



"""simple docstring"""
import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize




class 			snake_case_						:






							def __init__(   self			,     a_			,     a_=sys.maxsize      ):
									a_     :      Union[str, Any] 						=     "bilinear"
									a_     :      Union[str, Any] 						=     max_size
									a_     :      Tuple 						=     short_edge_length






							def __call__(   self			,     a_      ):
									a_     :      Any 						=     []
									for img in imgs:
											a_						,				a_     :      int 						=     img.shape[:2]
											# later: provide list and randomly choose index for resize
											a_     :      Optional[Any] 						=     np.random.randint(self.short_edge_length[0]			,     self.short_edge_length[1] + 1      )
											if size == 0:
													return img
											a_     :      Optional[int] 						=     size * 1.0 / min(snake_case_			,     snake_case_      )
											if h < w:
													a_						,				a_     :      Optional[Any] 						=     size, scale * w
											else:
													a_						,				a_     :      Any 						=     scale * h, size
											if max(snake_case_			,     snake_case_      ) > self.max_size:
													a_     :      Union[str, Any] 						=     self.max_size * 1.0 / max(snake_case_			,     snake_case_      )
													a_     :      Optional[Any] 						=     newh * scale
													a_     :      Union[str, Any] 						=     neww * scale
											a_     :      str 						=     int(neww + 0.5      )
											a_     :      Union[str, Any] 						=     int(newh + 0.5      )

											if img.dtype == np.uinta:
													a_     :      Union[str, Any] 						=     Image.fromarray(snake_case_      )
													a_     :      Dict 						=     pil_image.resize((neww, newh)			,     PILImageResampling.BILINEAR      )
													a_     :      List[str] 						=     np.asarray(snake_case_      )
											else:
													a_     :      Tuple 						=     img.permute(2			,     0			,     1      ).unsqueeze(0      )  # 3, 0, 1)  # hw(c) -> nchw
													a_     :      str 						=     nn.functional.interpolate(
													    snake_case_			,     (newh, neww)			,     mode=self.interp_method			,     align_corners=snake_case_      ).squeeze(0      )
											img_augs.append(snake_case_      )

									return img_augs




class 			snake_case_						:






							def __init__(   self			,     a_      ):
									a_     :      Any 						=     ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]			,     cfg.INPUT.MAX_SIZE_TEST      )
									a_     :      Optional[int] 						=     cfg.INPUT.FORMAT
									a_     :      str 						=     cfg.SIZE_DIVISIBILITY
									a_     :      str 						=     cfg.PAD_VALUE
									a_     :      Union[str, Any] 						=     cfg.INPUT.MAX_SIZE_TEST
									a_     :      Dict 						=     cfg.MODEL.DEVICE
									a_     :      Union[str, Any] 						=     torch.tensor(cfg.MODEL.PIXEL_STD      ).to(self.device      ).view(len(cfg.MODEL.PIXEL_STD      )			,     1			,     1      )
									a_     :      Optional[int] 						=     torch.tensor(cfg.MODEL.PIXEL_MEAN      ).to(self.device      ).view(len(cfg.MODEL.PIXEL_STD      )			,     1			,     1      )
									a_     :      Union[str, Any] 						=     lambda a_      : (x - self.pixel_mean) / self.pixel_std






							def snake_case_  (   self			,     a_      ):
									a_     :      Any 						=     tuple(max(snake_case_      ) for s in zip(*[img.shape for img in images]      )      )
									a_     :      Any 						=     [im.shape[-2:] for im in images]
									a_     :      Dict 						=     [
									    nn.functional.pad(
									        snake_case_			,     [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]			,     value=self.pad_value			,     )
									    for size, im in zip(snake_case_			,     snake_case_      )
									]

									return torch.stack(snake_case_      ), torch.tensor(snake_case_      )






