





'''simple docstring'''







import collections
import inspect
import unittest

from transformers import FocalNetConfig
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_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch
   from torch import nn

   from transformers import (
       FocalNetBackbone,
       FocalNetForImageClassification,
       FocalNetForMaskedImageModeling,
       FocalNetModel,
   )
   from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor
class 			_lowercase						:







     def __init__( self    ,   A__    ,   A__=13    ,   A__=32    ,   A__=2    ,   A__=3    ,   A__=16    ,   A__=[32, 64, 1_28]    ,   A__=[1, 2, 1]    ,   A__=[2, 2, 4]    ,   A__=2    ,   A__=2.0    ,   A__=True    ,   A__=0.0    ,   A__=0.0    ,   A__=0.1    ,   A__="gelu"    ,   A__=False    ,   A__=True    ,   A__=0.0_2    ,   A__=1e-5    ,   A__=True    ,   A__=None    ,   A__=True    ,   A__=10    ,   A__=8    ,   A__=["stage1", "stage2"]    ,   A__=[1, 2]    ,   ) ->	Union[str, Any]:
      snake_case				=			parent
      snake_case				=			batch_size
      snake_case				=			image_size
      snake_case				=			patch_size
      snake_case				=			num_channels
      snake_case				=			embed_dim
      snake_case				=			hidden_sizes
      snake_case				=			depths
      snake_case				=			num_heads
      snake_case				=			window_size
      snake_case				=			mlp_ratio
      snake_case				=			qkv_bias
      snake_case				=			hidden_dropout_prob
      snake_case				=			attention_probs_dropout_prob
      snake_case				=			drop_path_rate
      snake_case				=			hidden_act
      snake_case				=			use_absolute_embeddings
      snake_case				=			patch_norm
      snake_case				=			layer_norm_eps
      snake_case				=			initializer_range
      snake_case				=			is_training
      snake_case				=			scope
      snake_case				=			use_labels
      snake_case				=			type_sequence_label_size
      snake_case				=			encoder_stride
      snake_case				=			out_features
      snake_case				=			out_indices







     def     UpperCamelCase			( self    ) ->	Optional[int]:
      snake_case				=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

      snake_case				=			None
      if self.use_labels:
       snake_case				=			ids_tensor([self.batch_size]    ,   self.type_sequence_label_size    )

      snake_case				=			self.get_config()

      return config, pixel_values, labels







     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      return FocalNetConfig(
          image_size=self.image_size    ,   patch_size=self.patch_size    ,   num_channels=self.num_channels    ,   embed_dim=self.embed_dim    ,   hidden_sizes=self.hidden_sizes    ,   depths=self.depths    ,   num_heads=self.num_heads    ,   window_size=self.window_size    ,   mlp_ratio=self.mlp_ratio    ,   qkv_bias=self.qkv_bias    ,   hidden_dropout_prob=self.hidden_dropout_prob    ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,   drop_path_rate=self.drop_path_rate    ,   hidden_act=self.hidden_act    ,   use_absolute_embeddings=self.use_absolute_embeddings    ,   path_norm=self.patch_norm    ,   layer_norm_eps=self.layer_norm_eps    ,   initializer_range=self.initializer_range    ,   encoder_stride=self.encoder_stride    ,   out_features=self.out_features    ,   out_indices=self.out_indices    ,   )







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ) ->	str:
      snake_case				=			FocalNetModel(config=A__    )
      model.to(A__    )
      model.eval()
      snake_case				=			model(A__    )

      snake_case				=			((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths    ) - 1))
      snake_case				=			int(config.embed_dim * 2 ** (len(config.depths    ) - 1)    )

      self.parent.assertEqual(result.last_hidden_state.shape    ,   (self.batch_size, expected_seq_len, expected_dim)    )







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ) ->	Union[str, Any]:
      snake_case				=			FocalNetBackbone(config=A__    )
      model.to(A__    )
      model.eval()
      snake_case				=			model(A__    )

      # verify feature maps
      self.parent.assertEqual(len(result.feature_maps    )    ,   len(config.out_features    )    )
      self.parent.assertListEqual(list(result.feature_maps[0].shape    )    ,   [self.batch_size, self.image_size, 8, 8]    )

