


import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import SwiftFormerForImageClassification, SwiftFormerModel
  from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import ViTImageProcessor


class     __UpperCAmelCase							:


     """simple docstring"""


     def __init__(	self  ,       __A  ,       __A=13  ,       __A=3  ,       __A=True  ,       __A=True  ,       __A=0.1  ,       __A=0.1  ,       __A=224  ,       __A=1000  ,       __A=[3, 3, 6, 4]  ,       __A=[48, 56, 112, 220]  ,       ):
        __a					=  parent
        __a					=  batch_size
        __a					=  num_channels
        __a					=  is_training
        __a					=  use_labels
        __a					=  hidden_dropout_prob
        __a					=  attention_probs_dropout_prob
        __a					=  num_labels
        __a					=  image_size
        __a					=  layer_depths
        __a					=  embed_dims


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

        __a					=  None
        if self.use_labels:
           __a					=  ids_tensor([self.batch_size]  ,       self.num_labels    )

        __a					=  self.get_config()

        return config, pixel_values, labels


     def 	snake_case_  (	self    ):
        return SwiftFormerConfig(
            depths=self.layer_depths  ,       embed_dims=self.embed_dims  ,       mlp_ratio=4  ,       downsamples=[True, True, True, True]  ,       hidden_act="""gelu"""  ,       num_labels=self.num_labels  ,       down_patch_size=3  ,       down_stride=2  ,       down_pad=1  ,       drop_rate=0.0  ,       drop_path_rate=0.0  ,       use_layer_scale=__A  ,       layer_scale_init_value=1E-5  ,       )


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A    ):
        __a					=  SwiftFormerModel(config=__A    )
        model.to(__A    )
        model.eval()
        __a					=  model(__A    )
        self.parent.assertEqual(result.last_hidden_state.shape  ,       (self.batch_size, self.embed_dims[-1], 7, 7)    )


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A    ):
        __a					=  self.num_labels
        __a					=  SwiftFormerForImageClassification(__A    )
        model.to(__A    )
        model.eval()
        __a					=  model(__A  ,       labels=__A    )
        self.parent.assertEqual(result.logits.shape  ,       (self.batch_size, self.num_labels)    )

        __a					=  SwiftFormerForImageClassification(__A    )
        model.to(__A    )
        model.eval()

        __a					=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )
        __a					=  model(__A    )
        self.parent.assertEqual(result.logits.shape  ,       (self.batch_size, self.num_labels)    )


     def 	snake_case_  (	self    ):
        ((__a)	,    (__a)	,    (__a))					=  self.prepare_config_and_inputs()
        __a					=  {"""pixel_values""": pixel_values}
        return config, inputs_dict







@require_torch
class     __UpperCAmelCase							(       __A ,					__A ,					unittest.TestCase							):


     """simple docstring"""
     _lowerCamelCase   =      (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
     _lowerCamelCase   =      (
         {"""feature-extraction""": SwiftFormerModel, """image-classification""": SwiftFormerForImageClassification}
         if is_torch_available()
         else {}
     )

     _lowerCamelCase   =      False
     _lowerCamelCase   =      False
     _lowerCamelCase   =      False
     _lowerCamelCase   =      False
     _lowerCamelCase   =      False


     def 	snake_case_  (	self    ):
        __a					=  SwiftFormerModelTester(self    )
        __a					=  ConfigTester(
            self  ,       config_class=__A  ,       has_text_modality=__A  ,       hidden_size=37  ,       num_attention_heads=12  ,       num_hidden_layers=12  ,       )


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


     @unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""    )
     def 	snake_case_  (	self    ):
        pass


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

        for model_class in self.all_model_classes:
           __a					=  model_class(__A    )
           __a					=  model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(__A  ,       nn.Linear    )    )


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

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

           __a					=  ["""pixel_values"""]
           self.assertListEqual(arg_names[:1]  ,       __A    )


     def 	snake_case_  (	self    ):
        __a					=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__A    )


     def 	snake_case_  (	self    ):
        __a					=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__A    )


     @slow
     def 	snake_case_  (	self    ):
        for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __a					=  SwiftFormerModel.from_pretrained(__A    )
           self.assertIsNotNone(__A    )


     @unittest.skip(reason="""SwiftFormer does not output attentions"""    )
     def 	snake_case_  (	self    ):
        pass


     def 	snake_case_  (	self    ):
        def check_hidden_states_output(__A  ,       __A  ,       __A    ):
           __a					=  model_class(__A    )
           model.to(__A    )
           model.eval()

           with torch.no_grad():
              __a					=  model(**self._prepare_for_class(__A  ,       __A    )    )

           __a					=  outputs.hidden_states

           __a					=  8
           self.assertEqual(len(__A    )  ,       __A    )  # TODO

           # SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
           # with the width and height being successively divided by 2, after every 2 blocks
           for i in range(len(__A    )    ):
              self.assertEqual(
                  hidden_states[i].shape  ,       torch.Size(
                      [
                          self.model_tester.batch_size,
                          self.model_tester.embed_dims[i // 2],
                          (self.model_tester.image_size // 4) // 2 ** (i // 2),
                          (self.model_tester.image_size // 4) // 2 ** (i // 2),
                      ]    )  ,       )

        __a	,    __a					=  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           __a					=  True
           check_hidden_states_output(__A  ,       __A  ,       __A    )

