


import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


UpperCamelCase					:       Any			    =     False



class A__     ( unittest.TestCase  ):



  """simple docstring"""



  pass




@nightly
@require_torch_gpu
class A__     ( unittest.TestCase  ):



  """simple docstring"""



  def    _UpperCamelCase( self			:  Optional[Any]       ):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

  def    _UpperCamelCase( self			:  str       ):
        a__	:  Tuple     =   VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion"       )
        # remove text_unet
        pipe.remove_unused_weights()
        pipe.to(lowerCamelCase__       )
        pipe.set_progress_bar_config(disable=lowerCamelCase__       )

        a__	:  Tuple     =   "A painting of a squirrel eating a burger "
        a__	:  Any     =   torch.manual_seed(0       )
        a__	:  int     =   pipe(
            prompt=lowerCamelCase__				,				generator=lowerCamelCase__				,				guidance_scale=7.5				,				num_inference_steps=2				,				output_type="numpy"       ).images

        with tempfile.TemporaryDirectory() as tmpdirname:
              pipe.save_pretrained(lowerCamelCase__       )
              a__	:  int     =   VersatileDiffusionTextToImagePipeline.from_pretrained(lowerCamelCase__       )
        pipe.to(lowerCamelCase__       )
        pipe.set_progress_bar_config(disable=lowerCamelCase__       )

        a__	:  Optional[int]     =   generator.manual_seed(0       )
        a__	:  Optional[int]     =   pipe(
            prompt=lowerCamelCase__				,				generator=lowerCamelCase__				,				guidance_scale=7.5				,				num_inference_steps=2				,				output_type="numpy"       ).images

        assert np.abs(image - new_image       ).sum() < 1E-5, "Models don't have the same forward pass"

  def    _UpperCamelCase( self			:  Tuple       ):
        a__	:  List[str]     =   VersatileDiffusionTextToImagePipeline.from_pretrained(
            "shi-labs/versatile-diffusion"				,				torch_dtype=torch.floataa       )
        pipe.to(lowerCamelCase__       )
        pipe.set_progress_bar_config(disable=lowerCamelCase__       )

        a__	:  Optional[Any]     =   "A painting of a squirrel eating a burger "
        a__	:  Dict     =   torch.manual_seed(0       )
        a__	:  int     =   pipe(
            prompt=lowerCamelCase__				,				generator=lowerCamelCase__				,				guidance_scale=7.5				,				num_inference_steps=50				,				output_type="numpy"       ).images

        a__	:  Union[str, Any]     =   image[0, 253:256, 253:256, -1]

        assert image.shape == (1, 512, 512, 3)
        a__	:  List[Any]     =   np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]       )

        assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2






'''simple docstring'''





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_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




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




  def 		_lowercase	(					self :      Dict  )   ->   str:
    __lowerCamelCase			:			List[str]       			=					ids_tensor([self.batch_size, self.seq_length]     ,      self.vocab_size  )

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

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

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

    __lowerCamelCase			:			List[str]       			=					self.get_config()

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




  def 		_lowercase	(					self :      List[str]  )   ->   Optional[int]:
    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 		_lowercase	(					self :      Any     ,      _a :      Optional[Any]  )   ->   Any:
    self.parent.assertListEqual(list(result.loss.size()  )     ,      []  )




  def 		_lowercase	(					self :      List[str]     ,      _a :      Any     ,      _a :      List[str]     ,      _a :      List[Any]     ,      _a :      Dict     ,      _a :      Union[str, Any]     ,      _a :      Tuple     ,      _a :      List[Any]  )   ->   List[str]:
    __lowerCamelCase			:			str       			=					DebertaVaModel(config=_a  )
    model.to(_a  )
    model.eval()
    __lowerCamelCase			:			Optional[Any]       			=					model(_a     ,      attention_mask=_a     ,      token_type_ids=_a  )[0]
    __lowerCamelCase			:			List[Any]       			=					model(_a     ,      token_type_ids=_a  )[0]
    __lowerCamelCase			:			int       			=					model(_a  )[0]