      # verify channels
      self.parent.assertEqual(len(model.channels    )    ,   len(config.out_features    )    )
      self.parent.assertListEqual(model.channels    ,   config.hidden_sizes[:-1]    )

      # verify backbone works with out_features=None
      snake_case				=			None
      snake_case				=			FocalNetBackbone(config=A__    )
      model.to(A__    )
      model.eval()
      snake_case				=			model(A__    )

      # verify feature maps
      self.parent.assertEqual(len(result.feature_maps    )    ,   1    )
      self.parent.assertListEqual(list(result.feature_maps[0].shape    )    ,   [self.batch_size, self.image_size * 2, 4, 4]    )

      # verify channels
      self.parent.assertEqual(len(model.channels    )    ,   1    )
      self.parent.assertListEqual(model.channels    ,   [config.hidden_sizes[-1]]    )







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ) ->	Dict:
      snake_case				=			FocalNetForMaskedImageModeling(config=A__    )
      model.to(A__    )
      model.eval()
      snake_case				=			model(A__    )
      self.parent.assertEqual(
          result.reconstruction.shape    ,   (self.batch_size, self.num_channels, self.image_size, self.image_size)    )

      # test greyscale images
      snake_case				=			1
      snake_case				=			FocalNetForMaskedImageModeling(A__    )
      model.to(A__    )
      model.eval()

      snake_case				=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
      snake_case				=			model(A__    )
      self.parent.assertEqual(result.reconstruction.shape    ,   (self.batch_size, 1, self.image_size, self.image_size)    )







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ) ->	Optional[Any]:
      snake_case				=			self.type_sequence_label_size
      snake_case				=			FocalNetForImageClassification(A__    )
      model.to(A__    )
      model.eval()
      snake_case				=			model(A__    ,   labels=A__    )
      self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.type_sequence_label_size)    )

      # test greyscale images
      snake_case				=			1
      snake_case				=			FocalNetForImageClassification(A__    )
      model.to(A__    )
      model.eval()

      snake_case				=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
      snake_case				=			model(A__    )
      self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.type_sequence_label_size)    )







     def     UpperCamelCase			( self    ) ->	int:
      snake_case				=			self.prepare_config_and_inputs()

      snake_case				,		snake_case				,		snake_case				=			config_and_inputs
      snake_case				=			{'''pixel_values''': pixel_values}
      return config, inputs_dict




@require_torch
class 			_lowercase						(       __a				,		__a				,		unittest.TestCase					):
     _UpperCAmelCase										=					(
         (
             FocalNetModel,
             FocalNetForImageClassification,
             FocalNetForMaskedImageModeling,
             FocalNetBackbone,
         )
         if is_torch_available()
         else ()
     )
     _UpperCAmelCase										=					(
         {'''feature-extraction''': FocalNetModel, '''image-classification''': FocalNetForImageClassification}
         if is_torch_available()
         else {}
     )
     _UpperCAmelCase										=					False

     _UpperCAmelCase										=					False
     _UpperCAmelCase										=					False
     _UpperCAmelCase										=					False
     _UpperCAmelCase										=					False







     def     UpperCamelCase			( self    ) ->	Dict:
      snake_case				=			FocalNetModelTester(self    )
      snake_case				=			ConfigTester(self    ,   config_class=A__    ,   embed_dim=37    ,   has_text_modality=A__    )







     def     UpperCamelCase			( self    ) ->	Dict:
      self.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()







     def     UpperCamelCase			( self    ) ->	List[Any]:
      return







     def     UpperCamelCase			( self    ) ->	Tuple:
      snake_case				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A__    )







     def     UpperCamelCase			( self    ) ->	List[Any]:
      snake_case				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_backbone(*A__    )







     def     UpperCamelCase			( self    ) ->	Optional[int]:
      snake_case				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_image_modeling(*A__    )







     def     UpperCamelCase			( self    ) ->	Any:
      snake_case				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*A__    )







     @unittest.skip(reason='''FocalNet does not use inputs_embeds'''    )
     def     UpperCamelCase			( self    ) ->	List[Any]:
      pass







