






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-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json",
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}




class 			UpperCamelCase_				(      A__    ):



	'''simple docstring'''




	lowerCAmelCase													=			'''blenderbot'''
	lowerCAmelCase													=			['''past_key_values''']
	lowerCAmelCase													=			{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




	def __init__(	self	,      a=80_08	,      a=1_28	,      a=2	,      a=1_02_40	,      a=32	,      a=24	,      a=1_02_40	,      a=32	,      a=0.0	,      a=0.0	,      a=True	,      a=True	,      a="gelu"	,      a=25_60	,      a=0.1	,      a=0.0	,      a=0.0	,      a=0.02	,      a=1	,      a=False	,      a=0	,      a=1	,      a=2	,      a=3	,      a=2	,      **a	,      )	->   Any:
								snake_case_           =							vocab_size
								snake_case_           =							max_position_embeddings
								snake_case_           =							d_model
								snake_case_           =							encoder_ffn_dim
								snake_case_           =							encoder_layers
								snake_case_           =							encoder_attention_heads
								snake_case_           =							decoder_ffn_dim
								snake_case_           =							decoder_layers
								snake_case_           =							decoder_attention_heads
								snake_case_           =							dropout
								snake_case_           =							attention_dropout
								snake_case_           =							activation_dropout
								snake_case_           =							activation_function
								snake_case_           =							init_std
								snake_case_           =							encoder_layerdrop
								snake_case_           =							decoder_layerdrop
								snake_case_           =							use_cache
								snake_case_           =							encoder_layers
								snake_case_           =							scale_embedding  # scale factor will be sqrt(d_model) if True

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






class 			UpperCamelCase_				(      A__    ):



	'''simple docstring'''




	@property
	def 							_UpperCamelCase      (	self )	->   Mapping[str, Mapping[int, str]]:
								if self.task in ["default", "seq2seq-lm"]:
															snake_case_           =							OrderedDict(
															    [
															        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
															        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
															    ] )
															if self.use_past:
																						snake_case_           =							{0: '''batch'''}
																						snake_case_           =							{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
															else:
																						snake_case_           =							{0: '''batch''', 1: '''decoder_sequence'''}
																						snake_case_           =							{0: '''batch''', 1: '''decoder_sequence'''}
															if self.use_past:
																						self.fill_with_past_key_values_(__snake_case	,      direction='inputs' )
								elif self.task == "causal-lm":
															snake_case_           =							OrderedDict(
															    [
															        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
															        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
															    ] )
															if self.use_past:
																						snake_case_           =							self.num_layers
																						for i in range(__snake_case ):
																													snake_case_           =							{0: '''batch''', 2: '''past_sequence + sequence'''}
																													snake_case_           =							{0: '''batch''', 2: '''past_sequence + sequence'''}
								else:
															snake_case_           =							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 							_UpperCamelCase      (	self )	->   Mapping[str, Mapping[int, str]]:
								if self.task in ["default", "seq2seq-lm"]:
															snake_case_           =							super().outputs
								else:
															snake_case_           =							super(__snake_case	,      self ).outputs
															if self.use_past:
																						snake_case_           =							self.num_layers
																						for i in range(__snake_case ):
																													snake_case_           =							{0: '''batch''', 2: '''past_sequence + sequence'''}
																													snake_case_           =							{0: '''batch''', 2: '''past_sequence + sequence'''}
								return common_outputs




	def 							_UpperCamelCase      (	self	,      a	,      a = -1	,      a = -1	,      a = False	,      a = None	,      )	->   Mapping[str, Any]:
								snake_case_           =							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    __snake_case	,      __snake_case	,      __snake_case	,      __snake_case	,      __snake_case )
								# Generate decoder inputs
								snake_case_           =							seq_length if not self.use_past else 1
								snake_case_           =							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    __snake_case	,      __snake_case	,      __snake_case	,      __snake_case	,      __snake_case )
								snake_case_           =							{F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
								snake_case_           =							dict(**__snake_case	,      **__snake_case )

