
'''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						,				A_						,				A_=3						,				A_=32						,				A_=3						,				A_=10						,				A_=[8, 16, 32, 64]						,				A_=[1, 1, 2, 1]						,				A_=True						,				A_=True						,				A_="relu"						,				A_=3						,				A_=None						,				A_=["stage2", "stage3", "stage4"]						,				A_=[2, 3, 4]						,				A_=1						,				)->		Union[str, Any]:







											'''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(A_     )
											UpperCamelCase   			=     out_features
											UpperCamelCase   			=     out_indices
											UpperCamelCase   			=     num_groups







				def 						UpperCAmelCase_    (       self     )->		List[Any]:







											'''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     )->		Union[str, 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						,				A_						,				A_						,				A_     )->		Tuple:







											'''simple docstring'''






											UpperCamelCase   			=     BitModel(config=A_     )
											model.to(A_     )
											model.eval()
											UpperCamelCase   			=     model(A_     )
											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						,				A_						,				A_						,				A_     )->		int:







											'''simple docstring'''






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







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







											'''simple docstring'''






											UpperCamelCase   			=     BitBackbone(config=A_     )
											model.to(A_     )
											model.eval()
											UpperCamelCase   			=     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.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=A_     )
											model.to(A_     )
											model.eval()
											UpperCamelCase   			=     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.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     )->		Optional[int]:







											'''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 			SCREAMING_SNAKE_CASE__      (     snake_case_							,       snake_case_							,       unittest.TestCase):
				lowerCAmelCase_     			=							(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
				lowerCAmelCase_     			=							(
				    {"""feature-extraction""": BitModel, """image-classification""": BitForImageClassification}
				    if is_torch_available()
				    else {}
				)

				lowerCAmelCase_     			=							False
				lowerCAmelCase_     			=							False
				lowerCAmelCase_     			=							False
				lowerCAmelCase_     			=							False
				lowerCAmelCase_     			=							False







				def 						UpperCAmelCase_    (       self     )->		Tuple:







											'''simple docstring'''






											UpperCamelCase   			=     BitModelTester(self     )
											UpperCamelCase   			=     ConfigTester(self						,				config_class=A_						,				has_text_modality=A_     )







				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     )->		Union[str, Any]:







											'''simple docstring'''






											return







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







											'''simple docstring'''






											pass







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







											'''simple docstring'''






											pass







				@unittest.skip(reason='Bit does not support input and output embeddings'     )
				def 						UpperCAmelCase_    (       self     )->		List[str]:







											'''simple docstring'''






											pass







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







											'''simple docstring'''






											UpperCamelCase			,	UpperCamelCase   			=     self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		UpperCamelCase   			=     model_class(A_     )
																		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]						,				A_     )







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







											'''simple docstring'''






											UpperCamelCase   			=     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*A_     )







				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






											UpperCamelCase   			=     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_backbone(*A_     )







				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''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=A_     )
																		for name, module in model.named_modules():
																									if isinstance(A_						,				(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     )->		Dict:







											'''simple docstring'''






											def check_hidden_states_output(A_						,				A_						,				A_     ):
																		UpperCamelCase   			=     model_class(A_     )
																		model.to(A_     )
																		model.eval()

																		with torch.no_grad():
																									UpperCamelCase   			=     model(**self._prepare_for_class(A_						,				A_     )     )

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

																		UpperCamelCase   			=     self.model_tester.num_stages
																		self.assertEqual(len(A_     )						,				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(A_						,				A_						,				A_     )

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

																									check_hidden_states_output(A_						,				A_						,				A_     )







				@unittest.skip(reason='Bit does not use feedforward chunking'     )
				def 						UpperCAmelCase_    (       self     )->		Optional[Any]:







											'''simple docstring'''






											pass







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







											'''simple docstring'''






											UpperCamelCase   			=     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*A_     )







				@slow
				def 						UpperCAmelCase_    (       self     )->		Any:







											'''simple docstring'''






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







def 		A_(     ):
							UpperCamelCase   			=     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     )->		Tuple:







											'''simple docstring'''






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







				@slow
				def 						UpperCAmelCase_    (       self     )->		Any:







											'''simple docstring'''






											UpperCamelCase   			=     BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]     ).to(A_     )

											UpperCamelCase   			=     self.default_image_processor
											UpperCamelCase   			=     prepare_img()
											UpperCamelCase   			=     image_processor(images=A_						,				return_tensors='pt'     ).to(A_     )

											# forward pass
											with torch.no_grad():
																		UpperCamelCase   			=     model(**A_     )

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

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

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


@require_torch
class 			SCREAMING_SNAKE_CASE__      (     snake_case_							,       unittest.TestCase):
				lowerCAmelCase_     			=							(BitBackbone,) if is_torch_available() else ()
				lowerCAmelCase_     			=							BitConfig

				lowerCAmelCase_     			=							False







				def 						UpperCAmelCase_    (       self     )->		Optional[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     BitModelTester(self     )










# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch

from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool


if is_vision_available():
     from PIL import Image

class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = (
        '''This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'''
        '''It takes two arguments named `image` which should be the original image, and `label` which should be a text '''
        '''describing the elements what should be identified in the segmentation mask. The tool returns the mask.'''
    )
    lowerCAmelCase         = '''CIDAS/clipseg-rd64-refined'''
    lowerCAmelCase         = '''image_segmenter'''
    lowerCAmelCase         = CLIPSegForImageSegmentation

    lowerCAmelCase         = ['''image''', '''text''']
    lowerCAmelCase         = ['''image''']





    def __init__( self  ,*_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )					->						Dict:
         requires_backends(self  ,['''vision''']    )
         super().__init__(*_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )					->						Tuple:
         return self.pre_processor(text=[label]  ,images=[image]  ,padding=_SCREAMING_SNAKE_CASE  ,return_tensors='''pt'''    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						str:
         with torch.no_grad():
              UpperCAmelCase_     :				Dict  =     self.model(**_SCREAMING_SNAKE_CASE    ).logits
         return logits





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						Dict:
         UpperCAmelCase_     :				Dict  =     outputs.cpu().detach().numpy()
         UpperCAmelCase_     :				Any  =     0
         UpperCAmelCase_     :				List[Any]  =     1
         return Image.fromarray((array * 255).astype(np.uinta    )    )