

"""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


_SCREAMING_SNAKE_CASE       :  Any        = logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE       :  Optional[int]        = {
    """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     __a   (      snake_case__    ):

			"""simple docstring"""

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






			def __init__(							self     :					Dict						,  lowercase_     :					Any=8008						,  lowercase_     :					Dict=128						,  lowercase_     :					Tuple=2						,  lowercase_     :					List[Any]=1_0240						,  lowercase_     :					Optional[int]=32						,  lowercase_     :					Optional[Any]=24						,  lowercase_     :					str=1_0240						,  lowercase_     :					Tuple=32						,  lowercase_     :					Optional[int]=0.0						,  lowercase_     :					Optional[int]=0.0						,  lowercase_     :					str=True						,  lowercase_     :					List[str]=True						,  lowercase_     :					Union[str, Any]="gelu"						,  lowercase_     :					List[str]=2560						,  lowercase_     :					Any=0.1						,  lowercase_     :					List[Any]=0.0						,  lowercase_     :					Any=0.0						,  lowercase_     :					Union[str, Any]=0.0_2						,  lowercase_     :					Any=1						,  lowercase_     :					Optional[int]=False						,  lowercase_     :					int=0						,  lowercase_     :					Optional[int]=1						,  lowercase_     :					Any=2						,  lowercase_     :					Any=3						,  lowercase_     :					Dict=2						,  **lowercase_     :					Optional[int]						,  ):
				UpperCamelCase__		:    int         =vocab_size
				UpperCamelCase__		:    List[Any]         =max_position_embeddings
				UpperCamelCase__		:    List[Any]         =d_model
				UpperCamelCase__		:    Union[str, Any]         =encoder_ffn_dim
				UpperCamelCase__		:    Optional[Any]         =encoder_layers
				UpperCamelCase__		:    List[str]         =encoder_attention_heads
				UpperCamelCase__		:    Optional[Any]         =decoder_ffn_dim
				UpperCamelCase__		:    Dict         =decoder_layers
				UpperCamelCase__		:    int         =decoder_attention_heads
				UpperCamelCase__		:    List[Any]         =dropout
				UpperCamelCase__		:    Optional[int]         =attention_dropout
				UpperCamelCase__		:    List[Any]         =activation_dropout
				UpperCamelCase__		:    Dict         =activation_function
				UpperCamelCase__		:    Optional[int]         =init_std
				UpperCamelCase__		:    Optional[int]         =encoder_layerdrop
				UpperCamelCase__		:    Tuple         =decoder_layerdrop
				UpperCamelCase__		:    List[str]         =use_cache
				UpperCamelCase__		:    int         =encoder_layers
				UpperCamelCase__		:    str         =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_						,  encoder_no_repeat_ngram_size=lowercase_						,  forced_eos_token_id=lowercase_						,  **lowercase_						,  )



class     __a   (      snake_case__    ):

			"""simple docstring"""

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






			def    _lowerCAmelCase			(							self     :					Optional[int]						,  lowercase_     :					PreTrainedTokenizer						,  lowercase_     :					int = -1						,  lowercase_     :					int = -1						,  lowercase_     :					bool = False						,  lowercase_     :					Optional[TensorType] = None						,  ):
				UpperCamelCase__		:    List[Any]         =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    lowercase_						,  lowercase_						,  lowercase_						,  lowercase_						,  lowercase_						)
				# Generate decoder inputs
				UpperCamelCase__		:    List[Any]         =seq_length if not self.use_past else 1
				UpperCamelCase__		:    int         =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    lowercase_						,  lowercase_						,  lowercase_						,  lowercase_						,  lowercase_						)
				UpperCamelCase__		:    str         ={f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
				UpperCamelCase__		:    str         =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
					UpperCamelCase__		:    Optional[int]         =common_inputs['''input_ids'''].shape
					UpperCamelCase__		:    Any         =common_inputs['''decoder_input_ids'''].shape[1]
					UpperCamelCase__		:    Union[str, Any]         =self.num_attention_heads
					UpperCamelCase__		:    Any         =(
					    batch,
					    num_encoder_attention_heads,
					    encoder_seq_length,
					    self._config.hidden_size // num_encoder_attention_heads,
					)
					UpperCamelCase__		:    Tuple         =decoder_seq_length
					UpperCamelCase__		:    Optional[Any]         =(
					    batch,
					    num_decoder_attention_heads,
					    decoder_past_length,
					    self._config.hidden_size // num_decoder_attention_heads,
					)
					UpperCamelCase__		:    Union[str, Any]         =torch.cat(
					    [common_inputs['''decoder_attention_mask'''], torch.ones(lowercase_						,  lowercase_						)]						,  dim=1						)
					UpperCamelCase__		:    Any         =[]
					UpperCamelCase__		:    Union[str, Any]         =self.num_layers

