


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A							:					Any     						=     {
    'configuration_altclip': [
        'ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AltCLIPConfig',
        'AltCLIPTextConfig',
        'AltCLIPVisionConfig',
    ],
    'processing_altclip': ['AltCLIPProcessor'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A							:					Any     						=     [
	    'ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'AltCLIPPreTrainedModel',
	    'AltCLIPModel',
	    'AltCLIPTextModel',
	    'AltCLIPVisionModel',
	]


if TYPE_CHECKING:
	from .configuration_altclip import (
	    ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    AltCLIPConfig,
	    AltCLIPTextConfig,
	    AltCLIPVisionConfig,
	)
	from .processing_altclip import AltCLIPProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_altclip import (
		    ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    AltCLIPModel,
		    AltCLIPPreTrainedModel,
		    AltCLIPTextModel,
		    AltCLIPVisionModel,
		)


else:
	import sys

	A							:					List[str]     						=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)









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							:					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     UpperCamelCase(  _a   ):
					snake_case_		:				str										=	"""blenderbot-small"""
					snake_case_		:				List[Any]										=	["""past_key_values"""]
					snake_case_		:				Any										=	{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}


					def __init__(						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=5_0_2_6_5     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=5_1_2     ,    SCREAMING_SNAKE_CASE	:		Any=8     ,    SCREAMING_SNAKE_CASE	:		int=2_0_4_8     ,    SCREAMING_SNAKE_CASE	:		Any=1_6     ,    SCREAMING_SNAKE_CASE	:		Tuple=8     ,    SCREAMING_SNAKE_CASE	:		List[Any]=2_0_4_8     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=1_6     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=0.0     ,    SCREAMING_SNAKE_CASE	:		str=0.0     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=True     ,    SCREAMING_SNAKE_CASE	:		int=True     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]="gelu"     ,    SCREAMING_SNAKE_CASE	:		Dict=5_1_2     ,    SCREAMING_SNAKE_CASE	:		Any=0.1     ,    SCREAMING_SNAKE_CASE	:		Dict=0.0     ,    SCREAMING_SNAKE_CASE	:		Any=0.0     ,    SCREAMING_SNAKE_CASE	:		int=0.02     ,    SCREAMING_SNAKE_CASE	:		List[Any]=1     ,    SCREAMING_SNAKE_CASE	:		int=False     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=0     ,    SCREAMING_SNAKE_CASE	:		str=1     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=2     ,    SCREAMING_SNAKE_CASE	:		Any=2     ,    **SCREAMING_SNAKE_CASE	:		Dict     ,    )			->	Optional[int]:




											'''simple docstring'''

											__snake_case				=		vocab_size
											__snake_case				=		max_position_embeddings
											__snake_case				=		d_model
											__snake_case				=		encoder_ffn_dim
											__snake_case				=		encoder_layers
											__snake_case				=		encoder_attention_heads
											__snake_case				=		decoder_ffn_dim
											__snake_case				=		decoder_layers
											__snake_case				=		decoder_attention_heads
											__snake_case				=		dropout
											__snake_case				=		attention_dropout
											__snake_case				=		activation_dropout
											__snake_case				=		activation_function
											__snake_case				=		init_std
											__snake_case				=		encoder_layerdrop
											__snake_case				=		decoder_layerdrop
											__snake_case				=		use_cache
											__snake_case				=		encoder_layers
											__snake_case				=		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     UpperCamelCase(  _a   ):


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




											'''simple docstring'''

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

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




											'''simple docstring'''

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


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




											'''simple docstring'''

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

																	__snake_case				=		torch.cat(
																	    [common_inputs["decoder_attention_mask"], torch.ones(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)]     ,    dim=1						)

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




											'''simple docstring'''

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

																	__snake_case				=		common_inputs["attention_mask"].dtype
																	__snake_case				=		torch.cat(
																	    [common_inputs["attention_mask"], torch.ones(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    dtype=SCREAMING_SNAKE_CASE						)]     ,    dim=1						)
																	__snake_case				=		[
																	    (torch.zeros(SCREAMING_SNAKE_CASE						), torch.zeros(SCREAMING_SNAKE_CASE						)) for _ in range(SCREAMING_SNAKE_CASE						)
																	]
											return common_inputs


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




											'''simple docstring'''

											__snake_case				=		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
											__snake_case				=		tokenizer.num_special_tokens_to_add(SCREAMING_SNAKE_CASE						)
											__snake_case				=		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
											__snake_case				=		[" ".join([tokenizer.unk_token]						) * seq_length] * batch_size
											__snake_case				=		dict(tokenizer(SCREAMING_SNAKE_CASE     ,    return_tensors=SCREAMING_SNAKE_CASE						)						)
											return common_inputs


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




											'''simple docstring'''

											if self.task in ["default", "seq2seq-lm"]:
																	__snake_case				=		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":
																	__snake_case				=		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:
																	__snake_case				=		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	:		int     ,    SCREAMING_SNAKE_CASE	:		Any     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		int						)			->	Union[str, Any]:




											'''simple docstring'''

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





