"""simple docstring"""


import functools
import operator

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


__UpperCamelCase									=			logging.get_logger(__name__)

__UpperCamelCase									=			{
    '''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json''',
    # See all SEW models at https://huggingface.co/models?filter=sew
}




class  UpperCamelCase					(				lowerCAmelCase__   ):
       SCREAMING_SNAKE_CASE_			=      "sew"







       def __init__( self,							lowerCAmelCase__=32,							lowerCAmelCase__=768,							lowerCAmelCase__=12,							lowerCAmelCase__=12,							lowerCAmelCase__=3072,							lowerCAmelCase__=2,							lowerCAmelCase__="gelu",							lowerCAmelCase__=0.1,							lowerCAmelCase__=0.1,							lowerCAmelCase__=0.1,							lowerCAmelCase__=0.0,							lowerCAmelCase__=0.1,							lowerCAmelCase__=0.1,							lowerCAmelCase__=0.02,							lowerCAmelCase__=1e-5,							lowerCAmelCase__="group",							lowerCAmelCase__="gelu",							lowerCAmelCase__=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512),							lowerCAmelCase__=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1),							lowerCAmelCase__=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1),							lowerCAmelCase__=False,							lowerCAmelCase__=128,							lowerCAmelCase__=16,							lowerCAmelCase__=True,							lowerCAmelCase__=0.05,							lowerCAmelCase__=10,							lowerCAmelCase__=2,							lowerCAmelCase__=0.0,							lowerCAmelCase__=10,							lowerCAmelCase__=0,							lowerCAmelCase__="mean",							lowerCAmelCase__=False,							lowerCAmelCase__=False,							lowerCAmelCase__=256,							lowerCAmelCase__=0,							lowerCAmelCase__=1,							lowerCAmelCase__=2,							**lowerCAmelCase__,							)      ->      str:
            super().__init__(**lowerCAmelCase__,							pad_token_id=lowerCAmelCase__,							bos_token_id=lowerCAmelCase__,							eos_token_id=lowerCAmelCase__)
            snake_case_					       =					hidden_size
            snake_case_					       =					feat_extract_norm
            snake_case_					       =					feat_extract_activation
            snake_case_					       =					list(lowerCAmelCase__)
            snake_case_					       =					list(lowerCAmelCase__)
            snake_case_					       =					list(lowerCAmelCase__)
            snake_case_					       =					conv_bias
            snake_case_					       =					num_conv_pos_embeddings
            snake_case_					       =					num_conv_pos_embedding_groups
            snake_case_					       =					len(self.conv_dim)
            snake_case_					       =					num_hidden_layers
            snake_case_					       =					intermediate_size
            snake_case_					       =					squeeze_factor
            snake_case_					       =					hidden_act
            snake_case_					       =					num_attention_heads
            snake_case_					       =					hidden_dropout
            snake_case_					       =					attention_dropout
            snake_case_					       =					activation_dropout
            snake_case_					       =					feat_proj_dropout
            snake_case_					       =					final_dropout
            snake_case_					       =					layerdrop
            snake_case_					       =					layer_norm_eps
            snake_case_					       =					initializer_range
            snake_case_					       =					vocab_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)`,'
                     f'but is `len(config.conv_dim) = {len(self.conv_dim)}`, `len(config.conv_stride)'
                     f'= {len(self.conv_stride)}`, `len(config.conv_kernel) = {len(self.conv_kernel)}`.')

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

            # ctc loss
            snake_case_					       =					ctc_loss_reduction
            snake_case_					       =					ctc_zero_infinity

            # sequence classification
            snake_case_					       =					use_weighted_layer_sum
            snake_case_					       =					classifier_proj_size







       @property
       def 		a_						( self)      ->      Optional[Any]:
            return functools.reduce(operator.mul,							self.conv_stride,							1)


"""simple docstring"""




from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
				import PIL

__lowerCamelCase							 =     logging.get_logger(__name__)






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							if isinstance(UpperCamelCase__				,			(list, tuple) ) and isinstance(videos[0]				,			(list, tuple) ) and is_valid_image(videos[0][0] ):
														return videos

							elif isinstance(UpperCamelCase__				,			(list, tuple) ) and is_valid_image(videos[0] ):
														return [videos]

							elif is_valid_image(UpperCamelCase__ ):
														return [[videos]]

							raise ValueError(F'''Could not make batched video from {videos}''' )


class 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  List[Any]  							=						['pixel_values']





						def __init__(				self						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,__UpperCAmelCase = PILImageResampling.BILINEAR						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,__UpperCAmelCase = True						,__UpperCAmelCase = 1 / 2_55						,__UpperCAmelCase = True						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> None:
													super().__init__(**__UpperCAmelCase   )
													A__		   =							size if size is not None else {'shortest_edge': 2_56}
													A__		   =							get_size_dict(__UpperCAmelCase						,default_to_square=__UpperCAmelCase   )
													A__		   =							crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
													A__		   =							get_size_dict(__UpperCAmelCase						,param_name='crop_size'   )