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






			def    _lowerCAmelCase			(							self     :					Tuple						,  lowercase_     :					PreTrainedTokenizer						,  lowercase_     :					int = -1						,  lowercase_     :					int = -1						,  lowercase_     :					bool = False						,  lowercase_     :					Optional[TensorType] = None						,  ):
				UpperCamelCase__		:    str         =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
					UpperCamelCase__		:    Tuple         =common_inputs['''input_ids'''].shape
					UpperCamelCase__		:    List[Any]         =seqlen
					UpperCamelCase__		:    Any         =self.num_layers
					UpperCamelCase__		:    Tuple         =self.num_attention_heads
					UpperCamelCase__		:    Optional[int]         =(
					    batch,
					    num_encoder_attention_heads,
					    past_key_values_length,
					    self._config.hidden_size // num_encoder_attention_heads,
					)
					UpperCamelCase__		:    Union[str, Any]         =common_inputs['''attention_mask'''].dtype
					UpperCamelCase__		:    int         =torch.cat(
					    [common_inputs['''attention_mask'''], torch.ones(lowercase_						,  lowercase_						,  dtype=lowercase_						)]						,  dim=1						)
					UpperCamelCase__		:    List[str]         =[
					    (torch.zeros(lowercase_						), torch.zeros(lowercase_						)) for _ in range(lowercase_						)
					]
				return common_inputs






			def    _lowerCAmelCase			(							self     :					Tuple						,  lowercase_     :					PreTrainedTokenizer						,  lowercase_     :					int = -1						,  lowercase_     :					int = -1						,  lowercase_     :					bool = False						,  lowercase_     :					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__		:    str         =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
				UpperCamelCase__		:    Union[str, Any]         =tokenizer.num_special_tokens_to_add(lowercase_						)
				UpperCamelCase__		:    str         =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
				UpperCamelCase__		:    Union[str, Any]         =[''' '''.join([tokenizer.unk_token]						) * seq_length] * batch_size
				UpperCamelCase__		:    Tuple         =dict(tokenizer(lowercase_						,  return_tensors=lowercase_						)						)
				return common_inputs






			def    _lowerCAmelCase			(							self     :					int						,  lowercase_     :					PreTrainedTokenizer						,  lowercase_     :					int = -1						,  lowercase_     :					int = -1						,  lowercase_     :					bool = False						,  lowercase_     :					Optional[TensorType] = None						,  ):
				if self.task in ["default", "seq2seq-lm"]:
					UpperCamelCase__		:    Tuple         =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":
					UpperCamelCase__		:    Union[str, Any]         =self._generate_dummy_inputs_for_causal_lm(
					    lowercase_						,  batch_size=lowercase_						,  seq_length=lowercase_						,  is_pair=lowercase_						,  framework=lowercase_						)
				else:
					UpperCamelCase__		:    Optional[int]         =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    _lowerCAmelCase			(							self     :					List[str]						,  lowercase_     :					int						,  lowercase_     :					Union[str, Any]						,  lowercase_     :					Optional[Any]						,  lowercase_     :					List[Any]						):
				if self.task in ["default", "seq2seq-lm"]:
					UpperCamelCase__		:    Optional[int]         =super()._flatten_past_key_values_(lowercase_						,  lowercase_						,  lowercase_						,  lowercase_						)
				else:
					UpperCamelCase__		:    Optional[int]         =super(lowercase_						,  self						)._flatten_past_key_values_(
					    lowercase_						,  lowercase_						,  lowercase_						,  lowercase_						)






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

				UpperCamelCase__		:    Optional[int]         ='''past_key_values''' if direction == '''inputs''' else '''present'''
				UpperCamelCase__		:    Optional[int]         =self.num_layers

				UpperCamelCase__		:    List[str]         ='''past_encoder_sequence'''
				UpperCamelCase__		:    Optional[int]         ='''past_decoder_sequence''' if direction == '''inputs''' else '''past_decoder_sequence + sequence'''

				for i in range(lowercase_						):
					UpperCamelCase__		:    Tuple         ={0: '''batch''', 2: decoder_sequence}
					UpperCamelCase__		:    Union[str, Any]         ={0: '''batch''', 2: decoder_sequence}
					UpperCamelCase__		:    str         ={0: '''batch''', 2: encoder_sequence}
					UpperCamelCase__		:    List[Any]         ={0: '''batch''', 2: encoder_sequence}





"""simple docstring"""





from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE       :  List[Any]        = {
    """configuration_deta""": ["""DETA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DetaConfig"""],
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  int        = ["""DetaImageProcessor"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Dict        = [
				    """DETA_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """DetaForObjectDetection""",
				    """DetaModel""",
				    """DetaPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .image_processing_deta import DetaImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_deta import (
								    DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DetaForObjectDetection,
								    DetaModel,
								    DetaPreTrainedModel,
								)

else:
				import sys

				_SCREAMING_SNAKE_CASE       :  Optional[int]        = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


