
"""simple docstring"""

import inspect
import unittest

from transformers import BitConfig
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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch
      from torch import nn

      from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
      from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

class __lowerCAmelCase	:





     def __init__(			self			,  __UpperCAmelCase			,  __UpperCAmelCase=3			,  __UpperCAmelCase=32			,  __UpperCAmelCase=3			,  __UpperCAmelCase=10			,  __UpperCAmelCase=[8, 16, 32, 64]			,  __UpperCAmelCase=[1, 1, 2, 1]			,  __UpperCAmelCase=True			,  __UpperCAmelCase=True			,  __UpperCAmelCase="relu"			,  __UpperCAmelCase=3			,  __UpperCAmelCase=None			,  __UpperCAmelCase=["stage2", "stage3", "stage4"]			,  __UpperCAmelCase=[2, 3, 4]			,  __UpperCAmelCase=1			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= parent
      __UpperCamelCase       						= batch_size
      __UpperCamelCase       						= image_size
      __UpperCamelCase       						= num_channels
      __UpperCamelCase       						= embeddings_size
      __UpperCamelCase       						= hidden_sizes
      __UpperCamelCase       						= depths
      __UpperCamelCase       						= is_training
      __UpperCamelCase       						= use_labels
      __UpperCamelCase       						= hidden_act
      __UpperCamelCase       						= num_labels
      __UpperCamelCase       						= scope
      __UpperCamelCase       						= len(__UpperCAmelCase   )
      __UpperCamelCase       						= out_features
      __UpperCamelCase       						= out_indices
      __UpperCamelCase       						= num_groups





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

      __UpperCamelCase       						= self.get_config()

      return config, pixel_values, labels





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return BitConfig(
          num_channels=self.num_channels			,  embeddings_size=self.embeddings_size			,  hidden_sizes=self.hidden_sizes			,  depths=self.depths			,  hidden_act=self.hidden_act			,  num_labels=self.num_labels			,  out_features=self.out_features			,  out_indices=self.out_indices			,  num_groups=self.num_groups			,  )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= BitModel(config=__UpperCAmelCase   )
      model.to(__UpperCAmelCase   )
      model.eval()
      __UpperCamelCase       						= model(__UpperCAmelCase   )
      self.parent.assertEqual(
          result.last_hidden_state.shape			,  (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)			,  )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.num_labels
      __UpperCamelCase       						= BitForImageClassification(__UpperCAmelCase   )
      model.to(__UpperCAmelCase   )
      model.eval()
      __UpperCamelCase       						= model(__UpperCAmelCase			,  labels=__UpperCAmelCase   )
      self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.num_labels)   )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= BitBackbone(config=__UpperCAmelCase   )
      model.to(__UpperCAmelCase   )
      model.eval()
      __UpperCamelCase       						= model(__UpperCAmelCase   )

      # 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.hidden_sizes[1], 4, 4]   )

      # 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
      __UpperCamelCase       						= None
      __UpperCamelCase       						= BitBackbone(config=__UpperCAmelCase   )
      model.to(__UpperCAmelCase   )
      model.eval()
      __UpperCamelCase       						= model(__UpperCAmelCase   )

      # verify feature maps
      self.parent.assertEqual(len(result.feature_maps   )			,  1   )
      self.parent.assertListEqual(list(result.feature_maps[0].shape   )			,  [self.batch_size, self.hidden_sizes[-1], 1, 1]   )

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







     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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


@require_torch
class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE    ,						unittest.TestCase    ):
     lowercase		     =		(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
     lowercase		     =		(
         {"feature-extraction": BitModel, "image-classification": BitForImageClassification}
         if is_torch_available()
         else {}
     )

     lowercase		     =		False
     lowercase		     =		False
     lowercase		     =		False
     lowercase		     =		False
     lowercase		     =		False





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= BitModelTester(self   )
      __UpperCamelCase       						= ConfigTester(self			,  config_class=__UpperCAmelCase			,  has_text_modality=__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      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   ):

      '''simple docstring'''




      return





     @unittest.skip(reason='Bit does not output attentions'   )
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      pass





     @unittest.skip(reason='Bit does not use inputs_embeds'   )
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      pass





