
"""simple docstring"""

import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
  from PIL import Image
else:

  class __lowerCAmelCase		:





       @staticmethod
       def 						UpperCAmelCase (     *__UpperCAmelCase     ,					**__UpperCAmelCase				):

        '''simple docstring'''




        pass






def 							A						(    snake_case				:Image		)				->     str:
 __UpperCamelCase      =	hashlib.mda(image.tobytes()		)
 return m.hexdigest()[:1_0]






def 							A						(    snake_case				:Image		)				->     Dict:
 __UpperCamelCase      =	np.array(snake_case		)
 __UpperCamelCase      =	npimg.shape
 return {"hash": hashimage(snake_case		), "shape": shape}





@is_pipeline_test
@require_vision
@require_torch
class __lowerCAmelCase		(				unittest.TestCase      ):
     lowercase						       =						dict(
         (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()      ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])      )
     lowercase						       =						dict(
         (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()      ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])      )





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	MaskGenerationPipeline(model=__UpperCAmelCase     ,					image_processor=__UpperCAmelCase				)
      return image_segmenter, [
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
      ]





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      pass





     @require_tf
     @unittest.skip('Image segmentation not implemented in TF'				)
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      pass





     @slow
     @require_torch
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	pipeline('mask-generation'     ,					model='facebook/sam-vit-huge'				)

      __UpperCamelCase      =	image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg'     ,					points_per_batch=256				)

      # Shortening by hashing
      __UpperCamelCase      =	[]
      for i, o in enumerate(outputs['masks']				):
       new_outupt += [{"mask": mask_to_test_readable(__UpperCAmelCase				), "scores": outputs["scores"][i]}]

      # fmt: off
      self.assertEqual(
          nested_simplify(__UpperCAmelCase     ,					decimals=4				)     ,					[
              {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0_4_4_4},
              {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0_2_1},
              {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0_1_6_7},
              {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0_1_3_2},
              {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0_0_5_3},
              {'mask': {'hash': 'e2d0b7a0b7', 'shape': (480, 640)}, 'scores': 0.9_9_6_7},
              {'mask': {'hash': '453c7844bd', 'shape': (480, 640)}, 'scores': 0.9_9_3},
              {'mask': {'hash': '3d44f2926d', 'shape': (480, 640)}, 'scores': 0.9_9_0_9},
              {'mask': {'hash': '64033ddc3f', 'shape': (480, 640)}, 'scores': 0.9_8_7_9},
              {'mask': {'hash': '801064ff79', 'shape': (480, 640)}, 'scores': 0.9_8_3_4},
              {'mask': {'hash': '6172f276ef', 'shape': (480, 640)}, 'scores': 0.9_7_1_6},
              {'mask': {'hash': 'b49e60e084', 'shape': (480, 640)}, 'scores': 0.9_6_1_2},
              {'mask': {'hash': 'a811e775fd', 'shape': (480, 640)}, 'scores': 0.9_5_9_9},
              {'mask': {'hash': 'a6a8ebcf4b', 'shape': (480, 640)}, 'scores': 0.9_5_5_2},
              {'mask': {'hash': '9d8257e080', 'shape': (480, 640)}, 'scores': 0.9_5_3_2},
              {'mask': {'hash': '32de6454a8', 'shape': (480, 640)}, 'scores': 0.9_5_1_6},
              {'mask': {'hash': 'af3d4af2c8', 'shape': (480, 640)}, 'scores': 0.9_4_9_9},
              {'mask': {'hash': '3c6db475fb', 'shape': (480, 640)}, 'scores': 0.9_4_8_3},
              {'mask': {'hash': 'c290813fb9', 'shape': (480, 640)}, 'scores': 0.9_4_6_4},
              {'mask': {'hash': 'b6f0b8f606', 'shape': (480, 640)}, 'scores': 0.9_4_3},
              {'mask': {'hash': '92ce16bfdf', 'shape': (480, 640)}, 'scores': 0.9_4_3},
              {'mask': {'hash': 'c749b25868', 'shape': (480, 640)}, 'scores': 0.9_4_0_8},
              {'mask': {'hash': 'efb6cab859', 'shape': (480, 640)}, 'scores': 0.9_3_3_5},
              {'mask': {'hash': '1ff2eafb30', 'shape': (480, 640)}, 'scores': 0.9_3_2_6},
              {'mask': {'hash': '788b798e24', 'shape': (480, 640)}, 'scores': 0.9_2_6_2},
              {'mask': {'hash': 'abea804f0e', 'shape': (480, 640)}, 'scores': 0.8_9_9_9},
              {'mask': {'hash': '7b9e8ddb73', 'shape': (480, 640)}, 'scores': 0.8_9_8_6},
              {'mask': {'hash': 'cd24047c8a', 'shape': (480, 640)}, 'scores': 0.8_9_8_4},
              {'mask': {'hash': '6943e6bcbd', 'shape': (480, 640)}, 'scores': 0.8_8_7_3},
              {'mask': {'hash': 'b5f47c9191', 'shape': (480, 640)}, 'scores': 0.8_8_7_1}
          ]     ,					)
      # fmt: on