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




  def 		_lowercase	(					self :      Union[str, Any]     ,      _a :      str     ,      _a :      Optional[int]     ,      _a :      Dict     ,      _a :      Optional[int]     ,      _a :      Dict     ,      _a :      Optional[Any]     ,      _a :      Optional[Any]  )   ->   str:
    __lowerCamelCase			:			List[Any]       			=					DebertaVaForMaskedLM(config=_a  )
    model.to(_a  )
    model.eval()
    __lowerCamelCase			:			Optional[int]       			=					model(_a     ,      attention_mask=_a     ,      token_type_ids=_a     ,      labels=_a  )

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




  def 		_lowercase	(					self :      Optional[Any]     ,      _a :      List[str]     ,      _a :      str     ,      _a :      Any     ,      _a :      Dict     ,      _a :      Optional[Any]     ,      _a :      Optional[Any]     ,      _a :      str  )   ->   List[Any]:
    __lowerCamelCase			:			List[str]       			=					self.num_labels
    __lowerCamelCase			:			List[str]       			=					DebertaVaForSequenceClassification(_a  )
    model.to(_a  )
    model.eval()
    __lowerCamelCase			:			Tuple       			=					model(_a     ,      attention_mask=_a     ,      token_type_ids=_a     ,      labels=_a  )
    self.parent.assertListEqual(list(result.logits.size()  )     ,      [self.batch_size, self.num_labels]  )
    self.check_loss_output(_a  )




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




  def 		_lowercase	(					self :      Union[str, Any]     ,      _a :      Optional[int]     ,      _a :      List[Any]     ,      _a :      Optional[Any]     ,      _a :      List[str]     ,      _a :      Optional[int]     ,      _a :      Union[str, Any]     ,      _a :      Any  )   ->   Tuple:
    __lowerCamelCase			:			int       			=					DebertaVaForQuestionAnswering(config=_a  )
    model.to(_a  )
    model.eval()
    __lowerCamelCase			:			List[str]       			=					model(
        _a     ,      attention_mask=_a     ,      token_type_ids=_a     ,      start_positions=_a     ,      end_positions=_a     ,      )
    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 		_lowercase	(					self :      Union[str, Any]     ,      _a :      Any     ,      _a :      List[str]     ,      _a :      str     ,      _a :      str     ,      _a :      List[Any]     ,      _a :      Tuple     ,      _a :      str  )   ->   Optional[int]:
    __lowerCamelCase			:			Union[str, Any]       			=					DebertaVaForMultipleChoice(config=_a  )
    model.to(_a  )
    model.eval()
    __lowerCamelCase			:			List[Any]       			=					input_ids.unsqueeze(1  ).expand(-1     ,      self.num_choices     ,      -1  ).contiguous()
    __lowerCamelCase			:			List[str]       			=					token_type_ids.unsqueeze(1  ).expand(-1     ,      self.num_choices     ,      -1  ).contiguous()
    __lowerCamelCase			:			Union[str, Any]       			=					input_mask.unsqueeze(1  ).expand(-1     ,      self.num_choices     ,      -1  ).contiguous()
    __lowerCamelCase			:			Optional[int]       			=					model(
        _a     ,      attention_mask=_a     ,      token_type_ids=_a     ,      labels=_a     ,      )
    self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.num_choices)  )




  def 		_lowercase	(					self :      Any  )   ->   Any:
    __lowerCamelCase			:			Tuple       			=					self.prepare_config_and_inputs()
    (
        (
        __lowerCamelCase
    )			,(
        __lowerCamelCase
    )			,(
        __lowerCamelCase
    )			,(
        __lowerCamelCase
    )			,(
        __lowerCamelCase
    )			,(
        __lowerCamelCase
    )			,(
        __lowerCamelCase
    )			,
    )			:			Optional[int]       			=					config_and_inputs
    __lowerCamelCase			:			List[Any]       			=					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
    return config, inputs_dict






