




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  _lowerCamelCase						:

      """simple docstring"""

      def __init__(     self					: Dict ,       snake_case					: List[Any] ,       snake_case					: Optional[Any]=sys.maxsize				):
          __UpperCamelCase  		=						'''bilinear'''
          __UpperCamelCase  		=						max_size
          __UpperCamelCase  		=						short_edge_length







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

              if img.dtype == np.uinta:
                  __UpperCamelCase  		=						Image.fromarray(snake_case_				)
                  __UpperCamelCase  		=						pil_image.resize((neww, newh) ,       PILImageResampling.BILINEAR				)
                  __UpperCamelCase  		=						np.asarray(snake_case_				)
              else:
                  __UpperCamelCase  		=						img.permute(2 ,       0 ,       1				).unsqueeze(0				)  # 3, 0, 1)  # hw(c) -> nchw
                  __UpperCamelCase  		=						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  _lowerCamelCase						:

      """simple docstring"""

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







      def       snake_case	(     self					: Tuple ,       snake_case					: Optional[int]				):
          __UpperCamelCase  		=						tuple(max(snake_case_				) for s in zip(*[img.shape for img in images]				)				)
          __UpperCamelCase  		=						[im.shape[-2:] for im in images]
          __UpperCamelCase  		=						[
              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					: Optional[Any] ,       snake_case					: int ,       snake_case					: Optional[Any]=False				):
          with torch.no_grad():
              if not isinstance(snake_case_ ,       snake_case_				):
                  __UpperCamelCase  		=						[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
              __UpperCamelCase  		=						torch.tensor([im.shape[:2] for im in images]				)
              __UpperCamelCase  		=						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
              __UpperCamelCase  		=						[self.normalizer(snake_case_				) for x in images]
              # now pad them to do the following operations
              __UpperCamelCase		,      __UpperCamelCase  		=						self.pad(snake_case_				)
              # Normalize

              if self.size_divisibility > 0:
                  raise NotImplementedError()
              # pad
              __UpperCamelCase  		=						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 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	Optional[int]:

    """simple docstring"""


    boxes[:, 0::2] *= scale_yx[:, 1]
    boxes[:, 1::2] *= scale_yx[:, 0]
    return boxes




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	Optional[Any]:

    """simple docstring"""


    assert torch.isfinite(lowercase_      ).all(), "Box tensor contains infinite or NaN!"
    __UpperCamelCase		,      __UpperCamelCase  		=						box_size
    tensor[:, 0].clamp_(min=0					,     max=lowercase_      )
    tensor[:, 1].clamp_(min=0					,     max=lowercase_      )
    tensor[:, 2].clamp_(min=0					,     max=lowercase_      )
    tensor[:, 3].clamp_(min=0					,     max=lowercase_      )








import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_   		=     logging.get_logger(__name__)

a_   		=     {
    "microsoft/unispeech-large-1500h-cv": (
        "https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json"
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: Dict    =					"unispeech"







