




import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPanoramaPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

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()


@skip_mps
class     UpperCamelCase_   (			UpperCAmelCase__		, UpperCAmelCase__		, unittest.TestCase					):

    '''simple docstring'''






    UpperCAmelCase__							  =				StableDiffusionPanoramaPipeline
    UpperCAmelCase__							  =				TEXT_TO_IMAGE_PARAMS
    UpperCAmelCase__							  =				TEXT_TO_IMAGE_BATCH_PARAMS
    UpperCAmelCase__							  =				TEXT_TO_IMAGE_IMAGE_PARAMS
    UpperCAmelCase__							  =				TEXT_TO_IMAGE_IMAGE_PARAMS


    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any])						->str:





           '''simple docstring'''

           torch.manual_seed(0)
           A__							=   UNetaDConditionModel(
               block_out_channels=(32, 64) ,			layers_per_block=1 ,			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()
           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				:     str ,			UpperCAmelCase__				:     List[Any] ,			UpperCAmelCase__				:     Union[str, Any]=0)						->Optional[Any]:





           '''simple docstring'''

           A__							=   torch.manual_seed(UpperCAmelCase__)
           A__							=   {
               '''prompt''': '''a photo of the dolomites''',
               '''generator''': generator,
               # Setting height and width to None to prevent OOMs on CPU.
               '''height''': None,
               '''width''': None,
               '''num_inference_steps''': 1,
               '''guidance_scale''': 6.0,
               '''output_type''': '''numpy''',
           }
           return inputs


    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict)						->str:





           '''simple docstring'''

           A__							=   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           A__							=   self.get_dummy_components()
           A__							=   StableDiffusionPanoramaPipeline(**UpperCAmelCase__)
           A__							=   sd_pipe.to(UpperCAmelCase__)
           sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__)

           A__							=   self.get_dummy_inputs(UpperCAmelCase__)
           A__							=   sd_pipe(**UpperCAmelCase__).images
           A__							=   image[0, -3:, -3:, -1]
           assert image.shape == (1, 64, 64, 3)

           A__							=   np.array([0.6186, 0.5374, 0.4915, 0.4135, 0.4114, 0.4563, 0.5128, 0.4977, 0.4757])

           assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2


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





           '''simple docstring'''

           super().test_inference_batch_consistent(batch_sizes=[1, 2])


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





           '''simple docstring'''

           super().test_inference_batch_single_identical(batch_size=2 ,			expected_max_diff=3.25e-3)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple)						->int:





           '''simple docstring'''

           A__							=   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           A__							=   self.get_dummy_components()
           A__							=   StableDiffusionPanoramaPipeline(**UpperCAmelCase__)
           A__							=   sd_pipe.to(UpperCAmelCase__)
           sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__)

           A__							=   self.get_dummy_inputs(UpperCAmelCase__)
           A__							=   '''french fries'''
           A__							=   sd_pipe(**UpperCAmelCase__ ,			negative_prompt=UpperCAmelCase__)
           A__							=   output.images
           A__							=   image[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)

           A__							=   np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757])

           assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2


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





           '''simple docstring'''

           A__							=   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           A__							=   self.get_dummy_components()
           A__							=   StableDiffusionPanoramaPipeline(**UpperCAmelCase__)
           A__							=   sd_pipe.to(UpperCAmelCase__)
           sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__)

           A__							=   self.get_dummy_inputs(UpperCAmelCase__)
           A__							=   sd_pipe(**UpperCAmelCase__ ,			view_batch_size=2)
           A__							=   output.images
           A__							=   image[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)

           A__							=   np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757])

           assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2


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





           '''simple docstring'''

           A__							=   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           A__							=   self.get_dummy_components()
           A__							=   EulerAncestralDiscreteScheduler(
               beta_start=0.00085 ,			beta_end=0.012 ,			beta_schedule='''scaled_linear''')
           A__							=   StableDiffusionPanoramaPipeline(**UpperCAmelCase__)
           A__							=   sd_pipe.to(UpperCAmelCase__)
           sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__)

           A__							=   self.get_dummy_inputs(UpperCAmelCase__)
           A__							=   sd_pipe(**UpperCAmelCase__).images
           A__							=   image[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)

           A__							=   np.array([0.4024, 0.6510, 0.4901, 0.5378, 0.5813, 0.5622, 0.4795, 0.4467, 0.4952])

           assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2





    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple)						->Tuple:





           '''simple docstring'''

           A__							=   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           A__							=   self.get_dummy_components()
           A__							=   PNDMScheduler(
               beta_start=0.00085 ,			beta_end=0.012 ,			beta_schedule='''scaled_linear''' ,			skip_prk_steps=UpperCAmelCase__)
           A__							=   StableDiffusionPanoramaPipeline(**UpperCAmelCase__)
           A__							=   sd_pipe.to(UpperCAmelCase__)
           sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__)

           A__							=   self.get_dummy_inputs(UpperCAmelCase__)
           A__							=   sd_pipe(**UpperCAmelCase__).images
           A__							=   image[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)

           A__							=   np.array([0.6391, 0.6291, 0.4861, 0.5134, 0.5552, 0.4578, 0.5032, 0.5023, 0.4539])

           assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2


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

    '''simple docstring'''






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





           '''simple docstring'''

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any ,			UpperCAmelCase__				:     List[str]=0)						->Union[str, Any]:





           '''simple docstring'''

