




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 MobileNetVaImageProcessor


class        UpperCAmelCase__				(							unittest.TestCase				):



     '''simple docstring'''
     def __init__(				self ,    _lowerCAmelCase ,    _lowerCAmelCase=7 ,    _lowerCAmelCase=3 ,    _lowerCAmelCase=18 ,    _lowerCAmelCase=30 ,    _lowerCAmelCase=400 ,    _lowerCAmelCase=True ,    _lowerCAmelCase=None ,    _lowerCAmelCase=True ,    _lowerCAmelCase=None ,    ):
            a			   =size if size is not None else {"""shortest_edge""": 20}
            a			   =crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
            a			   =parent
            a			   =batch_size
            a			   =num_channels
            a			   =image_size
            a			   =min_resolution
            a			   =max_resolution
            a			   =do_resize
            a			   =size
            a			   =do_center_crop
            a			   =crop_size






     def        lowerCAmelCase__  (				self	):
            return {
                "do_resize": self.do_resize,
                "size": self.size,
                "do_center_crop": self.do_center_crop,
                "crop_size": self.crop_size,
            }



@require_torch
@require_vision
class        UpperCAmelCase__				(							UpperCAmelCase__      ,		unittest.TestCase				):



     '''simple docstring'''
     _SCREAMING_SNAKE_CASE      :					Tuple     					=    MobileNetVaImageProcessor if is_vision_available() else None
     def        lowerCAmelCase__  (				self	):
            a			   =MobileNetVaImageProcessingTester(self	)
     @property
     def        lowerCAmelCase__  (				self	):
            return self.image_processor_tester.prepare_image_processor_dict()
     def        lowerCAmelCase__  (				self	):
            a			   =self.image_processing_class(**self.image_processor_dict	)
            self.assertTrue(hasattr(_lowerCAmelCase ,    """do_resize"""	)	)
            self.assertTrue(hasattr(_lowerCAmelCase ,    """size"""	)	)
            self.assertTrue(hasattr(_lowerCAmelCase ,    """do_center_crop"""	)	)
            self.assertTrue(hasattr(_lowerCAmelCase ,    """center_crop"""	)	)
     def        lowerCAmelCase__  (				self	):
            a			   =self.image_processing_class.from_dict(self.image_processor_dict	)
            self.assertEqual(image_processor.size ,    {"""shortest_edge""": 20}	)
            self.assertEqual(image_processor.crop_size ,    {"""height""": 18, """width""": 18}	)

            a			   =self.image_processing_class.from_dict(self.image_processor_dict ,    size=42 ,    crop_size=84	)
            self.assertEqual(image_processor.size ,    {"""shortest_edge""": 42}	)
            self.assertEqual(image_processor.crop_size ,    {"""height""": 84, """width""": 84}	)
     def        lowerCAmelCase__  (				self	):
            pass
     def        lowerCAmelCase__  (				self	):
            # Initialize image_processing
            a			   =self.image_processing_class(**self.image_processor_dict	)
            # create random PIL images
            a			   =prepare_image_inputs(self.image_processor_tester ,    equal_resolution=_lowerCAmelCase	)
            for image in image_inputs:
                   self.assertIsInstance(_lowerCAmelCase ,    Image.Image	)

            # Test not batched input
            a			   =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
            a			   =image_processing(_lowerCAmelCase ,    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	):
            # Initialize image_processing
            a			   =self.image_processing_class(**self.image_processor_dict	)
            # create random numpy tensors
            a			   =prepare_image_inputs(self.image_processor_tester ,    equal_resolution=_lowerCAmelCase ,    numpify=_lowerCAmelCase	)
            for image in image_inputs:
                   self.assertIsInstance(_lowerCAmelCase ,    np.ndarray	)

            # Test not batched input
            a			   =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
            a			   =image_processing(_lowerCAmelCase ,    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	):
            # Initialize image_processing
            a			   =self.image_processing_class(**self.image_processor_dict	)
            # create random PyTorch tensors
            a			   =prepare_image_inputs(self.image_processor_tester ,    equal_resolution=_lowerCAmelCase ,    torchify=_lowerCAmelCase	)
            for image in image_inputs:
                   self.assertIsInstance(_lowerCAmelCase ,    torch.Tensor	)