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           __a					=  True

           check_hidden_states_output(__A  ,       __A  ,       __A    )


     def 	snake_case_  (	self    ):
        def _config_zero_init(__A    ):
           __a					=  copy.deepcopy(__A    )
           for key in configs_no_init.__dict__.keys():
              if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
                 setattr(__A  ,       __A  ,       1E-10    )
              if isinstance(getattr(__A  ,       __A  ,       __A    )  ,       __A    ):
                 __a					=  _config_zero_init(getattr(__A  ,       __A    )    )
                 setattr(__A  ,       __A  ,       __A    )
           return configs_no_init

        __a	,    __a					=  self.model_tester.prepare_config_and_inputs_for_common()

        __a					=  _config_zero_init(__A    )
        for model_class in self.all_model_classes:
           __a					=  model_class(config=__A    )
           for name, param in model.named_parameters():
              if param.requires_grad:
                 self.assertIn(
                     ((param.data.mean() * 1E9) / 1E9).round().item()  ,       [0.0, 1.0]  ,       msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''  ,       )


     @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""    )
     def 	snake_case_  (	self    ):
        pass







def  a   ():
   __a					=  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
   return image


@require_torch
@require_vision
class     __UpperCAmelCase							(       unittest.TestCase							):


     """simple docstring"""


     @cached_property
     def 	snake_case_  (	self    ):
        return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs"""    ) if is_vision_available() else None


     @slow
     def 	snake_case_  (	self    ):
        __a					=  SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""    ).to(__A    )

        __a					=  self.default_image_processor
        __a					=  prepare_img()
        __a					=  image_processor(images=__A  ,       return_tensors="""pt"""    ).to(__A    )

        # forward pass
        with torch.no_grad():
           __a					=  model(**__A    )

        # verify the logits
        __a					=  torch.Size((1, 1000)    )
        self.assertEqual(outputs.logits.shape  ,       __A    )

        __a					=  torch.tensor([[-2.1703E00, 2.1107E00, -2.0811E00]]    ).to(__A    )
        self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,       __A  ,       atol=1E-4    )    )









'''simple docstring'''

import functools
import operator

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


a    :							List[Any]										=    logging.get_logger(__name__)

a    :							List[str]										=    {
    '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							(  _lowerCamelCase	):
				snake_case_           =						"wavlm"


				def __init__(						self       :   Union[str, Any]			,							lowercase_       :   Union[str, Any]=32			,							lowercase_       :   Optional[int]=768			,							lowercase_       :   List[str]=12			,							lowercase_       :   Dict=12			,							lowercase_       :   Any=3072			,							lowercase_       :   List[Any]="gelu"			,							lowercase_       :   int=0.1			,							lowercase_       :   Dict=0.1			,							lowercase_       :   int=0.1			,							lowercase_       :   int=0.0			,							lowercase_       :   List[Any]=0.1			,							lowercase_       :   int=0.1			,							lowercase_       :   Optional[int]=0.02			,							lowercase_       :   Dict=1e-5			,							lowercase_       :   Tuple="group"			,							lowercase_       :   str="gelu"			,							lowercase_       :   Any=(512, 512, 512, 512, 512, 512, 512)			,							lowercase_       :   str=(5, 2, 2, 2, 2, 2, 2)			,							lowercase_       :   Any=(10, 3, 3, 3, 3, 2, 2)			,							lowercase_       :   Tuple=False			,							lowercase_       :   List[Any]=128			,							lowercase_       :   int=16			,							lowercase_       :   Tuple=320			,							lowercase_       :   Union[str, Any]=800			,							lowercase_       :   List[str]=False			,							lowercase_       :   str=True			,							lowercase_       :   List[Any]=0.05			,							lowercase_       :   Optional[Any]=10			,							lowercase_       :   Optional[Any]=2			,							lowercase_       :   Optional[int]=0.0			,							lowercase_       :   List[str]=10			,							lowercase_       :   Union[str, Any]=320			,							lowercase_       :   Optional[Any]=2			,							lowercase_       :   List[str]=0.1			,							lowercase_       :   int=100			,							lowercase_       :   Tuple=256			,							lowercase_       :   Tuple=256			,							lowercase_       :   Dict=0.1			,							lowercase_       :   Any="mean"			,							lowercase_       :   Tuple=False			,							lowercase_       :   Dict=False			,							lowercase_       :   str=256			,							lowercase_       :   Optional[int]=(512, 512, 512, 512, 1500)			,							lowercase_       :   List[str]=(5, 3, 3, 1, 1)			,							lowercase_       :   Any=(1, 2, 3, 1, 1)			,							lowercase_       :   int=512			,							lowercase_       :   List[Any]=80			,							lowercase_       :   Optional[Any]=0			,							lowercase_       :   int=1			,							lowercase_       :   List[str]=2			,							lowercase_       :   List[str]=False			,							lowercase_       :   Dict=3			,							lowercase_       :   List[Any]=2			,							lowercase_       :   Optional[Any]=3			,							lowercase_       :   int=None			,							**lowercase_       :   List[str]			,							):
									super().__init__(**lowercase_			,							pad_token_id=lowercase_			,							bos_token_id=lowercase_			,							eos_token_id=lowercase_   )
									snake_case_		     =							hidden_size
									snake_case_		     =							feat_extract_norm
									snake_case_		     =							feat_extract_activation
									snake_case_		     =							list(lowercase_   )
									snake_case_		     =							list(lowercase_   )
									snake_case_		     =							list(lowercase_   )
									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(lowercase_   )
									snake_case_		     =							list(lowercase_   )
									snake_case_		     =							list(lowercase_   )
									snake_case_		     =							xvector_output_dim


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

