






"""simple docstring"""





import functools
import operator

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


__lowerCamelCase							=  logging.get_logger(__name__)

__lowerCamelCase							=  {
    '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       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				"wavlm"

					def __init__(			self  :      List[str]     ,		__snake_case  :      Optional[Any]=3_2     ,		__snake_case  :      List[Any]=7_6_8     ,		__snake_case  :      Any=1_2     ,		__snake_case  :      Dict=1_2     ,		__snake_case  :      str=3_0_7_2     ,		__snake_case  :      str="gelu"     ,		__snake_case  :      Dict=0.1     ,		__snake_case  :      List[str]=0.1     ,		__snake_case  :      str=0.1     ,		__snake_case  :      Any=0.0     ,		__snake_case  :      Optional[int]=0.1     ,		__snake_case  :      int=0.1     ,		__snake_case  :      Optional[int]=0.02     ,		__snake_case  :      str=1E-5     ,		__snake_case  :      str="group"     ,		__snake_case  :      Any="gelu"     ,		__snake_case  :      Any=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)     ,		__snake_case  :      Union[str, Any]=(5, 2, 2, 2, 2, 2, 2)     ,		__snake_case  :      List[Any]=(1_0, 3, 3, 3, 3, 2, 2)     ,		__snake_case  :      Any=False     ,		__snake_case  :      List[str]=1_2_8     ,		__snake_case  :      Union[str, Any]=1_6     ,		__snake_case  :      List[Any]=3_2_0     ,		__snake_case  :      List[Any]=8_0_0     ,		__snake_case  :      Optional[Any]=False     ,		__snake_case  :      List[str]=True     ,		__snake_case  :      List[str]=0.05     ,		__snake_case  :      Tuple=1_0     ,		__snake_case  :      List[Any]=2     ,		__snake_case  :      Any=0.0     ,		__snake_case  :      Optional[Any]=1_0     ,		__snake_case  :      int=3_2_0     ,		__snake_case  :      Optional[int]=2     ,		__snake_case  :      List[Any]=0.1     ,		__snake_case  :      Any=1_0_0     ,		__snake_case  :      Union[str, Any]=2_5_6     ,		__snake_case  :      Any=2_5_6     ,		__snake_case  :      List[Any]=0.1     ,		__snake_case  :      int="mean"     ,		__snake_case  :      int=False     ,		__snake_case  :      Any=False     ,		__snake_case  :      List[str]=2_5_6     ,		__snake_case  :      Optional[int]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)     ,		__snake_case  :      List[str]=(5, 3, 3, 1, 1)     ,		__snake_case  :      Optional[Any]=(1, 2, 3, 1, 1)     ,		__snake_case  :      Dict=5_1_2     ,		__snake_case  :      Tuple=8_0     ,		__snake_case  :      Optional[int]=0     ,		__snake_case  :      Optional[Any]=1     ,		__snake_case  :      Optional[int]=2     ,		__snake_case  :      int=False     ,		__snake_case  :      Optional[Any]=3     ,		__snake_case  :      Optional[int]=2     ,		__snake_case  :      List[str]=3     ,		__snake_case  :      Optional[int]=None     ,		**__snake_case  :      List[str]     ,		)			->							Optional[int]:
											super().__init__(**__snake_case     ,		pad_token_id=__snake_case     ,		bos_token_id=__snake_case     ,		eos_token_id=__snake_case					)
											__magic_name__:	Tuple							=       hidden_size
											__magic_name__:	Any							=       feat_extract_norm
											__magic_name__:	List[str]							=       feat_extract_activation
											__magic_name__:	Optional[Any]							=       list(__snake_case					)
											__magic_name__:	str							=       list(__snake_case					)
											__magic_name__:	List[Any]							=       list(__snake_case					)
											__magic_name__:	Union[str, Any]							=       conv_bias
											__magic_name__:	Union[str, Any]							=       num_buckets
											__magic_name__:	Any							=       max_bucket_distance
											__magic_name__:	Any							=       num_conv_pos_embeddings
											__magic_name__:	List[str]							=       num_conv_pos_embedding_groups
											__magic_name__:	Any							=       len(self.conv_dim					)
											__magic_name__:	List[str]							=       num_hidden_layers
											__magic_name__:	Any							=       intermediate_size
											__magic_name__:	Union[str, Any]							=       hidden_act
											__magic_name__:	Any							=       num_attention_heads
											__magic_name__:	Tuple							=       hidden_dropout
											__magic_name__:	Any							=       attention_dropout
											__magic_name__:	List[str]							=       activation_dropout
											__magic_name__:	Union[str, Any]							=       feat_proj_dropout
											__magic_name__:	Optional[int]							=       final_dropout
											__magic_name__:	Tuple							=       layerdrop
											__magic_name__:	int							=       layer_norm_eps
											__magic_name__:	int							=       initializer_range
											__magic_name__:	List[Any]							=       num_ctc_classes
											__magic_name__:	List[Any]							=       vocab_size
											__magic_name__:	Dict							=       do_stable_layer_norm
											__magic_name__:	int							=       use_weighted_layer_sum
											__magic_name__:	List[str]							=       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
											__magic_name__:	Optional[int]							=       apply_spec_augment
											__magic_name__:	Union[str, Any]							=       mask_time_prob
											__magic_name__:	List[str]							=       mask_time_length
											__magic_name__:	Optional[Any]							=       mask_time_min_masks
											__magic_name__:	Optional[int]							=       mask_feature_prob
											__magic_name__:	Any							=       mask_feature_length

