

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE          = {
    '''configuration_open_llama''': ['''OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OpenLlamaConfig'''],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE          = ['''LlamaTokenizer''']

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE          = ['''LlamaTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE          = [
						    '''OpenLlamaForCausalLM''',
						    '''OpenLlamaModel''',
						    '''OpenLlamaPreTrainedModel''',
						    '''OpenLlamaForSequenceClassification''',
						]


if TYPE_CHECKING:
						from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from transformers import LlamaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from transformers import LlamaTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_open_llama import (
												    OpenLlamaForCausalLM,
												    OpenLlamaForSequenceClassification,
												    OpenLlamaModel,
												    OpenLlamaPreTrainedModel,
												)


else:
						import sys

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








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


_SCREAMING_SNAKE_CASE          = False


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




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




 def 						a					( self       ):
   __UpperCAmelCase  :     Dict		=    VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'       )
   # remove text_unet
   pipe.remove_unused_weights()
   pipe.to(_UpperCAmelCase       )
   pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

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

   __UpperCAmelCase  :     Optional[Any]		=    torch.manual_seed(0       )
   __UpperCAmelCase  :     Union[str, Any]		=    pipe(
       prompt='first prompt'     ,     image=_UpperCAmelCase     ,     text_to_image_strength=0.7_5     ,     generator=_UpperCAmelCase     ,     guidance_scale=7.5     ,     num_inference_steps=2     ,     output_type='numpy'     ,     ).images

   with tempfile.TemporaryDirectory() as tmpdirname:
     pipe.save_pretrained(_UpperCAmelCase       )
     __UpperCAmelCase  :     List[str]		=    VersatileDiffusionDualGuidedPipeline.from_pretrained(_UpperCAmelCase       )

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

   __UpperCAmelCase  :     List[str]		=    generator.manual_seed(0       )
   __UpperCAmelCase  :     Optional[int]		=    pipe(
       prompt='first prompt'     ,     image=_UpperCAmelCase     ,     text_to_image_strength=0.7_5     ,     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       ):
   __UpperCAmelCase  :     Optional[Any]		=    VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'       )
   pipe.remove_unused_weights()
   pipe.to(_UpperCAmelCase       )
   pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

   __UpperCAmelCase  :     Tuple		=    'cyberpunk 2077'
   __UpperCAmelCase  :     Dict		=    load_image(
       'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'       )
   __UpperCAmelCase  :     Optional[Any]		=    torch.manual_seed(0       )
   __UpperCAmelCase  :     List[Any]		=    pipe(
       prompt=_UpperCAmelCase     ,     image=_UpperCAmelCase     ,     text_to_image_strength=0.7_5     ,     generator=_UpperCAmelCase     ,     guidance_scale=7.5     ,     num_inference_steps=50     ,     output_type='numpy'     ,     ).images

   __UpperCAmelCase  :     Tuple		=    image[0, 253:256, 253:256, -1]

   assert image.shape == (1, 512, 512, 3)
   __UpperCAmelCase  :     Tuple		=    np.array([0.0_7_8_7, 0.0_8_4_9, 0.0_8_2_6, 0.0_8_1_2, 0.0_8_0_7, 0.0_7_9_5, 0.0_8_1_8, 0.0_7_9_8, 0.0_7_7_9]       )

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