							def __call__(   self			,     a_			,     a_=False      ):
									with torch.no_grad():
											if not isinstance(snake_case_			,     snake_case_      ):
													a_     :      Optional[Any] 						=     [images]
											if single_image:
													assert len(snake_case_      ) == 1
											for i in range(len(snake_case_      )      ):
													if isinstance(images[i]			,     torch.Tensor      ):
															images.insert(snake_case_			,     images.pop(snake_case_      ).to(self.device      ).float()      )
													elif not isinstance(images[i]			,     torch.Tensor      ):
															images.insert(
															    snake_case_			,     torch.as_tensor(img_tensorize(images.pop(snake_case_      )			,     input_format=self.input_format      )      )
															    .to(self.device      )
															    .float()			,     )
            # resize smallest edge
											a_     :      Dict 						=     torch.tensor([im.shape[:2] for im in images]      )
											a_     :      Any 						=     self.aug(snake_case_      )
											# transpose images and convert to torch tensors
											# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
											# now normalize before pad to avoid useless arithmetic
											a_     :      Optional[int] 						=     [self.normalizer(snake_case_      ) for x in images]
											# now pad them to do the following operations
											a_						,				a_     :      Any 						=     self.pad(snake_case_      )
											# Normalize

											if self.size_divisibility > 0:
													raise NotImplementedError()
											# pad
											a_     :      Any 						=     torch.true_divide(snake_case_			,     snake_case_      )
											if single_image:
													return images[0], sizes[0], scales_yx[0]
											else:
													return images, sizes, scales_yx



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Optional[int]:
		boxes[:, 0::2] *= scale_yx[:, 1]
		boxes[:, 1::2] *= scale_yx[:, 0]
		return boxes



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Optional[Any]:
		assert torch.isfinite(SCREAMING_SNAKE_CASE__	).all(), "Box tensor contains infinite or NaN!"
		a_						,				a_     :      Dict 						=     box_size
		tensor[:, 0].clamp_(min=0, max=SCREAMING_SNAKE_CASE__	)
		tensor[:, 1].clamp_(min=0, max=SCREAMING_SNAKE_CASE__	)
		tensor[:, 2].clamp_(min=0, max=SCREAMING_SNAKE_CASE__	)
		tensor[:, 3].clamp_(min=0, max=SCREAMING_SNAKE_CASE__	)




"""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_				= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_				= {
    """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json""",
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}




class 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					"blenderbot-small"
							__lowerCAmelCase    					=					["past_key_values"]
							__lowerCAmelCase    					=					{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






							def __init__(   self			,     a_=5_0_2_6_5			,     a_=5_1_2			,     a_=8			,     a_=2_0_4_8			,     a_=1_6			,     a_=8			,     a_=2_0_4_8			,     a_=1_6			,     a_=0.0			,     a_=0.0			,     a_=True			,     a_=True			,     a_="gelu"			,     a_=5_1_2			,     a_=0.1			,     a_=0.0			,     a_=0.0			,     a_=0.02			,     a_=1			,     a_=False			,     a_=0			,     a_=1			,     a_=2			,     a_=2			,     **a_			,     ):
									a_     :      int 						=     vocab_size
									a_     :      Any 						=     max_position_embeddings
									a_     :      Optional[int] 						=     d_model
									a_     :      Tuple 						=     encoder_ffn_dim
									a_     :      List[Any] 						=     encoder_layers
									a_     :      Optional[int] 						=     encoder_attention_heads
									a_     :      Optional[int] 						=     decoder_ffn_dim
									a_     :      List[str] 						=     decoder_layers
									a_     :      Dict 						=     decoder_attention_heads
									a_     :      List[str] 						=     dropout
									a_     :      List[Any] 						=     attention_dropout
									a_     :      List[str] 						=     activation_dropout
									a_     :      Optional[Any] 						=     activation_function
									a_     :      List[Any] 						=     init_std
									a_     :      int 						=     encoder_layerdrop
									a_     :      Optional[int] 						=     decoder_layerdrop
									a_     :      List[str] 						=     use_cache
									a_     :      Optional[int] 						=     encoder_layers
									a_     :      Optional[int] 						=     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_			,     decoder_start_token_id=a_			,     forced_eos_token_id=a_			,     **a_			,     )




class 			snake_case_						(						a_				):