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

											# ctc loss
											__magic_name__:	Tuple							=       ctc_loss_reduction
											__magic_name__:	List[str]							=       ctc_zero_infinity

											# adapter
											__magic_name__:	List[str]							=       add_adapter
											__magic_name__:	Any							=       adapter_kernel_size
											__magic_name__:	Tuple							=       adapter_stride
											__magic_name__:	List[Any]							=       num_adapter_layers
											__magic_name__:	List[Any]							=       output_hidden_size or hidden_size

											# SequenceClassification-specific parameter. Feel free to ignore for other classes.
											__magic_name__:	Union[str, Any]							=       classifier_proj_size

											# XVector-specific parameters. Feel free to ignore for other classes.
											__magic_name__:	Optional[Any]							=       list(__snake_case					)
											__magic_name__:	List[Any]							=       list(__snake_case					)
											__magic_name__:	Any							=       list(__snake_case					)
											__magic_name__:	Optional[int]							=       xvector_output_dim






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






"""simple docstring"""




import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
						import jax

						from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel





class  lowerCamelCase    (unittest.TestCase      ):

			def __init__(       self					:    List[Any]     ,					__UpperCAmelCase					:    Dict     ,					__UpperCAmelCase					:    Optional[Any]=1_3     ,					__UpperCAmelCase					:    Tuple=3_0     ,					__UpperCAmelCase					:    str=2     ,					__UpperCAmelCase					:    List[Any]=3     ,					__UpperCAmelCase					:    Any=True     ,					__UpperCAmelCase					:    Dict=True     ,					__UpperCAmelCase					:    Optional[Any]=3_2     ,					__UpperCAmelCase					:    Any=5     ,					__UpperCAmelCase					:    Optional[Any]=4     ,					__UpperCAmelCase					:    Tuple=3_7     ,					__UpperCAmelCase					:    List[str]="gelu"     ,					__UpperCAmelCase					:    Union[str, Any]=0.1     ,					__UpperCAmelCase					:    List[str]=0.1     ,					__UpperCAmelCase					:    int=1_0     ,					__UpperCAmelCase					:    Any=0.02     ,					) ->     Union[str, Any]:
						SCREAMING_SNAKE_CASE__										=   parent
						SCREAMING_SNAKE_CASE__										=   batch_size
						SCREAMING_SNAKE_CASE__										=   image_size
						SCREAMING_SNAKE_CASE__										=   patch_size
						SCREAMING_SNAKE_CASE__										=   num_channels
						SCREAMING_SNAKE_CASE__										=   is_training
						SCREAMING_SNAKE_CASE__										=   use_labels
						SCREAMING_SNAKE_CASE__										=   hidden_size
						SCREAMING_SNAKE_CASE__										=   num_hidden_layers
						SCREAMING_SNAKE_CASE__										=   num_attention_heads
						SCREAMING_SNAKE_CASE__										=   intermediate_size
						SCREAMING_SNAKE_CASE__										=   hidden_act
						SCREAMING_SNAKE_CASE__										=   hidden_dropout_prob
						SCREAMING_SNAKE_CASE__										=   attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__										=   type_sequence_label_size
						SCREAMING_SNAKE_CASE__										=   initializer_range

						# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
						SCREAMING_SNAKE_CASE__										=   (image_size // patch_size) ** 2
						SCREAMING_SNAKE_CASE__										=   num_patches + 1

			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     Any:
						SCREAMING_SNAKE_CASE__										=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

