



"""simple docstring"""





from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


lowerCamelCase      =		8
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__=BITS   ):
      UpperCAmelCase_    				=							x.device

      UpperCAmelCase_    				=							(x * 255).int().clamp(0			,    255   )

      UpperCAmelCase_    				=							2 ** torch.arange(bits - 1			,    -1			,    -1			,    device=lowerCAmelCase__   )
      UpperCAmelCase_    				=							rearrange(lowerCAmelCase__			,    "d -> d 1 1"   )
      UpperCAmelCase_    				=							rearrange(lowerCAmelCase__			,    "b c h w -> b c 1 h w"   )

      UpperCAmelCase_    				=							((x & mask) != 0).float()
      UpperCAmelCase_    				=							rearrange(lowerCAmelCase__			,    "b c d h w -> b (c d) h w"   )
      UpperCAmelCase_    				=							bits * 2 - 1
      return bits
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__=BITS   ):
      UpperCAmelCase_    				=							x.device

      UpperCAmelCase_    				=							(x > 0).int()
      UpperCAmelCase_    				=							2 ** torch.arange(bits - 1			,    -1			,    -1			,    device=lowerCAmelCase__			,    dtype=torch.intaa   )

      UpperCAmelCase_    				=							rearrange(lowerCAmelCase__			,    "d -> d 1 1"   )
      UpperCAmelCase_    				=							rearrange(lowerCAmelCase__			,    "b (c d) h w -> b c d h w"			,    d=8   )
      UpperCAmelCase_    				=							reduce(x * mask			,    "b c d h w -> b c h w"			,    "sum"   )
      return (dec / 255).clamp(0.0			,    1.0   )
def      a__			(   self			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__ = 0.0			,    lowerCAmelCase__ = True			,    lowerCAmelCase__=None			,    lowerCAmelCase__ = True			,    ):
      if self.num_inference_steps is None:
            raise ValueError(
                "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"   )

      # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
      # Ideally, read DDIM paper in-detail understanding

      # Notation (<variable name> -> <name in paper>
      # - pred_noise_t -> e_theta(x_t, t)
      # - pred_original_sample -> f_theta(x_t, t) or x_0
      # - std_dev_t -> sigma_t
      # - eta -> η
      # - pred_sample_direction -> "direction pointing to x_t"
      # - pred_prev_sample -> "x_t-1"

      # 1. get previous step value (=t-1)
      UpperCAmelCase_    				=							timestep - self.config.num_train_timesteps // self.num_inference_steps

      # 2. compute alphas, betas
      UpperCAmelCase_    				=							self.alphas_cumprod[timestep]
      UpperCAmelCase_    				=							self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

      UpperCAmelCase_    				=							1 - alpha_prod_t

      # 3. compute predicted original sample from predicted noise also called
      # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      UpperCAmelCase_    				=							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

      # 4. Clip "predicted x_0"
      UpperCAmelCase_    				=							self.bit_scale
      if self.config.clip_sample:
            UpperCAmelCase_    				=							torch.clamp(lowerCAmelCase__			,    -scale			,    lowerCAmelCase__   )

      # 5. compute variance: "sigma_t(η)" -> see formula (16)
      # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
      UpperCAmelCase_    				=							self._get_variance(lowerCAmelCase__			,    lowerCAmelCase__   )
      UpperCAmelCase_    				=							eta * variance ** 0.5

      if use_clipped_model_output:
            # the model_output is always re-derived from the clipped x_0 in Glide
            UpperCAmelCase_    				=							(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

      # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      UpperCAmelCase_    				=							(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

      # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      UpperCAmelCase_    				=							alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

      if eta > 0:
            # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
            UpperCAmelCase_    				=							model_output.device if torch.is_tensor(lowerCAmelCase__   ) else "cpu"
            UpperCAmelCase_    				=							torch.randn(model_output.shape			,    dtype=model_output.dtype			,    generator=lowerCAmelCase__   ).to(lowerCAmelCase__   )
            UpperCAmelCase_    				=							self._get_variance(lowerCAmelCase__			,    lowerCAmelCase__   ) ** 0.5 * eta * noise

