

import functools
import operator

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


a_						     =    logging.get_logger(__name__)

a_						     =    {
    '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 _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			lowerCamelCase__           ='wavlm'
			def __init__(			self							: Optional[int]      ,      a							: Optional[Any]=32      ,      a							: int=768      ,      a							: Tuple=12      ,      a							: List[str]=12      ,      a							: str=3072      ,      a							: Any="gelu"      ,      a							: Dict=0.1      ,      a							: int=0.1      ,      a							: str=0.1      ,      a							: Optional[Any]=0.0      ,      a							: Any=0.1      ,      a							: Any=0.1      ,      a							: List[str]=0.02      ,      a							: List[Any]=1e-5      ,      a							: Any="group"      ,      a							: Optional[int]="gelu"      ,      a							: List[str]=(512, 512, 512, 512, 512, 512, 512)      ,      a							: Any=(5, 2, 2, 2, 2, 2, 2)      ,      a							: Union[str, Any]=(10, 3, 3, 3, 3, 2, 2)      ,      a							: Optional[Any]=False      ,      a							: Dict=128      ,      a							: Optional[Any]=16      ,      a							: Optional[Any]=320      ,      a							: str=800      ,      a							: Optional[int]=False      ,      a							: Tuple=True      ,      a							: Optional[Any]=0.05      ,      a							: Any=10      ,      a							: Optional[int]=2      ,      a							: Dict=0.0      ,      a							: str=10      ,      a							: Tuple=320      ,      a							: Optional[int]=2      ,      a							: int=0.1      ,      a							: List[str]=100      ,      a							: Tuple=256      ,      a							: str=256      ,      a							: Tuple=0.1      ,      a							: str="mean"      ,      a							: int=False      ,      a							: int=False      ,      a							: Optional[Any]=256      ,      a							: Any=(512, 512, 512, 512, 1500)      ,      a							: Tuple=(5, 3, 3, 1, 1)      ,      a							: str=(1, 2, 3, 1, 1)      ,      a							: Optional[Any]=512      ,      a							: Optional[Any]=80      ,      a							: Tuple=0      ,      a							: Any=1      ,      a							: Optional[Any]=2      ,      a							: int=False      ,      a							: Dict=3      ,      a							: Any=2      ,      a							: List[Any]=3      ,      a							: int=None      ,      **a							: Any      ,      )							->       Dict:

				"""simple docstring"""

				super().__init__(**a      ,      pad_token_id=a      ,      bos_token_id=a      ,      eos_token_id=a	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       hidden_size
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       feat_extract_norm
				SCREAMING_SNAKE_CASE   :      Any				     =       feat_extract_activation
				SCREAMING_SNAKE_CASE   :      Any				     =       list(a	)
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       list(a	)
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       list(a	)
				SCREAMING_SNAKE_CASE   :      Any				     =       conv_bias
				SCREAMING_SNAKE_CASE   :      str				     =       num_buckets
				SCREAMING_SNAKE_CASE   :      str				     =       max_bucket_distance
				SCREAMING_SNAKE_CASE   :      List[str]				     =       num_conv_pos_embeddings
				SCREAMING_SNAKE_CASE   :      Any				     =       num_conv_pos_embedding_groups
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       len(self.conv_dim	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       num_hidden_layers
				SCREAMING_SNAKE_CASE   :      List[str]				     =       intermediate_size
				SCREAMING_SNAKE_CASE   :      Dict				     =       hidden_act
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       num_attention_heads
				SCREAMING_SNAKE_CASE   :      int				     =       hidden_dropout
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       attention_dropout
				SCREAMING_SNAKE_CASE   :      List[str]				     =       activation_dropout
				SCREAMING_SNAKE_CASE   :      int				     =       feat_proj_dropout
				SCREAMING_SNAKE_CASE   :      Any				     =       final_dropout
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       layerdrop
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       layer_norm_eps
				SCREAMING_SNAKE_CASE   :      int				     =       initializer_range
				SCREAMING_SNAKE_CASE   :      Dict				     =       num_ctc_classes
				SCREAMING_SNAKE_CASE   :      Tuple				     =       vocab_size
				SCREAMING_SNAKE_CASE   :      int				     =       do_stable_layer_norm
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       use_weighted_layer_sum
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       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
				SCREAMING_SNAKE_CASE   :      int				     =       apply_spec_augment
				SCREAMING_SNAKE_CASE   :      int				     =       mask_time_prob
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       mask_time_length
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       mask_time_min_masks
				SCREAMING_SNAKE_CASE   :      Tuple				     =       mask_feature_prob
				SCREAMING_SNAKE_CASE   :      List[str]				     =       mask_feature_length

