

"""simple docstring"""





import functools
import operator

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


__UpperCamelCase    : Optional[int]				 =						logging.get_logger(__name__)

__UpperCamelCase    : int				 =						{
    '''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 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					lowercase__												=			"sew"







					def __init__(			self     :							int  ,lowercase_     :							Union[str, Any]=3_2  ,lowercase_     :							Optional[int]=7_6_8  ,lowercase_     :							Tuple=1_2  ,lowercase_     :							Union[str, Any]=1_2  ,lowercase_     :							Optional[int]=3_0_7_2  ,lowercase_     :							Tuple=2  ,lowercase_     :							List[Any]="gelu"  ,lowercase_     :							Optional[int]=0.1  ,lowercase_     :							Optional[Any]=0.1  ,lowercase_     :							str=0.1  ,lowercase_     :							int=0.0  ,lowercase_     :							str=0.1  ,lowercase_     :							Dict=0.1  ,lowercase_     :							List[Any]=0.02  ,lowercase_     :							int=1E-5  ,lowercase_     :							str="group"  ,lowercase_     :							Optional[int]="gelu"  ,lowercase_     :							int=(6_4, 1_2_8, 1_2_8, 1_2_8, 1_2_8, 2_5_6, 2_5_6, 2_5_6, 2_5_6, 5_1_2, 5_1_2, 5_1_2, 5_1_2)  ,lowercase_     :							List[str]=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)  ,lowercase_     :							Optional[Any]=(1_0, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)  ,lowercase_     :							List[Any]=False  ,lowercase_     :							Union[str, Any]=1_2_8  ,lowercase_     :							Optional[int]=1_6  ,lowercase_     :							str=True  ,lowercase_     :							List[str]=0.05  ,lowercase_     :							Dict=1_0  ,lowercase_     :							str=2  ,lowercase_     :							List[str]=0.0  ,lowercase_     :							Any=1_0  ,lowercase_     :							str=0  ,lowercase_     :							List[str]="mean"  ,lowercase_     :							List[Any]=False  ,lowercase_     :							List[Any]=False  ,lowercase_     :							List[Any]=2_5_6  ,lowercase_     :							Optional[int]=0  ,lowercase_     :							int=1  ,lowercase_     :							Optional[int]=2  ,**lowercase_     :							Dict  ,):
						super().__init__(**lowercase_  ,pad_token_id=lowercase_  ,bos_token_id=lowercase_  ,eos_token_id=lowercase_						)
						lowerCAmelCase__      :		Optional[int]    							=      hidden_size
						lowerCAmelCase__      :		Union[str, Any]    							=      feat_extract_norm
						lowerCAmelCase__      :		str    							=      feat_extract_activation
						lowerCAmelCase__      :		Optional[Any]    							=      list(lowercase_						)
						lowerCAmelCase__      :		Optional[int]    							=      list(lowercase_						)
						lowerCAmelCase__      :		Dict    							=      list(lowercase_						)
						lowerCAmelCase__      :		Any    							=      conv_bias
						lowerCAmelCase__      :		int    							=      num_conv_pos_embeddings
						lowerCAmelCase__      :		List[str]    							=      num_conv_pos_embedding_groups
						lowerCAmelCase__      :		Dict    							=      len(self.conv_dim						)
						lowerCAmelCase__      :		Tuple    							=      num_hidden_layers
						lowerCAmelCase__      :		Optional[int]    							=      intermediate_size
						lowerCAmelCase__      :		Dict    							=      squeeze_factor
						lowerCAmelCase__      :		Dict    							=      hidden_act
						lowerCAmelCase__      :		List[str]    							=      num_attention_heads
						lowerCAmelCase__      :		Dict    							=      hidden_dropout
						lowerCAmelCase__      :		List[Any]    							=      attention_dropout
						lowerCAmelCase__      :		str    							=      activation_dropout
						lowerCAmelCase__      :		Optional[Any]    							=      feat_proj_dropout
						lowerCAmelCase__      :		Optional[int]    							=      final_dropout
						lowerCAmelCase__      :		Optional[int]    							=      layerdrop
						lowerCAmelCase__      :		Tuple    							=      layer_norm_eps
						lowerCAmelCase__      :		List[Any]    							=      initializer_range
						lowerCAmelCase__      :		Dict    							=      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__      :		Optional[int]    							=      apply_spec_augment
						lowerCAmelCase__      :		Union[str, Any]    							=      mask_time_prob
						lowerCAmelCase__      :		Tuple    							=      mask_time_length
						lowerCAmelCase__      :		Optional[int]    							=      mask_time_min_masks
						lowerCAmelCase__      :		List[Any]    							=      mask_feature_prob
						lowerCAmelCase__      :		Optional[int]    							=      mask_feature_length
						lowerCAmelCase__      :		int    							=      mask_feature_min_masks

