"""simple docstring"""




import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import (
			    DebertaForMaskedLM,
			    DebertaForQuestionAnswering,
			    DebertaForSequenceClassification,
			    DebertaForTokenClassification,
			    DebertaModel,
			)
			from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST

class       a_      (     __UpperCamelCase			):




					def __init__(	self						:     List[str]  ,						snake_case__						:     List[Any]  ,						snake_case__						:     Tuple=13  ,						snake_case__						:     str=7  ,						snake_case__						:     int=True  ,						snake_case__						:     Any=True  ,						snake_case__						:     Optional[int]=True  ,						snake_case__						:     List[str]=True  ,						snake_case__						:     Union[str, Any]=99  ,						snake_case__						:     List[Any]=32  ,						snake_case__						:     List[Any]=5  ,						snake_case__						:     Union[str, Any]=4  ,						snake_case__						:     Optional[int]=37  ,						snake_case__						:     Any="gelu"  ,						snake_case__						:     Any=0.1  ,						snake_case__						:     int=0.1  ,						snake_case__						:     Any=512  ,						snake_case__						:     str=16  ,						snake_case__						:     Optional[Any]=2  ,						snake_case__						:     List[str]=0.02  ,						snake_case__						:     List[Any]=False  ,						snake_case__						:     Dict=True  ,						snake_case__						:     List[Any]="None"  ,						snake_case__						:     List[str]=3  ,						snake_case__						:     Dict=4  ,						snake_case__						:     List[Any]=None  ,						):
							lowerCAmelCase__        =   parent
							lowerCAmelCase__        =   batch_size
							lowerCAmelCase__        =   seq_length
							lowerCAmelCase__        =   is_training
							lowerCAmelCase__        =   use_input_mask
							lowerCAmelCase__        =   use_token_type_ids
							lowerCAmelCase__        =   use_labels
							lowerCAmelCase__        =   vocab_size
							lowerCAmelCase__        =   hidden_size
							lowerCAmelCase__        =   num_hidden_layers
							lowerCAmelCase__        =   num_attention_heads
							lowerCAmelCase__        =   intermediate_size
							lowerCAmelCase__        =   hidden_act
							lowerCAmelCase__        =   hidden_dropout_prob
							lowerCAmelCase__        =   attention_probs_dropout_prob
							lowerCAmelCase__        =   max_position_embeddings
							lowerCAmelCase__        =   type_vocab_size
							lowerCAmelCase__        =   type_sequence_label_size
							lowerCAmelCase__        =   initializer_range
							lowerCAmelCase__        =   num_labels
							lowerCAmelCase__        =   num_choices
							lowerCAmelCase__        =   relative_attention
							lowerCAmelCase__        =   position_biased_input
							lowerCAmelCase__        =   pos_att_type
							lowerCAmelCase__        =   scope




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							lowerCAmelCase__        =   ids_tensor([self.batch_size, self.seq_length]  ,						self.vocab_size     )

							lowerCAmelCase__        =   None
							if self.use_input_mask:
									lowerCAmelCase__        =   ids_tensor([self.batch_size, self.seq_length]  ,						vocab_size=2     )

							lowerCAmelCase__        =   None
							if self.use_token_type_ids:
									lowerCAmelCase__        =   ids_tensor([self.batch_size, self.seq_length]  ,						self.type_vocab_size     )

							lowerCAmelCase__        =   None
							lowerCAmelCase__        =   None
							lowerCAmelCase__        =   None
							if self.use_labels:
									lowerCAmelCase__        =   ids_tensor([self.batch_size]  ,						self.type_sequence_label_size     )
									lowerCAmelCase__        =   ids_tensor([self.batch_size, self.seq_length]  ,						self.num_labels     )
									lowerCAmelCase__        =   ids_tensor([self.batch_size]  ,						self.num_choices     )

							lowerCAmelCase__        =   self.get_config()

							return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							return DebertaConfig(
							    vocab_size=self.vocab_size  ,						hidden_size=self.hidden_size  ,						num_hidden_layers=self.num_hidden_layers  ,						num_attention_heads=self.num_attention_heads  ,						intermediate_size=self.intermediate_size  ,						hidden_act=self.hidden_act  ,						hidden_dropout_prob=self.hidden_dropout_prob  ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,						max_position_embeddings=self.max_position_embeddings  ,						type_vocab_size=self.type_vocab_size  ,						initializer_range=self.initializer_range  ,						relative_attention=self.relative_attention  ,						position_biased_input=self.position_biased_input  ,						pos_att_type=self.pos_att_type  ,						)