     @unittest.skip(reason='''FocalNet does not use feedforward chunking'''    )
     def     UpperCamelCase			( self    ) ->	Optional[int]:
      pass







     def     UpperCamelCase			( self    ) ->	int:
      snake_case				,		snake_case				=			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes[:-1]:
       snake_case				=			model_class(A__    )
       self.assertIsInstance(model.get_input_embeddings()    ,   (nn.Module)    )
       snake_case				=			model.get_output_embeddings()
       self.assertTrue(x is None or isinstance(A__    ,   nn.Linear    )    )







     def     UpperCamelCase			( self    ) ->	List[str]:
      snake_case				,		snake_case				=			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes[:-1]:
       snake_case				=			model_class(A__    )
       snake_case				=			inspect.signature(model.forward    )
       # 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]    ,   A__    )







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__    ) ->	Optional[Any]:
      snake_case				=			model_class(A__    )
      model.to(A__    )
      model.eval()

      with torch.no_grad():
       snake_case				=			model(**self._prepare_for_class(A__    ,   A__    )    )

      snake_case				=			outputs.hidden_states

      snake_case				=			getattr(
          self.model_tester    ,   '''expected_num_hidden_layers'''    ,   len(self.model_tester.depths    ) + 1    )
      self.assertEqual(len(A__    )    ,   A__    )

      # FocalNet has a different seq_length
      snake_case				=			(
          config.patch_size
          if isinstance(config.patch_size    ,   collections.abc.Iterable    )
          else (config.patch_size, config.patch_size)
      )

      snake_case				=			(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

      self.assertListEqual(
          list(hidden_states[0].shape[-2:]    )    ,   [num_patches, self.model_tester.embed_dim]    ,   )

      snake_case				=			outputs.reshaped_hidden_states
      self.assertEqual(len(A__    )    ,   A__    )

      snake_case				,		snake_case				,		snake_case				,		snake_case				=			reshaped_hidden_states[0].shape
      snake_case				=			(
          reshaped_hidden_states[0].view(A__    ,   A__    ,   height * width    ).permute(0    ,   2    ,   1    )
      )
      self.assertListEqual(
          list(reshaped_hidden_states.shape[-2:]    )    ,   [num_patches, self.model_tester.embed_dim]    ,   )







     def     UpperCamelCase			( self    ) ->	Optional[int]:
      snake_case				,		snake_case				=			self.model_tester.prepare_config_and_inputs_for_common()

      snake_case				=			(
          self.model_tester.image_size
          if isinstance(self.model_tester.image_size    ,   collections.abc.Iterable    )
          else (self.model_tester.image_size, self.model_tester.image_size)
      )

      for model_class in self.all_model_classes[:-1]:
       snake_case				=			True
       self.check_hidden_states_output(A__    ,   A__    ,   A__    ,   A__    )

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

       self.check_hidden_states_output(A__    ,   A__    ,   A__    ,   A__    )







     def     UpperCamelCase			( self    ) ->	Dict:
      snake_case				,		snake_case				=			self.model_tester.prepare_config_and_inputs_for_common()
      snake_case				=			3

      snake_case				=			(
          self.model_tester.image_size
          if isinstance(self.model_tester.image_size    ,   collections.abc.Iterable    )
          else (self.model_tester.image_size, self.model_tester.image_size)
      )
      snake_case				=			(
          config.patch_size
          if isinstance(config.patch_size    ,   collections.abc.Iterable    )
          else (config.patch_size, config.patch_size)
      )

      snake_case				=			image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
      snake_case				=			image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

      for model_class in self.all_model_classes[:-1]:
       snake_case				=			True
       self.check_hidden_states_output(A__    ,   A__    ,   A__    ,   (padded_height, padded_width)    )

       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       snake_case				=			True
       self.check_hidden_states_output(A__    ,   A__    ,   A__    ,   (padded_height, padded_width)    )







     @slow
     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       snake_case				=			FocalNetModel.from_pretrained(A__    )
       self.assertIsNotNone(A__    )







     def     UpperCamelCase			( self    ) ->	int:
      snake_case				,		snake_case				=			self.model_tester.prepare_config_and_inputs_for_common()

      snake_case				=			_config_zero_init(A__    )
      for model_class in self.all_model_classes:
       snake_case				=			model_class(config=A__    )
       for name, param in model.named_parameters():
        if "embeddings" not in name and param.requires_grad:
         self.assertIn(
             ((param.data.mean() * 1e9).round() / 1e9).item()    ,   [0.0, 1.0]    ,   msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""    ,   )




