



"""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    =			logging.get_logger(__name__)

A    =			{
    "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      UpperCAmelCase__		(      _UpperCAmelCase     ):
		lowerCAmelCase_       :	Union[str, Any] 							=     """blenderbot-small"""
		lowerCAmelCase_       :	Optional[int] 							=     ["""past_key_values"""]
		lowerCAmelCase_       :	Tuple 							=     {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



		def __init__( self  :    Any      ,							snake_case  :    Dict=50_265      ,							snake_case  :    str=512      ,							snake_case  :    Tuple=8      ,							snake_case  :    int=2_048      ,							snake_case  :    str=16      ,							snake_case  :    Optional[int]=8      ,							snake_case  :    str=2_048      ,							snake_case  :    Optional[Any]=16      ,							snake_case  :    Union[str, Any]=0.0      ,							snake_case  :    List[str]=0.0      ,							snake_case  :    Tuple=True      ,							snake_case  :    Tuple=True      ,							snake_case  :    int="gelu"      ,							snake_case  :    Tuple=512      ,							snake_case  :    Optional[int]=0.1      ,							snake_case  :    int=0.0      ,							snake_case  :    Tuple=0.0      ,							snake_case  :    Optional[int]=0.02      ,							snake_case  :    Union[str, Any]=1      ,							snake_case  :    Dict=False      ,							snake_case  :    Optional[int]=0      ,							snake_case  :    List[Any]=1      ,							snake_case  :    Any=2      ,							snake_case  :    Optional[Any]=2      ,							**snake_case  :    Dict      ,							)			->						Any:






									'''simple docstring'''

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

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






class      UpperCAmelCase__		(      _UpperCAmelCase     ):



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






									'''simple docstring'''

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

																if self.use_past:
																							A         =					{0: '''batch'''}
																							A         =					{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
																else:
																							A         =					{0: '''batch''', 1: '''decoder_sequence'''}
																							A         =					{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         =					OrderedDict(
																    [
																        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
																        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
																    ]				)
																if self.use_past:
																							A         =					self.num_layers
																							for i in range(lowerCamelCase_				):
																														A         =					{0: '''batch''', 2: '''past_sequence + sequence'''}
																														A         =					{0: '''batch''', 2: '''past_sequence + sequence'''}
									else:
																A         =					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 					A_		( self  :    List[str]				)			->						Mapping[str, Mapping[int, str]]:






									'''simple docstring'''

									if self.task in ["default", "seq2seq-lm"]:
																A         =					super().outputs
									else:
																A         =					super(lowerCamelCase_      ,							self				).outputs
																if self.use_past:
																							A         =					self.num_layers
																							for i in range(lowerCamelCase_				):
																														A         =					{0: '''batch''', 2: '''past_sequence + sequence'''}
																														A         =					{0: '''batch''', 2: '''past_sequence + sequence'''}
									return common_outputs



		def 					A_		( self  :    int      ,							snake_case  :    PreTrainedTokenizer      ,							snake_case  :    int = -1      ,							snake_case  :    int = -1      ,							snake_case  :    bool = False      ,							snake_case  :    Optional[TensorType] = None      ,							)			->						Mapping[str, Any]:






									'''simple docstring'''

									A         =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    lowerCamelCase_      ,							lowerCamelCase_      ,							lowerCamelCase_      ,							lowerCamelCase_      ,							lowerCamelCase_				)

									# Generate decoder inputs
									A         =					seq_length if not self.use_past else 1
									A         =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    lowerCamelCase_      ,							lowerCamelCase_      ,							lowerCamelCase_      ,							lowerCamelCase_      ,							lowerCamelCase_				)
									A         =					{f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
									A         =					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         =					common_inputs['''input_ids'''].shape
																A         =					common_inputs['''decoder_input_ids'''].shape[1]
																A         =					self.num_attention_heads
																A         =					(
																    batch,
																    num_encoder_attention_heads,
																    encoder_seq_length,
																    self._config.hidden_size // num_encoder_attention_heads,
																)
																A         =					decoder_seq_length + 3
																A         =					(
																    batch,
																    num_decoder_attention_heads,
																    decoder_past_length,
																    self._config.hidden_size // num_decoder_attention_heads,
																)

																A         =					torch.cat(
																    [common_inputs['decoder_attention_mask'], torch.ones(lowerCamelCase_      ,							lowerCamelCase_				)]      ,							dim=1				)