            UpperCAmelCase_    				=							prev_sample + variance

      if not return_dict:
            return (prev_sample,)

      return DDIMSchedulerOutput(prev_sample=lowerCAmelCase__			,    pred_original_sample=lowerCAmelCase__   )
def      a__			(   self			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__="epsilon"			,    lowerCAmelCase__=None			,    lowerCAmelCase__ = True			,    ):
      UpperCAmelCase_    				=							timestep

      if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
            UpperCAmelCase_				,					UpperCAmelCase_    				=							torch.split(lowerCAmelCase__			,    sample.shape[1]			,    dim=1   )
      else:
            UpperCAmelCase_    				=							None

      # 1. compute alphas, betas
      UpperCAmelCase_    				=							self.alphas_cumprod[t]
      UpperCAmelCase_    				=							self.alphas_cumprod[t - 1] if t > 0 else self.one
      UpperCAmelCase_    				=							1 - alpha_prod_t
      UpperCAmelCase_    				=							1 - alpha_prod_t_prev

      # 2. compute predicted original sample from predicted noise also called
      # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
      if prediction_type == "epsilon":
            UpperCAmelCase_    				=							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
      elif prediction_type == "sample":
            UpperCAmelCase_    				=							model_output
      else:
            raise ValueError(f"""Unsupported prediction_type {prediction_type}."""   )

      # 3. Clip "predicted x_0"
      UpperCAmelCase_    				=							self.bit_scale
      if self.config.clip_sample:
            UpperCAmelCase_    				=							torch.clamp(lowerCAmelCase__			,    -scale			,    lowerCAmelCase__   )

      # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
      # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
      UpperCAmelCase_    				=							(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
      UpperCAmelCase_    				=							self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

      # 5. Compute predicted previous sample µ_t
      # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
      UpperCAmelCase_    				=							pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

      # 6. Add noise
      UpperCAmelCase_    				=							0
      if t > 0:
            UpperCAmelCase_    				=							torch.randn(
                model_output.size()			,    dtype=model_output.dtype			,    layout=model_output.layout			,    generator=lowerCAmelCase__   ).to(model_output.device   )
            UpperCAmelCase_    				=							(self._get_variance(lowerCAmelCase__			,    predicted_variance=lowerCAmelCase__   ) ** 0.5) * noise

      UpperCAmelCase_    				=							pred_prev_sample + variance

      if not return_dict:
            return (pred_prev_sample,)

      return DDPMSchedulerOutput(prev_sample=lowerCAmelCase__			,    pred_original_sample=lowerCAmelCase__   )


class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       def __init__(							self    :						Union[str, Any]	,       _UpperCAmelCase    :						UNetaDConditionModel	,       _UpperCAmelCase    :						Union[DDIMScheduler, DDPMScheduler]	,       _UpperCAmelCase    :						Optional[float] = 1.0	,       )				->		str:


             '''simple docstring'''




             super().__init__()
             UpperCAmelCase_    				=							bit_scale
             UpperCAmelCase_    				=							(
                 ddim_bit_scheduler_step if isinstance(_UpperCAmelCase	,       _UpperCAmelCase					) else ddpm_bit_scheduler_step
             )

             self.register_modules(unet=_UpperCAmelCase	,       scheduler=_UpperCAmelCase					)







       @torch.no_grad()
       def __call__(							self    :						Dict	,       _UpperCAmelCase    :						Optional[int] = 256	,       _UpperCAmelCase    :						Optional[int] = 256	,       _UpperCAmelCase    :						Optional[int] = 50	,       _UpperCAmelCase    :						Optional[torch.Generator] = None	,       _UpperCAmelCase    :						Optional[int] = 1	,       _UpperCAmelCase    :						Optional[str] = "pil"	,       _UpperCAmelCase    :						bool = True	,       **_UpperCAmelCase    :						List[Any]	,       )				->		Union[Tuple, ImagePipelineOutput]:


