


"""simple docstring"""




import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput

def        lowercase							(		lowerCAmelCase__ :     Any    ,					lowerCAmelCase__ :     Union[str, Any]=0.9_99    ,					lowerCAmelCase__ :     Union[str, Any]="cosine"    ,					) ->      Dict:
		if alpha_transform_type == "cosine":

				def alpha_bar_fn(lowerCAmelCase__ :     Any			):
						return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2			) ** 2

		elif alpha_transform_type == "exp":

				def alpha_bar_fn(lowerCAmelCase__ :     Optional[int]			):
						return math.exp(t * -12.0			)

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

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




class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			Optional[int]										=				[e.name for e in KarrasDiffusionSchedulers]
		__UpperCAmelCase		:			Tuple										=				2

		@register_to_config
		def __init__(  self							, _a = 1_000							, _a = 0.0_0085							, _a = 0.012							, _a = "linear"							, _a = None							, _a = "epsilon"							, _a = False							, _a = False							, _a = 1.0							, _a = "linspace"							, _a = 0							, ):





				if trained_betas is not None:
						__a           =			torch.tensor(_SCREAMING_SNAKE_CASE							, dtype=torch.floataa      )
				elif beta_schedule == "linear":
						__a           =			torch.linspace(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, dtype=torch.floataa      )
				elif beta_schedule == "scaled_linear":
						# this schedule is very specific to the latent diffusion model.
						__a           =			(
						    torch.linspace(beta_start**0.5							, beta_end**0.5							, _SCREAMING_SNAKE_CASE							, dtype=torch.floataa      ) ** 2
						)
				elif beta_schedule == "squaredcos_cap_v2":
						# Glide cosine schedule
						__a           =			betas_for_alpha_bar(_SCREAMING_SNAKE_CASE							, alpha_transform_type='''cosine'''      )
				elif beta_schedule == "exp":
						__a           =			betas_for_alpha_bar(_SCREAMING_SNAKE_CASE							, alpha_transform_type='''exp'''      )
				else:
						raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''      )

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

				#  set all values
				self.set_timesteps(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE      )
				__a           =			use_karras_sigmas

		def 			__UpperCAmelCase       (  self							, _a							, _a=None      ):





				if schedule_timesteps is None:
						__a           =			self.timesteps

				__a           =			(schedule_timesteps == timestep).nonzero()

				# The sigma index that is taken for the **very** first `step`
				# is always the second index (or the last index if there is only 1)
				# This way we can ensure we don't accidentally skip a sigma in
				# case we start in the middle of the denoising schedule (e.g. for image-to-image)
				if len(self._index_counter      ) == 0:
						__a           =			1 if len(_SCREAMING_SNAKE_CASE      ) > 1 else 0
				else:
						__a           =			timestep.cpu().item() if torch.is_tensor(_SCREAMING_SNAKE_CASE      ) else timestep
						__a           =			self._index_counter[timestep_int]

				return indices[pos].item()

		@property
		def 			__UpperCAmelCase       (  self      ):





				if self.config.timestep_spacing in ["linspace", "trailing"]:
						return self.sigmas.max()

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

		def 			__UpperCAmelCase       (  self							, _a							, _a							, ):
				__a           =			self.index_for_timestep(_SCREAMING_SNAKE_CASE      )

				__a           =			self.sigmas[step_index]
				__a           =			sample / ((sigma**2 + 1) ** 0.5)
				return sample

		def 			__UpperCAmelCase       (  self							, _a							, _a = None							, _a = None							, ):
				__a           =			num_inference_steps

				__a           =			num_train_timesteps or self.config.num_train_timesteps

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

				__a           =			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
				__a           =			np.log(_SCREAMING_SNAKE_CASE      )
				__a           =			np.interp(_SCREAMING_SNAKE_CASE							, np.arange(0							, len(_SCREAMING_SNAKE_CASE      )      )							, _SCREAMING_SNAKE_CASE      )

				if self.config.use_karras_sigmas:
						__a           =			self._convert_to_karras(in_sigmas=_SCREAMING_SNAKE_CASE							, num_inference_steps=self.num_inference_steps      )
						__a           =			np.array([self._sigma_to_t(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE      ) for sigma in sigmas]      )

				__a           =			np.concatenate([sigmas, [0.0]]      ).astype(np.floataa      )
				__a           =			torch.from_numpy(_SCREAMING_SNAKE_CASE      ).to(device=_SCREAMING_SNAKE_CASE      )
				__a           =			torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2      ), sigmas[-1:]]      )

