownloadManager()
 for num_file, file in enumerate(dl_manager.iter_files(UpperCamelCase						)			,		start=1						):
  assert os.path.basename(UpperCamelCase						) == ("test.txt" if num_file == 1 else "train.txt")
 assert num_file == 2





"""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


A:   Tuple								=		logging.get_logger(__name__)

A:   List[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       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):
   __lowerCAmelCase  :   int						=						'blenderbot-small'
   __lowerCAmelCase  :   Any						=						['past_key_values']
   __lowerCAmelCase  :   str						=						{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE=50265      ,     _SCREAMING_SNAKE_CASE=512      ,     _SCREAMING_SNAKE_CASE=8      ,     _SCREAMING_SNAKE_CASE=2048      ,     _SCREAMING_SNAKE_CASE=16      ,     _SCREAMING_SNAKE_CASE=8      ,     _SCREAMING_SNAKE_CASE=2048      ,     _SCREAMING_SNAKE_CASE=16      ,     _SCREAMING_SNAKE_CASE=0.0      ,     _SCREAMING_SNAKE_CASE=0.0      ,     _SCREAMING_SNAKE_CASE=True      ,     _SCREAMING_SNAKE_CASE=True      ,     _SCREAMING_SNAKE_CASE="gelu"      ,     _SCREAMING_SNAKE_CASE=512      ,     _SCREAMING_SNAKE_CASE=0.1      ,     _SCREAMING_SNAKE_CASE=0.0      ,     _SCREAMING_SNAKE_CASE=0.0      ,     _SCREAMING_SNAKE_CASE=0.02      ,     _SCREAMING_SNAKE_CASE=1      ,     _SCREAMING_SNAKE_CASE=False      ,     _SCREAMING_SNAKE_CASE=0      ,     _SCREAMING_SNAKE_CASE=1      ,     _SCREAMING_SNAKE_CASE=2      ,     _SCREAMING_SNAKE_CASE=2      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      str:





    '''simple docstring'''




    UpperCAmelCase	:					Any	=	vocab_size
    UpperCAmelCase	:					Any	=	max_position_embeddings
    UpperCAmelCase	:					str	=	d_model
    UpperCAmelCase	:					List[Any]	=	encoder_ffn_dim
    UpperCAmelCase	:					Union[str, Any]	=	encoder_layers
    UpperCAmelCase	:					List[str]	=	encoder_attention_heads
    UpperCAmelCase	:					Optional[Any]	=	decoder_ffn_dim
    UpperCAmelCase	:					List[Any]	=	decoder_layers
    UpperCAmelCase	:					Any	=	decoder_attention_heads
    UpperCAmelCase	:					Union[str, Any]	=	dropout
    UpperCAmelCase	:					Union[str, Any]	=	attention_dropout
    UpperCAmelCase	:					int	=	activation_dropout
    UpperCAmelCase	:					int	=	activation_function
    UpperCAmelCase	:					Optional[Any]	=	init_std
    UpperCAmelCase	:					List[str]	=	encoder_layerdrop
    UpperCAmelCase	:					List[str]	=	decoder_layerdrop
    UpperCAmelCase	:					List[str]	=	use_cache
    UpperCAmelCase	:					Union[str, Any]	=	encoder_layers
    UpperCAmelCase	:					Any	=	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       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):

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





    '''simple docstring'''




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

     if self.use_past:
      UpperCAmelCase	:					int	=	{0: """batch"""}
      UpperCAmelCase	:					List[Any]	=	{0: """batch""", 1: """past_decoder_sequence + sequence"""}
     else:
      UpperCAmelCase	:					Optional[Any]	=	{0: """batch""", 1: """decoder_sequence"""}
      UpperCAmelCase	:					Any	=	{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.
     UpperCAmelCase	:					int	=	OrderedDict(
         [
             ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
             ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
         ]						)
     if self.use_past:
      UpperCAmelCase	,		UpperCAmelCase	:					Tuple	=	self.num_layers
      for i in range(_SCREAMING_SNAKE_CASE						):
       UpperCAmelCase	:					Optional[Any]	=	{0: """batch""", 2: """past_sequence + sequence"""}
       UpperCAmelCase	:					List[Any]	=	{0: """batch""", 2: """past_sequence + sequence"""}
    else:
     UpperCAmelCase	:					Dict	=	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						)  ->      Mapping[str, Mapping[int, str]]:





    '''simple docstring'''




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

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





    '''simple docstring'''