						# ctc loss
						lowerCAmelCase__      :		List[Any]    							=      ctc_loss_reduction
						lowerCAmelCase__      :		Union[str, Any]    							=      ctc_zero_infinity

						# sequence classification
						lowerCAmelCase__      :		Any    							=      use_weighted_layer_sum
						lowerCAmelCase__      :		Union[str, Any]    							=      classifier_proj_size







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







"""simple docstring"""





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import LevitImageProcessor







class 							SCREAMING_SNAKE_CASE   (						unittest.TestCase			):






					"""simple docstring"""



					def __init__(			self     :							Any  ,lowercase_     :							Dict  ,lowercase_     :							List[str]=7  ,lowercase_     :							Tuple=3  ,lowercase_     :							List[str]=1_8  ,lowercase_     :							Optional[Any]=3_0  ,lowercase_     :							List[Any]=4_0_0  ,lowercase_     :							List[Any]=True  ,lowercase_     :							Any=None  ,lowercase_     :							Optional[Any]=True  ,lowercase_     :							str=None  ,lowercase_     :							List[Any]=True  ,lowercase_     :							Dict=[0.5, 0.5, 0.5]  ,lowercase_     :							Dict=[0.5, 0.5, 0.5]  ,):
						lowerCAmelCase__      :		Any    							=      size if size is not None else {'''shortest_edge''': 1_8}
						lowerCAmelCase__      :		Union[str, Any]    							=      crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
						lowerCAmelCase__      :		Dict    							=      parent
						lowerCAmelCase__      :		Dict    							=      batch_size
						lowerCAmelCase__      :		List[str]    							=      num_channels
						lowerCAmelCase__      :		Any    							=      image_size
						lowerCAmelCase__      :		Union[str, Any]    							=      min_resolution
						lowerCAmelCase__      :		Dict    							=      max_resolution
						lowerCAmelCase__      :		List[str]    							=      do_resize
						lowerCAmelCase__      :		Optional[Any]    							=      size
						lowerCAmelCase__      :		Tuple    							=      do_center_crop
						lowerCAmelCase__      :		Optional[int]    							=      crop_size
						lowerCAmelCase__      :		List[str]    							=      do_normalize
						lowerCAmelCase__      :		Tuple    							=      image_mean
						lowerCAmelCase__      :		int    							=      image_std







					def 			__lowerCAmelCase					(			self     :							List[str]						):
						return {
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_normalize": self.do_normalize,
						    "do_resize": self.do_resize,
						    "do_center_crop": self.do_center_crop,
						    "size": self.size,
						    "crop_size": self.crop_size,
						}







@require_torch
@require_vision
class 							SCREAMING_SNAKE_CASE   (						a_       ,				unittest.TestCase			):






					"""simple docstring"""



					lowercase__												=			LevitImageProcessor if is_vision_available() else None







					def 			__lowerCAmelCase					(			self     :							List[str]						):
						lowerCAmelCase__      :		Optional[Any]    							=      LevitImageProcessingTester(self						)







					@property
					def 			__lowerCAmelCase					(			self     :							Any						):
						return self.image_processor_tester.prepare_image_processor_dict()







					def 			__lowerCAmelCase					(			self     :							str						):
						lowerCAmelCase__      :		Optional[int]    							=      self.image_processing_class(**self.image_processor_dict						)
						self.assertTrue(hasattr(lowercase_  ,'''image_mean'''						)						)
						self.assertTrue(hasattr(lowercase_  ,'''image_std'''						)						)
						self.assertTrue(hasattr(lowercase_  ,'''do_normalize'''						)						)
						self.assertTrue(hasattr(lowercase_  ,'''do_resize'''						)						)
						self.assertTrue(hasattr(lowercase_  ,'''do_center_crop'''						)						)
						self.assertTrue(hasattr(lowercase_  ,'''size'''						)						)