													A__		   =							do_resize
													A__		   =							size
													A__		   =							do_center_crop
													A__		   =							crop_size
													A__		   =							resample
													A__		   =							do_rescale
													A__		   =							rescale_factor
													A__		   =							offset
													A__		   =							do_normalize
													A__		   =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
													A__		   =							image_std if image_std is not None else IMAGENET_STANDARD_STD





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = PILImageResampling.BILINEAR						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> np.ndarray:
													A__		   =							get_size_dict(__UpperCAmelCase						,default_to_square=__UpperCAmelCase   )
													if "shortest_edge" in size:
																				A__		   =							get_resize_output_image_size(__UpperCAmelCase						,size['shortest_edge']						,default_to_square=__UpperCAmelCase   )
													elif "height" in size and "width" in size:
																				A__		   =							(size['height'], size['width'])
													else:
																				raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''   )
													return resize(__UpperCAmelCase						,size=__UpperCAmelCase						,resample=__UpperCAmelCase						,data_format=__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> np.ndarray:
													A__		   =							get_size_dict(__UpperCAmelCase   )
													if "height" not in size or "width" not in size:
																				raise ValueError(f'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}'''   )
													return center_crop(__UpperCAmelCase						,size=(size['height'], size['width'])						,data_format=__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> Optional[Any]:
													A__		   =							image.astype(np.floataa   )
													if offset:
																				A__		   =							image - (scale / 2)
													return rescale(__UpperCAmelCase						,scale=__UpperCAmelCase						,data_format=__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> np.ndarray:
													return normalize(__UpperCAmelCase						,mean=__UpperCAmelCase						,std=__UpperCAmelCase						,data_format=__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = ChannelDimension.FIRST						,)    -> np.ndarray:
													if do_resize and size is None or resample is None:
																				raise ValueError('Size and resample must be specified if do_resize is True.'   )

													if do_center_crop and crop_size is None:
																				raise ValueError('Crop size must be specified if do_center_crop is True.'   )

													if do_rescale and rescale_factor is None:
																				raise ValueError('Rescale factor must be specified if do_rescale is True.'   )

													if do_normalize and (image_mean is None or image_std is None):
																				raise ValueError('Image mean and std must be specified if do_normalize is True.'   )

													if offset and not do_rescale:
																				raise ValueError('For offset, do_rescale must also be set to True.'   )

													# All transformations expect numpy arrays.
													A__		   =							to_numpy_array(__UpperCAmelCase   )

													if do_resize:
																				A__		   =							self.resize(image=__UpperCAmelCase						,size=__UpperCAmelCase						,resample=__UpperCAmelCase   )

													if do_center_crop:
																				A__		   =							self.center_crop(__UpperCAmelCase						,size=__UpperCAmelCase   )

													if do_rescale:
																				A__		   =							self.rescale(image=__UpperCAmelCase						,scale=__UpperCAmelCase						,offset=__UpperCAmelCase   )

													if do_normalize:
																				A__		   =							self.normalize(image=__UpperCAmelCase						,mean=__UpperCAmelCase						,std=__UpperCAmelCase   )

													A__		   =							to_channel_dimension_format(__UpperCAmelCase						,__UpperCAmelCase   )
													return image





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = ChannelDimension.FIRST						,**__UpperCAmelCase						,)    -> PIL.Image.Image:
													A__		   =							do_resize if do_resize is not None else self.do_resize
													A__		   =							resample if resample is not None else self.resample
													A__		   =							do_center_crop if do_center_crop is not None else self.do_center_crop
													A__		   =							do_rescale if do_rescale is not None else self.do_rescale
													A__		   =							rescale_factor if rescale_factor is not None else self.rescale_factor
													A__		   =							offset if offset is not None else self.offset
													A__		   =							do_normalize if do_normalize is not None else self.do_normalize
													A__		   =							image_mean if image_mean is not None else self.image_mean
													A__		   =							image_std if image_std is not None else self.image_std

													A__		   =							size if size is not None else self.size
													A__		   =							get_size_dict(__UpperCAmelCase						,default_to_square=__UpperCAmelCase   )
													A__		   =							crop_size if crop_size is not None else self.crop_size
													A__		   =							get_size_dict(__UpperCAmelCase						,param_name='crop_size'   )

													if not valid_images(__UpperCAmelCase   ):
																				raise ValueError(
																				    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
																				    'torch.Tensor, tf.Tensor or jax.ndarray.'   )

													A__		   =							make_batched(__UpperCAmelCase   )

													A__		   =							[
													    [
													        self._preprocess_image(
													            image=__UpperCAmelCase						,do_resize=__UpperCAmelCase						,size=__UpperCAmelCase						,resample=__UpperCAmelCase						,do_center_crop=__UpperCAmelCase						,crop_size=__UpperCAmelCase						,do_rescale=__UpperCAmelCase						,rescale_factor=__UpperCAmelCase						,offset=__UpperCAmelCase						,do_normalize=__UpperCAmelCase						,image_mean=__UpperCAmelCase						,image_std=__UpperCAmelCase						,data_format=__UpperCAmelCase						,)
													        for img in video
													    ]
													    for video in videos
													]

													A__		   =							{'pixel_values': videos}
													return BatchFeature(data=__UpperCAmelCase						,tensor_type=__UpperCAmelCase   )