    UpperCAmelCase	:					Dict	=	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
    UpperCAmelCase	:					Optional[Any]	=	seq_length if not self.use_past else 1
    UpperCAmelCase	:					Dict	=	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						)
    UpperCAmelCase	:					Any	=	{F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
    UpperCAmelCase	:					Union[str, Any]	=	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
     UpperCAmelCase	,		UpperCAmelCase	:					int	=	common_inputs["""input_ids"""].shape
     UpperCAmelCase	:					Any	=	common_inputs["""decoder_input_ids"""].shape[1]
     UpperCAmelCase	,		UpperCAmelCase	:					str	=	self.num_attention_heads
     UpperCAmelCase	:					Union[str, Any]	=	(
         batch,
         num_encoder_attention_heads,
         encoder_seq_length,
         self._config.hidden_size // num_encoder_attention_heads,
     )
     UpperCAmelCase	:					Union[str, Any]	=	decoder_seq_length + 3
     UpperCAmelCase	:					Union[str, Any]	=	(
         batch,
         num_decoder_attention_heads,
         decoder_past_length,
         self._config.hidden_size // num_decoder_attention_heads,
     )

     UpperCAmelCase	:					Optional[Any]	=	torch.cat(
         [common_inputs["""decoder_attention_mask"""], torch.ones(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)]      ,     dim=1						)

     UpperCAmelCase	:					List[str]	=	[]
     # If the number of encoder and decoder layers are present in the model configuration, both are considered
     UpperCAmelCase	,		UpperCAmelCase	:					List[Any]	=	self.num_layers
     UpperCAmelCase	:					Optional[Any]	=	min(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)
     UpperCAmelCase	:					Optional[Any]	=	max(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						) - min_num_layers
     UpperCAmelCase	:					Optional[int]	=	"""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.
     UpperCAmelCase	:					Dict	=	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 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = -1      ,     _SCREAMING_SNAKE_CASE = -1      ,     _SCREAMING_SNAKE_CASE = False      ,     _SCREAMING_SNAKE_CASE = None      ,     )  ->      Mapping[str, Any]:





    '''simple docstring'''




    UpperCAmelCase	:					Tuple	=	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
     UpperCAmelCase	,		UpperCAmelCase	:					Optional[Any]	=	common_inputs["""input_ids"""].shape
     # Not using the same length for past_key_values
     UpperCAmelCase	:					Union[str, Any]	=	seqlen + 2
     UpperCAmelCase	,		UpperCAmelCase	:					Optional[int]	=	self.num_layers
     UpperCAmelCase	,		UpperCAmelCase	:					Dict	=	self.num_attention_heads
     UpperCAmelCase	:					Any	=	(
         batch,
         num_encoder_attention_heads,
         past_key_values_length,
         self._config.hidden_size // num_encoder_attention_heads,
     )

     UpperCAmelCase	:					List[Any]	=	common_inputs["""attention_mask"""].dtype
     UpperCAmelCase	:					Any	=	torch.cat(
         [common_inputs["""attention_mask"""], torch.ones(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     dtype=_SCREAMING_SNAKE_CASE						)]      ,     dim=1						)
     UpperCAmelCase	:					List[str]	=	[
         (torch.zeros(_SCREAMING_SNAKE_CASE						), torch.zeros(_SCREAMING_SNAKE_CASE						)) for _ in range(_SCREAMING_SNAKE_CASE						)
     ]
    return common_inputs

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





    '''simple docstring'''




    UpperCAmelCase	:					str	=	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
    UpperCAmelCase	:					List[str]	=	tokenizer.num_special_tokens_to_add(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					int	=	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
    UpperCAmelCase	:					Dict	=	[""" """.join([tokenizer.unk_token]						) * seq_length] * batch_size
    UpperCAmelCase	:					Optional[int]	=	dict(tokenizer(_SCREAMING_SNAKE_CASE      ,     return_tensors=_SCREAMING_SNAKE_CASE						)						)
    return common_inputs

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





    '''simple docstring'''




    if self.task in ["default", "seq2seq-lm"]:
     UpperCAmelCase	:					Optional[int]	=	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":
     UpperCAmelCase	:					Optional[int]	=	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:
     UpperCAmelCase	:					Tuple	=	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 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)  ->      List[Any]:





    '''simple docstring'''




    if self.task in ["default", "seq2seq-lm"]:
     UpperCAmelCase	:					List[Any]	=	super()._flatten_past_key_values_(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)
    else:
     UpperCAmelCase	:					Dict	=	super(_SCREAMING_SNAKE_CASE      ,     self						)._flatten_past_key_values_(
         _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)
