"""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


lowerCAmelCase__    :    Dict			=      logging.get_logger(__name__)

lowerCAmelCase__    :    List[Any]			=      {
    'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json',
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}
class 							snake_case			(					__UpperCAmelCase							):




					"""simple docstring"""

					snake_case__							       =					"blenderbot"
					snake_case__							       =					["past_key_values"]
					snake_case__							       =					{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







					def __init__(							self  :      Any       ,lowerCamelCase__  :      Tuple=8_008       ,lowerCamelCase__  :      Dict=128       ,lowerCamelCase__  :      str=2       ,lowerCamelCase__  :      str=10_240       ,lowerCamelCase__  :      List[Any]=32       ,lowerCamelCase__  :      Dict=24       ,lowerCamelCase__  :      Union[str, Any]=10_240       ,lowerCamelCase__  :      List[Any]=32       ,lowerCamelCase__  :      List[Any]=0.0       ,lowerCamelCase__  :      int=0.0       ,lowerCamelCase__  :      Dict=True       ,lowerCamelCase__  :      Optional[Any]=True       ,lowerCamelCase__  :      Dict="gelu"       ,lowerCamelCase__  :      List[str]=2_560       ,lowerCamelCase__  :      List[Any]=0.1       ,lowerCamelCase__  :      Optional[int]=0.0       ,lowerCamelCase__  :      Dict=0.0       ,lowerCamelCase__  :      Any=0.0_2       ,lowerCamelCase__  :      str=1       ,lowerCamelCase__  :      Optional[int]=False       ,lowerCamelCase__  :      List[Any]=0       ,lowerCamelCase__  :      Union[str, Any]=1       ,lowerCamelCase__  :      Dict=2       ,lowerCamelCase__  :      Any=3       ,lowerCamelCase__  :      List[str]=2       ,**lowerCamelCase__  :      int       ,):
									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__			 =				use_cache
									UpperCAmelCase__			 =				encoder_layers
									UpperCAmelCase__			 =				scale_embedding  # scale factor will be sqrt(d_model) if True

									super().__init__(
									    pad_token_id=lowerCamelCase__       ,bos_token_id=lowerCamelCase__       ,eos_token_id=lowerCamelCase__       ,is_encoder_decoder=lowerCamelCase__       ,decoder_start_token_id=lowerCamelCase__       ,encoder_no_repeat_ngram_size=lowerCamelCase__       ,forced_eos_token_id=lowerCamelCase__       ,**lowerCamelCase__       ,)






class 							snake_case			(					__UpperCAmelCase							):




					"""simple docstring"""

					@property
					def 						__lowerCAmelCase		(							self  :      Optional[int]     ):
									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_(lowerCamelCase__       ,direction='inputs'     )
									elif self.task == "causal-lm":
													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(lowerCamelCase__     ):
																					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
					# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
					def 						__lowerCAmelCase		(							self  :      Any     ):
									if self.task in ["default", "seq2seq-lm"]:
													UpperCAmelCase__			 =				super().outputs
									else:
													UpperCAmelCase__			 =				super(lowerCamelCase__       ,self     ).outputs
													if self.use_past:
																	UpperCAmelCase__ ,      UpperCAmelCase__			 =				self.num_layers
																	for i in range(lowerCamelCase__     ):
																					UpperCAmelCase__			 =				{0: 'batch', 2: 'past_sequence + sequence'}
																					UpperCAmelCase__			 =				{0: 'batch', 2: 'past_sequence + sequence'}
									return common_outputs







					def 						__lowerCAmelCase		(							self  :      Optional[int]       ,lowerCamelCase__  :      PreTrainedTokenizer       ,lowerCamelCase__  :      int = -1       ,lowerCamelCase__  :      int = -1       ,lowerCamelCase__  :      bool = False       ,lowerCamelCase__  :      Optional[TensorType] = None       ,):
									UpperCAmelCase__			 =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    lowerCamelCase__       ,lowerCamelCase__       ,lowerCamelCase__       ,lowerCamelCase__       ,lowerCamelCase__     )
									# Generate decoder inputs
									UpperCAmelCase__			 =				seq_length if not self.use_past else 1
									UpperCAmelCase__			 =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    lowerCamelCase__       ,lowerCamelCase__       ,lowerCamelCase__       ,lowerCamelCase__       ,lowerCamelCase__     )
									UpperCAmelCase__			 =				{f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
									UpperCAmelCase__			 =				dict(**lowerCamelCase__       ,**lowerCamelCase__     )

									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
													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(lowerCamelCase__       ,lowerCamelCase__     )]       ,dim=1     )
													UpperCAmelCase__			 =				[]
													UpperCAmelCase__ ,      UpperCAmelCase__			 =				self.num_layers

													for _ in range(lowerCamelCase__     ):
																	common_inputs["past_key_values"].append(
																	    (
																	        torch.zeros(lowerCamelCase__     ),
																	        torch.zeros(lowerCamelCase__     ),
																	        torch.zeros(lowerCamelCase__     ),
																	        torch.zeros(lowerCamelCase__     ),
																	    )     )
									return common_inputs







					def 						__lowerCAmelCase		(							self  :      List[Any]       ,lowerCamelCase__  :      PreTrainedTokenizer       ,lowerCamelCase__  :      int = -1       ,lowerCamelCase__  :      int = -1       ,lowerCamelCase__  :      bool = False       ,lowerCamelCase__  :      Optional[TensorType] = None       ,):
									UpperCAmelCase__			 =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    lowerCamelCase__       ,lowerCamelCase__       ,lowerCamelCase__       ,lowerCamelCase__       ,lowerCamelCase__     )

