



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: UNetaDModel
    __lowerCamelCase			: KarrasVeScheduler






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__		):
        super().__init__()
        self.register_modules(unet=lowerCamelCase__,		scheduler=lowerCamelCase__		)






    @torch.no_grad()
    def __call__(       self,		lowerCamelCase__ = 1,		lowerCamelCase__ = 50,		lowerCamelCase__ = None,		lowerCamelCase__ = "pil",		lowerCamelCase__ = True,		**lowerCamelCase__,		):
        A :       Tuple          =   self.unet.config.sample_size
        A :       Dict          =   (batch_size, 3, img_size, img_size)

        A :       Optional[Any]          =   self.unet

        # sample x_0 ~ N(0, sigma_0^2 * I)
        A :       Dict          =   randn_tensor(lowerCamelCase__,		generator=lowerCamelCase__,		device=self.device		) * self.scheduler.init_noise_sigma

        self.scheduler.set_timesteps(lowerCamelCase__		)

        for t in self.progress_bar(self.scheduler.timesteps		):
            # here sigma_t == t_i from the paper
            A :       Any          =   self.scheduler.schedule[t]
            A :       List[Any]          =   self.scheduler.schedule[t - 1] if t > 0 else 0

            # 1. Select temporarily increased noise level sigma_hat
            # 2. Add new noise to move from sample_i to sample_hat
            A     ,							A :       Union[str, Any]          =   self.scheduler.add_noise_to_input(lowerCamelCase__,		lowerCamelCase__,		generator=lowerCamelCase__		)

            # 3. Predict the noise residual given the noise magnitude `sigma_hat`
            # The model inputs and output are adjusted by following eq. (213) in [1].
            A :       str          =   (sigma_hat / 2) * model((sample_hat + 1) / 2,		sigma_hat / 2		).sample

            # 4. Evaluate dx/dt at sigma_hat
            # 5. Take Euler step from sigma to sigma_prev
            A :       List[str]          =   self.scheduler.step(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)

            if sigma_prev != 0:
                # 6. Apply 2nd order correction
                # The model inputs and output are adjusted by following eq. (213) in [1].
                A :       Tuple          =   (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2,		sigma_prev / 2		).sample
                A :       List[str]          =   self.scheduler.step_correct(
                    lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		step_output.prev_sample,		step_output["""derivative"""],		)
            A :       List[str]          =   step_output.prev_sample

        A :       str          =   (sample / 2 + 0.5).clamp(0,		1		)
        A :       Any          =   sample.cpu().permute(0,		2,		3,		1		).numpy()
        if output_type == "pil":
            A :       List[str]          =   self.numpy_to_pil(lowerCamelCase__		)

        if not return_dict:
            return (image,)

        return ImagePipelineOutput(images=lowerCamelCase__		)







from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
     import tensorflow as tf

     from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM






@require_tf
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE__     (		unittest.TestCase    ):




    '''simple docstring'''






    @slow
    def 			_lowerCAmelCase					(       self		):
        A :       Any          =   TFAutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""		)
        A :       Dict          =   AutoTokenizer.from_pretrained("""google/mt5-small"""		)

        A :       Tuple          =   tokenizer("""Hello there""",		return_tensors="""tf"""		).input_ids
        A :       Optional[int]          =   tokenizer("""Hi I am""",		return_tensors="""tf"""		).input_ids

        A :       List[Any]          =   model(lowerCamelCase__,		labels=lowerCamelCase__		).loss
        A :       str          =   -tf.math.reduce_mean(lowerCamelCase__		).numpy()

        A :       Tuple          =   -21.22_8168
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 2e-4		)


