


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


snake_case							:	Optional[Any]					      =				logging.get_logger(__name__)

snake_case							:	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 	snake_case_       (lowerCamelCase_				):
					UpperCAmelCase__ :   int   				=      '''blenderbot-small'''
					UpperCAmelCase__ :   str   				=      ['''past_key_values''']
					UpperCAmelCase__ :   int   				=      {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
					def __init__(				self       :Any      ,__snake_case       :Union[str, Any]=5_02_65      ,__snake_case       :Optional[int]=5_12      ,__snake_case       :Optional[Any]=8      ,__snake_case       :Tuple=20_48      ,__snake_case       :Union[str, Any]=16      ,__snake_case       :Union[str, Any]=8      ,__snake_case       :int=20_48      ,__snake_case       :Optional[int]=16      ,__snake_case       :List[Any]=0.0      ,__snake_case       :List[str]=0.0      ,__snake_case       :Optional[Any]=True      ,__snake_case       :Dict=True      ,__snake_case       :int="gelu"      ,__snake_case       :Optional[int]=5_12      ,__snake_case       :str=0.1      ,__snake_case       :int=0.0      ,__snake_case       :str=0.0      ,__snake_case       :Any=0.02      ,__snake_case       :int=1      ,__snake_case       :List[Any]=False      ,__snake_case       :List[str]=0      ,__snake_case       :List[Any]=1      ,__snake_case       :List[Any]=2      ,__snake_case       :Dict=2      ,**__snake_case       :Optional[int]      ,) ->   Any:
												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=__snake_case      ,bos_token_id=__snake_case      ,eos_token_id=__snake_case      ,is_encoder_decoder=__snake_case      ,decoder_start_token_id=__snake_case      ,forced_eos_token_id=__snake_case      ,**__snake_case      ,)







class 	snake_case_       (lowerCamelCase_				):
					@property
					def 			lowerCamelCase__(				self       :Optional[int]				) ->   Mapping[str, Mapping[int, str]]:
												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_(__snake_case      ,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__				, a__					=					self.num_layers
																										for i in range(__snake_case				):
																																	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 			lowerCamelCase__(				self       :Dict				) ->   Mapping[str, Mapping[int, str]]:
												if self.task in ["default", "seq2seq-lm"]:
																			a__					=					super().outputs
												else:
																			a__					=					super(__snake_case      ,self				).outputs
																			if self.use_past:
																										a__				, a__					=					self.num_layers
																										for i in range(__snake_case				):
																																	a__					=					{0: 'batch', 2: 'past_sequence + sequence'}
																																	a__					=					{0: 'batch', 2: 'past_sequence + sequence'}
												return common_outputs
					def 			lowerCamelCase__(				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]:
												a__					=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
												    __snake_case      ,__snake_case      ,__snake_case      ,__snake_case      ,__snake_case				)

												# Generate decoder inputs
												a__					=					seq_length if not self.use_past else 1
												a__					=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
												    __snake_case      ,__snake_case      ,__snake_case      ,__snake_case      ,__snake_case				)
												a__					=					{F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
												a__					=					dict(**__snake_case      ,**__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
																			a__				, a__					=					common_inputs['input_ids'].shape
																			a__					=					common_inputs['decoder_input_ids'].shape[1]
																			a__				, 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(__snake_case      ,__snake_case				)]      ,dim=1				)

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

																			for _ in range(__snake_case				):
																										common_inputs["past_key_values"].append(
																										    (
																										        torch.zeros(__snake_case				),
																										        torch.zeros(__snake_case				),
																										        torch.zeros(__snake_case				),
																										        torch.zeros(__snake_case				),
																										    )				)
																			# TODO: test this.
																			a__					=					encoder_shape if remaining_side_name == 'encoder' else decoder_shape
																			for _ in range(__snake_case      ,__snake_case				):
																										common_inputs["past_key_values"].append((torch.zeros(__snake_case				), torch.zeros(__snake_case				))				)
												return common_inputs
					def 			lowerCamelCase__(				self       :Dict      ,__snake_case       :PreTrainedTokenizer      ,__snake_case       :int = -1      ,__snake_case       :int = -1      ,__snake_case       :bool = False      ,__snake_case       :Optional[TensorType] = None      ,) ->   Mapping[str, Any]:
												a__					=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
												    __snake_case      ,__snake_case      ,__snake_case      ,__snake_case      ,__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
																			a__				, a__					=					common_inputs['input_ids'].shape
																			# Not using the same length for past_key_values
																			a__					=					seqlen + 2
																			a__				, a__					=					self.num_layers
																			a__				, 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(__snake_case      ,__snake_case      ,dtype=__snake_case				)]      ,dim=1				)
																			a__					=					[
																			    (torch.zeros(__snake_case				), torch.zeros(__snake_case				)) for _ in range(__snake_case				)
																			]
												return common_inputs
					def 			lowerCamelCase__(				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]:
												# Copied from OnnxConfig.generate_dummy_inputs
												# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
												# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
												a__					=					compute_effective_axis_dimension(
												    __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
												a__					=					tokenizer.num_special_tokens_to_add(__snake_case				)
												a__					=					compute_effective_axis_dimension(
												    __snake_case      ,fixed_dimension=OnnxConfig.default_fixed_sequence      ,num_token_to_add=__snake_case				)

												# Generate dummy inputs according to compute batch and sequence
												a__					=					[' '.join([tokenizer.unk_token]				) * seq_length] * batch_size
												a__					=					dict(tokenizer(__snake_case      ,return_tensors=__snake_case				)				)
												return common_inputs
					def 			lowerCamelCase__(				self       :str      ,__snake_case       :PreTrainedTokenizer      ,__snake_case       :int = -1      ,__snake_case       :int = -1      ,__snake_case       :bool = False      ,__snake_case       :Optional[TensorType] = None      ,) ->   Mapping[str, Any]:
												if self.task in ["default", "seq2seq-lm"]:
																			a__					=					self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																			    __snake_case      ,batch_size=__snake_case      ,seq_length=__snake_case      ,is_pair=__snake_case      ,framework=__snake_case				)

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

												return common_inputs



					def 			lowerCamelCase__(				self       :List[str]      ,__snake_case       :Union[str, Any]      ,__snake_case       :List[str]      ,__snake_case       :List[str]      ,__snake_case       :Optional[Any]				) ->   List[str]:
												if self.task in ["default", "seq2seq-lm"]:
																			a__					=					super()._flatten_past_key_values_(__snake_case      ,__snake_case      ,__snake_case      ,__snake_case				)
												else:
																			a__					=					super(__snake_case      ,self				)._flatten_past_key_values_(
																			    __snake_case      ,__snake_case      ,__snake_case      ,__snake_case				)








from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *




