



"""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


snake_case   					=				8




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_=BITS	)						->				List[str]:
  _snake_case   	=      x.device

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

  _snake_case   	=      2 ** torch.arange(bits - 1  ,						-1  ,						-1  ,						device=lowerCAmelCase_	)
  _snake_case   	=      rearrange(lowerCAmelCase_  ,						'''d -> d 1 1'''	)
  _snake_case   	=      rearrange(lowerCAmelCase_  ,						'''b c h w -> b c 1 h w'''	)

  _snake_case   	=      ((x & mask) != 0).float()
  _snake_case   	=      rearrange(lowerCAmelCase_  ,						'''b c d h w -> b (c d) h w'''	)
  _snake_case   	=      bits * 2 - 1
  return bits




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_=BITS	)						->				Union[str, Any]:
  _snake_case   	=      x.device

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

  _snake_case   	=      rearrange(lowerCAmelCase_  ,						'''d -> d 1 1'''	)
  _snake_case   	=      rearrange(lowerCAmelCase_  ,						'''b (c d) h w -> b c d h w'''  ,						d=8	)
  _snake_case   	=      reduce(x * mask  ,						'''b c d h w -> b c h w'''  ,						'''sum'''	)
  return (dec / 255).clamp(0.0  ,						1.0	)




def 			snake_case		(		self  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_ = 0.0  ,						lowerCAmelCase_ = True  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_ = True  ,						)						->				Union[DDIMSchedulerOutput, Tuple]:
  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)
  _snake_case   	=      timestep - self.config.num_train_timesteps // self.num_inference_steps

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

  _snake_case   	=      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
  _snake_case   	=      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

  # 4. Clip "predicted x_0"
  _snake_case   	=      self.bit_scale
  if self.config.clip_sample:
    _snake_case   	=      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)
  _snake_case   	=      self._get_variance(lowerCAmelCase_  ,						lowerCAmelCase_	)
  _snake_case   	=      eta * variance ** 0.5

  if use_clipped_model_output:
    # the model_output is always re-derived from the clipped x_0 in Glide
    _snake_case   	=      (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
  _snake_case   	=      (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
  _snake_case   	=      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
    _snake_case   	=      model_output.device if torch.is_tensor(lowerCAmelCase_	) else '''cpu'''
    _snake_case   	=      torch.randn(model_output.shape  ,						dtype=model_output.dtype  ,						generator=lowerCAmelCase_	).to(lowerCAmelCase_	)
    _snake_case   	=      self._get_variance(lowerCAmelCase_  ,						lowerCAmelCase_	) ** 0.5 * eta * noise

    _snake_case   	=      prev_sample + variance

  if not return_dict:
    return (prev_sample,)

  return DDIMSchedulerOutput(prev_sample=lowerCAmelCase_  ,						pred_original_sample=lowerCAmelCase_	)




def 			snake_case		(		self  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_="epsilon"  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_ = True  ,						)						->				Union[DDPMSchedulerOutput, Tuple]:
  _snake_case   	=      timestep

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

  # 1. compute alphas, betas
  _snake_case   	=      self.alphas_cumprod[t]
  _snake_case   	=      self.alphas_cumprod[t - 1] if t > 0 else self.one
  _snake_case   	=      1 - alpha_prod_t
  _snake_case   	=      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":
    _snake_case   	=      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
  elif prediction_type == "sample":
    _snake_case   	=      model_output
  else:
    raise ValueError(f"""Unsupported prediction_type {prediction_type}."""	)

  # 3. Clip "predicted x_0"
  _snake_case   	=      self.bit_scale
  if self.config.clip_sample:
    _snake_case   	=      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
  _snake_case   	=      (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
  _snake_case   	=      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
  _snake_case   	=      pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

  _snake_case   	=      pred_prev_sample + variance

  if not return_dict:
    return (pred_prev_sample,)

  return DDPMSchedulerOutput(prev_sample=lowerCAmelCase_  ,						pred_original_sample=lowerCAmelCase_	)







class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):



   def __init__(       self    :	Dict							,       __lowerCamelCase    :	UNetaDConditionModel							,       __lowerCamelCase    :	Union[DDIMScheduler, DDPMScheduler]							,       __lowerCamelCase    :	Optional[float] = 1.0							,       ):



     """simple docstring"""
     super().__init__()
     _snake_case   	=      bit_scale
     _snake_case   	=      (
         ddim_bit_scheduler_step if isinstance(__lowerCamelCase							,       __lowerCamelCase				) else ddpm_bit_scheduler_step
     )

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





   @torch.no_grad()
   def __call__(       self    :	Union[str, Any]							,       __lowerCamelCase    :	Optional[int] = 2_5_6							,       __lowerCamelCase    :	Optional[int] = 2_5_6							,       __lowerCamelCase    :	Optional[int] = 5_0							,       __lowerCamelCase    :	Optional[torch.Generator] = None							,       __lowerCamelCase    :	Optional[int] = 1							,       __lowerCamelCase    :	Optional[str] = "pil"							,       __lowerCamelCase    :	bool = True							,       **__lowerCamelCase    :	List[str]							,       ):



     """simple docstring"""
     _snake_case   	=      torch.randn(
         (batch_size, self.unet.config.in_channels, height, width)							,       generator=__lowerCamelCase							,       )
     _snake_case   	=      decimal_to_bits(__lowerCamelCase				) * self.bit_scale
     _snake_case   	=      latents.to(self.device				)

     self.scheduler.set_timesteps(__lowerCamelCase				)

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

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

     _snake_case   	=      bits_to_decimal(__lowerCamelCase				)

     if output_type == "pil":
       _snake_case   	=      self.numpy_to_pil(__lowerCamelCase				)

     if not return_dict:
       return (image,)

     return ImagePipelineOutput(images=__lowerCamelCase				)










"""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

snake_case   					=				'''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
snake_case   					=				[
    '''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>''',
]
snake_case   					=				dict(zip(vocab, range(len(vocab))))
snake_case   					=				['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
     snake_case   					=				Path(tmpdirname)
     snake_case   					=				build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
     snake_case   					=				build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
     snake_case   					=				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))

     snake_case   					=				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,
     )

snake_case   					=				FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1_0_0_0,
    tgt_vocab_size=1_0_0_0,
    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,
)

snake_case   					=				FSMTForConditionalGeneration(config)
print(F"num of params {tiny_model.num_parameters()}")

# Test
snake_case   					=				tokenizer(['''Making tiny model'''], return_tensors='''pt''')
snake_case   					=				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