@require_torch
class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_  ,  unittest.TestCase	):
  """simple docstring"""




  a_				   =(
      (
          DebertaVaModel,
          DebertaVaForMaskedLM,
          DebertaVaForSequenceClassification,
          DebertaVaForTokenClassification,
          DebertaVaForQuestionAnswering,
          DebertaVaForMultipleChoice,
      )
      if is_torch_available()
      else ()
  )
  a_				   =(
      {
          """feature-extraction""": DebertaVaModel,
          """fill-mask""": DebertaVaForMaskedLM,
          """question-answering""": DebertaVaForQuestionAnswering,
          """text-classification""": DebertaVaForSequenceClassification,
          """token-classification""": DebertaVaForTokenClassification,
          """zero-shot""": DebertaVaForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )

  a_				   =True
  a_				   =False
  a_				   =False
  a_				   =False
  a_				   =False




  def 		_lowercase	(					self :      Tuple  )   ->   Dict:
    __lowerCamelCase			:			str       			=					DebertaVaModelTester(self  )
    __lowerCamelCase			:			Dict       			=					ConfigTester(self     ,      config_class=_a     ,      hidden_size=37  )




  def 		_lowercase	(					self :      int  )   ->   List[str]:
    self.config_tester.run_common_tests()




  def 		_lowercase	(					self :      Any  )   ->   int:
    __lowerCamelCase			:			Any       			=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_deberta_model(*_a  )




  def 		_lowercase	(					self :      Dict  )   ->   Tuple:
    __lowerCamelCase			:			List[str]       			=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_deberta_for_sequence_classification(*_a  )




  def 		_lowercase	(					self :      Tuple  )   ->   Optional[Any]:
    __lowerCamelCase			:			Tuple       			=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_deberta_for_masked_lm(*_a  )




  def 		_lowercase	(					self :      Union[str, Any]  )   ->   Optional[Any]:
    __lowerCamelCase			:			Union[str, Any]       			=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_deberta_for_question_answering(*_a  )




  def 		_lowercase	(					self :      Union[str, Any]  )   ->   List[Any]:
    __lowerCamelCase			:			int       			=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_deberta_for_token_classification(*_a  )




  def 		_lowercase	(					self :      Optional[Any]  )   ->   List[Any]:
    __lowerCamelCase			:			Tuple       			=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_deberta_for_multiple_choice(*_a  )




  @slow
  def 		_lowercase	(					self :      Optional[int]  )   ->   int:
    for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      __lowerCamelCase			:			Any       			=					DebertaVaModel.from_pretrained(_a  )
      self.assertIsNotNone(_a  )






@require_torch
@require_sentencepiece
@require_tokenizers
class    lowerCamelCase_       (							unittest.TestCase	):
  """simple docstring"""




  @unittest.skip(reason='Model not available yet'  )
  def 		_lowercase	(					self :      List[str]  )   ->   Dict:
    pass




  @slow
  def 		_lowercase	(					self :      List[str]  )   ->   List[Any]:
    __lowerCamelCase			:			List[Any]       			=					DebertaVaModel.from_pretrained('microsoft/deberta-v2-xlarge'  )

    __lowerCamelCase			:			Optional[Any]       			=					torch.tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]  )
    __lowerCamelCase			:			List[str]       			=					torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
    with torch.no_grad():
      __lowerCamelCase			:			List[str]       			=					model(_a     ,      attention_mask=_a  )[0]
    # compare the actual values for a slice.
    __lowerCamelCase			:			Tuple       			=					torch.tensor(
        [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]  )
    self.assertTrue(torch.allclose(output[:, 1:4, 1:4]     ,      _a     ,      atol=1e-4  )     ,      f'{output[:, 1:4, 1:4]}'  )