             '''simple docstring'''




             UpperCAmelCase_    				=							torch.randn(
                 (batch_size, self.unet.config.in_channels, height, width)	,       generator=_UpperCAmelCase	,       )
             UpperCAmelCase_    				=							decimal_to_bits(_UpperCAmelCase					) * self.bit_scale
             UpperCAmelCase_    				=							latents.to(self.device					)

             self.scheduler.set_timesteps(_UpperCAmelCase					)

             for t in self.progress_bar(self.scheduler.timesteps					):
                   # predict the noise residual
                   UpperCAmelCase_    				=							self.unet(_UpperCAmelCase	,       _UpperCAmelCase					).sample

                   # compute the previous noisy sample x_t -> x_t-1
                   UpperCAmelCase_    				=							self.scheduler.step(_UpperCAmelCase	,       _UpperCAmelCase	,       _UpperCAmelCase					).prev_sample

             UpperCAmelCase_    				=							bits_to_decimal(_UpperCAmelCase					)

             if output_type == "pil":
                   UpperCAmelCase_    				=							self.numpy_to_pil(_UpperCAmelCase					)

             if not return_dict:
                   return (image,)

             return ImagePipelineOutput(images=_UpperCAmelCase					)









"""simple docstring"""





import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase      =		get_tests_dir("""fixtures/test_sentencepiece.model""")
@require_sentencepiece
class    lowercase__     (					SCREAMING_SNAKE_CASE       ,      unittest.TestCase   ):







       '''simple docstring'''


       UpperCamelCase		=  XLMProphetNetTokenizer
       UpperCamelCase		=  False
       UpperCamelCase		=  True



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


             '''simple docstring'''




             super().setUp()

             # We have a SentencePiece fixture for testing
             UpperCAmelCase_    				=							XLMProphetNetTokenizer(_UpperCAmelCase	,       keep_accents=_UpperCAmelCase					)
             tokenizer.save_pretrained(self.tmpdirname					)



       def   lowercase__       (							self    :						Tuple					)				->		int:


             '''simple docstring'''




             UpperCAmelCase_    				=							"[PAD]"
             UpperCAmelCase_    				=							0

             self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCAmelCase					)	,       _UpperCAmelCase					)
             self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCAmelCase					)	,       _UpperCAmelCase					)



       def   lowercase__       (							self    :						Dict					)				->		int:


             '''simple docstring'''




             UpperCAmelCase_    				=							list(self.get_tokenizer().get_vocab().keys()					)

             self.assertEqual(vocab_keys[0]	,       "[PAD]"					)
             self.assertEqual(vocab_keys[1]	,       "[CLS]"					)
             self.assertEqual(vocab_keys[-1]	,       "j"					)
             self.assertEqual(len(_UpperCAmelCase					)	,       1012					)



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


             '''simple docstring'''




             self.assertEqual(self.get_tokenizer().vocab_size	,       1012					)



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


             '''simple docstring'''




             UpperCAmelCase_    				=							XLMProphetNetTokenizer(_UpperCAmelCase	,       keep_accents=_UpperCAmelCase					)

             UpperCAmelCase_    				=							tokenizer.tokenize("This is a test"					)
             self.assertListEqual(_UpperCAmelCase	,       ["▁This", "▁is", "▁a", "▁t", "est"]					)

             self.assertListEqual(
                 tokenizer.convert_tokens_to_ids(_UpperCAmelCase					)	,       [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]	,       )

