

'''simple docstring'''






import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						torch.FloatTensor
  SCREAMING_SNAKE_CASE					:						Optional[torch.FloatTensor]           =    None


def 	_A  (					A__  ,       A__=0.9_9_9  ,       A__="cosine"  ,       ):






 """simple docstring"""



 if alpha_transform_type == "cosine":

  def alpha_bar_fn(A__   ):
   return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2   ) ** 2

 elif alpha_transform_type == "exp":

  def alpha_bar_fn(A__   ):
   return math.exp(t * -12.0   )

 else:
  raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}"   )

 __lowercase													=				[]
 for i in range(A__   ):
  __lowercase													=				i / num_diffusion_timesteps
  __lowercase													=				(i + 1) / num_diffusion_timesteps
  betas.append(min(1 - alpha_bar_fn(A__   ) / alpha_bar_fn(A__   )  ,       A__   )   )
 return torch.tensor(A__  ,       dtype=torch.floataa   )






class     lowercase_			(lowerCamelCase__       , lowerCamelCase__   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						str           =    1






  @register_to_config
  def __init__(   self     :  Union[str, Any]      ,lowercase__     :  int = 1_0_0_0      ,lowercase__     :  float = 0.0_0_0_1      ,lowercase__     :  float = 0.0_2      ,lowercase__     :  str = "linear"      ,lowercase__     :  Optional[Union[np.ndarray, List[float]]] = None      ,lowercase__     :  bool = True      ,lowercase__     :  bool = True      ,lowercase__     :  int = 0      ,lowercase__     :  str = "epsilon"      ,lowercase__     :  float = 1.0      ,**lowercase__     :  Optional[int]      ,):
   if kwargs.get('''set_alpha_to_one'''      ,lowercase__    ) is not None:
    __lowercase													=				(
        '''The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'''
    )
    deprecate('''set_alpha_to_one'''      ,'''1.0.0'''      ,lowercase__      ,standard_warn=lowercase__    )
    __lowercase													=				kwargs['''set_alpha_to_one''']
   if trained_betas is not None:
    __lowercase													=				torch.tensor(lowercase__      ,dtype=torch.floataa    )
   elif beta_schedule == "linear":
    __lowercase													=				torch.linspace(lowercase__      ,lowercase__      ,lowercase__      ,dtype=torch.floataa    )
   elif beta_schedule == "scaled_linear":
    # this schedule is very specific to the latent diffusion model.
    __lowercase													=				(
        torch.linspace(beta_start**0.5      ,beta_end**0.5      ,lowercase__      ,dtype=torch.floataa    ) ** 2
    )
   elif beta_schedule == "squaredcos_cap_v2":
    # Glide cosine schedule
    __lowercase													=				betas_for_alpha_bar(lowercase__    )
   else:
    raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"    )

   __lowercase													=				1.0 - self.betas
   __lowercase													=				torch.cumprod(self.alphas      ,dim=0    )

   # At every step in inverted ddim, we are looking into the next alphas_cumprod
   # For the final step, there is no next alphas_cumprod, and the index is out of bounds
   # `set_alpha_to_zero` decides whether we set this parameter simply to zero
   # in this case, self.step() just output the predicted noise
   # or whether we use the final alpha of the "non-previous" one.
   __lowercase													=				torch.tensor(0.0    ) if set_alpha_to_zero else self.alphas_cumprod[-1]

   # standard deviation of the initial noise distribution
   __lowercase													=				1.0

   # setable values
   __lowercase													=				None
   __lowercase													=				torch.from_numpy(np.arange(0      ,lowercase__    ).copy().astype(np.intaa    )    )






  def       SCREAMING_SNAKE_CASE   (   self     :  str      ,lowercase__     :  torch.FloatTensor      ,lowercase__     :  Optional[int] = None    ):
   return sample






  def       SCREAMING_SNAKE_CASE   (   self     :  str      ,lowercase__     :  int      ,lowercase__     :  Union[str, torch.device] = None    ):

   if num_inference_steps > self.config.num_train_timesteps:
    raise ValueError(
        F"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
        F" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
        F" maximal {self.config.num_train_timesteps} timesteps."    )

   __lowercase													=				num_inference_steps
   __lowercase													=				self.config.num_train_timesteps // self.num_inference_steps
   # creates integer timesteps by multiplying by ratio
   # casting to int to avoid issues when num_inference_step is power of 3
   __lowercase													=				(np.arange(0      ,lowercase__    ) * step_ratio).round().copy().astype(np.intaa    )
   __lowercase													=				torch.from_numpy(lowercase__    ).to(lowercase__    )
   self.timesteps += self.config.steps_offset






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]      ,lowercase__     :  torch.FloatTensor      ,lowercase__     :  int      ,lowercase__     :  torch.FloatTensor      ,lowercase__     :  float = 0.0      ,lowercase__     :  bool = False      ,lowercase__     :  Optional[torch.FloatTensor] = None      ,lowercase__     :  bool = True      ,):
   # 1. get previous step value (=t+1)
   __lowercase													=				timestep + self.config.num_train_timesteps // self.num_inference_steps

   # 2. compute alphas, betas
   # change original implementation to exactly match noise levels for analogous forward process
   __lowercase													=				self.alphas_cumprod[timestep]
   __lowercase													=				(
       self.alphas_cumprod[prev_timestep]
       if prev_timestep < self.config.num_train_timesteps
       else self.final_alpha_cumprod
   )