				__a           =			torch.from_numpy(_SCREAMING_SNAKE_CASE      )
				__a           =			torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2      )]      )

				if str(_SCREAMING_SNAKE_CASE      ).startswith('''mps'''      ):
						# mps does not support float64
						__a           =			timesteps.to(_SCREAMING_SNAKE_CASE							, dtype=torch.floataa      )
				else:
						__a           =			timesteps.to(device=_SCREAMING_SNAKE_CASE      )

				# empty dt and derivative
				__a           =			None
				__a           =			None

				# for exp beta schedules, such as the one for `pipeline_shap_e.py`
				# we need an index counter
				__a           =			defaultdict(_SCREAMING_SNAKE_CASE      )

		def 			__UpperCAmelCase       (  self							, _a							, _a      ):
				__a           =			np.log(_SCREAMING_SNAKE_CASE      )

				# get distribution
				__a           =			log_sigma - log_sigmas[:, np.newaxis]

				# get sigmas range
				__a           =			np.cumsum((dists >= 0)							, axis=0      ).argmax(axis=0      ).clip(max=log_sigmas.shape[0] - 2      )
				__a           =			low_idx + 1

				__a           =			log_sigmas[low_idx]
				__a           =			log_sigmas[high_idx]

				# interpolate sigmas
				__a           =			(low - log_sigma) / (low - high)
				__a           =			np.clip(_SCREAMING_SNAKE_CASE							, 0							, 1      )

				# transform interpolation to time range
				__a           =			(1 - w) * low_idx + w * high_idx
				__a           =			t.reshape(sigma.shape      )
				return t

		def 			__UpperCAmelCase       (  self							, _a							, _a      ):
				__a           =			in_sigmas[-1].item()
				__a           =			in_sigmas[0].item()

				__a           =			7.0  # 7.0 is the value used in the paper
				__a           =			np.linspace(0							, 1							, _SCREAMING_SNAKE_CASE      )
				__a           =			sigma_min ** (1 / rho)
				__a           =			sigma_max ** (1 / rho)
				__a           =			(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
				return sigmas

		@property
		def 			__UpperCAmelCase       (  self      ):
				return self.dt is None

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a = True							, ):
				__a           =			self.index_for_timestep(_SCREAMING_SNAKE_CASE      )

				# advance index counter by 1
				__a           =			timestep.cpu().item() if torch.is_tensor(_SCREAMING_SNAKE_CASE      ) else timestep
				self._index_counter[timestep_int] += 1

				if self.state_in_first_order:
						__a           =			self.sigmas[step_index]
						__a           =			self.sigmas[step_index + 1]
				else:
						# 2nd order / Heun's method
						__a           =			self.sigmas[step_index - 1]
						__a           =			self.sigmas[step_index]

				# currently only gamma=0 is supported. This usually works best anyways.
				# We can support gamma in the future but then need to scale the timestep before
				# passing it to the model which requires a change in API
				__a           =			0
				__a           =			sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

				# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
				if self.config.prediction_type == "epsilon":
						__a           =			sigma_hat if self.state_in_first_order else sigma_next
						__a           =			sample - sigma_input * model_output
				elif self.config.prediction_type == "v_prediction":
						__a           =			sigma_hat if self.state_in_first_order else sigma_next
						__a           =			model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
						    sample / (sigma_input**2 + 1)
						)
				elif self.config.prediction_type == "sample":
						__a           =			model_output
				else:
						raise ValueError(
						    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''      )

				if self.config.clip_sample:
						__a           =			pred_original_sample.clamp(
						    -self.config.clip_sample_range							, self.config.clip_sample_range      )

				if self.state_in_first_order:
						# 2. Convert to an ODE derivative for 1st order
						__a           =			(sample - pred_original_sample) / sigma_hat
						# 3. delta timestep
						__a           =			sigma_next - sigma_hat

						# store for 2nd order step
						__a           =			derivative
						__a           =			dt
						__a           =			sample
				else:
						# 2. 2nd order / Heun's method
						__a           =			(sample - pred_original_sample) / sigma_next
						__a           =			(self.prev_derivative + derivative) / 2

						# 3. take prev timestep & sample
						__a           =			self.dt
						__a           =			self.sample

						# free dt and derivative
						# Note, this puts the scheduler in "first order mode"
						__a           =			None
						__a           =			None
						__a           =			None

				__a           =			sample + derivative * dt

				if not return_dict:
						return (prev_sample,)

				return SchedulerOutput(prev_sample=_SCREAMING_SNAKE_CASE      )