																A         =					[]
																# If the number of encoder and decoder layers are present in the model configuration, both are considered
																A         =					self.num_layers
																A         =					min(lowerCamelCase_      ,							lowerCamelCase_				)
																A         =					max(lowerCamelCase_      ,							lowerCamelCase_				) - min_num_layers
																A         =					'''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         =					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 					A_		( self  :    Any      ,							snake_case  :    PreTrainedTokenizer      ,							snake_case  :    int = -1      ,							snake_case  :    int = -1      ,							snake_case  :    bool = False      ,							snake_case  :    Optional[TensorType] = None      ,							)			->						Mapping[str, Any]:






									'''simple docstring'''

									A         =					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         =					common_inputs['''input_ids'''].shape
																# Not using the same length for past_key_values
																A         =					seqlen + 2
																A         =					self.num_layers
																A         =					self.num_attention_heads
																A         =					(
																    batch,
																    num_encoder_attention_heads,
																    past_key_values_length,
																    self._config.hidden_size // num_encoder_attention_heads,
																)

																A         =					common_inputs['''attention_mask'''].dtype
																A         =					torch.cat(
																    [common_inputs['attention_mask'], torch.ones(lowerCamelCase_      ,							lowerCamelCase_      ,							dtype=lowerCamelCase_				)]      ,							dim=1				)
																A         =					[
																    (torch.zeros(lowerCamelCase_				), torch.zeros(lowerCamelCase_				)) for _ in range(lowerCamelCase_				)
																]
									return common_inputs



		def 					A_		( self  :    Union[str, Any]      ,							snake_case  :    PreTrainedTokenizer      ,							snake_case  :    int = -1      ,							snake_case  :    int = -1      ,							snake_case  :    bool = False      ,							snake_case  :    Optional[TensorType] = None      ,							)			->						Mapping[str, Any]:






									'''simple docstring'''

									A         =					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         =					tokenizer.num_special_tokens_to_add(lowerCamelCase_				)
									A         =					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         =					[''' '''.join([tokenizer.unk_token]				) * seq_length] * batch_size
									A         =					dict(tokenizer(lowerCamelCase_      ,							return_tensors=lowerCamelCase_				)				)
									return common_inputs



		def 					A_		( self  :    List[Any]      ,							snake_case  :    PreTrainedTokenizer      ,							snake_case  :    int = -1      ,							snake_case  :    int = -1      ,							snake_case  :    bool = False      ,							snake_case  :    Optional[TensorType] = None      ,							)			->						Mapping[str, Any]:






									'''simple docstring'''

									if self.task in ["default", "seq2seq-lm"]:
																A         =					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         =					self._generate_dummy_inputs_for_causal_lm(
																    lowerCamelCase_      ,							batch_size=lowerCamelCase_      ,							seq_length=lowerCamelCase_      ,							is_pair=lowerCamelCase_      ,							framework=lowerCamelCase_				)
									else:
																A         =					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 					A_		( self  :    Dict      ,							snake_case  :    int      ,							snake_case  :    List[str]      ,							snake_case  :    List[str]      ,							snake_case  :    Dict				)			->						List[Any]:






									'''simple docstring'''

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











"""simple docstring"""







from datasets.utils.patching import _PatchedModuleObj, patch_submodule

from . import _test_patching

def   lowerCAmelCase__			(     ) ->							Optional[Any]:
							import os as original_os
							from os import path as original_path
							from os import rename as original_rename
							from os.path import dirname as original_dirname
							from os.path import join as original_join

							assert _test_patching.os is original_os
							assert _test_patching.path is original_path
							assert _test_patching.join is original_join

							assert _test_patching.renamed_os is original_os
							assert _test_patching.renamed_path is original_path
							assert _test_patching.renamed_join is original_join

							A         =					'__test_patch_submodule_mock__'
							with patch_submodule(_test_patching				,      'os.path.join'				,      lowerCamelCase__			):
														# Every way to access os.path.join must be patched, and the rest must stay untouched

														# check os.path.join
														assert isinstance(_test_patching.os				,      _PatchedModuleObj			)
														assert isinstance(_test_patching.os.path				,      _PatchedModuleObj			)
														assert _test_patching.os.path.join is mock

														# check path.join
														assert isinstance(_test_patching.path				,      _PatchedModuleObj			)
														assert _test_patching.path.join is mock

														# check join
														assert _test_patching.join is mock

														# check that the other attributes are untouched
														assert _test_patching.os.rename is original_rename
														assert _test_patching.path.dirname is original_dirname
														assert _test_patching.os.path.dirname is original_dirname

														# Even renamed modules or objects must be patched

														# check renamed_os.path.join
														assert isinstance(_test_patching.renamed_os				,      _PatchedModuleObj			)
														assert isinstance(_test_patching.renamed_os.path				,      _PatchedModuleObj			)
														assert _test_patching.renamed_os.path.join is mock

