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


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

lowerCamelCase_    :		Dict							=	{
    """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 						a__							(       __snake_case		):
			A__    :       Union[str, Any]        =      'blenderbot-small'
			A__    :       List[Any]        =      ['past_key_values']
			A__    :       int        =      {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}



			def __init__(						self				,						UpperCAmelCase=5_0_2_6_5				,						UpperCAmelCase=5_1_2				,						UpperCAmelCase=8				,						UpperCAmelCase=2_0_4_8				,						UpperCAmelCase=1_6				,						UpperCAmelCase=8				,						UpperCAmelCase=2_0_4_8				,						UpperCAmelCase=1_6				,						UpperCAmelCase=0.0				,						UpperCAmelCase=0.0				,						UpperCAmelCase=True				,						UpperCAmelCase=True				,						UpperCAmelCase="gelu"				,						UpperCAmelCase=5_1_2				,						UpperCAmelCase=0.1				,						UpperCAmelCase=0.0				,						UpperCAmelCase=0.0				,						UpperCAmelCase=0.02				,						UpperCAmelCase=1				,						UpperCAmelCase=False				,						UpperCAmelCase=0				,						UpperCAmelCase=1				,						UpperCAmelCase=2				,						UpperCAmelCase=2				,						**UpperCAmelCase				,						)			->   int:
								__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=UpperCAmelCase				,						bos_token_id=UpperCAmelCase				,						eos_token_id=UpperCAmelCase				,						is_encoder_decoder=UpperCAmelCase				,						decoder_start_token_id=UpperCAmelCase				,						forced_eos_token_id=UpperCAmelCase				,						**UpperCAmelCase				,						)






class 						a__							(       __snake_case		):



			@property
			def __SCREAMING_SNAKE_CASE							(						self   )			->   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_(UpperCAmelCase				,						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(UpperCAmelCase   ):
																							__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 __SCREAMING_SNAKE_CASE							(						self   )			->   Mapping[str, Mapping[int, str]]:
								if self.task in ["default", "seq2seq-lm"]:
													__a		 =       super().outputs
								else:
													__a		 =       super(UpperCAmelCase				,						self   ).outputs
													if self.use_past:
																		__a ,				__a		 =       self.num_layers
																		for i in range(UpperCAmelCase   ):
																							__a		 =       {0: 'batch', 2: 'past_sequence + sequence'}
																							__a		 =       {0: 'batch', 2: 'past_sequence + sequence'}
								return common_outputs



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = -1				,						UpperCAmelCase = -1				,						UpperCAmelCase = False				,						UpperCAmelCase = None				,						)			->   Mapping[str, Any]:
								__a		 =       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )

								# Generate decoder inputs
								__a		 =       seq_length if not self.use_past else 1
								__a		 =       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )
								__a		 =       {f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
								__a		 =       dict(**UpperCAmelCase				,						**UpperCAmelCase   )

								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(UpperCAmelCase				,						UpperCAmelCase   )]				,						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(UpperCAmelCase				,						UpperCAmelCase   )
													__a		 =       max(UpperCAmelCase				,						UpperCAmelCase   ) - min_num_layers
													__a		 =       'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

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



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = -1				,						UpperCAmelCase = -1				,						UpperCAmelCase = False				,						UpperCAmelCase = None				,						)			->   Mapping[str, Any]:
								__a		 =       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )

								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(UpperCAmelCase				,						UpperCAmelCase				,						dtype=UpperCAmelCase   )]				,						dim=1   )
													__a		 =       [
													    (torch.zeros(UpperCAmelCase   ), torch.zeros(UpperCAmelCase   )) for _ in range(UpperCAmelCase   )
													]
								return common_inputs



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = -1				,						UpperCAmelCase = -1				,						UpperCAmelCase = False				,						UpperCAmelCase = 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(
								    UpperCAmelCase				,						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(UpperCAmelCase   )
								__a		 =       compute_effective_axis_dimension(
								    UpperCAmelCase				,						fixed_dimension=OnnxConfig.default_fixed_sequence				,						num_token_to_add=UpperCAmelCase   )

								# Generate dummy inputs according to compute batch and sequence
								__a		 =       [' '.join([tokenizer.unk_token]   ) * seq_length] * batch_size
								__a		 =       dict(tokenizer(UpperCAmelCase				,						return_tensors=UpperCAmelCase   )   )
								return common_inputs



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = -1				,						UpperCAmelCase = -1				,						UpperCAmelCase = False				,						UpperCAmelCase = None				,						)			->   Mapping[str, Any]:
								if self.task in ["default", "seq2seq-lm"]:
													__a		 =       self._generate_dummy_inputs_for_default_and_seqaseq_lm(
													    UpperCAmelCase				,						batch_size=UpperCAmelCase				,						seq_length=UpperCAmelCase				,						is_pair=UpperCAmelCase				,						framework=UpperCAmelCase   )

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

								return common_inputs







			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )			->   int:
								if self.task in ["default", "seq2seq-lm"]:
													__a		 =       super()._flatten_past_key_values_(UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )
								else:
													__a		 =       super(UpperCAmelCase				,						self   )._flatten_past_key_values_(
													    UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )





from typing import TYPE_CHECKING

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


lowerCamelCase_    :		Dict							=	{"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig"""]}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		int							=	[
	    """SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """SwinForImageClassification""",
	    """SwinForMaskedImageModeling""",
	    """SwinModel""",
	    """SwinPreTrainedModel""",
	    """SwinBackbone""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Any							=	[
	    """TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFSwinForImageClassification""",
	    """TFSwinForMaskedImageModeling""",
	    """TFSwinModel""",
	    """TFSwinPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_swin import (
		    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SwinBackbone,
		    SwinForImageClassification,
		    SwinForMaskedImageModeling,
		    SwinModel,
		    SwinPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_swin import (
		    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFSwinForImageClassification,
		    TFSwinForMaskedImageModeling,
		    TFSwinModel,
		    TFSwinPreTrainedModel,
		)

else:
	import sys

	lowerCamelCase_    :		List[Any]							=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




