



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


lowercase_  =   logging.get_logger(__name__)

lowercase_  =   {
    '''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json''',
    # See all MBART models at https://huggingface.co/models?filter=mbart
}
class  _snake_case	(    UpperCamelCase__):
				UpperCamelCase__ :    List[str]   						="""mbart"""
				UpperCamelCase__ :    Union[str, Any]   						=["""past_key_values"""]
				UpperCamelCase__ :    Any   						={"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




				def __init__(  self    :  List[str],    __lowercase    :  Tuple=5_0265,    __lowercase    :  List[str]=1024,    __lowercase    :  List[str]=12,    __lowercase    :  Tuple=4096,    __lowercase    :  Optional[int]=16,    __lowercase    :  Union[str, Any]=12,    __lowercase    :  Optional[Any]=4096,    __lowercase    :  Optional[Any]=16,    __lowercase    :  Union[str, Any]=0.0,    __lowercase    :  List[Any]=0.0,    __lowercase    :  Dict=True,    __lowercase    :  List[str]=True,    __lowercase    :  Optional[Any]="gelu",    __lowercase    :  Optional[int]=1024,    __lowercase    :  str=0.1,    __lowercase    :  Tuple=0.0,    __lowercase    :  Tuple=0.0,    __lowercase    :  List[Any]=0.02,    __lowercase    :  Dict=0.0,    __lowercase    :  Any=False,    __lowercase    :  Union[str, Any]=1,    __lowercase    :  Tuple=0,    __lowercase    :  List[Any]=2,    __lowercase    :  List[str]=2,    **__lowercase    :  Union[str, Any],    ):
							lowercase__         = vocab_size
							lowercase__         = max_position_embeddings
							lowercase__         = d_model
							lowercase__         = encoder_ffn_dim
							lowercase__         = encoder_layers
							lowercase__         = encoder_attention_heads
							lowercase__         = decoder_ffn_dim
							lowercase__         = decoder_layers
							lowercase__         = decoder_attention_heads
							lowercase__         = dropout
							lowercase__         = attention_dropout
							lowercase__         = activation_dropout
							lowercase__         = activation_function
							lowercase__         = init_std
							lowercase__         = encoder_layerdrop
							lowercase__         = decoder_layerdrop
							lowercase__         = classifier_dropout
							lowercase__         = use_cache
							lowercase__         = encoder_layers
							lowercase__         = scale_embedding  # scale factor will be sqrt(d_model) if True
							super().__init__(
							    pad_token_id=_a,    bos_token_id=_a,    eos_token_id=_a,    is_encoder_decoder=_a,    forced_eos_token_id=_a,    **_a,    )
class  _snake_case	(    UpperCamelCase__):




				@property
				def 							A__					(  self    :  int  ):





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

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





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




				def 							A__					(  self    :  Optional[Any],    __lowercase    :  PreTrainedTokenizer,    __lowercase    :  int = -1,    __lowercase    :  int = -1,    __lowercase    :  bool = False,    __lowercase    :  Optional[TensorType] = None,    ):
							lowercase__         = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    _a,    _a,    _a,    _a,    _a  )

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

										lowercase__         = torch.cat(
										    [common_inputs["decoder_attention_mask"], torch.ones(_a,    _a  )],    dim=1  )

										lowercase__         = []
										# If the number of encoder and decoder layers are present in the model configuration, both are considered
										lowercase__     ,				lowercase__         = self.num_layers
										lowercase__         = min(_a,    _a  )
										lowercase__         = max(_a,    _a  ) - min_num_layers
										lowercase__         = "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.
										lowercase__         = 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    :  List[str],    __lowercase    :  PreTrainedTokenizer,    __lowercase    :  int = -1,    __lowercase    :  int = -1,    __lowercase    :  bool = False,    __lowercase    :  Optional[TensorType] = None,    ):
							lowercase__         = 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
										lowercase__     ,				lowercase__         = common_inputs["input_ids"].shape
										# Not using the same length for past_key_values
										lowercase__         = seqlen + 2
										lowercase__     ,				lowercase__         = self.num_layers
										lowercase__     ,				lowercase__         = self.num_attention_heads
										lowercase__         = (
										    batch,
										    num_encoder_attention_heads,
										    past_key_values_length,
										    self._config.hidden_size // num_encoder_attention_heads,
										)

