






'''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 				__SCREAMING_SNAKE_CASE							:




      def __init__(	self			, lowerCamelCase			, lowerCamelCase=3			, lowerCamelCase=32			, lowerCamelCase=3			, lowerCamelCase=10			, lowerCamelCase=[8, 16, 32, 64]			, lowerCamelCase=[1, 1, 2, 1]			, lowerCamelCase=True			, lowerCamelCase=True			, lowerCamelCase="relu"			, lowerCamelCase=3			, lowerCamelCase=None			, lowerCamelCase=["stage2", "stage3", "stage4"]			, lowerCamelCase=[2, 3, 4]			, lowerCamelCase=1			, )				->Union[str, Any]:



         '''simple docstring'''
         __a					  =     parent
         __a					  =     batch_size
         __a					  =     image_size
         __a					  =     num_channels
         __a					  =     embeddings_size
         __a					  =     hidden_sizes
         __a					  =     depths
         __a					  =     is_training
         __a					  =     use_labels
         __a					  =     hidden_act
         __a					  =     num_labels
         __a					  =     scope
         __a					  =     len(lowerCamelCase   )
         __a					  =     out_features
         __a					  =     out_indices
         __a					  =     num_groups




      def 			__UpperCamelCase				(	self   )				->Tuple:



         '''simple docstring'''
         __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 			__UpperCamelCase				(	self   )				->List[Any]:



         '''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			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase   )				->List[str]:



         '''simple docstring'''
         __a					  =     BitModel(config=lowerCamelCase   )
         model.to(lowerCamelCase   )
         model.eval()
         __a					  =     model(lowerCamelCase   )
         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			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase   )				->int:



         '''simple docstring'''
         __a					  =     self.num_labels
         __a					  =     BitForImageClassification(lowerCamelCase   )
         model.to(lowerCamelCase   )
         model.eval()
         __a					  =     model(lowerCamelCase			, labels=lowerCamelCase   )
         self.parent.assertEqual(result.logits.shape			, (self.batch_size, self.num_labels)   )




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase   )				->Union[str, Any]:



         '''simple docstring'''
         __a					  =     BitBackbone(config=lowerCamelCase   )
         model.to(lowerCamelCase   )
         model.eval()
         __a					  =     model(lowerCamelCase   )

         # 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
         __a					  =     None
         __a					  =     BitBackbone(config=lowerCamelCase   )
         model.to(lowerCamelCase   )
         model.eval()
         __a					  =     model(lowerCamelCase   )

         # 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   )				->List[Any]:



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







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

      __a	       =False
      __a	       =False
      __a	       =False
      __a	       =False
      __a	       =False




      def 			__UpperCamelCase				(	self   )				->Union[str, Any]:



         '''simple docstring'''
         __a					  =     BitModelTester(self   )
         __a					  =     ConfigTester(self			, config_class=lowerCamelCase			, has_text_modality=lowerCamelCase   )




      def 			__UpperCamelCase				(	self   )				->Optional[int]:



         '''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   )				->Optional[int]:



         '''simple docstring'''
         return




      @unittest.skip(reason='Bit does not output attentions'   )
      def 			__UpperCamelCase				(	self   )				->Any:



         '''simple docstring'''
         pass




      @unittest.skip(reason='Bit does not use inputs_embeds'   )
      def 			__UpperCamelCase				(	self   )				->str:



         '''simple docstring'''
         pass




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



         '''simple docstring'''
         pass




      def 			__UpperCamelCase				(	self   )				->Tuple:



         '''simple docstring'''
         __a  ,      __a					  =     self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            __a					  =     model_class(lowerCamelCase   )
            __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]			, lowerCamelCase   )




      def 			__UpperCamelCase				(	self   )				->Any:



         '''simple docstring'''
         __a					  =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCamelCase   )




      def 			__UpperCamelCase				(	self   )				->str:



         '''simple docstring'''
         __a					  =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_backbone(*lowerCamelCase   )




      def 			__UpperCamelCase				(	self   )				->Dict:



         '''simple docstring'''
         __a  ,      __a					  =     self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            __a					  =     model_class(config=lowerCamelCase   )
            for name, module in model.named_modules():
               if isinstance(lowerCamelCase			, (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   )				->Optional[Any]:



         '''simple docstring'''
         def check_hidden_states_output(lowerCamelCase			, lowerCamelCase			, lowerCamelCase   ):
            __a					  =     model_class(lowerCamelCase   )
            model.to(lowerCamelCase   )
            model.eval()

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

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

            __a					  =     self.model_tester.num_stages
            self.assertEqual(len(lowerCamelCase   )			, 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]			, )

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

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

               check_hidden_states_output(lowerCamelCase			, lowerCamelCase			, lowerCamelCase   )




      @unittest.skip(reason='Bit does not use feedforward chunking'   )
      def 			__UpperCamelCase				(	self   )				->Dict:



         '''simple docstring'''
         pass




      def 			__UpperCamelCase				(	self   )				->int:



         '''simple docstring'''
         __a					  =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*lowerCamelCase   )






      @slow
      def 			__UpperCamelCase				(	self   )				->List[Any]:



         '''simple docstring'''
         for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            __a					  =     BitModel.from_pretrained(lowerCamelCase   )
            self.assertIsNotNone(lowerCamelCase   )







def  __UpperCAmelCase						(					)	->      str:

   """simple docstring"""
   __a					  =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
   return image
@require_torch
@require_vision
class 				__SCREAMING_SNAKE_CASE							(				unittest.TestCase  ):




      @cached_property
      def 			__UpperCamelCase				(	self   )				->List[Any]:



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






      @slow
      def 			__UpperCamelCase				(	self   )				->Tuple:



         '''simple docstring'''
         __a					  =     BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ).to(lowerCamelCase   )

         __a					  =     self.default_image_processor
         __a					  =     prepare_img()
         __a					  =     image_processor(images=lowerCamelCase			, return_tensors='pt'   ).to(lowerCamelCase   )

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

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

         __a					  =     torch.tensor([[-0.6526, -0.5263, -1.4398]]   ).to(lowerCamelCase   )

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







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

      __a	       =False




      def 			__UpperCamelCase				(	self   )				->List[str]:



         '''simple docstring'''
         __a					  =     BitModelTester(self   )







'''simple docstring'''



import copy
import random

from transformers import CLIPTokenizer
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):




      def __init__(	self			, *lowerCamelCase			, **lowerCamelCase   )				->Union[str, Any]:



         '''simple docstring'''
         super().__init__(*lowerCamelCase			, **lowerCamelCase   )
         __a					  =     {}




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, *lowerCamelCase			, **lowerCamelCase   )				->Dict:



         '''simple docstring'''
         __a					  =     super().add_tokens(lowerCamelCase			, *lowerCamelCase			, **lowerCamelCase   )
         if num_added_tokens == 0:
            raise ValueError(
                F"""The tokenizer already contains the token {placeholder_token}. Please pass a different"""
                ' `placeholder_token` that is not already in the tokenizer.'   )




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, *lowerCamelCase			, lowerCamelCase=1			, **lowerCamelCase   )				->List[str]:



         '''simple docstring'''
         __a					  =     []
         if num_vec_per_token == 1:
            self.try_adding_tokens(lowerCamelCase			, *lowerCamelCase			, **lowerCamelCase   )
            output.append(lowerCamelCase   )
         else:
            __a					  =     []
            for i in range(lowerCamelCase   ):
               __a					  =     placeholder_token + F"""_{i}"""
               self.try_adding_tokens(lowerCamelCase			, *lowerCamelCase			, **lowerCamelCase   )
               output.append(lowerCamelCase   )
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
         for token in self.token_map:
            if token in placeholder_token:
               raise ValueError(
                   F"""The tokenizer already has placeholder token {token} that can get confused with"""
                   F""" {placeholder_token}keep placeholder tokens independent"""   )
         __a					  =     output




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase=False			, lowerCamelCase=1.0   )				->int:



         '''simple docstring'''
         if isinstance(lowerCamelCase			, lowerCamelCase   ):
            __a					  =     []
            for i in range(len(lowerCamelCase   )   ):
               output.append(self.replace_placeholder_tokens_in_text(text[i]			, vector_shuffle=lowerCamelCase   )   )
            return output
         for placeholder_token in self.token_map:
            if placeholder_token in text:
               __a					  =     self.token_map[placeholder_token]
               __a					  =     tokens[: 1 + int(len(lowerCamelCase   ) * prop_tokens_to_load   )]
               if vector_shuffle:
                  __a					  =     copy.copy(lowerCamelCase   )
                  random.shuffle(lowerCamelCase   )
               __a					  =     text.replace(lowerCamelCase			, ' '.join(lowerCamelCase   )   )
         return text




      def __call__(	self			, lowerCamelCase			, *lowerCamelCase			, lowerCamelCase=False			, lowerCamelCase=1.0			, **lowerCamelCase   )				->Optional[int]:



         '''simple docstring'''
         return super().__call__(
             self.replace_placeholder_tokens_in_text(
                 lowerCamelCase			, vector_shuffle=lowerCamelCase			, prop_tokens_to_load=lowerCamelCase   )			, *lowerCamelCase			, **lowerCamelCase			, )






      def 			__UpperCamelCase				(	self			, lowerCamelCase			, *lowerCamelCase			, lowerCamelCase=False			, lowerCamelCase=1.0			, **lowerCamelCase   )				->List[Any]:



         '''simple docstring'''
         return super().encode(
             self.replace_placeholder_tokens_in_text(
                 lowerCamelCase			, vector_shuffle=lowerCamelCase			, prop_tokens_to_load=lowerCamelCase   )			, *lowerCamelCase			, **lowerCamelCase			, )