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							lowerCAmelCase__        =   self.get_config()
							lowerCAmelCase__        =   300
							return config




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]  ,						snake_case__						:     Dict     ):
							self.parent.assertListEqual(list(result.loss.size()     )  ,						[]     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple  ,						snake_case__						:     List[str]  ,						snake_case__						:     int  ,						snake_case__						:     Union[str, Any]  ,						snake_case__						:     Optional[int]  ,						snake_case__						:     str  ,						snake_case__						:     Union[str, Any]  ,						snake_case__						:     Union[str, Any]     ):
							lowerCAmelCase__        =   DebertaModel(config=snake_case__     )
							model.to(snake_case__     )
							model.eval()
							lowerCAmelCase__        =   model(snake_case__  ,						attention_mask=snake_case__  ,						token_type_ids=snake_case__     )[0]
							lowerCAmelCase__        =   model(snake_case__  ,						token_type_ids=snake_case__     )[0]
							lowerCAmelCase__        =   model(snake_case__     )[0]

							self.parent.assertListEqual(list(sequence_output.size()     )  ,						[self.batch_size, self.seq_length, self.hidden_size]     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any  ,						snake_case__						:     Union[str, Any]  ,						snake_case__						:     Union[str, Any]  ,						snake_case__						:     Tuple  ,						snake_case__						:     Union[str, Any]  ,						snake_case__						:     Tuple  ,						snake_case__						:     int  ,						snake_case__						:     str     ):
							lowerCAmelCase__        =   DebertaForMaskedLM(config=snake_case__     )
							model.to(snake_case__     )
							model.eval()
							lowerCAmelCase__        =   model(snake_case__  ,						attention_mask=snake_case__  ,						token_type_ids=snake_case__  ,						labels=snake_case__     )

							self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.seq_length, self.vocab_size)     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any  ,						snake_case__						:     Union[str, Any]  ,						snake_case__						:     int  ,						snake_case__						:     Dict  ,						snake_case__						:     str  ,						snake_case__						:     int  ,						snake_case__						:     Optional[Any]  ,						snake_case__						:     Tuple     ):
							lowerCAmelCase__        =   self.num_labels
							lowerCAmelCase__        =   DebertaForSequenceClassification(snake_case__     )
							model.to(snake_case__     )
							model.eval()
							lowerCAmelCase__        =   model(snake_case__  ,						attention_mask=snake_case__  ,						token_type_ids=snake_case__  ,						labels=snake_case__     )
							self.parent.assertListEqual(list(result.logits.size()     )  ,						[self.batch_size, self.num_labels]     )
							self.check_loss_output(snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     int  ,						snake_case__						:     Optional[Any]  ,						snake_case__						:     List[Any]  ,						snake_case__						:     str  ,						snake_case__						:     Tuple  ,						snake_case__						:     Any  ,						snake_case__						:     Optional[int]     ):
							lowerCAmelCase__        =   self.num_labels
							lowerCAmelCase__        =   DebertaForTokenClassification(config=snake_case__     )
							model.to(snake_case__     )
							model.eval()
							lowerCAmelCase__        =   model(snake_case__  ,						attention_mask=snake_case__  ,						token_type_ids=snake_case__  ,						labels=snake_case__     )
							self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.seq_length, self.num_labels)     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]  ,						snake_case__						:     List[Any]  ,						snake_case__						:     Union[str, Any]  ,						snake_case__						:     Dict  ,						snake_case__						:     Union[str, Any]  ,						snake_case__						:     Any  ,						snake_case__						:     str  ,						snake_case__						:     Union[str, Any]     ):
							lowerCAmelCase__        =   DebertaForQuestionAnswering(config=snake_case__     )
							model.to(snake_case__     )
							model.eval()
							lowerCAmelCase__        =   model(
							    snake_case__  ,						attention_mask=snake_case__  ,						token_type_ids=snake_case__  ,						start_positions=snake_case__  ,						end_positions=snake_case__  ,						)
							self.parent.assertEqual(result.start_logits.shape  ,						(self.batch_size, self.seq_length)     )
							self.parent.assertEqual(result.end_logits.shape  ,						(self.batch_size, self.seq_length)     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							lowerCAmelCase__        =   self.prepare_config_and_inputs()
							(
							    (
							    lowerCAmelCase__
							)   ,					(
							    lowerCAmelCase__
							)   ,					(
							    lowerCAmelCase__
							)   ,					(
							    lowerCAmelCase__
							)   ,					(
							    lowerCAmelCase__
							)   ,					(
							    lowerCAmelCase__
							)   ,					(
							    lowerCAmelCase__
							)   ,					
							)        =   config_and_inputs
							lowerCAmelCase__        =   {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
							return config, inputs_dict

