



# Copyright 2021 The HuggingFace 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 warnings
from typing import List
from unittest.mock import Mock

import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType




class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					def __init__(			self    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				data



					def __iter__(			self     ):
							for element in self.data:
									yield element







def snake_case__  (			__SCREAMING_SNAKE_CASE=True )		->							Optional[int]:
		UpperCAmelCase_						=				Accelerator(even_batches=__SCREAMING_SNAKE_CASE )
		assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
		return accelerator







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE = False )		->							Any:
		if iterable:
				UpperCAmelCase_						=				DummyIterableDataset(torch.as_tensor(range(__SCREAMING_SNAKE_CASE ) ) )
		else:
				UpperCAmelCase_						=				TensorDataset(torch.as_tensor(range(__SCREAMING_SNAKE_CASE ) ) )

		UpperCAmelCase_						=				DataLoader(__SCREAMING_SNAKE_CASE			,     batch_size=__SCREAMING_SNAKE_CASE )
		UpperCAmelCase_						=				accelerator.prepare(__SCREAMING_SNAKE_CASE )

		return dl







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     )		->							Optional[Any]:
		UpperCAmelCase_						=				create_dataloader(accelerator=__SCREAMING_SNAKE_CASE			,     dataset_size=__SCREAMING_SNAKE_CASE			,     batch_size=__SCREAMING_SNAKE_CASE )

		UpperCAmelCase_						=				[len(batch[0] ) for batch in dl]

		if accelerator.process_index == 0:
				assert batch_sizes == process_0_expected_batch_sizes
		elif accelerator.process_index == 1:
				assert batch_sizes == process_1_expected_batch_sizes







def snake_case__  (			)		->							Optional[int]:
		UpperCAmelCase_						=				create_accelerator()

		# without padding, we would expect a different number of batches
		verify_dataloader_batch_sizes(
		    __SCREAMING_SNAKE_CASE			,     dataset_size=3			,     batch_size=1			,     process_0_expected_batch_sizes=[1, 1]			,     process_1_expected_batch_sizes=[1, 1]			,     )

		# without padding, we would expect the same number of batches, but different sizes
		verify_dataloader_batch_sizes(
		    __SCREAMING_SNAKE_CASE			,     dataset_size=7			,     batch_size=2			,     process_0_expected_batch_sizes=[2, 2]			,     process_1_expected_batch_sizes=[2, 2]			,     )







def snake_case__  (			)		->							Optional[int]:
		UpperCAmelCase_						=				create_accelerator(even_batches=__SCREAMING_SNAKE_CASE )

		verify_dataloader_batch_sizes(
		    __SCREAMING_SNAKE_CASE			,     dataset_size=3			,     batch_size=1			,     process_0_expected_batch_sizes=[1, 1]			,     process_1_expected_batch_sizes=[1]			,     )

		verify_dataloader_batch_sizes(
		    __SCREAMING_SNAKE_CASE			,     dataset_size=7			,     batch_size=2			,     process_0_expected_batch_sizes=[2, 2]			,     process_1_expected_batch_sizes=[2, 1]			,     )







def snake_case__  (			)		->							Dict:
		UpperCAmelCase_						=				create_accelerator(even_batches=__SCREAMING_SNAKE_CASE )

		UpperCAmelCase_						=				torch.nn.Linear(1			,     1 )
		UpperCAmelCase_						=				accelerator.prepare(__SCREAMING_SNAKE_CASE )

		UpperCAmelCase_						=				create_dataloader(__SCREAMING_SNAKE_CASE			,     dataset_size=3			,     batch_size=1 )