     @require_torch
     @slow
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	'facebook/sam-vit-huge'
      __UpperCamelCase      =	pipeline('mask-generation'     ,					model=__UpperCAmelCase				)

      __UpperCamelCase      =	image_segmenter(
          'http://images.cocodataset.org/val2017/000000039769.jpg'     ,					pred_iou_thresh=1     ,					points_per_batch=256				)

      # Shortening by hashing
      __UpperCamelCase      =	[]
      for i, o in enumerate(outputs['masks']				):
       new_outupt += [{"mask": mask_to_test_readable(__UpperCAmelCase				), "scores": outputs["scores"][i]}]

      self.assertEqual(
          nested_simplify(__UpperCAmelCase     ,					decimals=4				)     ,					[
              {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0_4_4_4},
              {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0_2_1_0},
              {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0_1_6_7},
              {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0_1_3_2},
              {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0_0_5_3},
          ]     ,					)





"""simple docstring"""

import functools
import operator

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


UpperCamelCase     :    str       =			logging.get_logger(__name__)

UpperCamelCase     :    Dict       =			{
    "microsoft/wavlm-base": "https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json",
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}

class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ):
     lowercase						       =						"wavlm"





     def __init__(     self     ,					__UpperCAmelCase=32     ,					__UpperCAmelCase=768     ,					__UpperCAmelCase=12     ,					__UpperCAmelCase=12     ,					__UpperCAmelCase=3072     ,					__UpperCAmelCase="gelu"     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.0     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.0_2     ,					__UpperCAmelCase=1E-5     ,					__UpperCAmelCase="group"     ,					__UpperCAmelCase="gelu"     ,					__UpperCAmelCase=(512, 512, 512, 512, 512, 512, 512)     ,					__UpperCAmelCase=(5, 2, 2, 2, 2, 2, 2)     ,					__UpperCAmelCase=(10, 3, 3, 3, 3, 2, 2)     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=128     ,					__UpperCAmelCase=16     ,					__UpperCAmelCase=320     ,					__UpperCAmelCase=800     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=0.0_5     ,					__UpperCAmelCase=10     ,					__UpperCAmelCase=2     ,					__UpperCAmelCase=0.0     ,					__UpperCAmelCase=10     ,					__UpperCAmelCase=320     ,					__UpperCAmelCase=2     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=100     ,					__UpperCAmelCase=256     ,					__UpperCAmelCase=256     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase="mean"     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=256     ,					__UpperCAmelCase=(512, 512, 512, 512, 1500)     ,					__UpperCAmelCase=(5, 3, 3, 1, 1)     ,					__UpperCAmelCase=(1, 2, 3, 1, 1)     ,					__UpperCAmelCase=512     ,					__UpperCAmelCase=80     ,					__UpperCAmelCase=0     ,					__UpperCAmelCase=1     ,					__UpperCAmelCase=2     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=3     ,					__UpperCAmelCase=2     ,					__UpperCAmelCase=3     ,					__UpperCAmelCase=None     ,					**__UpperCAmelCase     ,					):

      '''simple docstring'''




      super().__init__(**__UpperCAmelCase     ,					pad_token_id=__UpperCAmelCase     ,					bos_token_id=__UpperCAmelCase     ,					eos_token_id=__UpperCAmelCase				)
      __UpperCamelCase      =	hidden_size
      __UpperCamelCase      =	feat_extract_norm
      __UpperCamelCase      =	feat_extract_activation
      __UpperCamelCase      =	list(__UpperCAmelCase				)
      __UpperCamelCase      =	list(__UpperCAmelCase				)
      __UpperCamelCase      =	list(__UpperCAmelCase				)
      __UpperCamelCase      =	conv_bias
      __UpperCamelCase      =	num_buckets
      __UpperCamelCase      =	max_bucket_distance
      __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

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

      # ctc loss
      __UpperCamelCase      =	ctc_loss_reduction
      __UpperCamelCase      =	ctc_zero_infinity

      # adapter
      __UpperCamelCase      =	add_adapter
      __UpperCamelCase      =	adapter_kernel_size
      __UpperCamelCase      =	adapter_stride
      __UpperCamelCase      =	num_adapter_layers
      __UpperCamelCase      =	output_hidden_size or hidden_size

      # SequenceClassification-specific parameter. Feel free to ignore for other classes.
      __UpperCamelCase      =	classifier_proj_size

      # XVector-specific parameters. Feel free to ignore for other classes.
      __UpperCamelCase      =	list(__UpperCAmelCase				)
      __UpperCamelCase      =	list(__UpperCAmelCase				)
      __UpperCamelCase      =	list(__UpperCAmelCase				)
      __UpperCamelCase      =	xvector_output_dim







     @property
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      return functools.reduce(operator.mul     ,					self.conv_stride     ,					1				)