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, ):
				__a           =			self.sigmas.to(device=original_samples.device							, dtype=original_samples.dtype      )
				if original_samples.device.type == "mps" and torch.is_floating_point(_SCREAMING_SNAKE_CASE      ):
						# mps does not support float64
						__a           =			self.timesteps.to(original_samples.device							, dtype=torch.floataa      )
						__a           =			timesteps.to(original_samples.device							, dtype=torch.floataa      )
				else:
						__a           =			self.timesteps.to(original_samples.device      )
						__a           =			timesteps.to(original_samples.device      )

				__a           =			[self.index_for_timestep(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE      ) for t in timesteps]

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

				__a           =			original_samples + noise * sigma
				return noisy_samples





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










import copy
import tempfile
import unittest

from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError

from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test






class   _lowerCamelCase (					unittest.TestCase			):




       """simple docstring"""



       @parameterized.expand([(None,), ('''foo.json''',)]		)
       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->Tuple:




         '''simple docstring'''





         A_      :				Dict		     =					GenerationConfig(
             do_sample=_SCREAMING_SNAKE_CASE				,       temperature=0.7				,       length_penalty=1.0				,       bad_words_ids=[[1, 2, 3], [4, 5]]				,       )
         with tempfile.TemporaryDirectory() as tmp_dir:
           config.save_pretrained(_SCREAMING_SNAKE_CASE				,       config_name=_SCREAMING_SNAKE_CASE		)
           A_      :				Tuple		     =					GenerationConfig.from_pretrained(_SCREAMING_SNAKE_CASE				,       config_name=_SCREAMING_SNAKE_CASE		)

         # Checks parameters that were specified
         self.assertEqual(loaded_config.do_sample				,       _SCREAMING_SNAKE_CASE		)
         self.assertEqual(loaded_config.temperature				,       0.7		)
         self.assertEqual(loaded_config.length_penalty				,       1.0		)
         self.assertEqual(loaded_config.bad_words_ids				,       [[1, 2, 3], [4, 5]]		)

         # Checks parameters that were not specified (defaults)
         self.assertEqual(loaded_config.top_k				,       50		)
         self.assertEqual(loaded_config.max_length				,       20		)
         self.assertEqual(loaded_config.max_time				,       _SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self		)->Dict:




         '''simple docstring'''





         A_      :				str		     =					AutoConfig.from_pretrained('''gpt2'''		)
         A_      :				int		     =					GenerationConfig.from_model_config(_SCREAMING_SNAKE_CASE		)
         A_      :				Dict		     =					GenerationConfig()

         # The generation config has loaded a few non-default parameters from the model config
         self.assertNotEqual(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)

         # One of those parameters is eos_token_id -- check if it matches
         self.assertNotEqual(generation_config_from_model.eos_token_id				,       default_generation_config.eos_token_id		)
         self.assertEqual(generation_config_from_model.eos_token_id				,       model_config.eos_token_id		)



       def        _snake_case       (      self		)->Optional[int]:




         '''simple docstring'''





         A_      :				List[Any]		     =					GenerationConfig()
         A_      :				Optional[Any]		     =					{
             '''max_new_tokens''': 1024,
             '''foo''': '''bar''',
         }
         A_      :				Dict		     =					copy.deepcopy(_SCREAMING_SNAKE_CASE		)
         A_      :				Optional[int]		     =					generation_config.update(**_SCREAMING_SNAKE_CASE		)

         # update_kwargs was not modified (no side effects)
         self.assertEqual(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)

         # update_kwargs was used to update the config on valid attributes
         self.assertEqual(generation_config.max_new_tokens				,       1024		)

         # `.update()` returns a dictionary of unused kwargs
         self.assertEqual(_SCREAMING_SNAKE_CASE				,       {'''foo''': '''bar'''}		)



       def        _snake_case       (      self		)->str:




         '''simple docstring'''





         A_      :				List[str]		     =					GenerationConfig()
         A_      :				int		     =					'''bar'''

         with tempfile.TemporaryDirectory('''test-generation-config'''		) as tmp_dir:
           generation_config.save_pretrained(_SCREAMING_SNAKE_CASE		)

           A_      :				Optional[Any]		     =					GenerationConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
         # update_kwargs was used to update the config on valid attributes
         self.assertEqual(new_config.foo				,       '''bar'''		)

         A_      :				Optional[int]		     =					GenerationConfig.from_model_config(_SCREAMING_SNAKE_CASE		)
         assert not hasattr(_SCREAMING_SNAKE_CASE				,       '''foo'''		)  # no new kwargs should be initialized if from config







       def        _snake_case       (      self		)->List[Any]:




         '''simple docstring'''





