



import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np

class   _A   :


						'''simple docstring'''





						def     _snake_case	(	self	:							Union[str, Any]	):






							'''simple docstring'''



							torch.manual_seed(0	)
							__lowercase		       =			TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"	)

							torch.manual_seed(0	)
							__lowercase		       =			AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"	)

							torch.manual_seed(0	)
							__lowercase		       =			UNetaDConditionModel(
							    sample_size=32							,      layers_per_block=1							,      block_out_channels=[32, 64]							,      down_block_types=[
							        "ResnetDownsampleBlock2D",
							        "SimpleCrossAttnDownBlock2D",
							    ]							,      mid_block_type="UNetMidBlock2DSimpleCrossAttn"							,      up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]							,      in_channels=3							,      out_channels=6							,      cross_attention_dim=32							,      encoder_hid_dim=32							,      attention_head_dim=8							,      addition_embed_type="text"							,      addition_embed_type_num_heads=2							,      cross_attention_norm="group_norm"							,      resnet_time_scale_shift="scale_shift"							,      act_fn="gelu"							,      )
							unet.set_attn_processor(AttnAddedKVProcessor()	)  # For reproducibility tests

							torch.manual_seed(0	)
							__lowercase		       =			DDPMScheduler(
							    num_train_timesteps=1_000							,      beta_schedule="squaredcos_cap_v2"							,      beta_start=0.0001							,      beta_end=0.02							,      thresholding=lowerCamelCase							,      dynamic_thresholding_ratio=0.95							,      sample_max_value=1.0							,      prediction_type="epsilon"							,      variance_type="learned_range"							,      )

							torch.manual_seed(0	)
							__lowercase		       =			IFWatermarker()

							return {
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "unet": unet,
							    "scheduler": scheduler,
							    "watermarker": watermarker,
							    "safety_checker": None,
							    "feature_extractor": None,
							}



						def     _snake_case	(	self	:							Tuple	):






							'''simple docstring'''



							torch.manual_seed(0	)
							__lowercase		       =			TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"	)

							torch.manual_seed(0	)
							__lowercase		       =			AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"	)

							torch.manual_seed(0	)
							__lowercase		       =			UNetaDConditionModel(
							    sample_size=32							,      layers_per_block=[1, 2]							,      block_out_channels=[32, 64]							,      down_block_types=[
							        "ResnetDownsampleBlock2D",
							        "SimpleCrossAttnDownBlock2D",
							    ]							,      mid_block_type="UNetMidBlock2DSimpleCrossAttn"							,      up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]							,      in_channels=6							,      out_channels=6							,      cross_attention_dim=32							,      encoder_hid_dim=32							,      attention_head_dim=8							,      addition_embed_type="text"							,      addition_embed_type_num_heads=2							,      cross_attention_norm="group_norm"							,      resnet_time_scale_shift="scale_shift"							,      act_fn="gelu"							,      class_embed_type="timestep"							,      mid_block_scale_factor=1.414							,      time_embedding_act_fn="gelu"							,      time_embedding_dim=32							,      )
							unet.set_attn_processor(AttnAddedKVProcessor()	)  # For reproducibility tests

							torch.manual_seed(0	)
							__lowercase		       =			DDPMScheduler(
							    num_train_timesteps=1_000							,      beta_schedule="squaredcos_cap_v2"							,      beta_start=0.0001							,      beta_end=0.02							,      thresholding=lowerCamelCase							,      dynamic_thresholding_ratio=0.95							,      sample_max_value=1.0							,      prediction_type="epsilon"							,      variance_type="learned_range"							,      )

							torch.manual_seed(0	)
							__lowercase		       =			DDPMScheduler(
							    num_train_timesteps=1_000							,      beta_schedule="squaredcos_cap_v2"							,      beta_start=0.0001							,      beta_end=0.02							,      )

							torch.manual_seed(0	)
							__lowercase		       =			IFWatermarker()

							return {
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "unet": unet,
							    "scheduler": scheduler,
							    "image_noising_scheduler": image_noising_scheduler,
							    "watermarker": watermarker,
							    "safety_checker": None,
							    "feature_extractor": None,
							}



						def     _snake_case	(	self	:							str	):






							'''simple docstring'''



							__lowercase		       =			self.get_dummy_components()
							__lowercase		       =			self.pipeline_class(**lowerCamelCase	)
							pipe.to(lowerCamelCase	)
							pipe.set_progress_bar_config(disable=lowerCamelCase	)

