
"""simple docstring"""







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


lowerCamelCase__        =		False




class 			A__	(     unittest.TestCase):
		pass




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

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

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[str]						      =				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			)

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

			__lowerCAmelCase      :						Tuple						      =				torch.manual_seed(0			)
			__lowerCAmelCase      :						Optional[Any]						      =				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			)
				__lowerCAmelCase      :						Any						      =				VersatileDiffusionPipeline.from_pretrained(_SCREAMING_SNAKE_CASE	,		torch_dtype=torch.floataa			)
			pipe.to(_SCREAMING_SNAKE_CASE			)
			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						Dict						      =				generator.manual_seed(0			)
			__lowerCAmelCase      :						str						      =				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 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						int						      =				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			)

			__lowerCAmelCase      :						Optional[int]						      =				'cyberpunk 2077'
			__lowerCAmelCase      :						Optional[Any]						      =				load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'			)
			__lowerCAmelCase      :						Tuple						      =				torch.manual_seed(0			)
			__lowerCAmelCase      :						Optional[Any]						      =				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

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

			assert image.shape == (1, 5_12, 5_12, 3)
			__lowerCAmelCase      :						Optional[Any]						      =				np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001]			)

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

			__lowerCAmelCase      :						List[Any]						      =				'A painting of a squirrel eating a burger '
			__lowerCAmelCase      :						Tuple						      =				torch.manual_seed(0			)
			__lowerCAmelCase      :						Optional[Any]						      =				pipe.text_to_image(
			    prompt=_SCREAMING_SNAKE_CASE	,		generator=_SCREAMING_SNAKE_CASE	,		guidance_scale=7.5	,		num_inference_steps=50	,		output_type='numpy'			).images

			__lowerCAmelCase      :						List[str]						      =				image[0, 2_53:2_56, 2_53:2_56, -1]

			assert image.shape == (1, 5_12, 5_12, 3)
			__lowerCAmelCase      :						Union[str, 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-1

			__lowerCAmelCase      :						Optional[Any]						      =				pipe.image_variation(_SCREAMING_SNAKE_CASE	,		generator=_SCREAMING_SNAKE_CASE	,		output_type='numpy'			).images

			__lowerCAmelCase      :						Optional[int]						      =				image[0, 2_53:2_56, 2_53:2_56, -1]

			assert image.shape == (1, 5_12, 5_12, 3)
			__lowerCAmelCase      :						Dict						      =				np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456]			)

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

"""simple docstring"""







# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


lowerCamelCase__        =		logging.get_logger(__name__)

lowerCamelCase__        =		{}
lowerCamelCase__        =		{}
lowerCamelCase__        =		{}
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase = None	,  ):
	__lowerCAmelCase      :						Union[str, Any]						      =				aliases if aliases is not None else []
	if format_type in _FORMAT_TYPES:
		logger.warning(
		    F"Overwriting format type '{format_type}' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})"		)
	__lowerCAmelCase      :						str						      =				formatter_cls
	for alias in set(aliases + [format_type]		):
		if alias in _FORMAT_TYPES_ALIASES:
			logger.warning(
			    F"Overwriting format type alias '{alias}' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})"		)
		__lowerCAmelCase      :						Any						      =				format_type
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase = None		):
	__lowerCAmelCase      :						int						      =				aliases if aliases is not None else []
	for alias in set(aliases + [format_type]		):
		__lowerCAmelCase      :						str						      =				unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=["""python"""])
_register_formatter(ArrowFormatter, """arrow""", aliases=["""pa""", """pyarrow"""])
_register_formatter(NumpyFormatter, """numpy""", aliases=["""np"""])
_register_formatter(PandasFormatter, """pandas""", aliases=["""pd"""])
_register_formatter(CustomFormatter, """custom""")

if config.TORCH_AVAILABLE:
							from .torch_formatter import TorchFormatter

							_register_formatter(TorchFormatter, """torch""", aliases=["""pt""", """pytorch"""])
else:
							lowerCamelCase__        =		ValueError("""PyTorch needs to be installed to be able to return PyTorch tensors.""")
							_register_unavailable_formatter(_torch_error, """torch""", aliases=["""pt""", """pytorch"""])

if config.TF_AVAILABLE:
							from .tf_formatter import TFFormatter

							_register_formatter(TFFormatter, """tensorflow""", aliases=["""tf"""])
else:
							lowerCamelCase__        =		ValueError("""Tensorflow needs to be installed to be able to return Tensorflow tensors.""")
							_register_unavailable_formatter(_tf_error, """tensorflow""", aliases=["""tf"""])

if config.JAX_AVAILABLE:
							from .jax_formatter import JaxFormatter

							_register_formatter(JaxFormatter, """jax""", aliases=[])
else:
							lowerCamelCase__        =		ValueError("""JAX needs to be installed to be able to return JAX arrays.""")
							_register_unavailable_formatter(_jax_error, """jax""", aliases=[])
def    __lowerCAmelCase    (_UpperCamelCase		):
	if format_type in _FORMAT_TYPES_ALIASES:
		return _FORMAT_TYPES_ALIASES[format_type]
	else:
		return format_type
def    __lowerCAmelCase    (_UpperCamelCase	,  **_UpperCamelCase		):
	__lowerCAmelCase      :						Any						      =				get_format_type_from_alias(_UpperCamelCase		)
	if format_type in _FORMAT_TYPES:
		return _FORMAT_TYPES[format_type](**_UpperCamelCase		)
	if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
		raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
	else:
		raise ValueError(
		    F"Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None		)}, but got '{format_type}'"		)