




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					:       str    					=      False

class 		__lowerCAmelCase	(	unittest.TestCase ):
							pass







@nightly
@require_torch_gpu
class 		__lowerCAmelCase	(	unittest.TestCase ):
							def       lowerCamelCase__							(						self						:int				):







								'''simple docstring'''







								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()
							def       lowerCamelCase__							(						self						:Tuple				):







								'''simple docstring'''







								a										=							VersatileDiffusionTextToImagePipeline.from_pretrained("""shi-labs/versatile-diffusion"""				)
								# remove text_unet
								pipe.remove_unused_weights()
								pipe.to(__magic_name__				)
								pipe.set_progress_bar_config(disable=__magic_name__				)

								a										=							"""A painting of a squirrel eating a burger """
								a										=							torch.manual_seed(0				)
								a										=							pipe(
								    prompt=__magic_name__		,      generator=__magic_name__		,      guidance_scale=7.5		,      num_inference_steps=2		,      output_type="""numpy"""				).images

								with tempfile.TemporaryDirectory() as tmpdirname:
									pipe.save_pretrained(__magic_name__				)
									a										=							VersatileDiffusionTextToImagePipeline.from_pretrained(__magic_name__				)
								pipe.to(__magic_name__				)
								pipe.set_progress_bar_config(disable=__magic_name__				)

								a										=							generator.manual_seed(0				)
								a										=							pipe(
								    prompt=__magic_name__		,      generator=__magic_name__		,      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						:Optional[int]				):







								'''simple docstring'''







								a										=							VersatileDiffusionTextToImagePipeline.from_pretrained(
								    """shi-labs/versatile-diffusion"""		,      torch_dtype=torch.floataa				)
								pipe.to(__magic_name__				)
								pipe.set_progress_bar_config(disable=__magic_name__				)

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

								a										=							image[0, 253:256, 253:256, -1]

								assert image.shape == (1, 512, 512, 3)
								a										=							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







# 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


__UpperCamelCase					:       List[str]    					=      logging.get_logger(__name__)

__UpperCamelCase					:       Dict[Optional[str], Type[Formatter]]    					=      {}
__UpperCamelCase					:       Dict[Optional[str], str]    					=      {}
__UpperCamelCase					:       Dict[Optional[str], Exception]    					=      {}





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase = None							,			)      ->       Optional[int]:
	a										=							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__})'    )
	a										=							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})'    )
		a										=							format_type





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase = None    )      ->       List[str]:
	a										=							aliases if aliases is not None else []
	for alias in set(aliases + [format_type]    ):
		a										=							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:
		__UpperCamelCase					:       str    					=      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:
		__UpperCamelCase					:       List[str]    					=      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:
		__UpperCamelCase					:       List[str]    					=      ValueError("JAX needs to be installed to be able to return JAX arrays.")
		_register_unavailable_formatter(_jax_error, "jax", aliases=[])





def 		__A		(	__lowerCamelCase    )      ->       Optional[str]:
	if format_type in _FORMAT_TYPES_ALIASES:
		return _FORMAT_TYPES_ALIASES[format_type]
	else:
		return format_type





def 		__A		(	__lowerCamelCase							,			**__lowerCamelCase    )      ->       Formatter:
	a										=							get_format_type_from_alias(__lowerCamelCase    )
	if format_type in _FORMAT_TYPES:
		return _FORMAT_TYPES[format_type](**__lowerCamelCase    )
	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}\''    )