							__lowercase		       =			self.get_dummy_inputs(lowerCamelCase	)

							__lowercase		       =			inputs["prompt"]
							__lowercase		       =			inputs["generator"]
							__lowercase		       =			inputs["num_inference_steps"]
							__lowercase		       =			inputs["output_type"]

							if "image" in inputs:
								__lowercase		       =			inputs["image"]
							else:
								__lowercase		       =			None

							if "mask_image" in inputs:
								__lowercase		       =			inputs["mask_image"]
							else:
								__lowercase		       =			None

							if "original_image" in inputs:
								__lowercase		       =			inputs["original_image"]
							else:
								__lowercase		       =			None

							__lowercase       ,    __lowercase		       =			pipe.encode_prompt(lowerCamelCase	)

							# inputs with prompt converted to embeddings
							__lowercase		       =			{
							    "prompt_embeds": prompt_embeds,
							    "negative_prompt_embeds": negative_prompt_embeds,
							    "generator": generator,
							    "num_inference_steps": num_inference_steps,
							    "output_type": output_type,
							}

							if image is not None:
								__lowercase		       =			image

							if mask_image is not None:
								__lowercase		       =			mask_image

							if original_image is not None:
								__lowercase		       =			original_image

							# set all optional components to None
							for optional_component in pipe._optional_components:
								setattr(lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase	)

							__lowercase		       =			pipe(**lowerCamelCase	)[0]

							with tempfile.TemporaryDirectory() as tmpdir:
								pipe.save_pretrained(lowerCamelCase	)
								__lowercase		       =			self.pipeline_class.from_pretrained(lowerCamelCase	)
								pipe_loaded.to(lowerCamelCase	)
								pipe_loaded.set_progress_bar_config(disable=lowerCamelCase	)

							pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()	)  # For reproducibility tests

							for optional_component in pipe._optional_components:
								self.assertTrue(
								    getattr(lowerCamelCase							,      lowerCamelCase	) is None							,      f"""`{optional_component}` did not stay set to None after loading."""							,      )

							__lowercase		       =			self.get_dummy_inputs(lowerCamelCase	)

							__lowercase		       =			inputs["generator"]
							__lowercase		       =			inputs["num_inference_steps"]
							__lowercase		       =			inputs["output_type"]

							# inputs with prompt converted to embeddings
							__lowercase		       =			{
							    "prompt_embeds": prompt_embeds,
							    "negative_prompt_embeds": negative_prompt_embeds,
							    "generator": generator,
							    "num_inference_steps": num_inference_steps,
							    "output_type": output_type,
							}

							if image is not None:
								__lowercase		       =			image

							if mask_image is not None:
								__lowercase		       =			mask_image

							if original_image is not None:
								__lowercase		       =			original_image

							__lowercase		       =			pipe_loaded(**lowerCamelCase	)[0]

							__lowercase		       =			np.abs(to_np(lowerCamelCase	) - to_np(lowerCamelCase	)	).max()
							self.assertLess(lowerCamelCase							,      1e-4	)



						def     _snake_case	(	self	:							Optional[Any]	):






							'''simple docstring'''



							__lowercase		       =			self.get_dummy_components()
							__lowercase		       =			self.pipeline_class(**lowerCamelCase	)
							pipe.to(lowerCamelCase	)
							pipe.set_progress_bar_config(disable=lowerCamelCase	)

							__lowercase		       =			self.get_dummy_inputs(lowerCamelCase	)
							__lowercase		       =			pipe(**lowerCamelCase	)[0]

							with tempfile.TemporaryDirectory() as tmpdir:
								pipe.save_pretrained(lowerCamelCase	)
								__lowercase		       =			self.pipeline_class.from_pretrained(lowerCamelCase	)
								pipe_loaded.to(lowerCamelCase	)
								pipe_loaded.set_progress_bar_config(disable=lowerCamelCase	)

							pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()	)  # For reproducibility tests

							__lowercase		       =			self.get_dummy_inputs(lowerCamelCase	)
							__lowercase		       =			pipe_loaded(**lowerCamelCase	)[0]

							__lowercase		       =			np.abs(to_np(lowerCamelCase	) - to_np(lowerCamelCase	)	).max()
							self.assertLess(lowerCamelCase							,      1e-4	)








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	)	)