		UpperCAmelCase_						=				[]
		with accelerator.join_uneven_inputs([ddp_model] ):
				for batch_idx, batch in enumerate(__SCREAMING_SNAKE_CASE ):
						UpperCAmelCase_						=				ddp_model(batch[0].float() )
						UpperCAmelCase_						=				output.sum()
						loss.backward()
						batch_idxs.append(__SCREAMING_SNAKE_CASE )

		accelerator.wait_for_everyone()

		if accelerator.process_index == 0:
				assert batch_idxs == [0, 1]
		elif accelerator.process_index == 1:
				assert batch_idxs == [0]







def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							str:
		with warnings.catch_warnings(record=__SCREAMING_SNAKE_CASE ) as w:
				with accelerator.join_uneven_inputs([Mock()] ):
						pass

				assert issubclass(w[-1].category			,     __SCREAMING_SNAKE_CASE )
				assert "only supported for multi-GPU" in str(w[-1].message )







def snake_case__  (			)		->							Optional[Any]:
		UpperCAmelCase_						=				True
		UpperCAmelCase_						=				False
		UpperCAmelCase_						=				create_accelerator(even_batches=__SCREAMING_SNAKE_CASE )
		UpperCAmelCase_						=				torch.nn.Linear(1			,     1 )
		UpperCAmelCase_						=				accelerator.prepare(__SCREAMING_SNAKE_CASE )
		UpperCAmelCase_						=				create_dataloader(__SCREAMING_SNAKE_CASE			,     dataset_size=3			,     batch_size=1 )
		UpperCAmelCase_						=				create_dataloader(__SCREAMING_SNAKE_CASE			,     dataset_size=3			,     batch_size=1 )

		with accelerator.join_uneven_inputs([ddp_model]			,     even_batches=__SCREAMING_SNAKE_CASE ):
				UpperCAmelCase_						=				train_dl.batch_sampler.even_batches
				UpperCAmelCase_						=				valid_dl.batch_sampler.even_batches

		assert train_dl_overridden_value == overridden_even_batches
		assert valid_dl_overridden_value == overridden_even_batches
		assert train_dl.batch_sampler.even_batches == default_even_batches
		assert valid_dl.batch_sampler.even_batches == default_even_batches







def snake_case__  (			)		->							int:
		UpperCAmelCase_						=				True
		UpperCAmelCase_						=				False
		UpperCAmelCase_						=				create_accelerator(even_batches=__SCREAMING_SNAKE_CASE )
		UpperCAmelCase_						=				torch.nn.Linear(1			,     1 )
		UpperCAmelCase_						=				accelerator.prepare(__SCREAMING_SNAKE_CASE )
		create_dataloader(__SCREAMING_SNAKE_CASE			,     dataset_size=3			,     batch_size=1			,     iterable=__SCREAMING_SNAKE_CASE )
		UpperCAmelCase_						=				create_dataloader(__SCREAMING_SNAKE_CASE			,     dataset_size=3			,     batch_size=1 )

		with warnings.catch_warnings():
				warnings.filterwarnings("ignore" )
				try:
						with accelerator.join_uneven_inputs([ddp_model]			,     even_batches=__SCREAMING_SNAKE_CASE ):
								UpperCAmelCase_						=				batch_dl.batch_sampler.even_batches
				except AttributeError:
						# ensure attribute error is not raised when processing iterable dl
						raise AssertionError

		assert batch_dl_overridden_value == overridden_even_batches
		assert batch_dl.batch_sampler.even_batches == default_even_batches







def snake_case__  (			)		->							List[Any]:
		UpperCAmelCase_						=				create_accelerator()
		UpperCAmelCase_						=				torch.nn.Linear(1			,     1 )
		UpperCAmelCase_						=				accelerator.prepare(__SCREAMING_SNAKE_CASE )
		create_dataloader(__SCREAMING_SNAKE_CASE			,     dataset_size=3			,     batch_size=1			,     iterable=__SCREAMING_SNAKE_CASE )

		with warnings.catch_warnings(record=__SCREAMING_SNAKE_CASE ) as w:
				with accelerator.join_uneven_inputs([ddp_model]			,     even_batches=__SCREAMING_SNAKE_CASE ):
						pass

				assert issubclass(w[-1].category			,     __SCREAMING_SNAKE_CASE )
				assert "only supported for map-style datasets" in str(w[-1].message )