            # Test not batched input
            a			   =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
            a			   =image_processing(_lowerCAmelCase ,    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"""],
                ) ,    )






import functools
import operator

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


_lowerCamelCase							=      logging.get_logger(__name__)

_lowerCamelCase							=      {
    '''facebook/hubert-base-ls960''': '''https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json''',
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}


class        UpperCAmelCase__				(							UpperCAmelCase__				):



     '''simple docstring'''
     _SCREAMING_SNAKE_CASE      :					Dict     					=    "hubert"
     def __init__(				self ,    _lowerCAmelCase=32 ,    _lowerCAmelCase=768 ,    _lowerCAmelCase=12 ,    _lowerCAmelCase=12 ,    _lowerCAmelCase=3_072 ,    _lowerCAmelCase="gelu" ,    _lowerCAmelCase=0.1 ,    _lowerCAmelCase=0.1 ,    _lowerCAmelCase=0.1 ,    _lowerCAmelCase=True ,    _lowerCAmelCase=0.0 ,    _lowerCAmelCase=0.1 ,    _lowerCAmelCase=0.1 ,    _lowerCAmelCase=0.02 ,    _lowerCAmelCase=1E-5 ,    _lowerCAmelCase="group" ,    _lowerCAmelCase="gelu" ,    _lowerCAmelCase=(512, 512, 512, 512, 512, 512, 512) ,    _lowerCAmelCase=(5, 2, 2, 2, 2, 2, 2) ,    _lowerCAmelCase=(10, 3, 3, 3, 3, 2, 2) ,    _lowerCAmelCase=False ,    _lowerCAmelCase=128 ,    _lowerCAmelCase=16 ,    _lowerCAmelCase=False ,    _lowerCAmelCase=True ,    _lowerCAmelCase=0.05 ,    _lowerCAmelCase=10 ,    _lowerCAmelCase=2 ,    _lowerCAmelCase=0.0 ,    _lowerCAmelCase=10 ,    _lowerCAmelCase=0 ,    _lowerCAmelCase="sum" ,    _lowerCAmelCase=False ,    _lowerCAmelCase=False ,    _lowerCAmelCase=256 ,    _lowerCAmelCase=0 ,    _lowerCAmelCase=1 ,    _lowerCAmelCase=2 ,    **_lowerCAmelCase ,    ):
            super().__init__(**_lowerCAmelCase ,    pad_token_id=_lowerCAmelCase ,    bos_token_id=_lowerCAmelCase ,    eos_token_id=_lowerCAmelCase	)
            a			   =hidden_size
            a			   =feat_extract_norm
            a			   =feat_extract_activation
            a			   =list(_lowerCAmelCase	)
            a			   =list(_lowerCAmelCase	)
            a			   =list(_lowerCAmelCase	)
            a			   =conv_bias
            a			   =num_conv_pos_embeddings
            a			   =num_conv_pos_embedding_groups
            a			   =len(self.conv_dim	)
            a			   =num_hidden_layers
            a			   =intermediate_size
            a			   =hidden_act
            a			   =num_attention_heads
            a			   =hidden_dropout
            a			   =attention_dropout
            a			   =activation_dropout
            a			   =feat_proj_layer_norm
            a			   =feat_proj_dropout
            a			   =final_dropout
            a			   =layerdrop
            a			   =layer_norm_eps
            a			   =initializer_range
            a			   =vocab_size
            a			   =do_stable_layer_norm
            a			   =use_weighted_layer_sum
            a			   =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
            a			   =apply_spec_augment
            a			   =mask_time_prob
            a			   =mask_time_length
            a			   =mask_time_min_masks
            a			   =mask_feature_prob
            a			   =mask_feature_length
            a			   =mask_feature_min_masks

            # ctc loss
            a			   =ctc_loss_reduction
            a			   =ctc_zero_infinity






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