														# check renamed_path.join
														assert isinstance(_test_patching.renamed_path				,      _PatchedModuleObj			)
														assert _test_patching.renamed_path.join is mock

														# check renamed_join
														assert _test_patching.renamed_join is mock

														# check that the other attributes are untouched
														assert _test_patching.renamed_os.rename is original_rename
														assert _test_patching.renamed_path.dirname is original_dirname
														assert _test_patching.renamed_os.path.dirname is original_dirname

							# check that everthing is back to normal when the patch is over

							assert _test_patching.os is original_os
							assert _test_patching.path is original_path
							assert _test_patching.join is original_join

							assert _test_patching.renamed_os is original_os
							assert _test_patching.renamed_path is original_path
							assert _test_patching.renamed_join is original_join

def   lowerCAmelCase__			(     ) ->							str:
							assert _test_patching.open is open

							A         =					'__test_patch_submodule_builtin_mock__'
							# _test_patching has "open" in its globals
							assert _test_patching.open is open
							with patch_submodule(_test_patching				,      'open'				,      lowerCamelCase__			):
														assert _test_patching.open is mock

							# check that everthing is back to normal when the patch is over

							assert _test_patching.open is open

def   lowerCAmelCase__			(     ) ->							List[Any]:
							# pandas.read_csv is not present in _test_patching
							A         =					'__test_patch_submodule_missing_mock__'
							with patch_submodule(_test_patching				,      'pandas.read_csv'				,      lowerCamelCase__			):
														pass

def   lowerCAmelCase__			(     ) ->							Union[str, Any]:
							# builtin should always be mocked even if they're not in the globals
							# in case they're loaded at one point
							A         =					'__test_patch_submodule_missing_builtin_mock__'
							# _test_patching doesn't have "len" in its globals
							assert getattr(_test_patching				,      'len'				,      lowerCamelCase__			) is None
							with patch_submodule(_test_patching				,      'len'				,      lowerCamelCase__			):
														assert _test_patching.len is mock
							assert _test_patching.len is len

def   lowerCAmelCase__			(     ) ->							Union[str, Any]:
							A         =					'__test_patch_submodule_start_and_stop_mock__'
							A         =					patch_submodule(_test_patching				,      'open'				,      lowerCamelCase__			)
							assert _test_patching.open is open
							patch.start()
							assert _test_patching.open is mock
							patch.stop()
							assert _test_patching.open is open

def   lowerCAmelCase__			(     ) ->							int:
							from os import rename as original_rename
							from os.path import dirname as original_dirname
							from os.path import join as original_join

							A         =					'__test_patch_submodule_successive_join__'
							A         =					'__test_patch_submodule_successive_dirname__'
							A         =					'__test_patch_submodule_successive_rename__'
							assert _test_patching.os.path.join is original_join
							assert _test_patching.os.path.dirname is original_dirname
							assert _test_patching.os.rename is original_rename

							with patch_submodule(_test_patching				,      'os.path.join'				,      lowerCamelCase__			):
														with patch_submodule(_test_patching				,      'os.rename'				,      lowerCamelCase__			):
																					with patch_submodule(_test_patching				,      'os.path.dirname'				,      lowerCamelCase__			):
																												assert _test_patching.os.path.join is mock_join
																												assert _test_patching.os.path.dirname is mock_dirname
																												assert _test_patching.os.rename is mock_rename

    # try another order
							with patch_submodule(_test_patching				,      'os.rename'				,      lowerCamelCase__			):
														with patch_submodule(_test_patching				,      'os.path.join'				,      lowerCamelCase__			):
																					with patch_submodule(_test_patching				,      'os.path.dirname'				,      lowerCamelCase__			):
																												assert _test_patching.os.path.join is mock_join
																												assert _test_patching.os.path.dirname is mock_dirname
																												assert _test_patching.os.rename is mock_rename

							assert _test_patching.os.path.join is original_join
							assert _test_patching.os.path.dirname is original_dirname
							assert _test_patching.os.rename is original_rename

def   lowerCAmelCase__			(     ) ->							Optional[Any]:
							A         =					'__test_patch_submodule_doesnt_exist_mock__'
							with patch_submodule(_test_patching				,      '__module_that_doesn_exist__.__attribute_that_doesn_exist__'				,      lowerCamelCase__			):
														pass
							with patch_submodule(_test_patching				,      'os.__attribute_that_doesn_exist__'				,      lowerCamelCase__			):
														pass



