
"""simple docstring"""

from typing import Dict, List, Optional, Union

import numpy as np

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


if is_vision_available():
					import PIL


_A						=			logging.get_logger(__name__)






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     ['pixel_values']




	def __init__(			self		,			UpperCAmelCase_ = True		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = PIL.Image.BICUBIC		,			UpperCAmelCase_ = True		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = 1 / 255		,			UpperCAmelCase_ = True		,			UpperCAmelCase_ = True		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			**UpperCAmelCase_		,			)		->							None:
			super().__init__(**UpperCAmelCase_ )
			lowerCamelCase					:      Optional[Any]          =   size if size is not None else {'height': 256, 'width': 256}
			lowerCamelCase					:      Tuple          =   get_size_dict(UpperCAmelCase_ )
			lowerCamelCase					:      Optional[Any]          =   crop_size if crop_size is not None else {'height': 224, 'width': 224}
			lowerCamelCase					:      List[str]          =   get_size_dict(UpperCAmelCase_		,			param_name='crop_size' )

			lowerCamelCase					:      Dict          =   do_resize
			lowerCamelCase					:      Any          =   size
			lowerCamelCase					:      Any          =   resample
			lowerCamelCase					:      int          =   do_center_crop
			lowerCamelCase					:      str          =   crop_size
			lowerCamelCase					:      Optional[Any]          =   do_rescale
			lowerCamelCase					:      Tuple          =   rescale_factor
			lowerCamelCase					:      Dict          =   do_normalize
			lowerCamelCase					:      str          =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
			lowerCamelCase					:      int          =   image_std if image_std is not None else IMAGENET_STANDARD_STD




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ = PIL.Image.BICUBIC		,			UpperCAmelCase_ = None		,			**UpperCAmelCase_		,			)		->							np.ndarray:
			lowerCamelCase					:      Optional[int]          =   get_size_dict(UpperCAmelCase_ )
			if "height" not in size or "width" not in size:
					raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}""" )
			return resize(
			    UpperCAmelCase_		,			size=(size['height'], size['width'])		,			resample=UpperCAmelCase_		,			data_format=UpperCAmelCase_		,			**UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ = None		,			**UpperCAmelCase_		,			)		->							np.ndarray:
			lowerCamelCase					:      Union[str, Any]          =   get_size_dict(UpperCAmelCase_ )
			if "height" not in size or "width" not in size:
					raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}""" )
			return center_crop(UpperCAmelCase_		,			size=(size['height'], size['width'])		,			data_format=UpperCAmelCase_		,			**UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ = None		,			**UpperCAmelCase_		,			)		->							Any:
			return rescale(UpperCAmelCase_		,			scale=UpperCAmelCase_		,			data_format=UpperCAmelCase_		,			**UpperCAmelCase_ )




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




	def       _UpperCamelCase   (			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		,			**UpperCAmelCase_		,			)		->							PIL.Image.Image:
			lowerCamelCase					:      List[Any]          =   do_resize if do_resize is not None else self.do_resize
			lowerCamelCase					:      Optional[int]          =   resample if resample is not None else self.resample
			lowerCamelCase					:      int          =   do_center_crop if do_center_crop is not None else self.do_center_crop
			lowerCamelCase					:      Optional[Any]          =   do_rescale if do_rescale is not None else self.do_rescale
			lowerCamelCase					:      Optional[Any]          =   rescale_factor if rescale_factor is not None else self.rescale_factor
			lowerCamelCase					:      Tuple          =   do_normalize if do_normalize is not None else self.do_normalize
			lowerCamelCase					:      Any          =   image_mean if image_mean is not None else self.image_mean
			lowerCamelCase					:      int          =   image_std if image_std is not None else self.image_std

			lowerCamelCase					:      str          =   size if size is not None else self.size
			lowerCamelCase					:      Any          =   get_size_dict(UpperCAmelCase_ )
			lowerCamelCase					:      List[str]          =   crop_size if crop_size is not None else self.crop_size
			lowerCamelCase					:      List[Any]          =   get_size_dict(UpperCAmelCase_		,			param_name='crop_size' )

			lowerCamelCase					:      Dict          =   make_list_of_images(UpperCAmelCase_ )

			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.' )

			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.' )

			# All transformations expect numpy arrays.
			lowerCamelCase					:      Dict          =   [to_numpy_array(UpperCAmelCase_ ) for image in images]

			if do_resize:
					lowerCamelCase					:      Any          =   [self.resize(image=UpperCAmelCase_		,			size=UpperCAmelCase_		,			resample=UpperCAmelCase_ ) for image in images]

			if do_center_crop:
					lowerCamelCase					:      Tuple          =   [self.center_crop(image=UpperCAmelCase_		,			size=UpperCAmelCase_ ) for image in images]

			if do_rescale:
					lowerCamelCase					:      int          =   [self.rescale(image=UpperCAmelCase_		,			scale=UpperCAmelCase_ ) for image in images]

			if do_normalize:
					lowerCamelCase					:      List[str]          =   [self.normalize(image=UpperCAmelCase_		,			mean=UpperCAmelCase_		,			std=UpperCAmelCase_ ) for image in images]

			lowerCamelCase					:      Optional[Any]          =   [to_channel_dimension_format(UpperCAmelCase_		,			UpperCAmelCase_ ) for image in images]

			lowerCamelCase					:      Optional[Any]          =   {'pixel_values': images}
			return BatchFeature(data=UpperCAmelCase_		,			tensor_type=UpperCAmelCase_ )








