


import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class _lowerCamelCase(		unittest.TestCase     ):
       @property
       def     UpperCamelCase  (      self) ->     int:




           """simple docstring"""

           torch.manual_seed(0)
           _lowercase :							Union[str, Any]   	=	UNetaDModel(
               block_out_channels=(32, 64),   layers_per_block=2,   sample_size=32,   in_channels=3,   out_channels=3,   down_block_types=('DownBlock2D', 'AttnDownBlock2D'),   up_block_types=('AttnUpBlock2D', 'UpBlock2D'),   )
           return model
       @property
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           torch.manual_seed(0)
           _lowercase :							str   	=	VQModel(
               block_out_channels=[32, 64],   in_channels=3,   out_channels=3,   down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'],   up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'],   latent_channels=3,   )
           return model
       @property
       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

           torch.manual_seed(0)
           _lowercase :							Optional[int]   	=	CLIPTextConfig(
               bos_token_id=0,   eos_token_id=2,   hidden_size=32,   intermediate_size=37,   layer_norm_eps=1E-05,   num_attention_heads=4,   num_hidden_layers=5,   pad_token_id=1,   vocab_size=10_00,   )
           return CLIPTextModel(lowerCamelCase)




       def     UpperCamelCase  (      self) ->     int:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	self.dummy_uncond_unet
           _lowercase :							Optional[Any]   	=	DDIMScheduler()
           _lowercase :							Any   	=	self.dummy_vq_model

           _lowercase :							Optional[int]   	=	LDMPipeline(unet=lowerCamelCase,   vqvae=lowerCamelCase,   scheduler=lowerCamelCase)
           ldm.to(lowerCamelCase)
           ldm.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase :							Tuple   	=	torch.manual_seed(0)
           _lowercase :							Dict   	=	ldm(generator=lowerCamelCase,   num_inference_steps=2,   output_type='numpy').images

           _lowercase :							str   	=	torch.manual_seed(0)
           _lowercase :							Optional[Any]   	=	ldm(generator=lowerCamelCase,   num_inference_steps=2,   output_type='numpy',   return_dict=lowerCamelCase)[0]

           _lowercase :							List[str]   	=	image[0, -3:, -3:, -1]
           _lowercase :							int   	=	image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)
           _lowercase :							str   	=	np.array([0.8_5_1_2, 0.8_1_8, 0.6_4_1_1, 0.6_8_0_8, 0.4_4_6_5, 0.5_6_1_8, 0.4_6, 0.6_2_3_1, 0.5_1_7_2])
           _lowercase :							Any   	=	1E-2 if torch_device != 'mps' else 3E-2

           assert np.abs(image_slice.flatten() - expected_slice).max() < tolerance
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < tolerance






@slow
@require_torch
class _lowerCamelCase(		unittest.TestCase     ):
       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           _lowercase :							List[str]   	=	LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256')
           ldm.to(lowerCamelCase)
           ldm.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase :							Optional[Any]   	=	torch.manual_seed(0)
           _lowercase :							int   	=	ldm(generator=lowerCamelCase,   num_inference_steps=5,   output_type='numpy').images

           _lowercase :							Optional[int]   	=	image[0, -3:, -3:, -1]

           assert image.shape == (1, 2_56, 2_56, 3)
           _lowercase :							Tuple   	=	np.array([0.4_3_9_9, 0.4_4_9_7_5, 0.4_6_8_2_5, 0.4_7_4, 0.4_3_5_9, 0.4_5_8_1, 0.4_5_0_9_5, 0.4_3_4_1, 0.4_4_4_7])
           _lowercase :							Optional[int]   	=	1E-2 if torch_device != 'mps' else 3E-2

           assert np.abs(image_slice.flatten() - expected_slice).max() < tolerance






import unittest

from transformers import DebertaVaConfig, 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 (
     DebertaVaForMaskedLM,
     DebertaVaForMultipleChoice,
     DebertaVaForQuestionAnswering,
     DebertaVaForSequenceClassification,
     DebertaVaForTokenClassification,
     DebertaVaModel,
 )
 from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST






class _lowerCamelCase(		_a     ):
       def __init__(      self,   lowerCamelCase,   lowerCamelCase=13,   lowerCamelCase=7,   lowerCamelCase=True,   lowerCamelCase=True,   lowerCamelCase=True,   lowerCamelCase=True,   lowerCamelCase=99,   lowerCamelCase=32,   lowerCamelCase=5,   lowerCamelCase=4,   lowerCamelCase=37,   lowerCamelCase="gelu",   lowerCamelCase=0.1,   lowerCamelCase=0.1,   lowerCamelCase=5_12,   lowerCamelCase=16,   lowerCamelCase=2,   lowerCamelCase=0.0_2,   lowerCamelCase=False,   lowerCamelCase=True,   lowerCamelCase="None",   lowerCamelCase=3,   lowerCamelCase=4,   lowerCamelCase=None,   ) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							Union[str, Any]   	=	parent
           _lowercase :							Optional[Any]   	=	batch_size
           _lowercase :							Optional[Any]   	=	seq_length
           _lowercase :							Dict   	=	is_training
           _lowercase :							Optional[Any]   	=	use_input_mask
           _lowercase :							Optional[int]   	=	use_token_type_ids
           _lowercase :							str   	=	use_labels
           _lowercase :							List[Any]   	=	vocab_size
           _lowercase :							Dict   	=	hidden_size
           _lowercase :							Any   	=	num_hidden_layers
           _lowercase :							Union[str, Any]   	=	num_attention_heads
           _lowercase :							int   	=	intermediate_size
           _lowercase :							List[str]   	=	hidden_act
           _lowercase :							Tuple   	=	hidden_dropout_prob
           _lowercase :							Optional[Any]   	=	attention_probs_dropout_prob
           _lowercase :							int   	=	max_position_embeddings
           _lowercase :							Any   	=	type_vocab_size
           _lowercase :							Tuple   	=	type_sequence_label_size
           _lowercase :							List[Any]   	=	initializer_range
           _lowercase :							Optional[Any]   	=	num_labels
           _lowercase :							Tuple   	=	num_choices
           _lowercase :							Dict   	=	relative_attention
           _lowercase :							Optional[int]   	=	position_biased_input
           _lowercase :							str   	=	pos_att_type
           _lowercase :							Optional[Any]   	=	scope
       def     UpperCamelCase  (      self) ->     Optional[int]:




           """simple docstring"""

           _lowercase :							int   	=	ids_tensor([self.batch_size, self.seq_length],   self.vocab_size)

           _lowercase :							Union[str, Any]   	=	None
           if self.use_input_mask:
               _lowercase :							int   	=	ids_tensor([self.batch_size, self.seq_length],   vocab_size=2)

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

           _lowercase :							Union[str, Any]   	=	None
           _lowercase :							Tuple   	=	None
           _lowercase :							str   	=	None
           if self.use_labels:
               _lowercase :							List[Any]   	=	ids_tensor([self.batch_size],   self.type_sequence_label_size)
               _lowercase :							Optional[int]   	=	ids_tensor([self.batch_size, self.seq_length],   self.num_labels)
               _lowercase :							str   	=	ids_tensor([self.batch_size],   self.num_choices)

           _lowercase :							Union[str, Any]   	=	self.get_config()

           return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
       def     UpperCamelCase  (      self) ->     Optional[int]:




           """simple docstring"""

           return DebertaVaConfig(
               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     UpperCamelCase  (      self,   lowerCamelCase) ->     int:




           """simple docstring"""

           self.parent.assertListEqual(list(result.loss.size()),   [])
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase :							List[str]   	=	DebertaVaModel(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							int   	=	model(lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase)[0]
           _lowercase :							Optional[int]   	=	model(lowerCamelCase,   token_type_ids=lowerCamelCase)[0]
           _lowercase :							Dict   	=	model(lowerCamelCase)[0]

           self.parent.assertListEqual(list(sequence_output.size()),   [self.batch_size, self.seq_length, self.hidden_size])
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     List[Any]:




           """simple docstring"""

           _lowercase :							List[Any]   	=	DebertaVaForMaskedLM(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							str   	=	model(lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase,   labels=lowerCamelCase)

           self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.seq_length, self.vocab_size))
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     List[str]:




           """simple docstring"""

           _lowercase :							Optional[int]   	=	self.num_labels
           _lowercase :							Any   	=	DebertaVaForSequenceClassification(lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							Any   	=	model(lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase,   labels=lowerCamelCase)
           self.parent.assertListEqual(list(result.logits.size()),   [self.batch_size, self.num_labels])
           self.check_loss_output(lowerCamelCase)
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     Optional[int]:




           """simple docstring"""

