




'''simple docstring'''





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast




@require_vision
class      UpperCamelCase       (	unittest.TestCase       ):



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[Any]							       =      tempfile.mkdtemp()

      _a				:							Any							       =      CLIPImageProcessor()
      _a				:							Any							       =      BertTokenizer.from_pretrained(
          """hf-internal-testing/tiny-random-BertModel"""		,     model_input_names=["""input_ids""", """attention_mask"""] )

      _a				:							Optional[int]							       =      GitProcessor(__lowerCAmelCase		,     __lowerCAmelCase )

      processor.save_pretrained(self.tmpdirname )



  def     a_							(			self		,     **__lowerCAmelCase ):







      '''simple docstring'''


      return AutoProcessor.from_pretrained(self.tmpdirname		,     **__lowerCAmelCase ).tokenizer



  def     a_							(			self		,     **__lowerCAmelCase ):







      '''simple docstring'''


      return AutoProcessor.from_pretrained(self.tmpdirname		,     **__lowerCAmelCase ).image_processor



  def     a_							(			self ):







      '''simple docstring'''


      shutil.rmtree(self.tmpdirname )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Dict							       =      [np.random.randint(255		,     size=(3, 30, 400)		,     dtype=np.uinta )]

      _a				:							Union[str, Any]							       =      [Image.fromarray(np.moveaxis(__lowerCAmelCase		,     0		,     -1 ) ) for x in image_inputs]

      return image_inputs



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Any							       =      GitProcessor(tokenizer=self.get_tokenizer()		,     image_processor=self.get_image_processor() )
      processor.save_pretrained(self.tmpdirname )

      _a				:							Optional[int]							       =      self.get_tokenizer(bos_token="""(BOS)"""		,     eos_token="""(EOS)""" )
      _a				:							Dict							       =      self.get_image_processor(do_normalize=__lowerCAmelCase		,     padding_value=1.0 )

      _a				:							Dict							       =      GitProcessor.from_pretrained(
          self.tmpdirname		,     bos_token="""(BOS)"""		,     eos_token="""(EOS)"""		,     do_normalize=__lowerCAmelCase		,     padding_value=1.0 )

      self.assertEqual(processor.tokenizer.get_vocab()		,     tokenizer_add_kwargs.get_vocab() )
      self.assertIsInstance(processor.tokenizer		,     __lowerCAmelCase )

      self.assertEqual(processor.image_processor.to_json_string()		,     image_processor_add_kwargs.to_json_string() )
      self.assertIsInstance(processor.image_processor		,     __lowerCAmelCase )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Any							       =      self.get_image_processor()
      _a				:							Tuple							       =      self.get_tokenizer()

      _a				:							int							       =      GitProcessor(tokenizer=__lowerCAmelCase		,     image_processor=__lowerCAmelCase )

      _a				:							Optional[Any]							       =      self.prepare_image_inputs()

      _a				:							List[str]							       =      image_processor(__lowerCAmelCase		,     return_tensors="""np""" )
      _a				:							Any							       =      processor(images=__lowerCAmelCase		,     return_tensors="""np""" )

      for key in input_feat_extract.keys():
          self.assertAlmostEqual(input_feat_extract[key].sum()		,     input_processor[key].sum()		,     delta=1e-2 )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[int]							       =      self.get_image_processor()
      _a				:							str							       =      self.get_tokenizer()

      _a				:							List[Any]							       =      GitProcessor(tokenizer=__lowerCAmelCase		,     image_processor=__lowerCAmelCase )

      _a				:							Optional[Any]							       =      """lower newer"""

      _a				:							str							       =      processor(text=__lowerCAmelCase )

      _a				:							List[str]							       =      tokenizer(__lowerCAmelCase		,     return_token_type_ids=__lowerCAmelCase )

      for key in encoded_tok.keys():
          self.assertListEqual(encoded_tok[key]		,     encoded_processor[key] )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[Any]							       =      self.get_image_processor()
      _a				:							Union[str, Any]							       =      self.get_tokenizer()

      _a				:							List[Any]							       =      GitProcessor(tokenizer=__lowerCAmelCase		,     image_processor=__lowerCAmelCase )

      _a				:							Optional[int]							       =      """lower newer"""
      _a				:							List[Any]							       =      self.prepare_image_inputs()

      _a				:							Any							       =      processor(text=__lowerCAmelCase		,     images=__lowerCAmelCase )

      self.assertListEqual(list(inputs.keys() )		,     ["""input_ids""", """attention_mask""", """pixel_values"""] )

      # test if it raises when no input is passed
      with pytest.raises(__lowerCAmelCase ):
          processor()



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							str							       =      self.get_image_processor()
      _a				:							Optional[Any]							       =      self.get_tokenizer()