def snake_case__  (			)		->							Union[str, Any]:
		UpperCAmelCase_						=				create_accelerator()

		accelerator.print("Test that even_batches variable ensures uniform batches across processes" )
		test_default_ensures_even_batch_sizes()

		accelerator.print("Run tests with even_batches disabled" )
		test_can_disable_even_batches()

		accelerator.print("Test joining uneven inputs" )
		test_can_join_uneven_inputs()

		accelerator.print("Test overriding even_batches when joining uneven inputs" )
		test_join_can_override_even_batches()

		accelerator.print("Test overriding even_batches for mixed dataloader types" )
		test_join_can_override_for_mixed_type_dataloaders()

		accelerator.print("Test overriding even_batches raises a warning for iterable dataloaders" )
		test_join_raises_warning_for_iterable_when_overriding_even_batches()

		accelerator.print("Test join with non DDP distributed raises warning" )
		UpperCAmelCase_						=				accelerator.state.distributed_type
		UpperCAmelCase_						=				DistributedType.FSDP
		test_join_raises_warning_for_non_ddp_distributed(__SCREAMING_SNAKE_CASE )
		UpperCAmelCase_						=				original_state


if __name__ == "__main__":
			main()






import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    TextToVideoSDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



@skip_mps
class 	lowerCamelCase      (						lowercase__,     unittest.TestCase  ):







					'''simple docstring'''







					lowerCAmelCase_				:    int									=  TextToVideoSDPipeline
					lowerCAmelCase_				:    Dict									=  TEXT_TO_IMAGE_PARAMS
					lowerCAmelCase_				:    Optional[Any]									=  TEXT_TO_IMAGE_BATCH_PARAMS
					# No `output_type`.
					lowerCAmelCase_				:    Optional[Any]									=  frozenset(
					    [
					        'num_inference_steps',
					        'generator',
					        'latents',
					        'return_dict',
					        'callback',
					        'callback_steps',
					    ]  )



					def    A__		(			self     ):
							torch.manual_seed(0     )
							UpperCAmelCase_						=				UNetaDConditionModel(
							    block_out_channels=(32, 64, 64, 64)    ,	layers_per_block=2    ,	sample_size=32    ,	in_channels=4    ,	out_channels=4    ,	down_block_types=("CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "DownBlock3D")    ,	up_block_types=("UpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D")    ,	cross_attention_dim=32    ,	attention_head_dim=4    ,	)
							UpperCAmelCase_						=				DDIMScheduler(
							    beta_start=0.00085    ,	beta_end=0.012    ,	beta_schedule="scaled_linear"    ,	clip_sample=lowerCAmelCase    ,	set_alpha_to_one=lowerCAmelCase    ,	)
							torch.manual_seed(0     )
							UpperCAmelCase_						=				AutoencoderKL(
							    block_out_channels=[32, 64]    ,	in_channels=3    ,	out_channels=3    ,	down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]    ,	up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]    ,	latent_channels=4    ,	sample_size=128    ,	)
							torch.manual_seed(0     )
							UpperCAmelCase_						=				CLIPTextConfig(
							    bos_token_id=0    ,	eos_token_id=2    ,	hidden_size=32    ,	intermediate_size=37    ,	layer_norm_eps=1e-0_5    ,	num_attention_heads=4    ,	num_hidden_layers=5    ,	pad_token_id=1    ,	vocab_size=1000    ,	hidden_act="gelu"    ,	projection_dim=512    ,	)
							UpperCAmelCase_						=				CLIPTextModel(lowerCAmelCase     )
							UpperCAmelCase_						=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

							UpperCAmelCase_						=				{
							    "unet": unet,
							    "scheduler": scheduler,
							    "vae": vae,
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							}
							return components



