
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 						__magic_name__			(   __a      :	str	,		__a      :	Tuple=BITS						):



     '''simple docstring'''




     UpperCamelCase__				=    x.device

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

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

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






def 						__magic_name__			(   __a      :	Tuple	,		__a      :	List[Any]=BITS						):



     '''simple docstring'''




     UpperCamelCase__				=    x.device

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

     UpperCamelCase__				=    rearrange(__a	,		"""d -> d 1 1"""						)
     UpperCamelCase__				=    rearrange(__a	,		"""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 						__magic_name__			(   self      :	int	,		__a      :	torch.FloatTensor	,		__a      :	int	,		__a      :	torch.FloatTensor	,		__a      :	float = 0.0	,		__a      :	bool = True	,		__a      :	int=None	,		__a      :	bool = True	,		):



     '''simple docstring'''




     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(__a	,		-scale	,		__a						)

     # 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(__a	,		__a						)
     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(__a						) else """cpu"""
          UpperCamelCase__				=    torch.randn(model_output.shape	,		dtype=model_output.dtype	,		generator=__a						).to(__a						)
          UpperCamelCase__				=    self._get_variance(__a	,		__a						) ** 0.5 * eta * noise

          UpperCamelCase__				=    prev_sample + variance

     if not return_dict:
          return (prev_sample,)

     return DDIMSchedulerOutput(prev_sample=__a	,		pred_original_sample=__a						)






def 						__magic_name__			(   self      :	Union[str, Any]	,		__a      :	torch.FloatTensor	,		__a      :	int	,		__a      :	torch.FloatTensor	,		__a      :	Any="epsilon"	,		__a      :	Tuple=None	,		__a      :	bool = True	,		):



     '''simple docstring'''




     UpperCamelCase__				=    timestep

     if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
          UpperCamelCase__		,	UpperCamelCase__				=    torch.split(__a	,		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(__a	,		-scale	,		__a						)

     # 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=__a						).to(model_output.device						)
          UpperCamelCase__				=    (self._get_variance(__a	,		predicted_variance=__a						) ** 0.5) * noise

     UpperCamelCase__				=    pred_prev_sample + variance

     if not return_dict:
          return (pred_prev_sample,)

     return DDPMSchedulerOutput(prev_sample=__a	,		pred_original_sample=__a						)




class      __A(    __lowerCamelCase			):

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_ = 1.0	,      ):
       super().__init__()
       UpperCamelCase__				=    bit_scale
       UpperCamelCase__				=    (
           ddim_bit_scheduler_step if isinstance(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						) else ddpm_bit_scheduler_step
       )

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







  @torch.no_grad()
  def __call__(self	,      SCREAMING_SNAKE_CASE_ = 2_56	,      SCREAMING_SNAKE_CASE_ = 2_56	,      SCREAMING_SNAKE_CASE_ = 50	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = 1	,      SCREAMING_SNAKE_CASE_ = "pil"	,      SCREAMING_SNAKE_CASE_ = True	,      **SCREAMING_SNAKE_CASE_	,      ):
       UpperCamelCase__				=    torch.randn(
           (batch_size, self.unet.config.in_channels, height, width)	,      generator=SCREAMING_SNAKE_CASE_	,      )
       UpperCamelCase__				=    decimal_to_bits(SCREAMING_SNAKE_CASE_						) * self.bit_scale
       UpperCamelCase__				=    latents.to(self.device						)

       self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_						)

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

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

       UpperCamelCase__				=    bits_to_decimal(SCREAMING_SNAKE_CASE_						)

       if output_type == "pil":
            UpperCamelCase__				=    self.numpy_to_pil(SCREAMING_SNAKE_CASE_						)

       if not return_dict:
            return (image,)

       return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_						)







import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
    import torch

    from transformers import AutoModelForCausalLM




@require_torch
class      __A(    unittest.TestCase			):

  """simple docstring"""







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
       UpperCamelCase__				=    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    -1