"""simple docstring"""

import functools
import operator

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


_A						=			logging.get_logger(__name__)

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






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     'sew-d'




	def __init__(			self		,			UpperCAmelCase_=32		,			UpperCAmelCase_=768		,			UpperCAmelCase_=12		,			UpperCAmelCase_=12		,			UpperCAmelCase_=3072		,			UpperCAmelCase_=2		,			UpperCAmelCase_=512		,			UpperCAmelCase_=256		,			UpperCAmelCase_=True		,			UpperCAmelCase_=True		,			UpperCAmelCase_=("p2c", "c2p")		,			UpperCAmelCase_="layer_norm"		,			UpperCAmelCase_="gelu_python"		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=0.0		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=0.02		,			UpperCAmelCase_=1E-7		,			UpperCAmelCase_=1E-5		,			UpperCAmelCase_="group"		,			UpperCAmelCase_="gelu"		,			UpperCAmelCase_=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)		,			UpperCAmelCase_=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)		,			UpperCAmelCase_=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)		,			UpperCAmelCase_=False		,			UpperCAmelCase_=128		,			UpperCAmelCase_=16		,			UpperCAmelCase_=True		,			UpperCAmelCase_=0.05		,			UpperCAmelCase_=10		,			UpperCAmelCase_=2		,			UpperCAmelCase_=0.0		,			UpperCAmelCase_=10		,			UpperCAmelCase_=0		,			UpperCAmelCase_="mean"		,			UpperCAmelCase_=False		,			UpperCAmelCase_=False		,			UpperCAmelCase_=256		,			UpperCAmelCase_=0		,			UpperCAmelCase_=1		,			UpperCAmelCase_=2		,			**UpperCAmelCase_		,			)		->							Optional[Any]:
			super().__init__(**UpperCAmelCase_		,			pad_token_id=UpperCAmelCase_		,			bos_token_id=UpperCAmelCase_		,			eos_token_id=UpperCAmelCase_ )
			lowerCamelCase					:      Any          =   hidden_size
			lowerCamelCase					:      Any          =   feat_extract_norm
			lowerCamelCase					:      List[str]          =   feat_extract_activation
			lowerCamelCase					:      str          =   list(UpperCAmelCase_ )
			lowerCamelCase					:      Any          =   list(UpperCAmelCase_ )
			lowerCamelCase					:      str          =   list(UpperCAmelCase_ )
			lowerCamelCase					:      List[Any]          =   conv_bias
			lowerCamelCase					:      Optional[int]          =   num_conv_pos_embeddings
			lowerCamelCase					:      str          =   num_conv_pos_embedding_groups
			lowerCamelCase					:      Optional[int]          =   len(self.conv_dim )
			lowerCamelCase					:      Optional[int]          =   num_hidden_layers
			lowerCamelCase					:      Union[str, Any]          =   intermediate_size
			lowerCamelCase					:      str          =   squeeze_factor
			lowerCamelCase					:      Any          =   max_position_embeddings
			lowerCamelCase					:      List[Any]          =   position_buckets
			lowerCamelCase					:      Union[str, Any]          =   share_att_key
			lowerCamelCase					:      Optional[int]          =   relative_attention
			lowerCamelCase					:      Tuple          =   norm_rel_ebd
			lowerCamelCase					:      Union[str, Any]          =   list(UpperCAmelCase_ )
			lowerCamelCase					:      List[Any]          =   hidden_act
			lowerCamelCase					:      Optional[Any]          =   num_attention_heads
			lowerCamelCase					:      Tuple          =   hidden_dropout
			lowerCamelCase					:      List[Any]          =   attention_dropout
			lowerCamelCase					:      Optional[Any]          =   activation_dropout
			lowerCamelCase					:      List[str]          =   feat_proj_dropout
			lowerCamelCase					:      List[str]          =   final_dropout
			lowerCamelCase					:      str          =   layer_norm_eps
			lowerCamelCase					:      int          =   feature_layer_norm_eps
			lowerCamelCase					:      Optional[Any]          =   initializer_range
			lowerCamelCase					:      int          =   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
			lowerCamelCase					:      Any          =   apply_spec_augment
			lowerCamelCase					:      Optional[int]          =   mask_time_prob
			lowerCamelCase					:      Optional[Any]          =   mask_time_length
			lowerCamelCase					:      str          =   mask_time_min_masks
			lowerCamelCase					:      List[Any]          =   mask_feature_prob
			lowerCamelCase					:      int          =   mask_feature_length
			lowerCamelCase					:      List[Any]          =   mask_feature_min_masks

			# ctc loss
			lowerCamelCase					:      Optional[Any]          =   ctc_loss_reduction
			lowerCamelCase					:      Union[str, Any]          =   ctc_zero_infinity

			# sequence classification
			lowerCamelCase					:      Optional[Any]          =   use_weighted_layer_sum
			lowerCamelCase					:      Dict          =   classifier_proj_size




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






