






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:					str				,		UpperCamelCase:					Tuple				,		UpperCamelCase:					Optional[Any]=3				,		UpperCamelCase:					List[Any]=32				,		UpperCamelCase:					str=3				,		UpperCamelCase:					List[Any]=10				,		UpperCamelCase:					List[str]=[8, 16, 32, 64]				,		UpperCamelCase:					Union[str, Any]=[1, 1, 2, 1]				,		UpperCamelCase:					List[Any]=True				,		UpperCamelCase:					Tuple=True				,		UpperCamelCase:					Any="relu"				,		UpperCamelCase:					Optional[Any]=3				,		UpperCamelCase:					int=None				,		UpperCamelCase:					str=["stage2", "stage3", "stage4"]				,		UpperCamelCase:					Any=[2, 3, 4]				,		UpperCamelCase:					Optional[int]=1				,		)						->						Any:
											snake_case__													=   parent
											snake_case__													=   batch_size
											snake_case__													=   image_size
											snake_case__													=   num_channels
											snake_case__													=   embeddings_size
											snake_case__													=   hidden_sizes
											snake_case__													=   depths
											snake_case__													=   is_training
											snake_case__													=   use_labels
											snake_case__													=   hidden_act
											snake_case__													=   num_labels
											snake_case__													=   scope
											snake_case__													=   len(snake_case_  )
											snake_case__													=   out_features
											snake_case__													=   out_indices
											snake_case__													=   num_groups
							def lowerCAmelCase_       (   self:					int  )						->						Union[str, Any]:
											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.num_labels  )

											snake_case__													=   self.get_config()

											return config, pixel_values, labels
							def lowerCAmelCase_       (   self:					Optional[Any]  )						->						List[Any]:
											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 lowerCAmelCase_       (   self:					List[str]				,		UpperCamelCase:					List[str]				,		UpperCamelCase:					List[str]				,		UpperCamelCase:					List[str]  )						->						Any:
											snake_case__													=   BitModel(config=snake_case_  )
											model.to(snake_case_  )
											model.eval()
											snake_case__													=   model(snake_case_  )
											self.parent.assertEqual(
											    result.last_hidden_state.shape				,		(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)				,		)
							def lowerCAmelCase_       (   self:					List[Any]				,		UpperCamelCase:					Dict				,		UpperCamelCase:					Optional[Any]				,		UpperCamelCase:					str  )						->						List[str]:
											snake_case__													=   self.num_labels
											snake_case__													=   BitForImageClassification(snake_case_  )
											model.to(snake_case_  )
											model.eval()
											snake_case__													=   model(snake_case_				,		labels=snake_case_  )
											self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.num_labels)  )
							def lowerCAmelCase_       (   self:					int				,		UpperCamelCase:					List[Any]				,		UpperCamelCase:					int				,		UpperCamelCase:					int  )						->						Any:
											snake_case__													=   BitBackbone(config=snake_case_  )
											model.to(snake_case_  )
											model.eval()
											snake_case__													=   model(snake_case_  )

											# 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
											snake_case__													=   None
											snake_case__													=   BitBackbone(config=snake_case_  )
											model.to(snake_case_  )
											model.eval()
											snake_case__													=   model(snake_case_  )

											# 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 lowerCAmelCase_       (   self:					List[Any]  )						->						str:
											snake_case__													=   self.prepare_config_and_inputs()
											snake_case__													=   config_and_inputs
											snake_case__													=   {'pixel_values': pixel_values}
											return config, inputs_dict


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

							_UpperCAmelCase       			=				False
							_UpperCAmelCase       			=				False
							_UpperCAmelCase       			=				False
							_UpperCAmelCase       			=				False
							_UpperCAmelCase       			=				False
							def lowerCAmelCase_       (   self:					str  )						->						Optional[Any]:
											snake_case__													=   BitModelTester(self  )
											snake_case__													=   ConfigTester(self				,		config_class=snake_case_				,		has_text_modality=snake_case_  )
							def lowerCAmelCase_       (   self:					Optional[Any]  )						->						Tuple:
											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 lowerCAmelCase_       (   self:					Tuple  )						->						str:
											return
							@unittest.skip(reason='Bit does not output attentions'  )
							def lowerCAmelCase_       (   self:					Any  )						->						List[str]:
											pass
							@unittest.skip(reason='Bit does not use inputs_embeds'  )
							def lowerCAmelCase_       (   self:					Optional[Any]  )						->						Any:
											pass
							@unittest.skip(reason='Bit does not support input and output embeddings'  )
							def lowerCAmelCase_       (   self:					Any  )						->						int:
											pass
							def lowerCAmelCase_       (   self:					List[Any]  )						->						str:
											snake_case__													=   self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															snake_case__													=   model_class(snake_case_  )
															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]				,		snake_case_  )
							def lowerCAmelCase_       (   self:					str  )						->						Optional[Any]:
											snake_case__													=   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*snake_case_  )
							def lowerCAmelCase_       (   self:					Dict  )						->						Dict:
											snake_case__													=   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_backbone(*snake_case_  )
							def lowerCAmelCase_       (   self:					Any  )						->						List[str]:
											snake_case__													=   self.model_tester.prepare_config_and_inputs_for_common()