					def 			__lowerCAmelCase					(			self     :							Any						):
						lowerCAmelCase__      :		int    							=      self.image_processing_class.from_dict(self.image_processor_dict						)
						self.assertEqual(image_processor.size  ,{'''shortest_edge''': 1_8}						)
						self.assertEqual(image_processor.crop_size  ,{'''height''': 1_8, '''width''': 1_8}						)

						lowerCAmelCase__      :		List[str]    							=      self.image_processing_class.from_dict(self.image_processor_dict  ,size=4_2  ,crop_size=8_4						)
						self.assertEqual(image_processor.size  ,{'''shortest_edge''': 4_2}						)
						self.assertEqual(image_processor.crop_size  ,{'''height''': 8_4, '''width''': 8_4}						)







					def 			__lowerCAmelCase					(			self     :							Union[str, Any]						):
						pass







					def 			__lowerCAmelCase					(			self     :							Optional[int]						):
						# Initialize image_processing
						lowerCAmelCase__      :		List[str]    							=      self.image_processing_class(**self.image_processor_dict						)
						# create random PIL images
						lowerCAmelCase__      :		Union[str, Any]    							=      prepare_image_inputs(self.image_processor_tester  ,equal_resolution=lowercase_						)
						for image in image_inputs:
							self.assertIsInstance(lowercase_  ,Image.Image						)

						# Test not batched input
						lowerCAmelCase__      :		Any    							=      image_processing(image_inputs[0]  ,return_tensors='''pt'''						).pixel_values
						self.assertEqual(
						    encoded_images.shape  ,(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['''height'''],
						        self.image_processor_tester.crop_size['''width'''],
						    )  ,)

						# Test batched
						lowerCAmelCase__      :		Optional[int]    							=      image_processing(lowercase_  ,return_tensors='''pt'''						).pixel_values
						self.assertEqual(
						    encoded_images.shape  ,(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['''height'''],
						        self.image_processor_tester.crop_size['''width'''],
						    )  ,)







					def 			__lowerCAmelCase					(			self     :							Union[str, Any]						):
						# Initialize image_processing
						lowerCAmelCase__      :		str    							=      self.image_processing_class(**self.image_processor_dict						)
						# create random numpy tensors
						lowerCAmelCase__      :		Optional[Any]    							=      prepare_image_inputs(self.image_processor_tester  ,equal_resolution=lowercase_  ,numpify=lowercase_						)
						for image in image_inputs:
							self.assertIsInstance(lowercase_  ,np.ndarray						)

						# Test not batched input
						lowerCAmelCase__      :		Tuple    							=      image_processing(image_inputs[0]  ,return_tensors='''pt'''						).pixel_values
						self.assertEqual(
						    encoded_images.shape  ,(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['''height'''],
						        self.image_processor_tester.crop_size['''width'''],
						    )  ,)

						# Test batched
						lowerCAmelCase__      :		Union[str, Any]    							=      image_processing(lowercase_  ,return_tensors='''pt'''						).pixel_values
						self.assertEqual(
						    encoded_images.shape  ,(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['''height'''],
						        self.image_processor_tester.crop_size['''width'''],
						    )  ,)







					def 			__lowerCAmelCase					(			self     :							int						):
						# Initialize image_processing
						lowerCAmelCase__      :		Tuple    							=      self.image_processing_class(**self.image_processor_dict						)
						# create random PyTorch tensors
						lowerCAmelCase__      :		Dict    							=      prepare_image_inputs(self.image_processor_tester  ,equal_resolution=lowercase_  ,torchify=lowercase_						)
						for image in image_inputs:
							self.assertIsInstance(lowercase_  ,torch.Tensor						)

						# Test not batched input
						lowerCAmelCase__      :		Dict    							=      image_processing(image_inputs[0]  ,return_tensors='''pt'''						).pixel_values
						self.assertEqual(
						    encoded_images.shape  ,(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['''height'''],
						        self.image_processor_tester.crop_size['''width'''],
						    )  ,)

						# Test batched
						lowerCAmelCase__      :		Optional[int]    							=      image_processing(lowercase_  ,return_tensors='''pt'''						).pixel_values
						self.assertEqual(
						    encoded_images.shape  ,(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['''height'''],
						        self.image_processor_tester.crop_size['''width'''],
						    )  ,)




