



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[int]  =						logging.get_logger(__name__)

snake_case    :						Optional[int]  =						{
    '''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__  (	SCREAMING_SNAKE_CASE__						):
					__A     :    Tuple           =						'blenderbot-small'
					__A     :    Optional[int]           =						['past_key_values']
					__A     :    str           =						{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}







					def __init__(       self      :     Tuple			,		_A      :     Any=5_0265			,		_A      :     Tuple=512			,		_A      :     Optional[Any]=8			,		_A      :     Tuple=2048			,		_A      :     Optional[Any]=16			,		_A      :     List[str]=8			,		_A      :     Optional[int]=2048			,		_A      :     int=16			,		_A      :     List[Any]=0.0			,		_A      :     int=0.0			,		_A      :     Optional[Any]=True			,		_A      :     int=True			,		_A      :     Optional[int]="gelu"			,		_A      :     Dict=512			,		_A      :     str=0.1			,		_A      :     Any=0.0			,		_A      :     Union[str, Any]=0.0			,		_A      :     Optional[int]=0.02			,		_A      :     int=1			,		_A      :     int=False			,		_A      :     Any=0			,		_A      :     List[Any]=1			,		_A      :     Tuple=2			,		_A      :     Any=2			,		**_A      :     Optional[Any]			,		):
											A__	:							Optional[int]						    = vocab_size
											A__	:							Dict						    = max_position_embeddings
											A__	:							Union[str, Any]						    = d_model
											A__	:							List[str]						    = encoder_ffn_dim
											A__	:							Any						    = encoder_layers
											A__	:							Optional[Any]						    = encoder_attention_heads
											A__	:							int						    = decoder_ffn_dim
											A__	:							List[str]						    = decoder_layers
											A__	:							str						    = decoder_attention_heads
											A__	:							str						    = dropout
											A__	:							List[str]						    = attention_dropout
											A__	:							Any						    = activation_dropout
											A__	:							Optional[int]						    = activation_function
											A__	:							int						    = init_std
											A__	:							List[str]						    = encoder_layerdrop
											A__	:							Dict						    = decoder_layerdrop
											A__	:							List[Any]						    = use_cache
											A__	:							Optional[Any]						    = encoder_layers
											A__	:							Any						    = scale_embedding  # scale factor will be sqrt(d_model) if True

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




class 							lowerCAmelCase__  (	SCREAMING_SNAKE_CASE__						):







					@property
					def  _lowercase			(       self      :     Any):

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

																	if self.use_past:
																							A__	:							int						    = {0: """batch"""}
																							A__	:							str						    = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
																	else:
																							A__	:							Union[str, Any]						    = {0: """batch""", 1: """decoder_sequence"""}
																							A__	:							Optional[Any]						    = {0: """batch""", 1: """decoder_sequence"""}

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

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







					def  _lowercase			(       self      :     int			,		_A      :     List[Any]			,		_A      :     Union[str, Any] = -1			,		_A      :     List[Any] = -1			,		_A      :     Dict = False			,		_A      :     Optional[Any] = None			,		):
											A__	:							Tuple						    = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    _lowercase			,		_lowercase			,		_lowercase			,		_lowercase			,		_lowercase)

											# Generate decoder inputs
											A__	:							List[str]						    = seq_length if not self.use_past else 1
											A__	:							str						    = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    _lowercase			,		_lowercase			,		_lowercase			,		_lowercase			,		_lowercase)
											A__	:							Optional[Any]						    = {F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
											A__	:							Any						    = dict(**_lowercase			,		**_lowercase)

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

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

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

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







					def  _lowercase			(       self      :     Optional[int]			,		_A      :     int			,		_A      :     str = -1			,		_A      :     str = -1			,		_A      :     List[Any] = False			,		_A      :     List[str] = None			,		):
											A__	:							Union[str, Any]						    = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    _lowercase			,		_lowercase			,		_lowercase			,		_lowercase			,		_lowercase)

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

																	A__	:							str						    = common_inputs["""attention_mask"""].dtype
																	A__	:							Tuple						    = torch.cat(
																	    [common_inputs["attention_mask"], torch.ones(_lowercase			,		_lowercase			,		dtype=_lowercase)]			,		dim=1)
																	A__	:							int						    = [
																	    (torch.zeros(_lowercase), torch.zeros(_lowercase)) for _ in range(_lowercase)
																	]
											return common_inputs







					def  _lowercase			(       self      :     Optional[int]			,		_A      :     str			,		_A      :     str = -1			,		_A      :     List[Any] = -1			,		_A      :     Optional[int] = False			,		_A      :     str = None			,		):
											A__	:							Any						    = compute_effective_axis_dimension(
											    _lowercase			,		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__	:							Any						    = tokenizer.num_special_tokens_to_add(_lowercase)
											A__	:							Dict						    = compute_effective_axis_dimension(
											    _lowercase			,		fixed_dimension=OnnxConfig.default_fixed_sequence			,		num_token_to_add=_lowercase)