           _lowercase :							Union[str, Any]   	=	self.num_labels
           _lowercase :							Optional[int]   	=	DebertaVaForTokenClassification(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							Optional[Any]   	=	model(lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase,   labels=lowerCamelCase)
           self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.seq_length, self.num_labels))
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase :							Union[str, Any]   	=	DebertaVaForQuestionAnswering(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							Union[str, Any]   	=	model(
               lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase,   start_positions=lowerCamelCase,   end_positions=lowerCamelCase,   )
           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     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     Optional[int]:




           """simple docstring"""

           _lowercase :							Any   	=	DebertaVaForMultipleChoice(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							Optional[int]   	=	input_ids.unsqueeze(1).expand(-1,   self.num_choices,   -1).contiguous()
           _lowercase :							List[Any]   	=	token_type_ids.unsqueeze(1).expand(-1,   self.num_choices,   -1).contiguous()
           _lowercase :							int   	=	input_mask.unsqueeze(1).expand(-1,   self.num_choices,   -1).contiguous()
           _lowercase :							str   	=	model(
               lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase,   labels=lowerCamelCase,   )
           self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.num_choices))




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




           """simple docstring"""

           _lowercase :							Any   	=	self.prepare_config_and_inputs()
           (
               (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       
           ) :							List[str]   	=	config_and_inputs
           _lowercase :							int   	=	{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
           return config, inputs_dict






@require_torch
class _lowerCamelCase(		_a,				_a,				unittest.TestCase     ):
       lowercase_				:			Any									=    (
           (
               DebertaVaModel,
               DebertaVaForMaskedLM,
               DebertaVaForSequenceClassification,
               DebertaVaForTokenClassification,
               DebertaVaForQuestionAnswering,
               DebertaVaForMultipleChoice,
           )
           if is_torch_available()
           else ()
       )
       lowercase_				:			Any									=    (
           {
               """feature-extraction""": DebertaVaModel,
               """fill-mask""": DebertaVaForMaskedLM,
               """question-answering""": DebertaVaForQuestionAnswering,
               """text-classification""": DebertaVaForSequenceClassification,
               """token-classification""": DebertaVaForTokenClassification,
               """zero-shot""": DebertaVaForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )

       lowercase_				:			int									=    True
       lowercase_				:			str									=    False
       lowercase_				:			str									=    False
       lowercase_				:			str									=    False
       lowercase_				:			List[Any]									=    False
       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           _lowercase :							List[Any]   	=	DebertaVaModelTester(self)
           _lowercase :							List[Any]   	=	ConfigTester(self,   config_class=lowerCamelCase,   hidden_size=37)
       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           self.config_tester.run_common_tests()
       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase :							Tuple   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_deberta_model(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							int   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_deberta_for_sequence_classification(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     int:




           """simple docstring"""

           _lowercase :							Union[str, Any]   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_deberta_for_masked_lm(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           _lowercase :							List[Any]   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_deberta_for_question_answering(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     Tuple:




           """simple docstring"""

           _lowercase :							Union[str, Any]   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_deberta_for_token_classification(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							Tuple   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_deberta_for_multiple_choice(*lowerCamelCase)




       @slow
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _lowercase :							Dict   	=	DebertaVaModel.from_pretrained(lowerCamelCase)
               self.assertIsNotNone(lowerCamelCase)






@require_torch
@require_sentencepiece
@require_tokenizers
class _lowerCamelCase(		unittest.TestCase     ):
       @unittest.skip(reason='Model not available yet')
       def     UpperCamelCase  (      self) ->     Tuple:




           """simple docstring"""

           pass




       @slow
       def     UpperCamelCase  (      self) ->     Optional[int]:




           """simple docstring"""

           _lowercase :							Dict   	=	DebertaVaModel.from_pretrained('microsoft/deberta-v2-xlarge')

           _lowercase :							str   	=	torch.tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]])
           _lowercase :							Any   	=	torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
           with torch.no_grad():
               _lowercase :							Tuple   	=	model(lowerCamelCase,   attention_mask=lowerCamelCase)[0]
           # compare the actual values for a slice.
           _lowercase :							int   	=	torch.tensor(
               [[[0.2_3_5_6, 0.1_9_4_8, 0.0_3_6_9], [-0.1_0_6_3, 0.3_5_8_6, -0.5_1_5_2], [-0.6_3_9_9, -0.0_2_5_9, -0.2_5_2_5]]])
           self.assertTrue(torch.allclose(output[:, 1:4, 1:4],   lowerCamelCase,   atol=1E-4),   F'''{output[:, 1:4, 1:4]}''')


