


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	):

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=13		,				_UpperCAmelCase=30		,				_UpperCAmelCase=2		,				_UpperCAmelCase=3		,				_UpperCAmelCase=True		,				_UpperCAmelCase=True		,				_UpperCAmelCase=32		,				_UpperCAmelCase=5		,				_UpperCAmelCase=4		,				_UpperCAmelCase=37		,				_UpperCAmelCase="gelu"		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=10		,				_UpperCAmelCase=0.02		,				):
       snake_case_	=				parent
       snake_case_	=				batch_size
       snake_case_	=				image_size
       snake_case_	=				patch_size
       snake_case_	=				num_channels
       snake_case_	=				is_training
       snake_case_	=				use_labels
       snake_case_	=				hidden_size
       snake_case_	=				num_hidden_layers
       snake_case_	=				num_attention_heads
       snake_case_	=				intermediate_size
       snake_case_	=				hidden_act
       snake_case_	=				hidden_dropout_prob
       snake_case_	=				attention_probs_dropout_prob
       snake_case_	=				type_sequence_label_size
       snake_case_	=				initializer_range

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



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

       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=__SCREAMING_SNAKE_CASE		,				initializer_range=self.initializer_range		,				)

       return config, pixel_values



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				FlaxViTModel(config=__SCREAMING_SNAKE_CASE )
       snake_case_	=				model(__SCREAMING_SNAKE_CASE )
       # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
       snake_case_	=				(self.image_size, self.image_size)
       snake_case_	=				(self.patch_size, self.patch_size)
       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 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				self.type_sequence_label_size
       snake_case_	=				FlaxViTForImageClassification(config=__SCREAMING_SNAKE_CASE )
       snake_case_	=				model(__SCREAMING_SNAKE_CASE )
       self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.type_sequence_label_size) )

       # test greyscale images
       snake_case_	=				1
       snake_case_	=				FlaxViTForImageClassification(__SCREAMING_SNAKE_CASE )

       snake_case_	=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
       snake_case_	=				model(__SCREAMING_SNAKE_CASE )





 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.prepare_config_and_inputs()
       (
           (
           snake_case_   
       )	,   (
           snake_case_   
       )	,      
       )	=				config_and_inputs
       snake_case_	=				{'''pixel_values''': pixel_values}
       return config, inputs_dict







@require_flax
class lowerCAmelCase_						(			_A ,						unittest.TestCase	):

 '''simple docstring'''

 __snake_case												=  (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				FlaxViTModelTester(self )
       snake_case_	=				ConfigTester(self		,				config_class=__SCREAMING_SNAKE_CASE		,				has_text_modality=__SCREAMING_SNAKE_CASE		,				hidden_size=37 )



 def 						UpperCamelCase__					(     self ):
       self.config_tester.run_common_tests()



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE )



 def 						UpperCamelCase__					(     self ):
       snake_case_	,   snake_case_	=				self.model_tester.prepare_config_and_inputs_for_common()

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

             snake_case_	=				['''pixel_values''']
             self.assertListEqual(arg_names[:1]		,				__SCREAMING_SNAKE_CASE )



 def 						UpperCamelCase__					(     self ):
       snake_case_	,   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__ ):
                   snake_case_	=				self._prepare_for_class(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE )
                   snake_case_	=				model_class(__SCREAMING_SNAKE_CASE )

                   @jax.jit
                   def model_jitted(_UpperCAmelCase		,				**_UpperCAmelCase ):
                         return model(pixel_values=__SCREAMING_SNAKE_CASE		,				**__SCREAMING_SNAKE_CASE )

                   with self.subTest('''JIT Enabled''' ):
                         snake_case_	=				model_jitted(**__SCREAMING_SNAKE_CASE ).to_tuple()

                   with self.subTest('''JIT Disabled''' ):
                         with jax.disable_jit():
                               snake_case_	=				model_jitted(**__SCREAMING_SNAKE_CASE ).to_tuple()

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





 @slow
 def 						UpperCamelCase__					(     self ):
       for model_class_name in self.all_model_classes:
             snake_case_	=				model_class_name.from_pretrained('''google/vit-base-patch16-224''' )
             snake_case_	=				model(np.ones((1, 3, 2_24, 2_24) ) )
             self.assertIsNotNone(__SCREAMING_SNAKE_CASE )



import functools
import operator

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


UpperCAmelCase      =   logging.get_logger(__name__)