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




				def 							A__					(  self    :  Optional[Any],    __lowercase    :  PreTrainedTokenizer,    __lowercase    :  int = -1,    __lowercase    :  int = -1,    __lowercase    :  bool = False,    __lowercase    :  Optional[TensorType] = None,    ):
							lowercase__         = 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
							lowercase__         = tokenizer.num_special_tokens_to_add(_a  )
							lowercase__         = 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
							lowercase__         = [" ".join([tokenizer.unk_token]  ) * seq_length] * batch_size
							lowercase__         = dict(tokenizer(_a,    return_tensors=_a  )  )
							return common_inputs




				def 							A__					(  self    :  List[str],    __lowercase    :  PreTrainedTokenizer,    __lowercase    :  int = -1,    __lowercase    :  int = -1,    __lowercase    :  bool = False,    __lowercase    :  Optional[TensorType] = None,    ):





							if self.task in ["default", "seq2seq-lm"]:
										lowercase__         = 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":
										lowercase__         = self._generate_dummy_inputs_for_causal_lm(
										    _a,    batch_size=_a,    seq_length=_a,    is_pair=_a,    framework=_a  )
							else:
										lowercase__         = 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    :  Union[str, Any],    __lowercase    :  Optional[Any],    __lowercase    :  List[str],    __lowercase    :  Tuple,    __lowercase    :  Any  ):





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








from collections import OrderedDict
from typing import Mapping

from packaging import version

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


snake_case_  :	Any							  =							logging.get_logger(__name__)

snake_case_  :	Any							  =							{
    '''apple/mobilevit-small''': '''https://huggingface.co/apple/mobilevit-small/resolve/main/config.json''',
    '''apple/mobilevit-x-small''': '''https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json''',
    '''apple/mobilevit-xx-small''': '''https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json''',
    '''apple/deeplabv3-mobilevit-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json'''
    ),
    '''apple/deeplabv3-mobilevit-x-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json'''
    ),
    '''apple/deeplabv3-mobilevit-xx-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json'''
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}

class   A__							(						UpperCamelCase__     ):
						UpperCAmelCase									=  "mobilevit"




						def __init__(				self    :					Tuple ,			_a    :					Any=3 ,			_a    :					Any=256 ,			_a    :					Any=2 ,			_a    :					List[str]=[144, 192, 240] ,			_a    :					Optional[int]=[16, 32, 64, 96, 128, 160, 640] ,			_a    :					Union[str, Any]=4 ,			_a    :					Any=2.0 ,			_a    :					List[str]=4.0 ,			_a    :					Tuple="silu" ,			_a    :					Optional[Any]=3 ,			_a    :					List[str]=32 ,			_a    :					Union[str, Any]=0.1 ,			_a    :					Tuple=0.0 ,			_a    :					Union[str, Any]=0.1 ,			_a    :					Union[str, Any]=0.02 ,			_a    :					Tuple=1E-5 ,			_a    :					Any=True ,			_a    :					str=256 ,			_a    :					Tuple=[6, 12, 18] ,			_a    :					int=0.1 ,			_a    :					str=255 ,			**_a    :					Any ,			)							->   Optional[int]:


										"""simple docstring"""





										super().__init__(**_a     )

										_SCREAMING_SNAKE_CASE	     =num_channels
										_SCREAMING_SNAKE_CASE	     =image_size
										_SCREAMING_SNAKE_CASE	     =patch_size
										_SCREAMING_SNAKE_CASE	     =hidden_sizes
										_SCREAMING_SNAKE_CASE	     =neck_hidden_sizes
										_SCREAMING_SNAKE_CASE	     =num_attention_heads
										_SCREAMING_SNAKE_CASE	     =mlp_ratio
										_SCREAMING_SNAKE_CASE	     =expand_ratio
										_SCREAMING_SNAKE_CASE	     =hidden_act
										_SCREAMING_SNAKE_CASE	     =conv_kernel_size
										_SCREAMING_SNAKE_CASE	     =output_stride
										_SCREAMING_SNAKE_CASE	     =hidden_dropout_prob
										_SCREAMING_SNAKE_CASE	     =attention_probs_dropout_prob
										_SCREAMING_SNAKE_CASE	     =classifier_dropout_prob
										_SCREAMING_SNAKE_CASE	     =initializer_range
										_SCREAMING_SNAKE_CASE	     =layer_norm_eps
										_SCREAMING_SNAKE_CASE	     =qkv_bias

										# decode head attributes for semantic segmentation
										_SCREAMING_SNAKE_CASE	     =aspp_out_channels
										_SCREAMING_SNAKE_CASE	     =atrous_rates
										_SCREAMING_SNAKE_CASE	     =aspp_dropout_prob
										_SCREAMING_SNAKE_CASE	     =semantic_loss_ignore_index

class   A__							(						UpperCamelCase__     ):
						UpperCAmelCase									=  version.parse("1.11"     )




						@property
						def 							__UpperCamelCase (				self    :					Union[str, Any]     )							->   Mapping[str, Mapping[int, str]]:


										"""simple docstring"""





										return OrderedDict([('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''})]     )




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


										"""simple docstring"""





										if self.task == "image-classification":
														return OrderedDict([('''logits''', {0: '''batch'''})]     )
										else:
														return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]     )




						@property
						def 							__UpperCamelCase (				self    :					List[Any]     )							->   float:


										"""simple docstring"""





										return 1E-4