
'''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


_lowercase          =						logging.get_logger(__name__)

_lowercase          =						{
    """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_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: str										=  '''blenderbot-small'''
						_lowercase						: List[Any]										=  ['''past_key_values''']
						_lowercase						: List[Any]										=  {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




						def __init__(   self       ,  _lowercase=50_265       ,  _lowercase=512       ,  _lowercase=8       ,  _lowercase=2_048       ,  _lowercase=16       ,  _lowercase=8       ,  _lowercase=2_048       ,  _lowercase=16       ,  _lowercase=0.0       ,  _lowercase=0.0       ,  _lowercase=True       ,  _lowercase=True       ,  _lowercase="gelu"       ,  _lowercase=512       ,  _lowercase=0.1       ,  _lowercase=0.0       ,  _lowercase=0.0       ,  _lowercase=0.02       ,  _lowercase=1       ,  _lowercase=False       ,  _lowercase=0       ,  _lowercase=1       ,  _lowercase=2       ,  _lowercase=2       ,  **_lowercase       ,  ):

												"""simple docstring"""



												_lowerCAmelCase    		=					vocab_size
												_lowerCAmelCase    		=					max_position_embeddings
												_lowerCAmelCase    		=					d_model
												_lowerCAmelCase    		=					encoder_ffn_dim
												_lowerCAmelCase    		=					encoder_layers
												_lowerCAmelCase    		=					encoder_attention_heads
												_lowerCAmelCase    		=					decoder_ffn_dim
												_lowerCAmelCase    		=					decoder_layers
												_lowerCAmelCase    		=					decoder_attention_heads
												_lowerCAmelCase    		=					dropout
												_lowerCAmelCase    		=					attention_dropout
												_lowerCAmelCase    		=					activation_dropout
												_lowerCAmelCase    		=					activation_function
												_lowerCAmelCase    		=					init_std
												_lowerCAmelCase    		=					encoder_layerdrop
												_lowerCAmelCase    		=					decoder_layerdrop
												_lowerCAmelCase    		=					use_cache
												_lowerCAmelCase    		=					encoder_layers
												_lowerCAmelCase    		=					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     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



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

																		if self.use_past:
																								_lowerCAmelCase    		=					{0: """batch"""}
																								_lowerCAmelCase    		=					{0: """batch""", 1: """past_decoder_sequence + sequence"""}
																		else:
																								_lowerCAmelCase    		=					{0: """batch""", 1: """decoder_sequence"""}
																								_lowerCAmelCase    		=					{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.
																		_lowerCAmelCase    		=					OrderedDict(
																		    [
																		        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
																		        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
																		    ] )
																		if self.use_past:
																								_lowerCAmelCase		,       _lowerCAmelCase    		=					self.num_layers
																								for i in range(_lowercase ):
																														_lowerCAmelCase    		=					{0: """batch""", 2: """past_sequence + sequence"""}
																														_lowerCAmelCase    		=					{0: """batch""", 2: """past_sequence + sequence"""}
												else:
																		_lowerCAmelCase    		=					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 ):

												"""simple docstring"""



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




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase = -1       ,  _lowercase = -1       ,  _lowercase = False       ,  _lowercase = None       ,  ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
												    _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase )

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

																		_lowerCAmelCase    		=					torch.cat(
																		    [common_inputs["""decoder_attention_mask"""], torch.ones(_lowercase       ,  _lowercase )]       ,  dim=1 )

																		_lowerCAmelCase    		=					[]
																		# If the number of encoder and decoder layers are present in the model configuration, both are considered
																		_lowerCAmelCase		,       _lowerCAmelCase    		=					self.num_layers
																		_lowerCAmelCase    		=					min(_lowercase       ,  _lowercase )
																		_lowerCAmelCase    		=					max(_lowercase       ,  _lowercase ) - min_num_layers
																		_lowerCAmelCase    		=					"""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.
																		_lowerCAmelCase    		=					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       ,  _lowercase       ,  _lowercase = -1       ,  _lowercase = -1       ,  _lowercase = False       ,  _lowercase = None       ,  ):

												"""simple docstring"""



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

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




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase = -1       ,  _lowercase = -1       ,  _lowercase = False       ,  _lowercase = None       ,  ):

												"""simple docstring"""



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




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase = -1       ,  _lowercase = -1       ,  _lowercase = False       ,  _lowercase = None       ,  ):

												"""simple docstring"""



												if self.task in ["default", "seq2seq-lm"]:
																		_lowerCAmelCase    		=					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":
																		_lowerCAmelCase    		=					self._generate_dummy_inputs_for_causal_lm(
																		    _lowercase       ,  batch_size=_lowercase       ,  seq_length=_lowercase       ,  is_pair=_lowercase       ,  framework=_lowercase )
												else:
																		_lowerCAmelCase    		=					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       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase ):

												"""simple docstring"""



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


'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_lowercase          =						{"""configuration_vit_mae""": ["""VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMAEConfig"""]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase          =						[
						    """VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """ViTMAEForPreTraining""",
						    """ViTMAELayer""",
						    """ViTMAEModel""",
						    """ViTMAEPreTrainedModel""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase          =						[
						    """TFViTMAEForPreTraining""",
						    """TFViTMAEModel""",
						    """TFViTMAEPreTrainedModel""",
						]

if TYPE_CHECKING:
						from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_vit_mae import (
												    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ViTMAEForPreTraining,
												    ViTMAELayer,
												    ViTMAEModel,
												    ViTMAEPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
						import sys

						_lowercase          =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
