


import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


UpperCAmelCase__        =				False







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





     def 	__A   (self			)	->  Dict:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()





     def 	__A   (self			)	->  Dict:
      _lowercase				 =VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''			)
      # remove text_unet
      pipe.remove_unused_weights()
      pipe.to(UpperCAmelCase			)
      pipe.set_progress_bar_config(disable=UpperCAmelCase			)

      _lowercase				 =load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''			)

      _lowercase				 =torch.manual_seed(0			)
      _lowercase				 =pipe(
          prompt='''first prompt'''						,	image=UpperCAmelCase						,	text_to_image_strength=0.75						,	generator=UpperCAmelCase						,	guidance_scale=7.5						,	num_inference_steps=2						,	output_type='''numpy'''						,	).images

      with tempfile.TemporaryDirectory() as tmpdirname:
       pipe.save_pretrained(UpperCAmelCase			)
       _lowercase				 =VersatileDiffusionDualGuidedPipeline.from_pretrained(UpperCAmelCase			)

      pipe.to(UpperCAmelCase			)
      pipe.set_progress_bar_config(disable=UpperCAmelCase			)

      _lowercase				 =generator.manual_seed(0			)
      _lowercase				 =pipe(
          prompt='''first prompt'''						,	image=UpperCAmelCase						,	text_to_image_strength=0.75						,	generator=UpperCAmelCase						,	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 	__A   (self			)	->  str:
      _lowercase				 =VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''			)
      pipe.remove_unused_weights()
      pipe.to(UpperCAmelCase			)
      pipe.set_progress_bar_config(disable=UpperCAmelCase			)

      _lowercase				 ='''cyberpunk 2077'''
      _lowercase				 =load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''			)
      _lowercase				 =torch.manual_seed(0			)
      _lowercase				 =pipe(
          prompt=UpperCAmelCase						,	image=UpperCAmelCase						,	text_to_image_strength=0.75						,	generator=UpperCAmelCase						,	guidance_scale=7.5						,	num_inference_steps=5_0						,	output_type='''numpy'''						,	).images

      _lowercase				 =image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

      assert image.shape == (1, 5_1_2, 5_1_2, 3)
      _lowercase				 =np.array([0.0787, 0.0849, 0.0826, 0.0812, 0.0807, 0.0795, 0.0818, 0.0798, 0.0779]			)

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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase__        =				{
    '''configuration_flaubert''': ['''FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FlaubertConfig''', '''FlaubertOnnxConfig'''],
    '''tokenization_flaubert''': ['''FlaubertTokenizer'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__        =				[
      '''FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''FlaubertForMultipleChoice''',
      '''FlaubertForQuestionAnswering''',
      '''FlaubertForQuestionAnsweringSimple''',
      '''FlaubertForSequenceClassification''',
      '''FlaubertForTokenClassification''',
      '''FlaubertModel''',
      '''FlaubertWithLMHeadModel''',
      '''FlaubertPreTrainedModel''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__        =				[
      '''TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFFlaubertForMultipleChoice''',
      '''TFFlaubertForQuestionAnsweringSimple''',
      '''TFFlaubertForSequenceClassification''',
      '''TFFlaubertForTokenClassification''',
      '''TFFlaubertModel''',
      '''TFFlaubertPreTrainedModel''',
      '''TFFlaubertWithLMHeadModel''',
  ]


if TYPE_CHECKING:
  from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
  from .tokenization_flaubert import FlaubertTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flaubert import (
        FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        FlaubertForMultipleChoice,
        FlaubertForQuestionAnswering,
        FlaubertForQuestionAnsweringSimple,
        FlaubertForSequenceClassification,
        FlaubertForTokenClassification,
        FlaubertModel,
        FlaubertPreTrainedModel,
        FlaubertWithLMHeadModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_flaubert import (
        TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFFlaubertForMultipleChoice,
        TFFlaubertForQuestionAnsweringSimple,
        TFFlaubertForSequenceClassification,
        TFFlaubertForTokenClassification,
        TFFlaubertModel,
        TFFlaubertPreTrainedModel,
        TFFlaubertWithLMHeadModel,
    )

else:
  import sys

  UpperCAmelCase__        =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