									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__			 =				seqlen
													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(lowerCamelCase__       ,lowerCamelCase__       ,dtype=lowerCamelCase__     )]       ,dim=1     )
													UpperCAmelCase__			 =				[
													    (torch.zeros(lowerCamelCase__     ), torch.zeros(lowerCamelCase__     )) for _ in range(lowerCamelCase__     )
													]
									return common_inputs







					def 						__lowerCAmelCase		(							self  :      int       ,lowerCamelCase__  :      PreTrainedTokenizer       ,lowerCamelCase__  :      int = -1       ,lowerCamelCase__  :      int = -1       ,lowerCamelCase__  :      bool = False       ,lowerCamelCase__  :      Optional[TensorType] = None       ,):
									# 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
									UpperCAmelCase__			 =				compute_effective_axis_dimension(
									    lowerCamelCase__       ,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(lowerCamelCase__     )
									UpperCAmelCase__			 =				compute_effective_axis_dimension(
									    lowerCamelCase__       ,fixed_dimension=OnnxConfig.default_fixed_sequence       ,num_token_to_add=lowerCamelCase__     )

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







					def 						__lowerCAmelCase		(							self  :      List[Any]       ,lowerCamelCase__  :      PreTrainedTokenizer       ,lowerCamelCase__  :      int = -1       ,lowerCamelCase__  :      int = -1       ,lowerCamelCase__  :      bool = False       ,lowerCamelCase__  :      Optional[TensorType] = None       ,):
									if self.task in ["default", "seq2seq-lm"]:
													UpperCAmelCase__			 =				self._generate_dummy_inputs_for_default_and_seqaseq_lm(
													    lowerCamelCase__       ,batch_size=lowerCamelCase__       ,seq_length=lowerCamelCase__       ,is_pair=lowerCamelCase__       ,framework=lowerCamelCase__     )

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

									return common_inputs







					def 						__lowerCAmelCase		(							self  :      str       ,lowerCamelCase__  :      Union[str, Any]       ,lowerCamelCase__  :      int       ,lowerCamelCase__  :      Tuple       ,lowerCamelCase__  :      List[str]     ):
									if self.task in ["default", "seq2seq-lm"]:
													UpperCAmelCase__			 =				super()._flatten_past_key_values_(lowerCamelCase__       ,lowerCamelCase__       ,lowerCamelCase__       ,lowerCamelCase__     )
									else:
													UpperCAmelCase__			 =				super(lowerCamelCase__       ,self     )._flatten_past_key_values_(
													    lowerCamelCase__       ,lowerCamelCase__       ,lowerCamelCase__       ,lowerCamelCase__     )







					def 						__lowerCAmelCase		(							self  :      Any       ,lowerCamelCase__  :      Mapping[str, Mapping[int, str]]       ,lowerCamelCase__  :      str     ):
									if direction not in ["inputs", "outputs"]:
													raise ValueError(f'''direction must either be "inputs" or "outputs", but {direction} was given'''     )

									UpperCAmelCase__			 =				'past_key_values' if direction == 'inputs' else 'present'
									UpperCAmelCase__ ,      UpperCAmelCase__			 =				self.num_layers

									UpperCAmelCase__			 =				'past_encoder_sequence'
									UpperCAmelCase__			 =				'past_decoder_sequence' if direction == 'inputs' else 'past_decoder_sequence + sequence'

									for i in range(lowerCamelCase__     ):
													UpperCAmelCase__			 =				{0: 'batch', 2: decoder_sequence}
													UpperCAmelCase__			 =				{0: 'batch', 2: decoder_sequence}
													UpperCAmelCase__			 =				{0: 'batch', 2: encoder_sequence}
													UpperCAmelCase__			 =				{0: 'batch', 2: encoder_sequence}




"""simple docstring"""






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


lowerCAmelCase__    :    List[Any]			=      logging.get_logger(__name__)

lowerCAmelCase__    :    Dict			=      {
    '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 							snake_case			(					__UpperCAmelCase							):




					"""simple docstring"""

					snake_case__							       =					"mobilevit"







					def __init__(							self  :      Union[str, Any]       ,lowerCamelCase__  :      Any=3       ,lowerCamelCase__  :      str=256       ,lowerCamelCase__  :      Optional[int]=2       ,lowerCamelCase__  :      Union[str, Any]=[144, 192, 240]       ,lowerCamelCase__  :      Optional[int]=[16, 32, 64, 96, 128, 160, 640]       ,lowerCamelCase__  :      Dict=4       ,lowerCamelCase__  :      int=2.0       ,lowerCamelCase__  :      Optional[Any]=4.0       ,lowerCamelCase__  :      Optional[int]="silu"       ,lowerCamelCase__  :      List[str]=3       ,lowerCamelCase__  :      int=32       ,lowerCamelCase__  :      int=0.1       ,lowerCamelCase__  :      List[str]=0.0       ,lowerCamelCase__  :      List[str]=0.1       ,lowerCamelCase__  :      Tuple=0.0_2       ,lowerCamelCase__  :      Union[str, Any]=1e-5       ,lowerCamelCase__  :      Tuple=True       ,lowerCamelCase__  :      Dict=256       ,lowerCamelCase__  :      Any=[6, 12, 18]       ,lowerCamelCase__  :      Optional[int]=0.1       ,lowerCamelCase__  :      List[str]=255       ,**lowerCamelCase__  :      List[Any]       ,):
									super().__init__(**lowerCamelCase__     )

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

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






class 							snake_case			(					__UpperCAmelCase							):




					"""simple docstring"""

					snake_case__							       =					version.parse("1.11"							)







					@property
					def 						__lowerCAmelCase		(							self  :      str     ):
									return OrderedDict([('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'})]     )







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







					@property
					def 						__lowerCAmelCase		(							self  :      str     ):
									return 1e-4



