



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   _A   ( UpperCAmelCase_  ,						unittest.TestCase	):


						'''simple docstring'''





						_snake_case			:     Union[str, Any]      							=      TextToVideoSDPipeline
						_snake_case			:     str      							=      TEXT_TO_IMAGE_PARAMS
						_snake_case			:     str      							=      TEXT_TO_IMAGE_BATCH_PARAMS
						# No `output_type`.
						_snake_case			:     Tuple      							=      frozenset(
						    [
						        """num_inference_steps""",
						        """generator""",
						        """latents""",
						        """return_dict""",
						        """callback""",
						        """callback_steps""",
						    ]	)



						def     _snake_case	(	self	:							Optional[Any]	):






							'''simple docstring'''



							torch.manual_seed(0	)
							__lowercase		       =			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							,      )
							__lowercase		       =			DDIMScheduler(
							    beta_start=0.0_0085							,      beta_end=0.012							,      beta_schedule="scaled_linear"							,      clip_sample=_lowercase							,      set_alpha_to_one=_lowercase							,      )
							torch.manual_seed(0	)
							__lowercase		       =			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	)
							__lowercase		       =			CLIPTextConfig(
							    bos_token_id=0							,      eos_token_id=2							,      hidden_size=32							,      intermediate_size=37							,      layer_norm_eps=1e-05							,      num_attention_heads=4							,      num_hidden_layers=5							,      pad_token_id=1							,      vocab_size=1_000							,      hidden_act="gelu"							,      projection_dim=512							,      )
							__lowercase		       =			CLIPTextModel(_lowercase	)
							__lowercase		       =			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

							__lowercase		       =			{
							    'unet': unet,
							    'scheduler': scheduler,
							    'vae': vae,
							    'text_encoder': text_encoder,
							    'tokenizer': tokenizer,
							}
							return components



						def     _snake_case	(	self	:							int							,      lowerCamelCase	:							List[str]							,      lowerCamelCase	:							int=0	):






							'''simple docstring'''



							if str(_lowercase	).startswith("mps"	):
								__lowercase		       =			torch.manual_seed(_lowercase	)
							else:
								__lowercase		       =			torch.Generator(device=_lowercase	).manual_seed(_lowercase	)
							__lowercase		       =			{
							    '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     _snake_case	(	self	:							int	):






							'''simple docstring'''



							__lowercase		       =			'cpu'  # ensure determinism for the device-dependent torch.Generator
							__lowercase		       =			self.get_dummy_components()
							__lowercase		       =			TextToVideoSDPipeline(**_lowercase	)
							__lowercase		       =			sd_pipe.to(_lowercase	)
							sd_pipe.set_progress_bar_config(disable=_lowercase	)

							__lowercase		       =			self.get_dummy_inputs(_lowercase	)
							__lowercase		       =			'np'
							__lowercase		       =			sd_pipe(**_lowercase	).frames
							__lowercase		       =			frames[0][-3:, -3:, -1]

							assert frames[0].shape == (64, 64, 3)
							__lowercase		       =			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     _snake_case	(	self	:							Any	):






							'''simple docstring'''



							self._test_attention_slicing_forward_pass(test_mean_pixel_difference=_lowercase							,      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     _snake_case	(	self	:							List[str]	):






							'''simple docstring'''



							self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=_lowercase							,      expected_max_diff=1e-2	)



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






							'''simple docstring'''



							pass



						@unittest.skip(reason="Batching needs to be properly figured out first for this pipeline."	)
						def     _snake_case	(	self	:							Union[str, Any]	):






							'''simple docstring'''



							pass



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






							'''simple docstring'''



							pass



						def     _snake_case	(	self	:							List[str]	):






							'''simple docstring'''



							return super().test_progress_bar()

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


						'''simple docstring'''





						def     _snake_case	(	self	:							Any	):






							'''simple docstring'''



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

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

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

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

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



						def     _snake_case	(	self	:							Optional[Any]	):






							'''simple docstring'''



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

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

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

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

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









import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
snake_case__					:     Optional[Any]    		=   logging.getLogger()