						SCREAMING_SNAKE_CASE__										=   ViTConfig(
						    image_size=self.image_size     ,					patch_size=self.patch_size     ,					num_channels=self.num_channels     ,					hidden_size=self.hidden_size     ,					num_hidden_layers=self.num_hidden_layers     ,					num_attention_heads=self.num_attention_heads     ,					intermediate_size=self.intermediate_size     ,					hidden_act=self.hidden_act     ,					hidden_dropout_prob=self.hidden_dropout_prob     ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,					is_decoder=__UpperCAmelCase     ,					initializer_range=self.initializer_range     ,					)

						return config, pixel_values

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict     ,					__UpperCAmelCase					:    int     ,					__UpperCAmelCase					:    int							) ->     Optional[Any]:
						SCREAMING_SNAKE_CASE__										=   FlaxViTModel(config=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase							)
						# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
						SCREAMING_SNAKE_CASE__										=   (self.image_size, self.image_size)
						SCREAMING_SNAKE_CASE__										=   (self.patch_size, self.patch_size)
						SCREAMING_SNAKE_CASE__										=   (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
						self.parent.assertEqual(result.last_hidden_state.shape     ,					(self.batch_size, num_patches + 1, self.hidden_size)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]     ,					__UpperCAmelCase					:    Optional[int]     ,					__UpperCAmelCase					:    Optional[Any]							) ->     int:
						SCREAMING_SNAKE_CASE__										=   self.type_sequence_label_size
						SCREAMING_SNAKE_CASE__										=   FlaxViTForImageClassification(config=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase							)
						self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.type_sequence_label_size)							)

						# test greyscale images
						SCREAMING_SNAKE_CASE__										=   1
						SCREAMING_SNAKE_CASE__										=   FlaxViTForImageClassification(__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
						SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase							)





			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     str:
						SCREAMING_SNAKE_CASE__										=   self.prepare_config_and_inputs()
						(
						    (
						    SCREAMING_SNAKE_CASE__
						)				,		(
						    SCREAMING_SNAKE_CASE__
						)				,		
						)										=   config_and_inputs
						SCREAMING_SNAKE_CASE__										=   {"""pixel_values""": pixel_values}
						return config, inputs_dict





@require_flax
class  lowerCamelCase    (A__      ,unittest.TestCase      ):
			lowerCamelCase__ :		Tuple       		=				(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()

			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     None:
						SCREAMING_SNAKE_CASE__										=   FlaxViTModelTester(self							)
						SCREAMING_SNAKE_CASE__										=   ConfigTester(self     ,					config_class=__UpperCAmelCase     ,					has_text_modality=__UpperCAmelCase     ,					hidden_size=3_7							)

			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     Dict:
						self.config_tester.run_common_tests()

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]							) ->     Optional[Any]:
						SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]							) ->     List[str]:
						SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     str:
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE__										=   model_class(__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__										=   inspect.signature(model.__call__							)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									SCREAMING_SNAKE_CASE__										=   [*signature.parameters.keys()]

									SCREAMING_SNAKE_CASE__										=   ["""pixel_values"""]
									self.assertListEqual(arg_names[:1]     ,					__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     Optional[Any]:
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__							):
												SCREAMING_SNAKE_CASE__										=   self._prepare_for_class(__UpperCAmelCase     ,					__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   model_class(__UpperCAmelCase							)

												@jax.jit
												def model_jitted(__UpperCAmelCase					:    int     ,					**__UpperCAmelCase					:    Tuple							):
															return model(pixel_values=__UpperCAmelCase     ,					**__UpperCAmelCase							)

												with self.subTest("""JIT Enabled"""							):
															SCREAMING_SNAKE_CASE__										=   model_jitted(**__UpperCAmelCase							).to_tuple()

												with self.subTest("""JIT Disabled"""							):
															with jax.disable_jit():
																		SCREAMING_SNAKE_CASE__										=   model_jitted(**__UpperCAmelCase							).to_tuple()

												self.assertEqual(len(__UpperCAmelCase							)     ,					len(__UpperCAmelCase							)							)
												for jitted_output, output in zip(__UpperCAmelCase     ,					__UpperCAmelCase							):
															self.assertEqual(jitted_output.shape     ,					output.shape							)





			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     Dict:
						for model_class_name in self.all_model_classes:
									SCREAMING_SNAKE_CASE__										=   model_class_name.from_pretrained("""google/vit-base-patch16-224"""							)
									SCREAMING_SNAKE_CASE__										=   model(np.ones((1, 3, 2_2_4, 2_2_4)							)							)
									self.assertIsNotNone(__UpperCAmelCase							)