								if self.use_past:
															if not is_torch_available():
																						raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
															else:
																						import torch
															snake_case_           =							common_inputs['''input_ids'''].shape
															snake_case_           =							common_inputs['''decoder_input_ids'''].shape[1]
															snake_case_           =							self.num_attention_heads
															snake_case_           =							(
															    batch,
															    num_encoder_attention_heads,
															    encoder_seq_length,
															    self._config.hidden_size // num_encoder_attention_heads,
															)
															snake_case_           =							decoder_seq_length
															snake_case_           =							(
															    batch,
															    num_decoder_attention_heads,
															    decoder_past_length,
															    self._config.hidden_size // num_decoder_attention_heads,
															)
															snake_case_           =							torch.cat(
															    [common_inputs['decoder_attention_mask'], torch.ones(__snake_case	,      __snake_case )]	,      dim=1 )
															snake_case_           =							[]
															snake_case_           =							self.num_layers

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




	def 							_UpperCamelCase      (	self	,      a	,      a = -1	,      a = -1	,      a = False	,      a = None	,      )	->   Mapping[str, Any]:
								snake_case_           =							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    __snake_case	,      __snake_case	,      __snake_case	,      __snake_case	,      __snake_case )

								if self.use_past:
															if not is_torch_available():
																						raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
															else:
																						import torch
															snake_case_           =							common_inputs['''input_ids'''].shape
															snake_case_           =							seqlen
															snake_case_           =							self.num_layers
															snake_case_           =							self.num_attention_heads
															snake_case_           =							(
															    batch,
															    num_encoder_attention_heads,
															    past_key_values_length,
															    self._config.hidden_size // num_encoder_attention_heads,
															)
															snake_case_           =							common_inputs['''attention_mask'''].dtype
															snake_case_           =							torch.cat(
															    [common_inputs['attention_mask'], torch.ones(__snake_case	,      __snake_case	,      dtype=__snake_case )]	,      dim=1 )
															snake_case_           =							[
															    (torch.zeros(__snake_case ), torch.zeros(__snake_case )) for _ in range(__snake_case )
															]
								return common_inputs




	def 							_UpperCamelCase      (	self	,      a	,      a = -1	,      a = -1	,      a = False	,      a = 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
								snake_case_           =							compute_effective_axis_dimension(
								    __snake_case	,      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
								snake_case_           =							tokenizer.num_special_tokens_to_add(__snake_case )
								snake_case_           =							compute_effective_axis_dimension(
								    __snake_case	,      fixed_dimension=OnnxConfig.default_fixed_sequence	,      num_token_to_add=__snake_case )

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




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

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

								return common_inputs




	def 							_UpperCamelCase      (	self	,      a	,      a	,      a	,      a )	->   Optional[Any]:
								if self.task in ["default", "seq2seq-lm"]:
															snake_case_           =							super()._flatten_past_key_values_(__snake_case	,      __snake_case	,      __snake_case	,      __snake_case )
								else:
															snake_case_           =							super(__snake_case	,      self )._flatten_past_key_values_(
															    __snake_case	,      __snake_case	,      __snake_case	,      __snake_case )






	def 							_UpperCamelCase      (	self	,      a	,      a )	->   Dict:
								if direction not in ["inputs", "outputs"]:
															raise ValueError(F'''direction must either be \"inputs\" or \"outputs\", but {direction} was given''' )

								snake_case_           =							'''past_key_values''' if direction == '''inputs''' else '''present'''
								snake_case_           =							self.num_layers

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

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













from typing import TYPE_CHECKING

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


lowercase  							= {
    "configuration_deformable_detr": ["DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeformableDetrConfig"],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase  							= ["DeformableDetrFeatureExtractor"]
	lowercase  							= ["DeformableDetrImageProcessor"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase  							= [
	    "DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "DeformableDetrForObjectDetection",
	    "DeformableDetrModel",
	    "DeformableDetrPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
		from .image_processing_deformable_detr import DeformableDetrImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_deformable_detr import (
		    DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DeformableDetrForObjectDetection,
		    DeformableDetrModel,
		    DeformableDetrPreTrainedModel,
		)

else:
	import sys

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





