
'''simple docstring'''
import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()






class 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE    ,					unittest.TestCase					):







		"""simple docstring"""

		lowerCamelCase     							=						AutoencoderKL
		lowerCamelCase     							=						"""sample"""
		lowerCamelCase     							=						1E-2



		@property
		def       lowerCAmelCase				(      self				:			List[str]      )						->			Tuple:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						4
				snake_case  :       Optional[Any]     			=						3
				snake_case  :       Optional[int]     			=						(32, 32)

				snake_case  :       str     			=						floats_tensor((batch_size, num_channels) + sizes      ).to(UpperCamelCase__      )

				return {"sample": image}



		@property
		def       lowerCAmelCase				(      self				:			Dict      )						->			Dict:






				"""simple docstring"""







				return (3, 32, 32)



		@property
		def       lowerCAmelCase				(      self				:			Any      )						->			Union[str, Any]:






				"""simple docstring"""







				return (3, 32, 32)



		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			List[str]:






				"""simple docstring"""







				snake_case  :       Optional[int]     			=						{
				    '''block_out_channels''': [32, 64],
				    '''in_channels''': 3,
				    '''out_channels''': 3,
				    '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
				    '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
				    '''latent_channels''': 4,
				}
				snake_case  :       str     			=						self.dummy_input
				return init_dict, inputs_dict



		def       lowerCAmelCase				(      self				:			Union[str, Any]      )						->			List[str]:






				"""simple docstring"""







				pass



		def       lowerCAmelCase				(      self				:			List[Any]      )						->			Any:






				"""simple docstring"""







				pass



		@unittest.skipIf(torch_device == '''mps'''  ,	'''Gradient checkpointing skipped on MPS'''      )
		def       lowerCAmelCase				(      self				:			int      )						->			Optional[Any]:






				"""simple docstring"""







				snake_case			,snake_case  :       str     			=						self.prepare_init_args_and_inputs_for_common()
				snake_case  :       Optional[Any]     			=						self.model_class(**UpperCamelCase__      )
				model.to(UpperCamelCase__      )

				assert not model.is_gradient_checkpointing and model.training

				snake_case  :       Union[str, Any]     			=						model(**UpperCamelCase__      ).sample
				# run the backwards pass on the model. For backwards pass, for simplicity purpose,
				# we won't calculate the loss and rather backprop on out.sum()
				model.zero_grad()

				snake_case  :       Tuple     			=						torch.randn_like(UpperCamelCase__      )
				snake_case  :       Any     			=						(out - labels).mean()
				loss.backward()

				# re-instantiate the model now enabling gradient checkpointing
				snake_case  :       List[Any]     			=						self.model_class(**UpperCamelCase__      )
				# clone model
				model_a.load_state_dict(model.state_dict()      )
				model_a.to(UpperCamelCase__      )
				model_a.enable_gradient_checkpointing()

				assert model_a.is_gradient_checkpointing and model_a.training

				snake_case  :       Any     			=						model_a(**UpperCamelCase__      ).sample
				# run the backwards pass on the model. For backwards pass, for simplicity purpose,
				# we won't calculate the loss and rather backprop on out.sum()
				model_a.zero_grad()
				snake_case  :       Tuple     			=						(out_a - labels).mean()
				loss_a.backward()

				# compare the output and parameters gradients
				self.assertTrue((loss - loss_a).abs() < 1e-5      )
				snake_case  :       Dict     			=						dict(model.named_parameters()      )
				snake_case  :       str     			=						dict(model_a.named_parameters()      )
				for name, param in named_params.items():
						self.assertTrue(torch_all_close(param.grad.data  ,	named_params_a[name].grad.data  ,	atol=5e-5      )      )



		def       lowerCAmelCase				(      self				:			int      )						->			List[Any]:






				"""simple docstring"""







				snake_case			,snake_case  :       int     			=						AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''  ,	output_loading_info=UpperCamelCase__      )
				self.assertIsNotNone(UpperCamelCase__      )
				self.assertEqual(len(loading_info['''missing_keys''']      )  ,	0      )

				model.to(UpperCamelCase__      )
				snake_case  :       List[str]     			=						model(**self.dummy_input      )

				assert image is not None, "Make sure output is not None"