           A__							=   torch.manual_seed(UpperCAmelCase__)
           A__							=   {
               '''prompt''': '''a photo of the dolomites''',
               '''generator''': generator,
               '''num_inference_steps''': 3,
               '''guidance_scale''': 7.5,
               '''output_type''': '''numpy''',
           }
           return inputs


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





           '''simple docstring'''

           A__							=   '''stabilityai/stable-diffusion-2-base'''
           A__							=   DDIMScheduler.from_pretrained(UpperCAmelCase__ ,			subfolder='''scheduler''')
           A__							=   StableDiffusionPanoramaPipeline.from_pretrained(UpperCAmelCase__ ,			scheduler=UpperCAmelCase__ ,			safety_checker=UpperCAmelCase__)
           pipe.to(UpperCAmelCase__)
           pipe.set_progress_bar_config(disable=UpperCAmelCase__)
           pipe.enable_attention_slicing()

           A__							=   self.get_inputs()
           A__							=   pipe(**UpperCAmelCase__).images
           A__							=   image[0, -3:, -3:, -1].flatten()

           assert image.shape == (1, 512, 2_048, 3)

           A__							=   np.array(
               [
                   0.36968392,
                   0.27025372,
                   0.32446766,
                   0.28379387,
                   0.36363274,
                   0.30733347,
                   0.27100027,
                   0.27054125,
                   0.25536096,
               ])

           assert np.abs(expected_slice - image_slice).max() < 1e-2


    def 	SCREAMING_SNAKE_CASE							(      self				:     str)						->Tuple:





           '''simple docstring'''

           A__							=   StableDiffusionPanoramaPipeline.from_pretrained(
               '''stabilityai/stable-diffusion-2-base''' ,			safety_checker=UpperCAmelCase__)
           A__							=   LMSDiscreteScheduler.from_config(pipe.scheduler.config)
           pipe.to(UpperCAmelCase__)
           pipe.set_progress_bar_config(disable=UpperCAmelCase__)
           pipe.enable_attention_slicing()

           A__							=   self.get_inputs()
           A__							=   pipe(**UpperCAmelCase__).images
           A__							=   image[0, -3:, -3:, -1].flatten()

           assert image.shape == (1, 512, 2_048, 3)

           A__							=   np.array(
               [
                   [
                       0.0,
                       0.0,
                       0.0,
                       0.0,
                       0.0,
                       0.0,
                       0.0,
                       0.0,
                       0.0,
                   ]
               ])

           assert np.abs(expected_slice - image_slice).max() < 1e-3


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





           '''simple docstring'''

           A__							=   0

           def callback_fn(UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     torch.FloatTensor) -> None:
                  A__							=   True
                  nonlocal number_of_steps
                  number_of_steps += 1
                  if step == 1:
                         A__							=   latents.detach().cpu().numpy()
                         assert latents.shape == (1, 4, 64, 256)
                         A__							=   latents[0, -3:, -3:, -1]

                         A__							=   np.array(
                             [
                                 0.18681869,
                                 0.33907816,
                                 0.5361276,
                                 0.14432865,
                                 -0.02856611,
                                 -0.73941123,
                                 0.23397987,
                                 0.47322682,
                                 -0.37823164,
                             ])
                         assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
                  elif step == 2:
                         A__							=   latents.detach().cpu().numpy()
                         assert latents.shape == (1, 4, 64, 256)
                         A__							=   latents[0, -3:, -3:, -1]

                         A__							=   np.array(
                             [
                                 0.18539645,
                                 0.33987248,
                                 0.5378559,
                                 0.14437142,
                                 -0.02455261,
                                 -0.7338317,
                                 0.23990755,
                                 0.47356272,
                                 -0.3786505,
                             ])

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

           A__							=   False

           A__							=   '''stabilityai/stable-diffusion-2-base'''
           A__							=   DDIMScheduler.from_pretrained(UpperCAmelCase__ ,			subfolder='''scheduler''')
           A__							=   StableDiffusionPanoramaPipeline.from_pretrained(UpperCAmelCase__ ,			scheduler=UpperCAmelCase__ ,			safety_checker=UpperCAmelCase__)
           A__							=   pipe.to(UpperCAmelCase__)
           pipe.set_progress_bar_config(disable=UpperCAmelCase__)
           pipe.enable_attention_slicing()

           A__							=   self.get_inputs()
           pipe(**UpperCAmelCase__ ,			callback=UpperCAmelCase__ ,			callback_steps=1)
           assert callback_fn.has_been_called
           assert number_of_steps == 3





    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->Union[str, Any]:





           '''simple docstring'''

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

           A__							=   '''stabilityai/stable-diffusion-2-base'''
           A__							=   DDIMScheduler.from_pretrained(UpperCAmelCase__ ,			subfolder='''scheduler''')
           A__							=   StableDiffusionPanoramaPipeline.from_pretrained(UpperCAmelCase__ ,			scheduler=UpperCAmelCase__ ,			safety_checker=UpperCAmelCase__)
           A__							=   pipe.to(UpperCAmelCase__)
           pipe.set_progress_bar_config(disable=UpperCAmelCase__)
           pipe.enable_attention_slicing(1)
           pipe.enable_sequential_cpu_offload()

           A__							=   self.get_inputs()
           A__							=   pipe(**UpperCAmelCase__)

           A__							=   torch.cuda.max_memory_allocated()
           # make sure that less than 5.2 GB is allocated
           assert mem_bytes < 5.5 * 10**9








# 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