UpperCAmelCase      =   {
    """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_						(			lowerCamelCase__	):

 '''simple docstring'''

 __snake_case												=  "wavlm"



 def __init__(     self		,				_UpperCAmelCase=32		,				_UpperCAmelCase=7_68		,				_UpperCAmelCase=12		,				_UpperCAmelCase=12		,				_UpperCAmelCase=30_72		,				_UpperCAmelCase="gelu"		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0.0		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0.02		,				_UpperCAmelCase=1E-5		,				_UpperCAmelCase="group"		,				_UpperCAmelCase="gelu"		,				_UpperCAmelCase=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)		,				_UpperCAmelCase=(5, 2, 2, 2, 2, 2, 2)		,				_UpperCAmelCase=(10, 3, 3, 3, 3, 2, 2)		,				_UpperCAmelCase=False		,				_UpperCAmelCase=1_28		,				_UpperCAmelCase=16		,				_UpperCAmelCase=3_20		,				_UpperCAmelCase=8_00		,				_UpperCAmelCase=False		,				_UpperCAmelCase=True		,				_UpperCAmelCase=0.05		,				_UpperCAmelCase=10		,				_UpperCAmelCase=2		,				_UpperCAmelCase=0.0		,				_UpperCAmelCase=10		,				_UpperCAmelCase=3_20		,				_UpperCAmelCase=2		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=1_00		,				_UpperCAmelCase=2_56		,				_UpperCAmelCase=2_56		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase="mean"		,				_UpperCAmelCase=False		,				_UpperCAmelCase=False		,				_UpperCAmelCase=2_56		,				_UpperCAmelCase=(5_12, 5_12, 5_12, 5_12, 15_00)		,				_UpperCAmelCase=(5, 3, 3, 1, 1)		,				_UpperCAmelCase=(1, 2, 3, 1, 1)		,				_UpperCAmelCase=5_12		,				_UpperCAmelCase=80		,				_UpperCAmelCase=0		,				_UpperCAmelCase=1		,				_UpperCAmelCase=2		,				_UpperCAmelCase=False		,				_UpperCAmelCase=3		,				_UpperCAmelCase=2		,				_UpperCAmelCase=3		,				_UpperCAmelCase=None		,				**_UpperCAmelCase		,				):
       super().__init__(**_UpperCAmelCase		,				pad_token_id=_UpperCAmelCase		,				bos_token_id=_UpperCAmelCase		,				eos_token_id=_UpperCAmelCase )
       snake_case_	=				hidden_size
       snake_case_	=				feat_extract_norm
       snake_case_	=				feat_extract_activation
       snake_case_	=				list(_UpperCAmelCase )
       snake_case_	=				list(_UpperCAmelCase )
       snake_case_	=				list(_UpperCAmelCase )
       snake_case_	=				conv_bias
       snake_case_	=				num_buckets
       snake_case_	=				max_bucket_distance
       snake_case_	=				num_conv_pos_embeddings
       snake_case_	=				num_conv_pos_embedding_groups
       snake_case_	=				len(self.conv_dim )
       snake_case_	=				num_hidden_layers
       snake_case_	=				intermediate_size
       snake_case_	=				hidden_act
       snake_case_	=				num_attention_heads
       snake_case_	=				hidden_dropout
       snake_case_	=				attention_dropout
       snake_case_	=				activation_dropout
       snake_case_	=				feat_proj_dropout
       snake_case_	=				final_dropout
       snake_case_	=				layerdrop
       snake_case_	=				layer_norm_eps
       snake_case_	=				initializer_range
       snake_case_	=				num_ctc_classes
       snake_case_	=				vocab_size
       snake_case_	=				do_stable_layer_norm
       snake_case_	=				use_weighted_layer_sum
       snake_case_	=				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
       snake_case_	=				apply_spec_augment
       snake_case_	=				mask_time_prob
       snake_case_	=				mask_time_length
       snake_case_	=				mask_time_min_masks
       snake_case_	=				mask_feature_prob
       snake_case_	=				mask_feature_length

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

       # ctc loss
       snake_case_	=				ctc_loss_reduction
       snake_case_	=				ctc_zero_infinity

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

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

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





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