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			"\n".join(_SCREAMING_SNAKE_CASE   )
	Path(_SCREAMING_SNAKE_CASE   ).open("w"   ).writelines(_SCREAMING_SNAKE_CASE   )


snake_case__					:     List[str]    		=   """patrickvonplaten/t5-tiny-random"""
snake_case__					:     int    		=   """sshleifer/bart-tiny-random"""
snake_case__					:     Union[str, Any]    		=   """sshleifer/tiny-mbart"""

snake_case__					:     List[str]    		=   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks

class   _A   ( _lowercase	):


						'''simple docstring'''





						def     _snake_case	(	self	:							str							,      lowerCamelCase	:							Optional[int]	):






							'''simple docstring'''



							__lowercase		       =			Path(self.get_auto_remove_tmp_dir()	) / "utest_input.source"
							__lowercase		       =			input_file_name.parent / "utest_output.txt"
							assert not output_file_name.exists()
							__lowercase		       =			[" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
							_dump_articles(lowerCamelCase							,      lowerCamelCase	)

							__lowercase		       =			str(Path(self.get_auto_remove_tmp_dir()	) / "scores.json"	)
							__lowercase		       =			"translation_en_to_de" if model == T5_TINY else "summarization"
							__lowercase		       =			f"""
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            """.split()

							with patch.object(lowerCamelCase							,      "argv"							,      lowerCamelCase	):
								run_generate()
								assert Path(lowerCamelCase	).exists()
								# os.remove(Path(output_file_name))



						def     _snake_case	(	self	:							Dict	):






							'''simple docstring'''



							self.run_eval_tester(lowerCamelCase	)



						@parameterized.expand([BART_TINY, MBART_TINY]	)
						@slow
						def     _snake_case	(	self	:							Optional[Any]							,      lowerCamelCase	:							str	):






							'''simple docstring'''



							self.run_eval_tester(lowerCamelCase	)



						@parameterized.expand([T5_TINY, MBART_TINY]	)
						@slow
						def     _snake_case	(	self	:							Optional[Any]							,      lowerCamelCase	:							Optional[int]	):






							'''simple docstring'''



							__lowercase		       =			Path(self.get_auto_remove_tmp_dir()	) / "utest_input.source"
							__lowercase		       =			input_file_name.parent / "utest_output.txt"
							assert not output_file_name.exists()

							__lowercase		       =			{
							    "en": ["Machine learning is great, isn't it?", "I like to eat bananas", "Tomorrow is another great day!"],
							    "de": [
							        "Maschinelles Lernen ist großartig, oder?",
							        "Ich esse gerne Bananen",
							        "Morgen ist wieder ein toller Tag!",
							    ],
							}

							__lowercase		       =			Path(self.get_auto_remove_tmp_dir()	)
							__lowercase		       =			str(tmp_dir / "scores.json"	)
							__lowercase		       =			str(tmp_dir / "val.target"	)
							_dump_articles(lowerCamelCase							,      text["en"]	)
							_dump_articles(lowerCamelCase							,      text["de"]	)
							__lowercase		       =			"translation_en_to_de" if model == T5_TINY else "summarization"
							__lowercase		       =			f"""
            run_eval_search.py
            {model}
            {str(lowerCamelCase	)}
            {str(lowerCamelCase	)}
            --score_path {score_path}
            --reference_path {reference_path}
            --task {task}
            """.split()
							testargs.extend(["--search", "num_beams=1:2 length_penalty=0.9:1.0"]	)

							with patch.object(lowerCamelCase							,      "argv"							,      lowerCamelCase	):
								with CaptureStdout() as cs:
									run_search()
								__lowercase		       =			[" num_beams | length_penalty", model, "Best score args"]
								__lowercase		       =			["Info"]
								if "translation" in task:
									expected_strings.append("bleu"	)
								else:
									expected_strings.extend(lowerCamelCase	)
								for w in expected_strings:
									assert w in cs.out
								for w in un_expected_strings:
									assert w not in cs.out
								assert Path(lowerCamelCase	).exists()
								os.remove(Path(lowerCamelCase	)	)