@require_torch
class       a_      (     __UpperCamelCase ,		__UpperCamelCase ,		unittest.TestCase			):
					UpperCamelCase_ :	str			=    (
					    (
					        DebertaModel,
					        DebertaForMaskedLM,
					        DebertaForSequenceClassification,
					        DebertaForTokenClassification,
					        DebertaForQuestionAnswering,
					    )
					    if is_torch_available()
					    else ()
					)
					UpperCamelCase_ :	Optional[int]			=    (
					    {
					        "feature-extraction": DebertaModel,
					        "fill-mask": DebertaForMaskedLM,
					        "question-answering": DebertaForQuestionAnswering,
					        "text-classification": DebertaForSequenceClassification,
					        "token-classification": DebertaForTokenClassification,
					        "zero-shot": DebertaForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)

					UpperCamelCase_ :	int			=    True
					UpperCamelCase_ :	Optional[int]			=    False
					UpperCamelCase_ :	Dict			=    False
					UpperCamelCase_ :	Optional[Any]			=    False
					UpperCamelCase_ :	Union[str, Any]			=    False




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							lowerCAmelCase__        =   DebertaModelTester(self     )
							lowerCAmelCase__        =   ConfigTester(self  ,						config_class=snake_case__  ,						hidden_size=37     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							self.config_tester.run_common_tests()




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_model(*snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_sequence_classification(*snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_masked_lm(*snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_question_answering(*snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_token_classification(*snake_case__     )




					@slow
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowerCAmelCase__        =   DebertaModel.from_pretrained(snake_case__     )
									self.assertIsNotNone(snake_case__     )

@require_torch
@require_sentencepiece
@require_tokenizers
class       a_      (     unittest.TestCase			):




					@unittest.skip(reason="""Model not available yet"""     )
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							pass




					@slow
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							lowerCAmelCase__        =   DebertaModel.from_pretrained("""microsoft/deberta-base"""     )

							lowerCAmelCase__        =   torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]     )
							lowerCAmelCase__        =   torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]     )
							with torch.no_grad():
									lowerCAmelCase__        =   model(snake_case__  ,						attention_mask=snake_case__     )[0]
							# compare the actual values for a slice.
							lowerCAmelCase__        =   torch.tensor(
							    [[[-0.5986, -0.8055, -0.8462], [1.4484, -0.9348, -0.8059], [0.3123, 0.0032, -1.4131]]]     )
							self.assertTrue(torch.allclose(output[:, 1:4, 1:4]  ,						snake_case__  ,						atol=1E-4     )  ,						F"""{output[:, 1:4, 1:4]}"""     )





"""simple docstring"""




# 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_      (     __UpperCamelCase			):
					UpperCamelCase_ :	List[str]			=    (
					    "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."
					)
					UpperCamelCase_ :	str			=    "CIDAS/clipseg-rd64-refined"
					UpperCamelCase_ :	Any			=    "image_segmenter"
					UpperCamelCase_ :	Optional[Any]			=    CLIPSegForImageSegmentation

					UpperCamelCase_ :	List[str]			=    ["image", "text"]
					UpperCamelCase_ :	int			=    ["image"]




					def __init__(	self						:     Tuple  ,						*snake_case__						:     str  ,						**snake_case__						:     Optional[Any]     ):
							requires_backends(self  ,						["""vision"""]     )
							super().__init__(*snake_case__  ,						**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     "Image"  ,						snake_case__						:     str     ):
							return self.pre_processor(text=[label]  ,						images=[image]  ,						padding=snake_case__  ,						return_tensors="""pt"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     Tuple     ):
							with torch.no_grad():
									lowerCAmelCase__        =   self.model(**snake_case__     ).logits
							return logits




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