     @unittest.skip(reason='Bit does not support input and output embeddings'   )
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      pass





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase						,			__UpperCamelCase       						= self.model_tester.prepare_config_and_inputs_for_common()

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

       __UpperCamelCase       						= ['pixel_values']
       self.assertListEqual(arg_names[:1]			,  __UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_backbone(*__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase						,			__UpperCamelCase       						= self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __UpperCamelCase       						= model_class(config=__UpperCAmelCase   )
       for name, module in model.named_modules():
        if isinstance(__UpperCAmelCase			,  (nn.BatchNormad, nn.GroupNorm)   ):
         self.assertTrue(
             torch.all(module.weight == 1   )			,  msg=F'Parameter {name} of model {model_class} seems not properly initialized'			,  )
         self.assertTrue(
             torch.all(module.bias == 0   )			,  msg=F'Parameter {name} of model {model_class} seems not properly initialized'			,  )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      def check_hidden_states_output(__UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   ):
       __UpperCamelCase       						= model_class(__UpperCAmelCase   )
       model.to(__UpperCAmelCase   )
       model.eval()

       with torch.no_grad():
        __UpperCamelCase       						= model(**self._prepare_for_class(__UpperCAmelCase			,  __UpperCAmelCase   )   )

       __UpperCamelCase       						= outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

       __UpperCamelCase       						= self.model_tester.num_stages
       self.assertEqual(len(__UpperCAmelCase   )			,  expected_num_stages + 1   )

       # Bit's feature maps are of shape (batch_size, num_channels, height, width)
       self.assertListEqual(
           list(hidden_states[0].shape[-2:]   )			,  [self.model_tester.image_size // 4, self.model_tester.image_size // 4]			,  )

      __UpperCamelCase						,			__UpperCamelCase       						= self.model_tester.prepare_config_and_inputs_for_common()
      __UpperCamelCase       						= ['preactivation', 'bottleneck']
      for model_class in self.all_model_classes:
       for layer_type in layers_type:
        __UpperCamelCase       						= layer_type
        __UpperCamelCase       						= True
        check_hidden_states_output(__UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   )

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

        check_hidden_states_output(__UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   )





     @unittest.skip(reason='Bit does not use feedforward chunking'   )
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      pass





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase   )







     @slow
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __UpperCamelCase       						= BitModel.from_pretrained(__UpperCAmelCase   )
       self.assertIsNotNone(__UpperCAmelCase   )






def 							A		(       )   ->			str:
 __UpperCamelCase       						= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
 return image

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





     @cached_property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return (
          BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ) if is_vision_available() else None
      )







     @slow
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ).to(__UpperCAmelCase   )

      __UpperCamelCase       						= self.default_image_processor
      __UpperCamelCase       						= prepare_img()
      __UpperCamelCase       						= image_processor(images=__UpperCAmelCase			,  return_tensors='pt'   ).to(__UpperCAmelCase   )

      # forward pass
      with torch.no_grad():
       __UpperCamelCase       						= model(**__UpperCAmelCase   )

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

      __UpperCamelCase       						= torch.tensor([[-0.6_5_2_6, -0.5_2_6_3, -1.4_3_9_8]]   ).to(__UpperCAmelCase   )

      self.assertTrue(torch.allclose(outputs.logits[0, :3]			,  __UpperCAmelCase			,  atol=1E-4   )   )


@require_torch
class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ,						unittest.TestCase    ):
     lowercase		     =		(BitBackbone,) if is_torch_available() else ()
     lowercase		     =		BitConfig

     lowercase		     =		False





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= BitModelTester(self   )





"""simple docstring"""

import re

from filelock import FileLock


try:
      import nltk

      UpperCamelCase			:       int									=				True
except (ImportError, ModuleNotFoundError):
      UpperCamelCase			:       List[str]									=				False

if NLTK_AVAILABLE:
      with FileLock(".lock") as lock:
            nltk.download("punkt", quiet=True)






def 							A		(       snake_case   :str	)   ->			str:
 re.sub('<n>'       ,      ''       ,      snake_case	)  # remove pegasus newline char
 assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
 return "\n".join(nltk.sent_tokenize(snake_case	)	)



