
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


lowerCamelCase__	=							logging.get_logger(__name__)

lowerCamelCase__	=							{
    "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/config.json",
    # See all BART models at https://huggingface.co/models?filter=bart
}







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: Optional[Any]	=		"bart"
						UpperCamelCase_							: Optional[Any]	=		["past_key_values"]
						UpperCamelCase_							: Any	=		{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}

						def __init__(			self       ,							a=5_02_65       ,							a=10_24       ,							a=12       ,							a=40_96       ,							a=16       ,							a=12       ,							a=40_96       ,							a=16       ,							a=0.0       ,							a=0.0       ,							a="gelu"       ,							a=10_24       ,							a=0.1       ,							a=0.0       ,							a=0.0       ,							a=0.02       ,							a=0.0       ,							a=False       ,							a=True       ,							a=3       ,							a=1       ,							a=0       ,							a=2       ,							a=True       ,							a=2       ,							a=2       ,							**a       ,							)					->      Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	vocab_size
								_UpperCamelCase						    =	max_position_embeddings
								_UpperCamelCase						    =	d_model
								_UpperCamelCase						    =	encoder_ffn_dim
								_UpperCamelCase						    =	encoder_layers
								_UpperCamelCase						    =	encoder_attention_heads
								_UpperCamelCase						    =	decoder_ffn_dim
								_UpperCamelCase						    =	decoder_layers
								_UpperCamelCase						    =	decoder_attention_heads
								_UpperCamelCase						    =	dropout
								_UpperCamelCase						    =	attention_dropout
								_UpperCamelCase						    =	activation_dropout
								_UpperCamelCase						    =	activation_function
								_UpperCamelCase						    =	init_std
								_UpperCamelCase						    =	encoder_layerdrop
								_UpperCamelCase						    =	decoder_layerdrop
								_UpperCamelCase						    =	classifier_dropout
								_UpperCamelCase						    =	use_cache
								_UpperCamelCase						    =	encoder_layers
								_UpperCamelCase						    =	scale_embedding  # scale factor will be sqrt(d_model) if True

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

								# ensure backward compatibility for BART CNN models
								if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated"""       ,							a				):
										_UpperCamelCase						    =	self.bos_token_id
										warnings.warn(
										    F'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
										    """The config can simply be saved and uploaded again to be fixed."""				)







class        lowerCAmelCase__      (   __lowercase	):

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


								'''simple docstring'''

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

										if self.use_past:
												_UpperCamelCase						    =	{0: """batch"""}
												_UpperCamelCase						    =	{0: """batch""", 1: """past_decoder_sequence + sequence"""}
										else:
												_UpperCamelCase						    =	{0: """batch""", 1: """decoder_sequence"""}
												_UpperCamelCase						    =	{0: """batch""", 1: """decoder_sequence"""}

										if self.use_past:
												self.fill_with_past_key_values_(a       ,							direction="""inputs"""				)
								elif self.task == "causal-lm":
										# TODO: figure this case out.
										_UpperCamelCase						    =	OrderedDict(
										    [
										        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
										        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
										    ]				)
										if self.use_past:
												_UpperCamelCase    ,	_UpperCamelCase						    =	self.num_layers
												for i in range(a				):
														_UpperCamelCase						    =	{0: """batch""", 2: """past_sequence + sequence"""}
														_UpperCamelCase						    =	{0: """batch""", 2: """past_sequence + sequence"""}
								else:
										_UpperCamelCase						    =	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  A_				(			self				)					->      Mapping[str, Mapping[int, str]]:


								'''simple docstring'''

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

						def  A_				(			self       ,							a       ,							a = -1       ,							a = -1       ,							a = False       ,							a = None       ,							)					->      Mapping[str, Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    a       ,							a       ,							a       ,							a       ,							a				)

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

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

										_UpperCamelCase						    =	torch.cat(
										    [common_inputs["""decoder_attention_mask"""], torch.ones(a       ,							a				)]       ,							dim=1				)

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

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

						def  A_				(			self       ,							a       ,							a = -1       ,							a = -1       ,							a = False       ,							a = None       ,							)					->      Mapping[str, Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    a       ,							a       ,							a       ,							a       ,							a				)

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

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

						def  A_				(			self       ,							a       ,							a = -1       ,							a = -1       ,							a = False       ,							a = None       ,							)					->      Mapping[str, Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	compute_effective_axis_dimension(
								    a       ,							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
								_UpperCamelCase						    =	tokenizer.num_special_tokens_to_add(a				)
								_UpperCamelCase						    =	compute_effective_axis_dimension(
								    a       ,							fixed_dimension=OnnxConfig.default_fixed_sequence       ,							num_token_to_add=a				)

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

						def  A_				(			self       ,							a       ,							a = -1       ,							a = -1       ,							a = False       ,							a = None       ,							)					->      Mapping[str, Any]:


								'''simple docstring'''

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

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

								return common_inputs







						def  A_				(			self       ,							a       ,							a       ,							a       ,							a				)					->      Optional[Any]:


								'''simple docstring'''

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





from typing import TYPE_CHECKING

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


lowerCamelCase__	=							{"configuration_yolos": ["YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP", "YolosConfig", "YolosOnnxConfig"]}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase__	=							["YolosFeatureExtractor"]
		lowerCamelCase__	=							["YolosImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase__	=							[
		    "YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "YolosForObjectDetection",
		    "YolosModel",
		    "YolosPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_yolos import YolosFeatureExtractor
				from .image_processing_yolos import YolosImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_yolos import (
				    YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    YolosForObjectDetection,
				    YolosModel,
				    YolosPreTrainedModel,
				)


else:
		import sys

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