				# parameters for pretraining with codevector quantized representations
				SCREAMING_SNAKE_CASE   :      str				     =       num_codevectors_per_group
				SCREAMING_SNAKE_CASE   :      Dict				     =       num_codevector_groups
				SCREAMING_SNAKE_CASE   :      Tuple				     =       contrastive_logits_temperature
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       num_negatives
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       codevector_dim
				SCREAMING_SNAKE_CASE   :      int				     =       proj_codevector_dim
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       diversity_loss_weight

				# ctc loss
				SCREAMING_SNAKE_CASE   :      Any				     =       ctc_loss_reduction
				SCREAMING_SNAKE_CASE   :      Dict				     =       ctc_zero_infinity

				# adapter
				SCREAMING_SNAKE_CASE   :      Any				     =       add_adapter
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       adapter_kernel_size
				SCREAMING_SNAKE_CASE   :      Any				     =       adapter_stride
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       num_adapter_layers
				SCREAMING_SNAKE_CASE   :      int				     =       output_hidden_size or hidden_size

				# SequenceClassification-specific parameter. Feel free to ignore for other classes.
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       classifier_proj_size

				# XVector-specific parameters. Feel free to ignore for other classes.
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       list(a	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       list(a	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       list(a	)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       xvector_output_dim






			@property
			def 							__UpperCamelCase (			self							: int	)							->       Union[str, Any]:

				"""simple docstring"""

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


from __future__ import annotations

import inspect
import unittest
from math import floor

import numpy as np

from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import TFCvtForImageClassification, TFCvtModel
			from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor






class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			def 							__UpperCamelCase (			self							: Any	)							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.config_class(**self.inputs_dict	)
				self.parent.assertTrue(hasattr(a      ,      "embed_dim"	)	)
				self.parent.assertTrue(hasattr(a      ,      "num_heads"	)	)






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: Dict      ,      a							: int      ,      a							: List[str]=13      ,      a							: Optional[int]=64      ,      a							: List[str]=3      ,      a							: Dict=[16, 48, 96]      ,      a							: List[Any]=[1, 3, 6]      ,      a							: int=[1, 2, 10]      ,      a							: List[str]=[7, 3, 3]      ,      a							: List[Any]=[4, 2, 2]      ,      a							: Tuple=[2, 1, 1]      ,      a							: Union[str, Any]=[2, 2, 2]      ,      a							: List[str]=[False, False, True]      ,      a							: int=[0.0, 0.0, 0.0]      ,      a							: Dict=0.02      ,      a							: List[Any]=1e-12      ,      a							: Tuple=True      ,      a							: Optional[int]=True      ,      a							: Union[str, Any]=2      ,      )							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       parent
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       batch_size
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       image_size
				SCREAMING_SNAKE_CASE   :      Dict				     =       patch_sizes
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       patch_stride
				SCREAMING_SNAKE_CASE   :      str				     =       patch_padding
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       is_training
				SCREAMING_SNAKE_CASE   :      Dict				     =       use_labels
				SCREAMING_SNAKE_CASE   :      List[str]				     =       num_labels
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       num_channels
				SCREAMING_SNAKE_CASE   :      Any				     =       embed_dim
				SCREAMING_SNAKE_CASE   :      str				     =       num_heads
				SCREAMING_SNAKE_CASE   :      Any				     =       stride_kv
				SCREAMING_SNAKE_CASE   :      Tuple				     =       depth
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       cls_token
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       attention_drop_rate
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       initializer_range
				SCREAMING_SNAKE_CASE   :      Tuple				     =       layer_norm_eps
			def 							__UpperCamelCase (			self							: str	)							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