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

											def check_hidden_states_output(UpperCamelCase:					Optional[Any]				,		UpperCamelCase:					int				,		UpperCamelCase:					Dict  ):
															snake_case__													=   model_class(snake_case_  )
															model.to(snake_case_  )
															model.eval()

															with torch.no_grad():
																			snake_case__													=   model(**self._prepare_for_class(snake_case_				,		snake_case_  )  )

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

															snake_case__													=   self.model_tester.num_stages
															self.assertEqual(len(snake_case_  )				,		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]				,		)

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

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

																			check_hidden_states_output(snake_case_				,		snake_case_				,		snake_case_  )
							@unittest.skip(reason='Bit does not use feedforward chunking'  )
							def lowerCAmelCase_       (   self:					str  )						->						Tuple:
											pass
							def lowerCAmelCase_       (   self:					Union[str, Any]  )						->						List[Any]:
											snake_case__													=   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*snake_case_  )


							@slow
							def lowerCAmelCase_       (   self:					int  )						->						int:

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


def    a_       (					)	->	Dict:







				"""simple docstring"""
				snake_case__													=   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
				return image


@require_torch
@require_vision
class      __SCREAMING_SNAKE_CASE(		unittest.TestCase		):
							@cached_property
							def lowerCAmelCase_       (   self:					int  )						->						Union[str, Any]:
											return (
											    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]  ) if is_vision_available() else None
											)


							@slow
							def lowerCAmelCase_       (   self:					Optional[int]  )						->						List[str]:
											snake_case__													=   BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]  ).to(snake_case_  )

											snake_case__													=   self.default_image_processor
											snake_case__													=   prepare_img()
											snake_case__													=   image_processor(images=snake_case_				,		return_tensors='pt'  ).to(snake_case_  )

											# forward pass
											with torch.no_grad():
															snake_case__													=   model(**snake_case_  )

											# verify the logits
											snake_case__													=   torch.Size((1, 10_00)  )
											self.assertEqual(outputs.logits.shape				,		snake_case_  )

											snake_case__													=   torch.tensor([[-0.6_526, -0.5_263, -1.4_398]]  ).to(snake_case_  )

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


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

							_UpperCAmelCase       			=				False
							def lowerCAmelCase_       (   self:					List[str]  )						->						Union[str, Any]:
											snake_case__													=   BitModelTester(self  )











"""simple docstring"""




import copy
import random

from transformers import CLIPTokenizer

class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''

      def __init__(     self					,      *snake_case_					,      **snake_case_  ):

        """simple docstring"""

        super().__init__(*snake_case_					,      **snake_case_  )
        A_  :       Tuple										=  {}

      def 					lowerCamelCase_			(     self					,      snake_case_					,      *snake_case_					,      **snake_case_  ):

        """simple docstring"""

        A_  :       str										=  super().add_tokens(snake_case_					,      *snake_case_					,      **snake_case_  )
        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 					lowerCamelCase_			(     self					,      snake_case_					,      *snake_case_					,      snake_case_=1					,      **snake_case_  ):

        """simple docstring"""

        A_  :       Tuple										=  []
        if num_vec_per_token == 1:
          self.try_adding_tokens(snake_case_					,      *snake_case_					,      **snake_case_  )
          output.append(snake_case_  )
        else:
          A_  :       Tuple										=  []
          for i in range(snake_case_  ):
            A_  :       List[str]										=  placeholder_token + F"""_{i}"""
            self.try_adding_tokens(snake_case_					,      *snake_case_					,      **snake_case_  )
            output.append(snake_case_  )
        # 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_  :       Any										=  output

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_=False					,      snake_case_=1.0  ):

        """simple docstring"""

        if isinstance(snake_case_					,      snake_case_  ):
          A_  :       Optional[Any]										=  []
          for i in range(len(snake_case_  )  ):
            output.append(self.replace_placeholder_tokens_in_text(text[i]					,      vector_shuffle=snake_case_  )  )
          return output
        for placeholder_token in self.token_map:
          if placeholder_token in text:
            A_  :       List[Any]										=  self.token_map[placeholder_token]
            A_  :       Optional[int]										=  tokens[: 1 + int(len(snake_case_  ) * prop_tokens_to_load  )]
            if vector_shuffle:
              A_  :       Optional[Any]										=  copy.copy(snake_case_  )
              random.shuffle(snake_case_  )
            A_  :       List[str]										=  text.replace(snake_case_					,      ' '.join(snake_case_  )  )
        return text

      def __call__(     self					,      snake_case_					,      *snake_case_					,      snake_case_=False					,      snake_case_=1.0					,      **snake_case_  ):

        """simple docstring"""

        return super().__call__(
            self.replace_placeholder_tokens_in_text(
                snake_case_					,      vector_shuffle=snake_case_					,      prop_tokens_to_load=snake_case_  )					,      *snake_case_					,      **snake_case_					,      )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      *snake_case_					,      snake_case_=False					,      snake_case_=1.0					,      **snake_case_  ):

        """simple docstring"""

        return super().encode(
            self.replace_placeholder_tokens_in_text(
                snake_case_					,      vector_shuffle=snake_case_					,      prop_tokens_to_load=snake_case_  )					,      *snake_case_					,      **snake_case_					,      )