       UpperCamelCase__				=    ids_tensor((1, 5)	,      vocab_size=model.config.vocab_size						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    model.generate(SCREAMING_SNAKE_CASE_	,      max_new_tokens=10	,      do_sample=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    tokenizer.decode(greedy_ids[0]						)

       with CaptureStdout() as cs:
            UpperCamelCase__				=    TextStreamer(SCREAMING_SNAKE_CASE_						)
            model.generate(SCREAMING_SNAKE_CASE_	,      max_new_tokens=10	,      do_sample=SCREAMING_SNAKE_CASE_	,      streamer=SCREAMING_SNAKE_CASE_						)
       # The greedy text should be printed to stdout, except for the final "\n" in the streamer
       UpperCamelCase__				=    cs.out[:-1]

       self.assertEqual(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
       UpperCamelCase__				=    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    -1

       UpperCamelCase__				=    ids_tensor((1, 5)	,      vocab_size=model.config.vocab_size						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    model.generate(SCREAMING_SNAKE_CASE_	,      max_new_tokens=10	,      do_sample=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    tokenizer.decode(greedy_ids[0]						)

       UpperCamelCase__				=    TextIteratorStreamer(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    {"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
       UpperCamelCase__				=    Thread(target=model.generate	,      kwargs=SCREAMING_SNAKE_CASE_						)
       thread.start()
       UpperCamelCase__				=    """"""
       for new_text in streamer:
            streamer_text += new_text

       self.assertEqual(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
       UpperCamelCase__				=    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    -1

       UpperCamelCase__				=    ids_tensor((1, 5)	,      vocab_size=model.config.vocab_size						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    model.generate(SCREAMING_SNAKE_CASE_	,      max_new_tokens=10	,      do_sample=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    greedy_ids[:, input_ids.shape[1] :]
       UpperCamelCase__				=    tokenizer.decode(new_greedy_ids[0]						)

       with CaptureStdout() as cs:
            UpperCamelCase__				=    TextStreamer(SCREAMING_SNAKE_CASE_	,      skip_prompt=SCREAMING_SNAKE_CASE_						)
            model.generate(SCREAMING_SNAKE_CASE_	,      max_new_tokens=10	,      do_sample=SCREAMING_SNAKE_CASE_	,      streamer=SCREAMING_SNAKE_CASE_						)
       # The greedy text should be printed to stdout, except for the final "\n" in the streamer
       UpperCamelCase__				=    cs.out[:-1]

       self.assertEqual(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       # Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
       # with actual models -- the dummy models' tokenizers are not aligned with their models, and
       # `skip_special_tokens=True` has no effect on them
       UpperCamelCase__				=    AutoTokenizer.from_pretrained("""distilgpt2"""						)
       UpperCamelCase__				=    AutoModelForCausalLM.from_pretrained("""distilgpt2"""						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    -1

       UpperCamelCase__				=    torch.ones((1, 5)	,      device=SCREAMING_SNAKE_CASE_						).long() * model.config.bos_token_id
       with CaptureStdout() as cs:
            UpperCamelCase__				=    TextStreamer(SCREAMING_SNAKE_CASE_	,      skip_special_tokens=SCREAMING_SNAKE_CASE_						)
            model.generate(SCREAMING_SNAKE_CASE_	,      max_new_tokens=1	,      do_sample=SCREAMING_SNAKE_CASE_	,      streamer=SCREAMING_SNAKE_CASE_						)

       # The prompt contains a special token, so the streamer should not print it. As such, the output text, when
       # re-tokenized, must only contain one token
       UpperCamelCase__				=    cs.out[:-1]  # Remove the final "\n"
       UpperCamelCase__				=    tokenizer(SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""						)
       self.assertEqual(streamer_text_tokenized.input_ids.shape	,      (1, 1)						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
       UpperCamelCase__				=    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    -1

       UpperCamelCase__				=    ids_tensor((1, 5)	,      vocab_size=model.config.vocab_size						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    TextIteratorStreamer(SCREAMING_SNAKE_CASE_	,      timeout=0.001						)
       UpperCamelCase__				=    {"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
       UpperCamelCase__				=    Thread(target=model.generate	,      kwargs=SCREAMING_SNAKE_CASE_						)
       thread.start()

       # The streamer will timeout after 0.001 seconds, so an exception will be raised
       with self.assertRaises(SCREAMING_SNAKE_CASE_						):
            UpperCamelCase__				=    """"""
            for new_text in streamer:
                 streamer_text += new_text





