import gc
import tempfile
import unittest

import numpy as np
import torch

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


SCREAMING_SNAKE_CASE			:						Dict				  =		False
class  A_			(							unittest.TestCase			):
  pass
@nightly
@require_torch_gpu
class  A_			(							unittest.TestCase			):

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

  def 		_UpperCAmelCase (		self			:   List[str]							):
   __a							=	VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"  ,				torch_dtype=torch.floataa							)
   pipe.to(__SCREAMING_SNAKE_CASE							)
   pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE							)

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

   __a							=	torch.manual_seed(0							)
   __a							=	pipe.dual_guided(
       prompt="first prompt"  ,				image=__SCREAMING_SNAKE_CASE  ,				text_to_image_strength=0.75  ,				generator=__SCREAMING_SNAKE_CASE  ,				guidance_scale=7.5  ,				num_inference_steps=2  ,				output_type="numpy"  ,				).images

   with tempfile.TemporaryDirectory() as tmpdirname:
    pipe.save_pretrained(__SCREAMING_SNAKE_CASE							)
    __a							=	VersatileDiffusionPipeline.from_pretrained(__SCREAMING_SNAKE_CASE  ,				torch_dtype=torch.floataa							)
   pipe.to(__SCREAMING_SNAKE_CASE							)
   pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE							)

   __a							=	generator.manual_seed(0							)
   __a							=	pipe.dual_guided(
       prompt="first prompt"  ,				image=__SCREAMING_SNAKE_CASE  ,				text_to_image_strength=0.75  ,				generator=__SCREAMING_SNAKE_CASE  ,				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			:   Optional[int]							):
   __a							=	VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"  ,				torch_dtype=torch.floataa							)
   pipe.to(__SCREAMING_SNAKE_CASE							)
   pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE							)

   __a							=	"cyberpunk 2077"
   __a							=	load_image(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"							)
   __a							=	torch.manual_seed(0							)
   __a							=	pipe.dual_guided(
       prompt=__SCREAMING_SNAKE_CASE  ,				image=__SCREAMING_SNAKE_CASE  ,				text_to_image_strength=0.75  ,				generator=__SCREAMING_SNAKE_CASE  ,				guidance_scale=7.5  ,				num_inference_steps=50  ,				output_type="numpy"  ,				).images

   __a							=	image[0, 2_53:2_56, 2_53:2_56, -1]

   assert image.shape == (1, 5_12, 5_12, 3)
   __a							=	np.array([0.14_48, 0.16_19, 0.17_41, 0.10_86, 0.11_47, 0.11_28, 0.11_99, 0.11_65, 0.10_01]							)

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

   __a							=	"A painting of a squirrel eating a burger "
   __a							=	torch.manual_seed(0							)
   __a							=	pipe.text_to_image(
       prompt=__SCREAMING_SNAKE_CASE  ,				generator=__SCREAMING_SNAKE_CASE  ,				guidance_scale=7.5  ,				num_inference_steps=50  ,				output_type="numpy"							).images

   __a							=	image[0, 2_53:2_56, 2_53:2_56, -1]

   assert image.shape == (1, 5_12, 5_12, 3)
   __a							=	np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78]							)

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

   __a							=	pipe.image_variation(__SCREAMING_SNAKE_CASE  ,				generator=__SCREAMING_SNAKE_CASE  ,				output_type="numpy"							).images

   __a							=	image[0, 2_53:2_56, 2_53:2_56, -1]

   assert image.shape == (1, 5_12, 5_12, 3)
   __a							=	np.array([0.30_76, 0.31_23, 0.32_84, 0.37_82, 0.37_70, 0.38_94, 0.42_97, 0.43_31, 0.44_56]							)

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



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE			:						Optional[int]				  =		{
    """configuration_bert""": ["""BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BertConfig""", """BertOnnxConfig"""],
    """tokenization_bert""": ["""BasicTokenizer""", """BertTokenizer""", """WordpieceTokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						Optional[int]				  =		["""BertTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						List[Any]				  =		[
      """BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """BertForMaskedLM""",
      """BertForMultipleChoice""",
      """BertForNextSentencePrediction""",
      """BertForPreTraining""",
      """BertForQuestionAnswering""",
      """BertForSequenceClassification""",
      """BertForTokenClassification""",
      """BertLayer""",
      """BertLMHeadModel""",
      """BertModel""",
      """BertPreTrainedModel""",
      """load_tf_weights_in_bert""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		[
      """TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFBertEmbeddings""",
      """TFBertForMaskedLM""",
      """TFBertForMultipleChoice""",
      """TFBertForNextSentencePrediction""",
      """TFBertForPreTraining""",
      """TFBertForQuestionAnswering""",
      """TFBertForSequenceClassification""",
      """TFBertForTokenClassification""",
      """TFBertLMHeadModel""",
      """TFBertMainLayer""",
      """TFBertModel""",
      """TFBertPreTrainedModel""",
  ]
try:
  if not is_tensorflow_text_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		["""TFBertTokenizer"""]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						Any				  =		[
      """FlaxBertForCausalLM""",
      """FlaxBertForMaskedLM""",
      """FlaxBertForMultipleChoice""",
      """FlaxBertForNextSentencePrediction""",
      """FlaxBertForPreTraining""",
      """FlaxBertForQuestionAnswering""",
      """FlaxBertForSequenceClassification""",
      """FlaxBertForTokenClassification""",
      """FlaxBertModel""",
      """FlaxBertPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
  from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_bert_fast import BertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_bert import (
        BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BertForMaskedLM,
        BertForMultipleChoice,
        BertForNextSentencePrediction,
        BertForPreTraining,
        BertForQuestionAnswering,
        BertForSequenceClassification,
        BertForTokenClassification,
        BertLayer,
        BertLMHeadModel,
        BertModel,
        BertPreTrainedModel,
        load_tf_weights_in_bert,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_bert import (
        TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFBertEmbeddings,
        TFBertForMaskedLM,
        TFBertForMultipleChoice,
        TFBertForNextSentencePrediction,
        TFBertForPreTraining,
        TFBertForQuestionAnswering,
        TFBertForSequenceClassification,
        TFBertForTokenClassification,
        TFBertLMHeadModel,
        TFBertMainLayer,
        TFBertModel,
        TFBertPreTrainedModel,
    )

  try:
    if not is_tensorflow_text_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_bert_tf import TFBertTokenizer

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_bert import (
        FlaxBertForCausalLM,
        FlaxBertForMaskedLM,
        FlaxBertForMultipleChoice,
        FlaxBertForNextSentencePrediction,
        FlaxBertForPreTraining,
        FlaxBertForQuestionAnswering,
        FlaxBertForSequenceClassification,
        FlaxBertForTokenClassification,
        FlaxBertModel,
        FlaxBertPreTrainedModel,
    )

else:
  import sys

  SCREAMING_SNAKE_CASE			:						Optional[int]				  =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


