


import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()






class _lowerCamelCase(		_a,				_a,				_a,				unittest.TestCase     ):
       lowercase_				:			Optional[int]									=    StableUnCLIPPipeline
       lowercase_				:			Any									=    TEXT_TO_IMAGE_PARAMS
       lowercase_				:			Optional[int]									=    TEXT_TO_IMAGE_BATCH_PARAMS
       lowercase_				:			Optional[Any]									=    TEXT_TO_IMAGE_IMAGE_PARAMS
       lowercase_				:			Tuple									=    TEXT_TO_IMAGE_IMAGE_PARAMS

       # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
       lowercase_				:			Tuple									=    False
       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase :							List[Any]   	=	32
           _lowercase :							Optional[int]   	=	embedder_hidden_size

           # prior components

           torch.manual_seed(0)
           _lowercase :							Dict   	=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

           torch.manual_seed(0)
           _lowercase :							Union[str, Any]   	=	CLIPTextModelWithProjection(
               CLIPTextConfig(
                   bos_token_id=0,   eos_token_id=2,   hidden_size=lowerCamelCase,   projection_dim=lowerCamelCase,   intermediate_size=37,   layer_norm_eps=1E-05,   num_attention_heads=4,   num_hidden_layers=5,   pad_token_id=1,   vocab_size=10_00,   ))

           torch.manual_seed(0)
           _lowercase :							Dict   	=	PriorTransformer(
               num_attention_heads=2,   attention_head_dim=12,   embedding_dim=lowerCamelCase,   num_layers=1,   )

           torch.manual_seed(0)
           _lowercase :							Union[str, Any]   	=	DDPMScheduler(
               variance_type='fixed_small_log',   prediction_type='sample',   num_train_timesteps=10_00,   clip_sample=lowerCamelCase,   clip_sample_range=5.0,   beta_schedule='squaredcos_cap_v2',   )

           # regular denoising components

           torch.manual_seed(0)
           _lowercase :							Optional[int]   	=	StableUnCLIPImageNormalizer(embedding_dim=lowerCamelCase)
           _lowercase :							Union[str, Any]   	=	DDPMScheduler(beta_schedule='squaredcos_cap_v2')

           torch.manual_seed(0)
           _lowercase :							Any   	=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

           torch.manual_seed(0)
           _lowercase :							List[Any]   	=	CLIPTextModel(
               CLIPTextConfig(
                   bos_token_id=0,   eos_token_id=2,   hidden_size=lowerCamelCase,   projection_dim=32,   intermediate_size=37,   layer_norm_eps=1E-05,   num_attention_heads=4,   num_hidden_layers=5,   pad_token_id=1,   vocab_size=10_00,   ))

           torch.manual_seed(0)
           _lowercase :							List[str]   	=	UNetaDConditionModel(
               sample_size=32,   in_channels=4,   out_channels=4,   down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D'),   up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D'),   block_out_channels=(32, 64),   attention_head_dim=(2, 4),   class_embed_type='projection',   projection_class_embeddings_input_dim=embedder_projection_dim * 2,   cross_attention_dim=lowerCamelCase,   layers_per_block=1,   upcast_attention=lowerCamelCase,   use_linear_projection=lowerCamelCase,   )

           torch.manual_seed(0)
           _lowercase :							Union[str, Any]   	=	DDIMScheduler(
               beta_schedule='scaled_linear',   beta_start=0.0_0_0_8_5,   beta_end=0.0_1_2,   prediction_type='v_prediction',   set_alpha_to_one=lowerCamelCase,   steps_offset=1,   )

           torch.manual_seed(0)
           _lowercase :							Optional[int]   	=	AutoencoderKL()

           _lowercase :							Any   	=	{
               # prior components
               'prior_tokenizer': prior_tokenizer,
               'prior_text_encoder': prior_text_encoder,
               'prior': prior,
               'prior_scheduler': prior_scheduler,
               # image noising components
               'image_normalizer': image_normalizer,
               'image_noising_scheduler': image_noising_scheduler,
               # regular denoising components
               'tokenizer': tokenizer,
               'text_encoder': text_encoder,
               'unet': unet,
               'scheduler': scheduler,
               'vae': vae,
           }

           return components
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase=0) ->     Tuple:




           """simple docstring"""

           if str(lowerCamelCase).startswith('mps'):
               _lowercase :							int   	=	torch.manual_seed(lowerCamelCase)
           else:
               _lowercase :							Dict   	=	torch.Generator(device=lowerCamelCase).manual_seed(lowerCamelCase)
           _lowercase :							List[Any]   	=	{
               'prompt': 'A painting of a squirrel eating a burger',
               'generator': generator,
               'num_inference_steps': 2,
               'prior_num_inference_steps': 2,
               'output_type': 'numpy',
           }
           return inputs
       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							Tuple   	=	torch_device == 'cpu'

           self._test_attention_slicing_forward_pass(test_max_difference=lowerCamelCase)




       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           _lowercase :							Dict   	=	torch_device in ['cpu', 'mps']

           self._test_inference_batch_single_identical(test_max_difference=lowerCamelCase)






@slow
@require_torch_gpu
class _lowerCamelCase(		unittest.TestCase     ):
       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()
       def     UpperCamelCase  (      self) ->     Optional[int]:




           """simple docstring"""

           _lowercase :							Union[str, Any]   	=	load_numpy(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy')

           _lowercase :							Any   	=	StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l',   torch_dtype=torch.floataa)
           pipe.to(lowerCamelCase)
           pipe.set_progress_bar_config(disable=lowerCamelCase)
           # stable unclip will oom when integration tests are run on a V100,
           # so turn on memory savings
           pipe.enable_attention_slicing()
           pipe.enable_sequential_cpu_offload()

           _lowercase :							str   	=	torch.Generator(device='cpu').manual_seed(0)
           _lowercase :							Optional[Any]   	=	pipe('anime turle',   generator=lowerCamelCase,   output_type='np')

           _lowercase :							Tuple   	=	output.images[0]

           assert image.shape == (7_68, 7_68, 3)

           assert_mean_pixel_difference(lowerCamelCase,   lowerCamelCase)




       def     UpperCamelCase  (      self) ->     Optional[int]:




           """simple docstring"""

           torch.cuda.empty_cache()
           torch.cuda.reset_max_memory_allocated()
           torch.cuda.reset_peak_memory_stats()

           _lowercase :							Any   	=	StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l',   torch_dtype=torch.floataa)
           _lowercase :							Tuple   	=	pipe.to(lowerCamelCase)
           pipe.set_progress_bar_config(disable=lowerCamelCase)
           pipe.enable_attention_slicing()
           pipe.enable_sequential_cpu_offload()

           _lowercase :							Optional[Any]   	=	pipe(
               'anime turtle',   prior_num_inference_steps=2,   num_inference_steps=2,   output_type='np',   )

           _lowercase :							List[str]   	=	torch.cuda.max_memory_allocated()
           # make sure that less than 7 GB is allocated
           assert mem_bytes < 7 * 10**9






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)
SCREAMING_SNAKE_CASE :   Optional[Any]							      =				logging.getLogger()



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		Optional[Any]:
    _lowercase :							List[Any]   	=	'\n'.join(lowerCamelCase_				)
    Path(lowerCamelCase_				).open('w'				).writelines(lowerCamelCase_				)


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

SCREAMING_SNAKE_CASE :   Optional[int]							      =				logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks






class _lowerCamelCase(		_a     ):
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     Dict:




           """simple docstring"""

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

           _lowercase :							List[str]   	=	str(Path(self.get_auto_remove_tmp_dir()) / 'scores.json')
           _lowercase :							Optional[int]   	=	'translation_en_to_de' if model == T5_TINY else 'summarization'
           _lowercase :							Any   	=	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     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           self.run_eval_tester(lowerCamelCase)
       @parameterized.expand([BART_TINY, MBART_TINY])
       @slow
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     int:




           """simple docstring"""

           self.run_eval_tester(lowerCamelCase)




       @parameterized.expand([T5_TINY, MBART_TINY])
       @slow
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     List[str]:




           """simple docstring"""

           _lowercase :							str   	=	Path(self.get_auto_remove_tmp_dir()) / 'utest_input.source'
           _lowercase :							Any   	=	input_file_name.parent / 'utest_output.txt'
           assert not output_file_name.exists()

           _lowercase :							List[str]   	=	{
               '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 :							Optional[Any]   	=	Path(self.get_auto_remove_tmp_dir())
           _lowercase :							Optional[Any]   	=	str(tmp_dir / 'scores.json')
           _lowercase :							str   	=	str(tmp_dir / 'val.target')
           _dump_articles(lowerCamelCase,   text['en'])
           _dump_articles(lowerCamelCase,   text['de'])
           _lowercase :							Tuple   	=	'translation_en_to_de' if model == T5_TINY else 'summarization'
           _lowercase :							Tuple   	=	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 :							Dict   	=	[' num_beams | length_penalty', model, 'Best score args']
               _lowercase :							Optional[Any]   	=	['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))