							@property
							def snake_case_  (   self      ):
									if self.task in ["default", "seq2seq-lm"]:
											a_     :      Optional[int] 						=     OrderedDict(
											    [
											        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
											        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
											    ]      )

											if self.use_past:
													a_     :      Tuple 						=     {0: "batch"}
													a_     :      int 						=     {0: "batch", 1: "past_decoder_sequence + sequence"}
											else:
													a_     :      List[str] 						=     {0: "batch", 1: "decoder_sequence"}
													a_     :      Optional[int] 						=     {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.
											a_     :      Dict 						=     OrderedDict(
											    [
											        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
											        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
											    ]      )
											if self.use_past:
													a_						,				a_     :      Optional[int] 						=     self.num_layers
													for i in range(a_      ):
															a_     :      Union[str, Any] 						=     {0: "batch", 2: "past_sequence + sequence"}
															a_     :      List[Any] 						=     {0: "batch", 2: "past_sequence + sequence"}
									else:
											a_     :      List[str] 						=     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 snake_case_  (   self      ):
									if self.task in ["default", "seq2seq-lm"]:
											a_     :      List[Any] 						=     super().outputs
									else:
											a_     :      Tuple 						=     super(a_			,     self      ).outputs
											if self.use_past:
													a_						,				a_     :      Dict 						=     self.num_layers
													for i in range(a_      ):
															a_     :      List[Any] 						=     {0: "batch", 2: "past_sequence + sequence"}
															a_     :      List[Any] 						=     {0: "batch", 2: "past_sequence + sequence"}
									return common_outputs






							def snake_case_  (   self			,     a_			,     a_ = -1			,     a_ = -1			,     a_ = False			,     a_ = None			,     ):
									a_     :      List[str] 						=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    a_			,     a_			,     a_			,     a_			,     a_      )

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

											a_     :      Dict 						=     torch.cat(
											    [common_inputs["decoder_attention_mask"], torch.ones(a_			,     a_      )]			,     dim=1      )

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

											a_     :      str 						=     common_inputs["attention_mask"].dtype
											a_     :      Tuple 						=     torch.cat(
											    [common_inputs["attention_mask"], torch.ones(a_			,     a_			,     dtype=a_      )]			,     dim=1      )
											a_     :      Optional[int] 						=     [
											    (torch.zeros(a_      ), torch.zeros(a_      )) for _ in range(a_      )
											]
									return common_inputs






							def snake_case_  (   self			,     a_			,     a_ = -1			,     a_ = -1			,     a_ = False			,     a_ = 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
									a_     :      Optional[Any] 						=     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
									a_     :      Tuple 						=     tokenizer.num_special_tokens_to_add(a_      )
									a_     :      Union[str, Any] 						=     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
									a_     :      Union[str, Any] 						=     [" ".join([tokenizer.unk_token]      ) * seq_length] * batch_size
									a_     :      str 						=     dict(tokenizer(a_			,     return_tensors=a_      )      )
									return common_inputs






							def snake_case_  (   self			,     a_			,     a_ = -1			,     a_ = -1			,     a_ = False			,     a_ = None			,     ):
									if self.task in ["default", "seq2seq-lm"]:
											a_     :      List[str] 						=     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":
											a_     :      List[Any] 						=     self._generate_dummy_inputs_for_causal_lm(
											    a_			,     batch_size=a_			,     seq_length=a_			,     is_pair=a_			,     framework=a_      )
									else:
											a_     :      Union[str, Any] 						=     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 snake_case_  (   self			,     a_			,     a_			,     a_			,     a_      ):
									if self.task in ["default", "seq2seq-lm"]:
											a_     :      Optional[int] 						=     super()._flatten_past_key_values_(a_			,     a_			,     a_			,     a_      )
									else:
											a_     :      int 						=     super(a_			,     self      )._flatten_past_key_values_(
											    a_			,     a_			,     a_			,     a_      )