				SCREAMING_SNAKE_CASE   :      Any				     =       None
				if self.use_labels:
					# create a random int32 tensor of given shape
					SCREAMING_SNAKE_CASE   :      str				     =       ids_tensor([self.batch_size]      ,      self.num_labels	)

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.get_config()
				return config, pixel_values, labels
			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       Optional[int]:

				"""simple docstring"""

				return CvtConfig(
				    image_size=self.image_size      ,      num_labels=self.num_labels      ,      num_channels=self.num_channels      ,      embed_dim=self.embed_dim      ,      num_heads=self.num_heads      ,      patch_sizes=self.patch_sizes      ,      patch_padding=self.patch_padding      ,      patch_stride=self.patch_stride      ,      stride_kv=self.stride_kv      ,      depth=self.depth      ,      cls_token=self.cls_token      ,      attention_drop_rate=self.attention_drop_rate      ,      initializer_range=self.initializer_range      ,      )
			def 							__UpperCamelCase (			self							: Tuple      ,      a							: str      ,      a							: List[str]      ,      a							: str	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      str				     =       TFCvtModel(config=a	)
				SCREAMING_SNAKE_CASE   :      Any				     =       model(a      ,      training=a	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       (self.image_size, self.image_size)
				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Any				     =       image_size[0], image_size[1]
				for i in range(len(self.depth	)	):
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1	)
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1	)
				self.parent.assertEqual(result.last_hidden_state.shape      ,      (self.batch_size, self.embed_dim[-1], height, width)	)
			def 							__UpperCamelCase (			self							: Union[str, Any]      ,      a							: int      ,      a							: List[Any]      ,      a							: List[str]	)							->       Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.num_labels
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       TFCvtForImageClassification(a	)
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       model(a      ,      labels=a      ,      training=a	)
				self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.num_labels)	)






			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       self.prepare_config_and_inputs()
				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      List[str]				     =       config_and_inputs
				SCREAMING_SNAKE_CASE   :      str				     =       {"pixel_values": pixel_values}
				return config, inputs_dict






@require_tf
class _UpperCamelCase							(							__A							,	__A							,	unittest.TestCase   ):
			'''simple docstring'''







			lowerCamelCase__           =(TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
			lowerCamelCase__           =(
			    {'feature-extraction': TFCvtModel, 'image-classification': TFCvtForImageClassification}
			    if is_tf_available()
			    else {}
			)
			lowerCamelCase__           =False
			lowerCamelCase__           =False
			lowerCamelCase__           =False
			lowerCamelCase__           =False
			lowerCamelCase__           =False
			def 							__UpperCamelCase (			self							: List[Any]	)							->       int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       TFCvtModelTester(self	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       TFCvtConfigTester(self      ,      config_class=a      ,      has_text_modality=a      ,      hidden_size=37	)
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Optional[int]:

				"""simple docstring"""

				self.config_tester.create_and_test_config_common_properties()
				self.config_tester.create_and_test_config_to_json_string()
				self.config_tester.create_and_test_config_to_json_file()
				self.config_tester.create_and_test_config_from_and_save_pretrained()
				self.config_tester.create_and_test_config_with_num_labels()
				self.config_tester.check_config_can_be_init_without_params()
				self.config_tester.check_config_arguments_init()
			@unittest.skip(reason="Cvt does not output attentions"	)
			def 							__UpperCamelCase (			self							: str	)							->       Optional[int]:

				"""simple docstring"""

				pass
			@unittest.skip(reason="Cvt does not use inputs_embeds"	)
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Optional[int]:

				"""simple docstring"""

				pass
			@unittest.skip(reason="Cvt does not support input and output embeddings"	)
			def 							__UpperCamelCase (			self							: List[Any]	)							->       Dict:

				"""simple docstring"""

				pass
			@unittest.skipIf(
			    not is_tf_available() or len(tf.config.list_physical_devices("GPU"	)	) == 0      ,      reason="TF does not support backprop for grouped convolutions on CPU."      ,      )
			def 							__UpperCamelCase (			self							: int	)							->       Tuple:

				"""simple docstring"""

				super().test_dataset_conversion()
			@unittest.skipIf(
			    not is_tf_available() or len(tf.config.list_physical_devices("GPU"	)	) == 0      ,      reason="TF does not support backprop for grouped convolutions on CPU."      ,      )
			@slow
			def 							__UpperCamelCase (			self							: List[str]	)							->       str:

				"""simple docstring"""

				super().test_keras_fit()
			@unittest.skip(reason="Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8"	)
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       tf.keras.mixed_precision.Policy("mixed_float16"	)
				tf.keras.mixed_precision.set_global_policy(a	)
				super().test_keras_fit()
				tf.keras.mixed_precision.set_global_policy("float32"	)
			def 							__UpperCamelCase (			self							: List[str]	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Optional[int]				     =       self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       model_class(a	)
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       inspect.signature(model.call	)
					# signature.parameters is an OrderedDict => so arg_names order is deterministic
					SCREAMING_SNAKE_CASE   :      Tuple				     =       [*signature.parameters.keys()]

					SCREAMING_SNAKE_CASE   :      Tuple				     =       ["pixel_values"]
					self.assertListEqual(arg_names[:1]      ,      a	)
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       str:

				"""simple docstring"""

				def check_hidden_states_output(a							: List[str]      ,      a							: Union[str, Any]      ,      a							: List[str]	):
					SCREAMING_SNAKE_CASE   :      Tuple				     =       model_class(a	)

					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       model(**self._prepare_for_class(a      ,      a	)	)
					SCREAMING_SNAKE_CASE   :      List[str]				     =       outputs.hidden_states

					SCREAMING_SNAKE_CASE   :      int				     =       len(self.model_tester.depth	)
					self.assertEqual(len(a	)      ,      a	)

					# verify the first hidden states (first block)
					self.assertListEqual(
					    list(hidden_states[0].shape[-3:]	)      ,      [
					        self.model_tester.embed_dim[0],
					        self.model_tester.image_size // 4,
					        self.model_tester.image_size // 4,
					    ]      ,      )

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      List[str]				     =       self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       True
					check_hidden_states_output(a      ,      a      ,      a	)

					# check that output_hidden_states also work using config
					del inputs_dict["output_hidden_states"]
					SCREAMING_SNAKE_CASE   :      str				     =       True

					check_hidden_states_output(a      ,      a      ,      a	)
			def 							__UpperCamelCase (			self							: Any	)							->       Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*a	)
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*a	)






			@slow
			def 							__UpperCamelCase (			self							: Tuple	)							->       Optional[int]:

				"""simple docstring"""

				for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					SCREAMING_SNAKE_CASE   :      Tuple				     =       TFCvtModel.from_pretrained(a	)
					self.assertIsNotNone(a	)

def  lowerCamelCase__ (					):
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
	return image






@require_tf
@require_vision
class _UpperCamelCase							(							unittest.TestCase   ):
			'''simple docstring'''







			@cached_property
			def 							__UpperCamelCase (			self							: Tuple	)							->       Tuple:

				"""simple docstring"""

				return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)






			@slow
			def 							__UpperCamelCase (			self							: Dict	)							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.default_image_processor
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       prepare_img()
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       image_processor(images=a      ,      return_tensors="tf"	)

				# forward pass
				SCREAMING_SNAKE_CASE   :      List[str]				     =       model(**a	)

				# verify the logits
				SCREAMING_SNAKE_CASE   :      Dict				     =       tf.TensorShape((1, 1000)	)
				self.assertEqual(outputs.logits.shape      ,      a	)

				SCREAMING_SNAKE_CASE   :      Tuple				     =       tf.constant([0.9285, 0.9015, -0.3150]	)
				self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()      ,      a      ,      atol=1e-4	)	)