


'''simple docstring'''






import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

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


enable_full_determinism()




class 						a__							(				a__							,							a__							,							unittest.TestCase    ):





							'''simple docstring'''






							lowercase__     :  int      		= StableDiffusionSAGPipeline
							lowercase__     :  Any      		= TEXT_TO_IMAGE_PARAMS
							lowercase__     :  Optional[Any]      		= TEXT_TO_IMAGE_BATCH_PARAMS
							lowercase__     :  Tuple      		= TEXT_TO_IMAGE_IMAGE_PARAMS
							lowercase__     :  Dict      		= TEXT_TO_IMAGE_IMAGE_PARAMS
							lowercase__     :  List[Any]      		= False







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														torch.manual_seed(0  )
														lowerCAmelCase__			  =      UNetaDConditionModel(
														    block_out_channels=(32, 64)   ,       layers_per_block=2   ,       sample_size=32   ,       in_channels=4   ,       out_channels=4   ,       down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')   ,       up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')   ,       cross_attention_dim=32   ,       )
														lowerCAmelCase__			  =      DDIMScheduler(
														    beta_start=0.00_085   ,       beta_end=0.012   ,       beta_schedule='''scaled_linear'''   ,       clip_sample=lowerCamelCase_   ,       set_alpha_to_one=lowerCamelCase_   ,       )
														torch.manual_seed(0  )
														lowerCAmelCase__			  =      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   ,       )
														torch.manual_seed(0  )
														lowerCAmelCase__			  =      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=10_00   ,       )
														lowerCAmelCase__			  =      CLIPTextModel(lowerCamelCase_  )
														lowerCAmelCase__			  =      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )

														lowerCAmelCase__			  =      {
														    '''unet''': unet,
														    '''scheduler''': scheduler,
														    '''vae''': vae,
														    '''text_encoder''': text_encoder,
														    '''tokenizer''': tokenizer,
														    '''safety_checker''': None,
														    '''feature_extractor''': None,
														}
														return components







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_=0  )			->				Tuple:
														if str(lowerCamelCase_  ).startswith('''mps'''  ):
																					lowerCAmelCase__			  =      torch.manual_seed(lowerCamelCase_  )
														else:
																					lowerCAmelCase__			  =      torch.Generator(device=lowerCamelCase_  ).manual_seed(lowerCamelCase_  )
														lowerCAmelCase__			  =      {
														    '''prompt''': '''.''',
														    '''generator''': generator,
														    '''num_inference_steps''': 2,
														    '''guidance_scale''': 1.0,
														    '''sag_scale''': 1.0,
														    '''output_type''': '''numpy''',
														}
														return inputs







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														super().test_inference_batch_single_identical(expected_max_diff=3e-3  )




@slow
@require_torch_gpu
class 						a__							(				unittest.TestCase    ):





							'''simple docstring'''






							def     __SCREAMING_SNAKE_CASE				(						self  )			->				str:
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														lowerCAmelCase__			  =      StableDiffusionSAGPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''  )
														lowerCAmelCase__			  =      sag_pipe.to(lowerCamelCase_  )
														sag_pipe.set_progress_bar_config(disable=lowerCamelCase_  )

														lowerCAmelCase__			  =      '''.'''
														lowerCAmelCase__			  =      torch.manual_seed(0  )
														lowerCAmelCase__			  =      sag_pipe(
														    [prompt]   ,       generator=lowerCamelCase_   ,       guidance_scale=7.5   ,       sag_scale=1.0   ,       num_inference_steps=20   ,       output_type='''np'''  )

														lowerCAmelCase__			  =      output.images

														lowerCAmelCase__			  =      image[0, -3:, -3:, -1]

														assert image.shape == (1, 5_12, 5_12, 3)
														lowerCAmelCase__			  =      np.array([0.1_568, 0.1_738, 0.1_695, 0.1_693, 0.1_507, 0.1_705, 0.1_547, 0.1_751, 0.1_949]  )