             UpperCAmelCase_    				=							tokenizer.tokenize("I was born in 92000, and this is falsé."					)
             self.assertListEqual(
                 _UpperCAmelCase	,       [
                     SPIECE_UNDERLINE + "I",
                     SPIECE_UNDERLINE + "was",
                     SPIECE_UNDERLINE + "b",
                     "or",
                     "n",
                     SPIECE_UNDERLINE + "in",
                     SPIECE_UNDERLINE + "",
                     "9",
                     "2",
                     "0",
                     "0",
                     "0",
                     ",",
                     SPIECE_UNDERLINE + "and",
                     SPIECE_UNDERLINE + "this",
                     SPIECE_UNDERLINE + "is",
                     SPIECE_UNDERLINE + "f",
                     "al",
                     "s",
                     "é",
                     ".",
                 ]	,       )
             UpperCAmelCase_    				=							tokenizer.convert_tokens_to_ids(_UpperCAmelCase					)
             self.assertListEqual(
                 _UpperCAmelCase	,       [
                     value + tokenizer.fairseq_offset
                     for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
                 ]	,       )

             UpperCAmelCase_    				=							tokenizer.convert_ids_to_tokens(_UpperCAmelCase					)
             self.assertListEqual(
                 _UpperCAmelCase	,       [
                     SPIECE_UNDERLINE + "I",
                     SPIECE_UNDERLINE + "was",
                     SPIECE_UNDERLINE + "b",
                     "or",
                     "n",
                     SPIECE_UNDERLINE + "in",
                     SPIECE_UNDERLINE + "",
                     "[UNK]",
                     "2",
                     "0",
                     "0",
                     "0",
                     ",",
                     SPIECE_UNDERLINE + "and",
                     SPIECE_UNDERLINE + "this",
                     SPIECE_UNDERLINE + "is",
                     SPIECE_UNDERLINE + "f",
                     "al",
                     "s",
                     "[UNK]",
                     ".",
                 ]	,       )



       @cached_property
       def   lowercase__       (							self    :						Union[str, Any]					)				->		Optional[Any]:


             '''simple docstring'''




             return XLMProphetNetTokenizer.from_pretrained("microsoft/xprophetnet-large-wiki100-cased"					)



       @slow
       def   lowercase__       (							self    :						List[Any]					)				->		int:


             '''simple docstring'''




             UpperCAmelCase_    				=							"Hello World!"
             UpperCAmelCase_    				=							[35389, 6672, 49, 2]
             self.assertListEqual(_UpperCAmelCase	,       self.big_tokenizer.encode(_UpperCAmelCase					)					)







       @slow
       def   lowercase__       (							self    :						Optional[int]					)				->		Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_    				=							{"input_ids": [[11073, 82783, 18, 26, 82783, 549, 51540, 248, 17209, 1301, 217, 20, 215186, 1325, 147, 17209, 1301, 217, 20, 56370, 53, 122020, 20, 16477, 27, 87355, 4548, 20, 4728, 78392, 17, 159969, 18, 26, 24491, 629, 15, 538, 22704, 5439, 15, 2788, 24491, 9885, 15, 43534, 605, 15, 814, 18403, 33200, 29, 15, 43534, 24458, 12410, 111, 24966, 83669, 9637, 144068, 26, 850, 22346, 27, 147, 24966, 83669, 83490, 26, 39113, 735, 27, 689, 656, 2800, 1339, 4600, 53, 122020, 115785, 34, 816, 1339, 46887, 18, 147, 53905, 1951, 42238, 41170, 17732, 834, 436, 15, 27523, 98733, 217, 147, 5542, 4981, 930, 17347, 16, 2], [20091, 629, 94, 82786, 58, 490, 20, 1528, 84, 53905, 344, 80592, 110128, 18822, 5267, 1306, 62, 152537, 308, 7997, 401, 124427, 549, 35442, 225, 109, 15055, 25748, 147, 7119, 43712, 34, 767, 135366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 63784, 119466, 17, 147808, 88214, 18, 656, 81, 32, 3296, 10280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
             # fmt: on

             self.tokenizer_integration_test_util(
                 expected_encoding=_UpperCAmelCase	,       model_name="microsoft/xprophetnet-large-wiki100-cased"	,       revision="1acad1643ddd54a44df6a1b797ada8373685d90e"	,       )