      _a				:							Dict							       =      GitProcessor(tokenizer=__lowerCAmelCase		,     image_processor=__lowerCAmelCase )

      _a				:							int							       =      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      _a				:							Union[str, Any]							       =      processor.batch_decode(__lowerCAmelCase )
      _a				:							Optional[Any]							       =      tokenizer.batch_decode(__lowerCAmelCase )

      self.assertListEqual(__lowerCAmelCase		,     __lowerCAmelCase )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[Any]							       =      self.get_image_processor()
      _a				:							Union[str, Any]							       =      self.get_tokenizer()

      _a				:							str							       =      GitProcessor(tokenizer=__lowerCAmelCase		,     image_processor=__lowerCAmelCase )

      _a				:							Any							       =      """lower newer"""
      _a				:							int							       =      self.prepare_image_inputs()

      _a				:							int							       =      processor(text=__lowerCAmelCase		,     images=__lowerCAmelCase )

      # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
      self.assertListEqual(list(inputs.keys() )		,     ["""input_ids""", """attention_mask""", """pixel_values"""] )








'''simple docstring'''







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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


_lowerCamelCase				:		Optional[Any]				      =    logging.get_logger(__name__)  # pylint: disable=invalid-name





@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 	lowerCamelCase_				(			UpperCAmelCase_				):




					'''simple docstring'''






					lowercase__       =					42
					lowercase__       =					None






def 						_lowercase						(      lowercase_		,				lowercase_=0.999		,				lowercase_="cosine"		,				):



		'''simple docstring'''







		if alpha_transform_type == "cosine":

				def alpha_bar_fn(lowercase_    ):
						return math.cos((t + 0.008) / 1.008 * math.pi / 2    ) ** 2

		elif alpha_transform_type == "exp":

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

		else:
				raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''    )

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





class 	lowerCamelCase_				(			UpperCAmelCase_,			UpperCAmelCase_				):




					'''simple docstring'''






					lowercase__       =					[e.name for e in KarrasDiffusionSchedulers]
					lowercase__       =					1






					@register_to_config
					def __init__(       self,    __UpperCamelCase = 1_000,    __UpperCamelCase = 0.00_01,    __UpperCamelCase = 0.02,    __UpperCamelCase = "linear",    __UpperCamelCase = None,    __UpperCamelCase = "epsilon",    __UpperCamelCase = "linspace",    __UpperCamelCase = 0,    ):
							if trained_betas is not None:
									_snake_case      			=			torch.tensor(__UpperCamelCase,    dtype=torch.floataa					)
							elif beta_schedule == "linear":
									_snake_case      			=			torch.linspace(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    dtype=torch.floataa					)
							elif beta_schedule == "scaled_linear":
									# this schedule is very specific to the latent diffusion model.
									_snake_case      			=			(
									    torch.linspace(beta_start**0.5,    beta_end**0.5,    __UpperCamelCase,    dtype=torch.floataa					) ** 2
									)
							elif beta_schedule == "squaredcos_cap_v2":
									# Glide cosine schedule
									_snake_case      			=			betas_for_alpha_bar(__UpperCamelCase					)
							else:
									raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''					)

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

							_snake_case      			=			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
							_snake_case      			=			np.concatenate([sigmas[::-1], [0.0]]					).astype(np.floataa					)
							_snake_case      			=			torch.from_numpy(__UpperCamelCase					)

							# setable values
							_snake_case      			=			None
							_snake_case      			=			np.linspace(0,    num_train_timesteps - 1,    __UpperCamelCase,    dtype=__UpperCamelCase					)[::-1].copy()
							_snake_case      			=			torch.from_numpy(__UpperCamelCase					)
							_snake_case      			=			False






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							# standard deviation of the initial noise distribution
							if self.config.timestep_spacing in ["linspace", "trailing"]:
									return self.sigmas.max()

							return (self.sigmas.max() ** 2 + 1) ** 0.5






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase					):
							if isinstance(__UpperCamelCase,    torch.Tensor					):
									_snake_case      			=			timestep.to(self.timesteps.device					)
							_snake_case      			=			(self.timesteps == timestep).nonzero().item()
							_snake_case      			=			self.sigmas[step_index]
							_snake_case      			=			sample / ((sigma**2 + 1) ** 0.5)
							_snake_case      			=			True
							return sample






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase = None					):
							_snake_case      			=			num_inference_steps

							# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
							if self.config.timestep_spacing == "linspace":
									_snake_case      			=			np.linspace(0,    self.config.num_train_timesteps - 1,    __UpperCamelCase,    dtype=__UpperCamelCase					)[
									    ::-1
									].copy()
							elif self.config.timestep_spacing == "leading":
									_snake_case      			=			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
									_snake_case      			=			(np.arange(0,    __UpperCamelCase					) * step_ratio).round()[::-1].copy().astype(__UpperCamelCase					)
									timesteps += self.config.steps_offset
							elif self.config.timestep_spacing == "trailing":
									_snake_case      			=			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
									_snake_case      			=			(np.arange(self.config.num_train_timesteps,    0,    -step_ratio					)).round().copy().astype(__UpperCamelCase					)
									timesteps -= 1
							else:
									raise ValueError(
									    f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''					)

							_snake_case      			=			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
							_snake_case      			=			np.interp(__UpperCamelCase,    np.arange(0,    len(__UpperCamelCase					)					),    __UpperCamelCase					)
							_snake_case      			=			np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
							_snake_case      			=			torch.from_numpy(__UpperCamelCase					).to(device=__UpperCamelCase					)
							if str(__UpperCamelCase					).startswith("mps"					):
									# mps does not support float64
									_snake_case      			=			torch.from_numpy(__UpperCamelCase					).to(__UpperCamelCase,    dtype=torch.floataa					)
							else:
									_snake_case      			=			torch.from_numpy(__UpperCamelCase					).to(device=__UpperCamelCase					)






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase = None,    __UpperCamelCase = True,    ):

							if (
							    isinstance(__UpperCamelCase,    __UpperCamelCase					)
							    or isinstance(__UpperCamelCase,    torch.IntTensor					)
							    or isinstance(__UpperCamelCase,    torch.LongTensor					)
							):
									raise ValueError(
									    (
									        "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
									        " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
									        " one of the `scheduler.timesteps` as a timestep."
									    ),    )

							if not self.is_scale_input_called:
									logger.warning(
									    "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
									    "See `StableDiffusionPipeline` for a usage example."					)

							if isinstance(__UpperCamelCase,    torch.Tensor					):
									_snake_case      			=			timestep.to(self.timesteps.device					)

							_snake_case      			=			(self.timesteps == timestep).nonzero().item()
							_snake_case      			=			self.sigmas[step_index]

							# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
							if self.config.prediction_type == "epsilon":
									_snake_case      			=			sample - sigma * model_output
							elif self.config.prediction_type == "v_prediction":
									# * c_out + input * c_skip
									_snake_case      			=			model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
							elif self.config.prediction_type == "sample":
									raise NotImplementedError("prediction_type not implemented yet: sample"					)
							else:
									raise ValueError(
									    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''					)

							_snake_case      			=			self.sigmas[step_index]
							_snake_case      			=			self.sigmas[step_index + 1]
							_snake_case      			=			(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
							_snake_case      			=			(sigma_to**2 - sigma_up**2) ** 0.5

							# 2. Convert to an ODE derivative
							_snake_case      			=			(sample - pred_original_sample) / sigma

							_snake_case      			=			sigma_down - sigma

							_snake_case      			=			sample + derivative * dt

							_snake_case      			=			model_output.device
							_snake_case      			=			randn_tensor(model_output.shape,    dtype=model_output.dtype,    device=__UpperCamelCase,    generator=__UpperCamelCase					)

							_snake_case      			=			prev_sample + noise * sigma_up

							if not return_dict:
									return (prev_sample,)

							return EulerAncestralDiscreteSchedulerOutput(
							    prev_sample=__UpperCamelCase,    pred_original_sample=__UpperCamelCase					)






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    ):
							# Make sure sigmas and timesteps have the same device and dtype as original_samples
							_snake_case      			=			self.sigmas.to(device=original_samples.device,    dtype=original_samples.dtype					)
							if original_samples.device.type == "mps" and torch.is_floating_point(__UpperCamelCase					):
									# mps does not support float64
									_snake_case      			=			self.timesteps.to(original_samples.device,    dtype=torch.floataa					)
									_snake_case      			=			timesteps.to(original_samples.device,    dtype=torch.floataa					)
							else:
									_snake_case      			=			self.timesteps.to(original_samples.device					)
									_snake_case      			=			timesteps.to(original_samples.device					)

							_snake_case      			=			[(schedule_timesteps == t).nonzero().item() for t in timesteps]

							_snake_case      			=			sigmas[step_indices].flatten()
							while len(sigma.shape					) < len(original_samples.shape					):
									_snake_case      			=			sigma.unsqueeze(-1					)

							_snake_case      			=			original_samples + noise * sigma
							return noisy_samples






					def __len__(       self					):
							return self.config.num_train_timesteps