											# Generate dummy inputs according to compute batch and sequence
											A__	:							Dict						    = [""" """.join([tokenizer.unk_token]) * seq_length] * batch_size
											A__	:							List[Any]						    = dict(tokenizer(_lowercase			,		return_tensors=_lowercase))
											return common_inputs







					def  _lowercase			(       self      :     str			,		_A      :     List[str]			,		_A      :     Union[str, Any] = -1			,		_A      :     Any = -1			,		_A      :     Optional[Any] = False			,		_A      :     List[Any] = None			,		):

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

											elif self.task == "causal-lm":
																	A__	:							int						    = self._generate_dummy_inputs_for_causal_lm(
																	    _lowercase			,		batch_size=_lowercase			,		seq_length=_lowercase			,		is_pair=_lowercase			,		framework=_lowercase)
											else:
																	A__	:							List[Any]						    = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
																	    _lowercase			,		batch_size=_lowercase			,		seq_length=_lowercase			,		is_pair=_lowercase			,		framework=_lowercase)

											return common_inputs







					def  _lowercase			(       self      :     Dict			,		_A      :     str			,		_A      :     List[str]			,		_A      :     Tuple			,		_A      :     List[str]):

											if self.task in ["default", "seq2seq-lm"]:
																	A__	:							Union[str, Any]						    = super()._flatten_past_key_values_(_lowercase			,		_lowercase			,		_lowercase			,		_lowercase)
											else:
																	A__	:							int						    = super(_lowercase			,		self)._flatten_past_key_values_(
																	    _lowercase			,		_lowercase			,		_lowercase			,		_lowercase)





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


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

snake_case    :						Union[str, Any]  =						{
    'facebook/data2vec-text-base': 'https://huggingface.co/data2vec/resolve/main/config.json',
}




class 							lowerCAmelCase__  (	UpperCamelCase						):
					__A     :    Optional[Any]           =						'data2vec-text'







					def __init__(       self      :     Optional[Any]			,		_A      :     List[str]=3_0522			,		_A      :     Optional[int]=768			,		_A      :     Union[str, Any]=12			,		_A      :     str=12			,		_A      :     Optional[int]=3072			,		_A      :     Dict="gelu"			,		_A      :     List[str]=0.1			,		_A      :     Any=0.1			,		_A      :     str=512			,		_A      :     Any=2			,		_A      :     str=0.02			,		_A      :     List[str]=1e-12			,		_A      :     Optional[int]=1			,		_A      :     Any=0			,		_A      :     Any=2			,		_A      :     List[Any]="absolute"			,		_A      :     Tuple=True			,		_A      :     str=None			,		**_A      :     List[Any]			,		):
											super().__init__(pad_token_id=_A			,		bos_token_id=_A			,		eos_token_id=_A			,		**_A)

											A__	:							Optional[int]						    = vocab_size
											A__	:							Tuple						    = hidden_size
											A__	:							str						    = num_hidden_layers
											A__	:							Union[str, Any]						    = num_attention_heads
											A__	:							Dict						    = hidden_act
											A__	:							List[Any]						    = intermediate_size
											A__	:							int						    = hidden_dropout_prob
											A__	:							Dict						    = attention_probs_dropout_prob
											A__	:							Union[str, Any]						    = max_position_embeddings
											A__	:							Tuple						    = type_vocab_size
											A__	:							Union[str, Any]						    = initializer_range
											A__	:							str						    = layer_norm_eps
											A__	:							int						    = position_embedding_type
											A__	:							str						    = use_cache
											A__	:							int						    = classifier_dropout




class 							lowerCAmelCase__  (	UpperCamelCase						):







					@property
					def  _lowercase			(       self      :     Tuple):
											if self.task == "multiple-choice":
																	A__	:							List[str]						    = {0: "batch", 1: "choice", 2: "sequence"}
											else:
																	A__	:							List[str]						    = {0: "batch", 1: "sequence"}
											return OrderedDict(
											    [
											        ("input_ids", dynamic_axis),
											        ("attention_mask", dynamic_axis),
											    ])