														assert np.abs(image_slice.flatten() - expected_slice  ).max() < 5e-2







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														lowerCAmelCase__			  =      StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''  )
														lowerCAmelCase__			  =      sag_pipe.to(lowerCamelCase_  )
														sag_pipe.set_progress_bar_config(disable=lowerCamelCase_  )

														lowerCAmelCase__			  =      '''.'''
														lowerCAmelCase__			  =      torch.manual_seed(0  )
														lowerCAmelCase__			  =      sag_pipe(
														    [prompt]   ,       generator=lowerCamelCase_   ,       guidance_scale=7.5   ,       sag_scale=1.0   ,       num_inference_steps=20   ,       output_type='''np'''  )

														lowerCAmelCase__			  =      output.images

														lowerCAmelCase__			  =      image[0, -3:, -3:, -1]

														assert image.shape == (1, 5_12, 5_12, 3)
														lowerCAmelCase__			  =      np.array([0.3_459, 0.2_876, 0.2_537, 0.3_002, 0.2_671, 0.2_160, 0.3_026, 0.2_262, 0.2_371]  )

														assert np.abs(image_slice.flatten() - expected_slice  ).max() < 5e-2







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														lowerCAmelCase__			  =      StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''  )
														lowerCAmelCase__			  =      sag_pipe.to(lowerCamelCase_  )
														sag_pipe.set_progress_bar_config(disable=lowerCamelCase_  )

														lowerCAmelCase__			  =      '''.'''
														lowerCAmelCase__			  =      torch.manual_seed(0  )
														lowerCAmelCase__			  =      sag_pipe(
														    [prompt]   ,       width=7_68   ,       height=5_12   ,       generator=lowerCamelCase_   ,       guidance_scale=7.5   ,       sag_scale=1.0   ,       num_inference_steps=20   ,       output_type='''np'''   ,       )

														lowerCAmelCase__			  =      output.images

														assert image.shape == (1, 5_12, 7_68, 3)



'''simple docstring'''






# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

__UpperCAmelCase       							=				'''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
__UpperCAmelCase       							=				[
    '''l''',
    '''o''',
    '''w''',
    '''e''',
    '''r''',
    '''s''',
    '''t''',
    '''i''',
    '''d''',
    '''n''',
    '''w</w>''',
    '''r</w>''',
    '''t</w>''',
    '''lo''',
    '''low''',
    '''er</w>''',
    '''low</w>''',
    '''lowest</w>''',
    '''newer</w>''',
    '''wider</w>''',
    '''<unk>''',
]
__UpperCAmelCase       							=				dict(zip(vocab, range(len(vocab))))
__UpperCAmelCase       							=				['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
		__UpperCAmelCase       							=				Path(tmpdirname)
		__UpperCAmelCase       							=				build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
		__UpperCAmelCase       							=				build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
		__UpperCAmelCase       							=				build_dir / VOCAB_FILES_NAMES['''merges_file''']
		with open(src_vocab_file, '''w''') as fp:
				fp.write(json.dumps(vocab_tokens))
		with open(tgt_vocab_file, '''w''') as fp:
				fp.write(json.dumps(vocab_tokens))
		with open(merges_file, '''w''') as fp:
				fp.write('''\n'''.join(merges))

		__UpperCAmelCase       							=				FSMTTokenizer(
		    langs=['''en''', '''ru'''],
		    src_vocab_size=len(vocab),
		    tgt_vocab_size=len(vocab),
		    src_vocab_file=src_vocab_file,
		    tgt_vocab_file=tgt_vocab_file,
		    merges_file=merges_file,
		)

__UpperCAmelCase       							=				FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1_000,
    tgt_vocab_size=1_000,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

__UpperCAmelCase       							=				FSMTForConditionalGeneration(config)
print(f"""num of params {tiny_model.num_parameters()}""")

# Test
__UpperCAmelCase       							=				tokenizer(['''Making tiny model'''], return_tensors='''pt''')
__UpperCAmelCase       							=				tiny_model(**batch)

print('''test output:''', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f"""Generated {mname_tiny}""")

# Upload
# transformers-cli upload tiny-wmt19-en-ru