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase=0     ):
							if str(lowerCAmelCase     ).startswith("mps"     ):
									UpperCAmelCase_						=				torch.manual_seed(lowerCAmelCase     )
							else:
									UpperCAmelCase_						=				torch.Generator(device=lowerCAmelCase     ).manual_seed(lowerCAmelCase     )
							UpperCAmelCase_						=				{
							    "prompt": "A painting of a squirrel eating a burger",
							    "generator": generator,
							    "num_inference_steps": 2,
							    "guidance_scale": 6.0,
							    "output_type": "pt",
							}
							return inputs



					def    A__		(			self     ):
							UpperCAmelCase_						=				"cpu"  # ensure determinism for the device-dependent torch.Generator
							UpperCAmelCase_						=				self.get_dummy_components()
							UpperCAmelCase_						=				TextToVideoSDPipeline(**lowerCAmelCase     )
							UpperCAmelCase_						=				sd_pipe.to(lowerCAmelCase     )
							sd_pipe.set_progress_bar_config(disable=lowerCAmelCase     )

							UpperCAmelCase_						=				self.get_dummy_inputs(lowerCAmelCase     )
							UpperCAmelCase_						=				"np"
							UpperCAmelCase_						=				sd_pipe(**lowerCAmelCase     ).frames
							UpperCAmelCase_						=				frames[0][-3:, -3:, -1]

							assert frames[0].shape == (64, 64, 3)
							UpperCAmelCase_						=				np.array([158.0, 160.0, 153.0, 125.0, 100.0, 121.0, 111.0, 93.0, 113.0]     )

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



					def    A__		(			self     ):
							self._test_attention_slicing_forward_pass(test_mean_pixel_difference=lowerCAmelCase    ,	expected_max_diff=3e-3     )



					@unittest.skipIf(
					    torch_device != "cuda" or not is_xformers_available()    ,	reason="XFormers attention is only available with CUDA and `xformers` installed"    ,	)
					def    A__		(			self     ):
							self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowerCAmelCase    ,	expected_max_diff=1e-2     )



					@unittest.skip(reason="Batching needs to be properly figured out first for this pipeline."     )
					def    A__		(			self     ):
							pass



					@unittest.skip(reason="Batching needs to be properly figured out first for this pipeline."     )
					def    A__		(			self     ):
							pass



					@unittest.skip(reason="`num_images_per_prompt` argument is not supported for this pipeline."     )
					def    A__		(			self     ):
							pass



					def    A__		(			self     ):
							return super().test_progress_bar()





@slow
@skip_mps
class 	lowerCamelCase      (						unittest.TestCase  ):







					'''simple docstring'''







					def    A__		(			self     ):
							UpperCAmelCase_						=				load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy"     )

							UpperCAmelCase_						=				TextToVideoSDPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b"     )
							UpperCAmelCase_						=				DPMSolverMultistepScheduler.from_config(pipe.scheduler.config     )
							UpperCAmelCase_						=				pipe.to("cuda"     )

							UpperCAmelCase_						=				"Spiderman is surfing"
							UpperCAmelCase_						=				torch.Generator(device="cpu"     ).manual_seed(0     )

							UpperCAmelCase_						=				pipe(lowerCAmelCase    ,	generator=lowerCAmelCase    ,	num_inference_steps=25    ,	output_type="pt"     ).frames
							UpperCAmelCase_						=				video_frames.cpu().numpy()

							assert np.abs(expected_video - video     ).mean() < 5e-2



					def    A__		(			self     ):
							UpperCAmelCase_						=				load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy"     )

							UpperCAmelCase_						=				TextToVideoSDPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b"     )
							UpperCAmelCase_						=				pipe.to("cuda"     )

							UpperCAmelCase_						=				"Spiderman is surfing"
							UpperCAmelCase_						=				torch.Generator(device="cpu"     ).manual_seed(0     )

							UpperCAmelCase_						=				pipe(lowerCAmelCase    ,	generator=lowerCAmelCase    ,	num_inference_steps=2    ,	output_type="pt"     ).frames
							UpperCAmelCase_						=				video_frames.cpu().numpy()

							assert np.abs(expected_video - video     ).mean() < 5e-2