   __lowercase													=				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
   if self.config.prediction_type == "epsilon":
    __lowercase													=				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
    __lowercase													=				model_output
   elif self.config.prediction_type == "sample":
    __lowercase													=				model_output
    __lowercase													=				(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
   elif self.config.prediction_type == "v_prediction":
    __lowercase													=				(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
    __lowercase													=				(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
   else:
    raise ValueError(
        F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
        ''' `v_prediction`'''    )

   # 4. Clip or threshold "predicted x_0"
   if self.config.clip_sample:
    __lowercase													=				pred_original_sample.clamp(
        -self.config.clip_sample_range      ,self.config.clip_sample_range    )

   # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
   __lowercase													=				(1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

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

   if not return_dict:
    return (prev_sample, pred_original_sample)
   return DDIMSchedulerOutput(prev_sample=lowercase__      ,pred_original_sample=lowercase__    )






  def __len__(   self     :  List[Any]    ):
   return self.config.num_train_timesteps




'''simple docstring'''






import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
 import tensorflow as tf

if is_keras_nlp_available():
 from transformers.models.gpta import TFGPTaTokenizer


lowerCAmelCase__      		=			['''gpt2''']
lowerCAmelCase__      		=			'''gpt2'''

if is_tf_available():






 class     lowercase_			(tf.Module   ):




   """simple docstring"""




   def __init__(   self     :  List[str]      ,lowercase__     :  Tuple    ):
    super().__init__()
    __lowercase													=				tokenizer
    __lowercase													=				AutoConfig.from_pretrained(lowercase__    )
    __lowercase													=				TFGPTaLMHeadModel.from_config(lowercase__    )






   @tf.function(input_signature=(tf.TensorSpec((None,)      ,tf.string      ,name='''text'''    ),)    )
   def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]      ,lowercase__     :  Dict    ):
    __lowercase													=				self.tokenizer(lowercase__    )
    __lowercase													=				tokenized['''input_ids'''].to_tensor()

    __lowercase													=				tf.cast(input_ids_dense > 0      ,tf.intaa    )
    # input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

    __lowercase													=				self.model(input_ids=lowercase__      ,attention_mask=lowercase__    )['''logits''']

    return outputs






@require_tf
@require_keras_nlp
class     lowercase_			(unittest.TestCase   ):




  """simple docstring"""




  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   super().setUp()

   __lowercase													=				[GPTaTokenizer.from_pretrained(lowercase__    ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
   __lowercase													=				[TFGPTaTokenizer.from_pretrained(lowercase__    ) for checkpoint in TOKENIZER_CHECKPOINTS]
   assert len(self.tokenizers    ) == len(self.tf_tokenizers    )

   __lowercase													=				[
       '''This is a straightforward English test sentence.''',
       '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
       '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
       '''And some much more rare Chinese: 齉 堃 齉堃''',
       '''Je vais aussi écrire en français pour tester les accents''',
       '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
   ]
   __lowercase													=				list(zip(self.test_sentences      ,self.test_sentences[::-1]    )    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   for tokenizer, tf_tokenizer in zip(self.tokenizers      ,self.tf_tokenizers    ):
    for test_inputs in self.test_sentences:
     __lowercase													=				tokenizer([test_inputs]      ,return_tensors='''tf'''    )
     __lowercase													=				tf_tokenizer([test_inputs]    )

     for key in python_outputs.keys():
      # convert them to numpy to avoid messing with ragged tensors
      __lowercase													=				python_outputs[key].numpy()
      __lowercase													=				tf_outputs[key].numpy()

      self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape    )    )
      self.assertTrue(tf.reduce_all(tf.cast(lowercase__      ,tf.intaa    ) == tf_outputs_values    )    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   for tf_tokenizer in self.tf_tokenizers:
    __lowercase													=				tf.function(lowercase__    )
    for test_inputs in self.test_sentences:
     __lowercase													=				tf.constant(lowercase__    )
     __lowercase													=				compiled_tokenizer(lowercase__    )
     __lowercase													=				tf_tokenizer(lowercase__    )

     for key in eager_outputs.keys():
      self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]    )    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   for tf_tokenizer in self.tf_tokenizers:
    __lowercase													=				ModelToSave(tokenizer=lowercase__    )
    __lowercase													=				tf.convert_to_tensor([self.test_sentences[0]]    )
    __lowercase													=				model.serving(lowercase__    )  # Build model with some sample inputs
    with TemporaryDirectory() as tempdir:
     __lowercase													=				Path(lowercase__    ) / '''saved.model'''
     tf.saved_model.save(lowercase__      ,lowercase__      ,signatures={'''serving_default''': model.serving}    )
     __lowercase													=				tf.saved_model.load(lowercase__    )
    __lowercase													=				loaded_model.signatures['''serving_default'''](lowercase__    )['''output_0''']
    # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
    self.assertTrue(tf.reduce_all(out == loaded_output    )    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   for tf_tokenizer in self.tf_tokenizers:
    __lowercase													=				tf.convert_to_tensor([self.test_sentences[0]]    )
    __lowercase													=				tf_tokenizer(lowercase__    )  # Build model with some sample inputs

    __lowercase													=				tf_tokenizer.get_config()
    __lowercase													=				TFGPTaTokenizer.from_config(lowercase__    )
    __lowercase													=				model_from_config(lowercase__    )

    for key in from_config_output.keys():
     self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]    )    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   for tf_tokenizer in self.tf_tokenizers:
    # for the test to run
    __lowercase													=				1_2_3_1_2_3

    for max_length in [3, 5, 1_0_2_4]:
     __lowercase													=				tf.convert_to_tensor([self.test_sentences[0]]    )
     __lowercase													=				tf_tokenizer(lowercase__      ,max_length=lowercase__    )

     __lowercase													=				out['''input_ids'''].numpy().shape[1]

     assert out_length == max_length