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







     @cached_property
     def     UpperCamelCase			( self    ) ->	Optional[int]:
      # TODO update organization
      return AutoImageProcessor.from_pretrained('''microsoft/focalnet-tiny'''    ) if is_vision_available() else None







     @slow
     def     UpperCamelCase			( self    ) ->	Tuple:
      snake_case				=			FocalNetForImageClassification.from_pretrained('''microsoft/focalnet-tiny'''    ).to(A__    )
      snake_case				=			self.default_image_processor

      snake_case				=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
      snake_case				=			image_processor(images=A__    ,   return_tensors='''pt'''    ).to(A__    )

      # forward pass
      with torch.no_grad():
       snake_case				=			model(**A__    )

      # verify the logits
      snake_case				=			torch.Size((1, 10_00)    )
      self.assertEqual(outputs.logits.shape    ,   A__    )
      snake_case				=			torch.tensor([0.2_1_6_6, -0.4_3_6_8, 0.2_1_9_1]    ).to(A__    )
      self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,   A__    ,   atol=1e-4    )    )
      self.assertTrue(outputs.logits.argmax(dim=-1    ).item()    ,   2_81    )




@require_torch
class 			_lowercase						(       __a				,		unittest.TestCase					):
     _UpperCAmelCase										=					(FocalNetBackbone,) if is_torch_available() else ()
     _UpperCAmelCase										=					FocalNetConfig

     _UpperCAmelCase										=					False







     def     UpperCamelCase			( self    ) ->	Tuple:
      snake_case				=			FocalNetModelTester(self    )








'''simple docstring'''







from ...processing_utils import ProcessorMixin
class 			_lowercase						(       __a					):
     _UpperCAmelCase										=					'''WhisperFeatureExtractor'''
     _UpperCAmelCase										=					'''WhisperTokenizer'''







     def __init__( self    ,   A__    ,   A__    ) ->	Optional[Any]:
      super().__init__(A__    ,   A__    )
      snake_case				=			self.feature_extractor
      snake_case				=			False







     def     UpperCamelCase			( self    ,   A__=None    ,   A__=None    ,   A__=True    ) ->	Union[str, Any]:
      return self.tokenizer.get_decoder_prompt_ids(task=A__    ,   language=A__    ,   no_timestamps=A__    )







     def __call__( self    ,   *A__    ,   **A__    ) ->	Dict:
      # For backward compatibility
      if self._in_target_context_manager:
       return self.current_processor(*A__    ,   **A__    )

      snake_case				=			kwargs.pop('''audio'''    ,   A__    )
      snake_case				=			kwargs.pop('''sampling_rate'''    ,   A__    )
      snake_case				=			kwargs.pop('''text'''    ,   A__    )
      if len(A__    ) > 0:
       snake_case				=			args[0]
       snake_case				=			args[1:]

      if audio is None and text is None:
       raise ValueError('''You need to specify either an `audio` or `text` input to process.'''    )

      if audio is not None:
       snake_case				=			self.feature_extractor(A__    ,   *A__    ,   sampling_rate=A__    ,   **A__    )
      if text is not None:
       snake_case				=			self.tokenizer(A__    ,   **A__    )

      if text is None:
       return inputs

      elif audio is None:
       return encodings
      else:
       snake_case				=			encodings['''input_ids''']
       return inputs







     def     UpperCamelCase			( self    ,   *A__    ,   **A__    ) ->	Optional[Any]:
      return self.tokenizer.batch_decode(*A__    ,   **A__    )







     def     UpperCamelCase			( self    ,   *A__    ,   **A__    ) ->	str:
      return self.tokenizer.decode(*A__    ,   **A__    )







     def     UpperCamelCase			( self    ,   A__    ,   A__="np"    ) ->	Optional[Any]:
      return self.tokenizer.get_prompt_ids(A__    ,   return_tensors=A__    )

