




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     UpperCamelCase_   (			UpperCAmelCase__		, UpperCAmelCase__		, unittest.TestCase					):

    '''simple docstring'''






    UpperCAmelCase__							  =				StableDiffusionSAGPipeline
    UpperCAmelCase__							  =				TEXT_TO_IMAGE_PARAMS
    UpperCAmelCase__							  =				TEXT_TO_IMAGE_BATCH_PARAMS
    UpperCAmelCase__							  =				TEXT_TO_IMAGE_IMAGE_PARAMS
    UpperCAmelCase__							  =				TEXT_TO_IMAGE_IMAGE_PARAMS
    UpperCAmelCase__							  =				False


    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict)						->str:





           '''simple docstring'''

           torch.manual_seed(0)
           A__							=   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 ,			)
           A__							=   DDIMScheduler(
               beta_start=0.00085 ,			beta_end=0.012 ,			beta_schedule='''scaled_linear''' ,			clip_sample=UpperCAmelCase__ ,			set_alpha_to_one=UpperCAmelCase__ ,			)
           torch.manual_seed(0)
           A__							=   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)
           A__							=   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 ,			)
           A__							=   CLIPTextModel(UpperCAmelCase__)
           A__							=   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

           A__							=   {
               '''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				:     Any ,			UpperCAmelCase__				:     List[Any] ,			UpperCAmelCase__				:     List[Any]=0)						->Optional[int]:





           '''simple docstring'''

           if str(UpperCAmelCase__).startswith('''mps'''):
                  A__							=   torch.manual_seed(UpperCAmelCase__)
           else:
                  A__							=   torch.Generator(device=UpperCAmelCase__).manual_seed(UpperCAmelCase__)
           A__							=   {
               '''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)						->str:





           '''simple docstring'''

           super().test_inference_batch_single_identical(expected_max_diff=3e-3)


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

    '''simple docstring'''






    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->Optional[Any]:





           '''simple docstring'''

           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()


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





           '''simple docstring'''

           A__							=   StableDiffusionSAGPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4''')
           A__							=   sag_pipe.to(UpperCAmelCase__)
           sag_pipe.set_progress_bar_config(disable=UpperCAmelCase__)

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

           A__							=   output.images

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

           assert image.shape == (1, 512, 512, 3)
           A__							=   np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949])

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any])						->int:





           '''simple docstring'''

           A__							=   StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''')
           A__							=   sag_pipe.to(UpperCAmelCase__)
           sag_pipe.set_progress_bar_config(disable=UpperCAmelCase__)

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

           A__							=   output.images

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

           assert image.shape == (1, 512, 512, 3)
           A__							=   np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371])

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





    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict)						->Dict:





           '''simple docstring'''

           A__							=   StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''')
           A__							=   sag_pipe.to(UpperCAmelCase__)
           sag_pipe.set_progress_bar_config(disable=UpperCAmelCase__)

           A__							=   '''.'''
           A__							=   torch.manual_seed(0)
           A__							=   sag_pipe(
               [prompt] ,			width=768 ,			height=512 ,			generator=UpperCAmelCase__ ,			guidance_scale=7.5 ,			sag_scale=1.0 ,			num_inference_steps=20 ,			output_type='''np''' ,			)

           A__							=   output.images

           assert image.shape == (1, 512, 768, 3)








# 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 model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

_lowerCamelCase     :		Optional[Any]								=       """facebook/wmt19-en-de"""
_lowerCamelCase     :		Optional[Any]								=       FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
_lowerCamelCase     :		int								=       FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        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,
    )
)

_lowerCamelCase     :		Union[str, Any]								=       FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
_lowerCamelCase     :		int								=       tokenizer(["""Making tiny model"""], return_tensors="""pt""")
_lowerCamelCase     :		int								=       tiny_model(**batch)

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

# Save
_lowerCamelCase     :		str								=       """tiny-wmt19-en-de"""
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-de