		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			str:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''      )
				snake_case  :       List[Any]     			=						model.to(UpperCamelCase__      )
				model.eval()

				if torch_device == "mps":
						snake_case  :       List[Any]     			=						torch.manual_seed(0      )
				else:
						snake_case  :       List[str]     			=						torch.Generator(device=UpperCamelCase__      ).manual_seed(0      )

				snake_case  :       Optional[Any]     			=						torch.randn(
				    1  ,	model.config.in_channels  ,	model.config.sample_size  ,	model.config.sample_size  ,	generator=torch.manual_seed(0      )  ,	)
				snake_case  :       Tuple     			=						image.to(UpperCamelCase__      )
				with torch.no_grad():
						snake_case  :       List[str]     			=						model(UpperCamelCase__  ,	sample_posterior=UpperCamelCase__  ,	generator=UpperCamelCase__      ).sample

				snake_case  :       int     			=						output[0, -1, -3:, -3:].flatten().cpu()

				# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
				# the expected output slices are not the same for CPU and GPU.
				if torch_device == "mps":
						snake_case  :       List[str]     			=						torch.tensor(
						    [
						        -4.0_0_7_8e-0_1,
						        -3.8_3_2_3e-0_4,
						        -1.2_6_8_1e-0_1,
						        -1.1_4_6_2e-0_1,
						        2.0_0_9_5e-0_1,
						        1.0_8_9_3e-0_1,
						        -8.8_2_4_7e-0_2,
						        -3.0_3_6_1e-0_1,
						        -9.8_6_4_4e-0_3,
						    ]      )
				elif torch_device == "cpu":
						snake_case  :       Tuple     			=						torch.tensor(
						    [-0.1_352, 0.0_878, 0.0_419, -0.0_818, -0.1_069, 0.0_688, -0.1_458, -0.4_446, -0.0_026]      )
				else:
						snake_case  :       str     			=						torch.tensor(
						    [-0.2_421, 0.4_642, 0.2_507, -0.0_438, 0.0_682, 0.3_160, -0.2_018, -0.0_727, 0.2_485]      )

				self.assertTrue(torch_all_close(UpperCamelCase__  ,	UpperCamelCase__  ,	rtol=1e-2      )      )







@slow
class 	snake_case__       (      unittest.TestCase					):







		"""simple docstring"""

		def       lowerCAmelCase				(      self				:			Tuple  ,	UpperCamelCase__				:			Optional[Any]  ,	UpperCamelCase__				:			Optional[int]      )						->			int:






				"""simple docstring"""







				return f'gaussian_noise_s={seed}_shape={"_".join([str(UpperCamelCase__      ) for s in shape]      )}.npy'



		def       lowerCAmelCase				(      self				:			Any      )						->			Tuple:






				"""simple docstring"""







				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()



		def       lowerCAmelCase				(      self				:			int  ,	UpperCamelCase__				:			Tuple=0  ,	UpperCamelCase__				:			int=(4, 3, 512, 512)  ,	UpperCamelCase__				:			Tuple=False      )						->			Dict:






				"""simple docstring"""







				snake_case  :       Any     			=						torch.floataa if fpaa else torch.floataa
				snake_case  :       Any     			=						torch.from_numpy(load_hf_numpy(self.get_file_format(UpperCamelCase__  ,	UpperCamelCase__      )      )      ).to(UpperCamelCase__      ).to(UpperCamelCase__      )
				return image



		def       lowerCAmelCase				(      self				:			int  ,	UpperCamelCase__				:			Dict="CompVis/stable-diffusion-v1-4"  ,	UpperCamelCase__				:			int=False      )						->			str:






				"""simple docstring"""







				snake_case  :       int     			=						'''fp16''' if fpaa else None
				snake_case  :       Optional[int]     			=						torch.floataa if fpaa else torch.floataa

				snake_case  :       List[str]     			=						AutoencoderKL.from_pretrained(
				    UpperCamelCase__  ,	subfolder='''vae'''  ,	torch_dtype=UpperCamelCase__  ,	revision=UpperCamelCase__  ,	)
				model.to(UpperCamelCase__      ).eval()

				return model



		def       lowerCAmelCase				(      self				:			List[Any]  ,	UpperCamelCase__				:			Optional[int]=0      )						->			str:






				"""simple docstring"""







				if torch_device == "mps":
						return torch.manual_seed(UpperCamelCase__      )
				return torch.Generator(device=UpperCamelCase__      ).manual_seed(UpperCamelCase__      )



		@parameterized.expand(
		    [
		        # fmt: off
		        [33, [-0.1_603, 0.9_878, -0.0_495, -0.0_790, -0.2_709, 0.8_375, -0.2_060, -0.0_824], [-0.2_395, 0.0_098, 0.0_102, -0.0_709, -0.2_840, -0.0_274, -0.0_718, -0.1_824]],
		        [47, [-0.2_376, 0.1_168, 0.1_332, -0.4_840, -0.2_508, -0.0_791, -0.0_493, -0.4_089], [0.0_350, 0.0_847, 0.0_467, 0.0_344, -0.0_842, -0.0_547, -0.0_633, -0.1_131]],
		        # fmt: on
		    ]      )
		def       lowerCAmelCase				(      self				:			Optional[int]  ,	UpperCamelCase__				:			Optional[Any]  ,	UpperCamelCase__				:			int  ,	UpperCamelCase__				:			Union[str, Any]      )						->			int:






				"""simple docstring"""







				snake_case  :       Any     			=						self.get_sd_vae_model()
				snake_case  :       List[Any]     			=						self.get_sd_image(UpperCamelCase__      )
				snake_case  :       Optional[Any]     			=						self.get_generator(UpperCamelCase__      )

				with torch.no_grad():
						snake_case  :       List[str]     			=						model(UpperCamelCase__  ,	generator=UpperCamelCase__  ,	sample_posterior=UpperCamelCase__      ).sample

				assert sample.shape == image.shape

				snake_case  :       Any     			=						sample[-1, -2:, -2:, :2].flatten().float().cpu()
				snake_case  :       Union[str, Any]     			=						torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice      )

				assert torch_all_close(UpperCamelCase__  ,	UpperCamelCase__  ,	atol=3e-3      )



		@parameterized.expand(
		    [
		        # fmt: off
		        [33, [-0.0_513, 0.0_289, 1.3_799, 0.2_166, -0.2_573, -0.0_871, 0.5_103, -0.0_999]],
		        [47, [-0.4_128, -0.1_320, -0.3_704, 0.1_965, -0.4_116, -0.2_332, -0.3_340, 0.2_247]],
		        # fmt: on
		    ]      )
		@require_torch_gpu
		def       lowerCAmelCase				(      self				:			Optional[Any]  ,	UpperCamelCase__				:			Optional[Any]  ,	UpperCamelCase__				:			Union[str, Any]      )						->			Tuple:






				"""simple docstring"""







				snake_case  :       str     			=						self.get_sd_vae_model(fpaa=UpperCamelCase__      )
				snake_case  :       Union[str, Any]     			=						self.get_sd_image(UpperCamelCase__  ,	fpaa=UpperCamelCase__      )
				snake_case  :       int     			=						self.get_generator(UpperCamelCase__      )

				with torch.no_grad():
						snake_case  :       List[str]     			=						model(UpperCamelCase__  ,	generator=UpperCamelCase__  ,	sample_posterior=UpperCamelCase__      ).sample

				assert sample.shape == image.shape

				snake_case  :       Tuple     			=						sample[-1, -2:, :2, -2:].flatten().float().cpu()
				snake_case  :       str     			=						torch.tensor(UpperCamelCase__      )

				assert torch_all_close(UpperCamelCase__  ,	UpperCamelCase__  ,	atol=1e-2      )



		@parameterized.expand(
		    [
		        # fmt: off
		        [33, [-0.1_609, 0.9_866, -0.0_487, -0.0_777, -0.2_716, 0.8_368, -0.2_055, -0.0_814], [-0.2_395, 0.0_098, 0.0_102, -0.0_709, -0.2_840, -0.0_274, -0.0_718, -0.1_824]],
		        [47, [-0.2_377, 0.1_147, 0.1_333, -0.4_841, -0.2_506, -0.0_805, -0.0_491, -0.4_085], [0.0_350, 0.0_847, 0.0_467, 0.0_344, -0.0_842, -0.0_547, -0.0_633, -0.1_131]],
		        # fmt: on
		    ]      )
		def       lowerCAmelCase				(      self				:			Tuple  ,	UpperCamelCase__				:			Optional[int]  ,	UpperCamelCase__				:			List[Any]  ,	UpperCamelCase__				:			List[str]      )						->			List[str]:






				"""simple docstring"""







				snake_case  :       Any     			=						self.get_sd_vae_model()
				snake_case  :       Union[str, Any]     			=						self.get_sd_image(UpperCamelCase__      )

				with torch.no_grad():
						snake_case  :       Optional[int]     			=						model(UpperCamelCase__      ).sample

				assert sample.shape == image.shape

				snake_case  :       int     			=						sample[-1, -2:, -2:, :2].flatten().float().cpu()
				snake_case  :       int     			=						torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice      )

				assert torch_all_close(UpperCamelCase__  ,	UpperCamelCase__  ,	atol=3e-3      )



		@parameterized.expand(
		    [
		        # fmt: off
		        [13, [-0.2_051, -0.1_803, -0.2_311, -0.2_114, -0.3_292, -0.3_574, -0.2_953, -0.3_323]],
		        [37, [-0.2_632, -0.2_625, -0.2_199, -0.2_741, -0.4_539, -0.4_990, -0.3_720, -0.4_925]],
		        # fmt: on
		    ]      )
		@require_torch_gpu
		def       lowerCAmelCase				(      self				:			int  ,	UpperCamelCase__				:			List[str]  ,	UpperCamelCase__				:			Union[str, Any]      )						->			int:






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						self.get_sd_vae_model()
				snake_case  :       Optional[int]     			=						self.get_sd_image(UpperCamelCase__  ,	shape=(3, 4, 64, 64)      )

				with torch.no_grad():
						snake_case  :       Tuple     			=						model.decode(UpperCamelCase__      ).sample

				assert list(sample.shape      ) == [3, 3, 512, 512]

				snake_case  :       Optional[int]     			=						sample[-1, -2:, :2, -2:].flatten().cpu()
				snake_case  :       Any     			=						torch.tensor(UpperCamelCase__      )

				assert torch_all_close(UpperCamelCase__  ,	UpperCamelCase__  ,	atol=1e-3      )



		@parameterized.expand(
		    [
		        # fmt: off
		        [27, [-0.0_369, 0.0_207, -0.0_776, -0.0_682, -0.1_747, -0.1_930, -0.1_465, -0.2_039]],
		        [16, [-0.1_628, -0.2_134, -0.2_747, -0.2_642, -0.3_774, -0.4_404, -0.3_687, -0.4_277]],
		        # fmt: on
		    ]      )
		@require_torch_gpu
		def       lowerCAmelCase				(      self				:			Optional[Any]  ,	UpperCamelCase__				:			Optional[int]  ,	UpperCamelCase__				:			Any      )						->			Tuple:






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						self.get_sd_vae_model(fpaa=UpperCamelCase__      )
				snake_case  :       List[Any]     			=						self.get_sd_image(UpperCamelCase__  ,	shape=(3, 4, 64, 64)  ,	fpaa=UpperCamelCase__      )

				with torch.no_grad():
						snake_case  :       Optional[int]     			=						model.decode(UpperCamelCase__      ).sample

				assert list(sample.shape      ) == [3, 3, 512, 512]

				snake_case  :       Optional[Any]     			=						sample[-1, -2:, :2, -2:].flatten().float().cpu()
				snake_case  :       Tuple     			=						torch.tensor(UpperCamelCase__      )

				assert torch_all_close(UpperCamelCase__  ,	UpperCamelCase__  ,	atol=5e-3      )



		@parameterized.expand([(13,), (16,), (27,)]      )
		@require_torch_gpu
		@unittest.skipIf(not is_xformers_available()  ,	reason='''xformers is not required when using PyTorch 2.0.'''      )
		def       lowerCAmelCase				(      self				:			Optional[Any]  ,	UpperCamelCase__				:			Any      )						->			List[Any]:






				"""simple docstring"""







				snake_case  :       Dict     			=						self.get_sd_vae_model(fpaa=UpperCamelCase__      )
				snake_case  :       Optional[Any]     			=						self.get_sd_image(UpperCamelCase__  ,	shape=(3, 4, 64, 64)  ,	fpaa=UpperCamelCase__      )

				with torch.no_grad():
						snake_case  :       Dict     			=						model.decode(UpperCamelCase__      ).sample

				model.enable_xformers_memory_efficient_attention()
				with torch.no_grad():
						snake_case  :       Dict     			=						model.decode(UpperCamelCase__      ).sample

				assert list(sample.shape      ) == [3, 3, 512, 512]

				assert torch_all_close(UpperCamelCase__  ,	UpperCamelCase__  ,	atol=1e-1      )



		@parameterized.expand([(13,), (16,), (37,)]      )
		@require_torch_gpu
		@unittest.skipIf(not is_xformers_available()  ,	reason='''xformers is not required when using PyTorch 2.0.'''      )
		def       lowerCAmelCase				(      self				:			Optional[Any]  ,	UpperCamelCase__				:			List[str]      )						->			int:






				"""simple docstring"""







				snake_case  :       Tuple     			=						self.get_sd_vae_model()
				snake_case  :       Optional[int]     			=						self.get_sd_image(UpperCamelCase__  ,	shape=(3, 4, 64, 64)      )

				with torch.no_grad():
						snake_case  :       List[Any]     			=						model.decode(UpperCamelCase__      ).sample

				model.enable_xformers_memory_efficient_attention()
				with torch.no_grad():
						snake_case  :       List[Any]     			=						model.decode(UpperCamelCase__      ).sample

				assert list(sample.shape      ) == [3, 3, 512, 512]

				assert torch_all_close(UpperCamelCase__  ,	UpperCamelCase__  ,	atol=1e-2      )



		@parameterized.expand(
		    [
		        # fmt: off
		        [33, [-0.3_001, 0.0_918, -2.6_984, -3.9_720, -3.2_099, -5.0_353, 1.7_338, -0.2_065, 3.4_267]],
		        [47, [-1.5_030, -4.3_871, -6.0_355, -9.1_157, -1.6_661, -2.7_853, 2.1_607, -5.0_823, 2.5_633]],
		        # fmt: on
		    ]      )
		def       lowerCAmelCase				(      self				:			Dict  ,	UpperCamelCase__				:			int  ,	UpperCamelCase__				:			int      )						->			str:






				"""simple docstring"""







				snake_case  :       int     			=						self.get_sd_vae_model()
				snake_case  :       Optional[int]     			=						self.get_sd_image(UpperCamelCase__      )
				snake_case  :       str     			=						self.get_generator(UpperCamelCase__      )

				with torch.no_grad():
						snake_case  :       Union[str, Any]     			=						model.encode(UpperCamelCase__      ).latent_dist
						snake_case  :       int     			=						dist.sample(generator=UpperCamelCase__      )

				assert list(sample.shape      ) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

				snake_case  :       List[str]     			=						sample[0, -1, -3:, -3:].flatten().cpu()
				snake_case  :       List[str]     			=						torch.tensor(UpperCamelCase__      )

				snake_case  :       List[Any]     			=						3e-3 if torch_device != '''mps''' else 1e-2
				assert torch_all_close(UpperCamelCase__  ,	UpperCamelCase__  ,	atol=UpperCamelCase__      )




'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..utils import cached_file


# docstyle-ignore
lowercase__						       =      "\nHuman: <<task>>\n\nAssistant: "


lowercase__						       =      "huggingface-tools/default-prompts"
lowercase__						       =      {"chat": "chat_prompt_template.txt", "run": "run_prompt_template.txt"}




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__="run"			)  ->							Dict:


		'''simple docstring'''


		if prompt_or_repo_id is None:
				snake_case  :       Union[str, Any]     			=						DEFAULT_PROMPTS_REPO

		# prompt is considered a repo ID when it does not contain any kind of space
		if re.search('''\\s''' ,       SCREAMING_SNAKE_CASE__			) is not None:
				return prompt_or_repo_id

		snake_case  :       List[Any]     			=						cached_file(
		    SCREAMING_SNAKE_CASE__ ,       PROMPT_FILES[mode] ,       repo_type='''dataset''' ,       user_agent={'''agent''': agent_name}			)
		with open(SCREAMING_SNAKE_CASE__ ,       '''r''' ,       encoding='''utf-8'''			) as f:
				return f.read()