         A_      :				str		     =					GenerationConfig()
         self.assertEqual(default_config.temperature				,       1.0		)
         self.assertEqual(default_config.do_sample				,       _SCREAMING_SNAKE_CASE		)
         self.assertEqual(default_config.num_beams				,       1		)

         A_      :				str		     =					GenerationConfig(
             do_sample=_SCREAMING_SNAKE_CASE				,       temperature=0.7				,       length_penalty=1.0				,       bad_words_ids=[[1, 2, 3], [4, 5]]				,       )
         self.assertEqual(config.temperature				,       0.7		)
         self.assertEqual(config.do_sample				,       _SCREAMING_SNAKE_CASE		)
         self.assertEqual(config.num_beams				,       1		)

         with tempfile.TemporaryDirectory() as tmp_dir:
           config.save_pretrained(_SCREAMING_SNAKE_CASE		)
           A_      :				Dict		     =					GenerationConfig.from_pretrained(_SCREAMING_SNAKE_CASE				,       temperature=1.0		)

         self.assertEqual(loaded_config.temperature				,       1.0		)
         self.assertEqual(loaded_config.do_sample				,       _SCREAMING_SNAKE_CASE		)
         self.assertEqual(loaded_config.num_beams				,       1		)  # default value






@is_staging_test
class   _lowerCamelCase (					unittest.TestCase			):




       """simple docstring"""



       @classmethod
       def        _snake_case       (      cls		)->Optional[int]:




         '''simple docstring'''





         A_      :				str		     =					TOKEN
         HfFolder.save_token(_SCREAMING_SNAKE_CASE		)



       @classmethod
       def        _snake_case       (      cls		)->List[Any]:




         '''simple docstring'''





         try:
           delete_repo(token=cls._token				,       repo_id='''test-generation-config'''		)
         except HTTPError:
           pass

         try:
           delete_repo(token=cls._token				,       repo_id='''valid_org/test-generation-config-org'''		)
         except HTTPError:
           pass



       def        _snake_case       (      self		)->List[Any]:




         '''simple docstring'''





         A_      :				List[Any]		     =					GenerationConfig(
             do_sample=_SCREAMING_SNAKE_CASE				,       temperature=0.7				,       length_penalty=1.0				,       )
         config.push_to_hub('''test-generation-config'''				,       use_auth_token=self._token		)

         A_      :				Optional[int]		     =					GenerationConfig.from_pretrained(F'''{USER}/test-generation-config'''		)
         for k, v in config.to_dict().items():
           if k != "transformers_version":
             self.assertEqual(_SCREAMING_SNAKE_CASE				,       getattr(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)		)

        # Reset repo
         delete_repo(token=self._token				,       repo_id='''test-generation-config'''		)

         # Push to hub via save_pretrained
         with tempfile.TemporaryDirectory() as tmp_dir:
           config.save_pretrained(
               _SCREAMING_SNAKE_CASE				,       repo_id='''test-generation-config'''				,       push_to_hub=_SCREAMING_SNAKE_CASE				,       use_auth_token=self._token		)

         A_      :				Optional[Any]		     =					GenerationConfig.from_pretrained(F'''{USER}/test-generation-config'''		)
         for k, v in config.to_dict().items():
           if k != "transformers_version":
             self.assertEqual(_SCREAMING_SNAKE_CASE				,       getattr(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)		)







       def        _snake_case       (      self		)->List[str]:




         '''simple docstring'''





         A_      :				Tuple		     =					GenerationConfig(
             do_sample=_SCREAMING_SNAKE_CASE				,       temperature=0.7				,       length_penalty=1.0				,       )
         config.push_to_hub('''valid_org/test-generation-config-org'''				,       use_auth_token=self._token		)

         A_      :				Union[str, Any]		     =					GenerationConfig.from_pretrained('''valid_org/test-generation-config-org'''		)
         for k, v in config.to_dict().items():
           if k != "transformers_version":
             self.assertEqual(_SCREAMING_SNAKE_CASE				,       getattr(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)		)

        # Reset repo
         delete_repo(token=self._token				,       repo_id='''valid_org/test-generation-config-org'''		)

         # Push to hub via save_pretrained
         with tempfile.TemporaryDirectory() as tmp_dir:
           config.save_pretrained(
               _SCREAMING_SNAKE_CASE				,       repo_id='''valid_org/test-generation-config-org'''				,       push_to_hub=_SCREAMING_SNAKE_CASE				,       use_auth_token=self._token		)

         A_      :				Dict		     =					GenerationConfig.from_pretrained('''valid_org/test-generation-config-org'''		)
         for k, v in config.to_dict().items():
           if k != "transformers_version":
             self.assertEqual(_SCREAMING_SNAKE_CASE				,       getattr(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)		)