      def __init__(     self					: str ,       snake_case					: Union[str, Any]=32 ,       snake_case					: Optional[Any]=768 ,       snake_case					: Dict=12 ,       snake_case					: Tuple=12 ,       snake_case					: Optional[Any]=3072 ,       snake_case					: Any="gelu" ,       snake_case					: Dict=0.1 ,       snake_case					: Tuple=0.1 ,       snake_case					: str=0.1 ,       snake_case					: Optional[int]=0.0 ,       snake_case					: Any=0.0 ,       snake_case					: Optional[Any]=0.1 ,       snake_case					: List[Any]=0.1 ,       snake_case					: Optional[int]=0.02 ,       snake_case					: List[str]=1E-5 ,       snake_case					: str="group" ,       snake_case					: List[Any]="gelu" ,       snake_case					: Union[str, Any]=(512, 512, 512, 512, 512, 512, 512) ,       snake_case					: List[Any]=(5, 2, 2, 2, 2, 2, 2) ,       snake_case					: Union[str, Any]=(10, 3, 3, 3, 3, 2, 2) ,       snake_case					: Tuple=False ,       snake_case					: Optional[int]=128 ,       snake_case					: List[str]=16 ,       snake_case					: List[str]=False ,       snake_case					: Dict=True ,       snake_case					: Optional[Any]=0.05 ,       snake_case					: Optional[Any]=10 ,       snake_case					: Union[str, Any]=2 ,       snake_case					: List[str]=0.0 ,       snake_case					: str=10 ,       snake_case					: int=0 ,       snake_case					: Tuple=320 ,       snake_case					: Any=2 ,       snake_case					: List[str]=0.1 ,       snake_case					: Optional[Any]=100 ,       snake_case					: List[Any]=256 ,       snake_case					: Union[str, Any]=256 ,       snake_case					: Any=0.1 ,       snake_case					: str="mean" ,       snake_case					: Union[str, Any]=False ,       snake_case					: str=False ,       snake_case					: Union[str, Any]=256 ,       snake_case					: Optional[Any]=80 ,       snake_case					: str=0 ,       snake_case					: int=1 ,       snake_case					: int=2 ,       snake_case					: Dict=0.5 ,       **snake_case					: Optional[int] ,       ):
          super().__init__(**snake_case ,       pad_token_id=snake_case ,       bos_token_id=snake_case ,       eos_token_id=snake_case				)
          __UpperCamelCase  		=						hidden_size
          __UpperCamelCase  		=						feat_extract_norm
          __UpperCamelCase  		=						feat_extract_activation
          __UpperCamelCase  		=						list(snake_case				)
          __UpperCamelCase  		=						list(snake_case				)
          __UpperCamelCase  		=						list(snake_case				)
          __UpperCamelCase  		=						conv_bias
          __UpperCamelCase  		=						num_conv_pos_embeddings
          __UpperCamelCase  		=						num_conv_pos_embedding_groups
          __UpperCamelCase  		=						len(self.conv_dim				)
          __UpperCamelCase  		=						num_hidden_layers
          __UpperCamelCase  		=						intermediate_size
          __UpperCamelCase  		=						hidden_act
          __UpperCamelCase  		=						num_attention_heads
          __UpperCamelCase  		=						hidden_dropout
          __UpperCamelCase  		=						attention_dropout
          __UpperCamelCase  		=						activation_dropout
          __UpperCamelCase  		=						feat_proj_dropout
          __UpperCamelCase  		=						final_dropout
          __UpperCamelCase  		=						layerdrop
          __UpperCamelCase  		=						layer_norm_eps
          __UpperCamelCase  		=						initializer_range
          __UpperCamelCase  		=						num_ctc_classes
          __UpperCamelCase  		=						vocab_size
          __UpperCamelCase  		=						do_stable_layer_norm
          __UpperCamelCase  		=						use_weighted_layer_sum
          __UpperCamelCase  		=						classifier_proj_size

          if (
              (len(self.conv_stride				) != self.num_feat_extract_layers)
              or (len(self.conv_kernel				) != self.num_feat_extract_layers)
              or (len(self.conv_dim				) != self.num_feat_extract_layers)
          ):
              raise ValueError(
                  '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
                  ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
                  F" {len(self.conv_dim				)}`, `len(config.conv_stride) = {len(self.conv_stride				)}`,"
                  F" `len(config.conv_kernel) = {len(self.conv_kernel				)}`."				)

          # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
          __UpperCamelCase  		=						apply_spec_augment
          __UpperCamelCase  		=						mask_time_prob
          __UpperCamelCase  		=						mask_time_length
          __UpperCamelCase  		=						mask_time_min_masks
          __UpperCamelCase  		=						mask_feature_prob
          __UpperCamelCase  		=						mask_feature_length
          __UpperCamelCase  		=						mask_feature_min_masks

          # parameters for pretraining with codevector quantized representations
          __UpperCamelCase  		=						num_codevectors_per_group
          __UpperCamelCase  		=						num_codevector_groups
          __UpperCamelCase  		=						contrastive_logits_temperature
          __UpperCamelCase  		=						feat_quantizer_dropout
          __UpperCamelCase  		=						num_negatives
          __UpperCamelCase  		=						codevector_dim
          __UpperCamelCase  		=						proj_codevector_dim
          __UpperCamelCase  		=						diversity_loss_weight

          # ctc loss
          __UpperCamelCase  		=						ctc_loss_reduction
          __UpperCamelCase  		=						ctc_zero_infinity

          # pretraining loss
          __UpperCamelCase  		=						replace_prob







      @property
      def       snake_case	(     self					: Dict				):
          return functools.reduce(operator.mul ,       self.conv_stride ,       1				)






