

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_	     =     logging.get_logger(__name__)  # pylint: disable=invalid-name





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







			'''simple docstring'''

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






def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__=0.999  ,		SCREAMING_SNAKE_CASE__="cosine"  ,		):
						if alpha_transform_type == "cosine":

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

						elif alpha_transform_type == "exp":

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

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

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





class      snake_case_       (		lowerCAmelCase__     ,	lowerCAmelCase__ ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE					:       str						      =    [e.name for e in KarrasDiffusionSchedulers]
			SCREAMING_SNAKE_CASE					:       Union[str, Any]						      =    1







			@register_to_config
			def __init__(					self			:    str							,       _UpperCamelCase			:    int = 1_0_0_0							,       _UpperCamelCase			:    float = 0.0001							,       _UpperCamelCase			:    float = 0.02							,       _UpperCamelCase			:    str = "linear"							,       _UpperCamelCase			:    Optional[Union[np.ndarray, List[float]]] = None							,       _UpperCamelCase			:    str = "epsilon"							,       _UpperCamelCase			:    str = "linspace"							,       _UpperCamelCase			:    int = 0							,       )				->int:


									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     snake_case__(					self			:    List[str]							)				->Optional[int]:


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

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







			def     snake_case__(					self			:    int							,       _UpperCamelCase			:    torch.FloatTensor							,       _UpperCamelCase			:    Union[float, torch.FloatTensor]							)				->torch.FloatTensor:


									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     snake_case__(					self			:    int							,       _UpperCamelCase			:    int							,       _UpperCamelCase			:    Union[str, torch.device] = None							)				->Any:
									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     snake_case__(					self			:    List[Any]							,       _UpperCamelCase			:    torch.FloatTensor							,       _UpperCamelCase			:    Union[float, torch.FloatTensor]							,       _UpperCamelCase			:    torch.FloatTensor							,       _UpperCamelCase			:    Optional[torch.Generator] = None							,       _UpperCamelCase			:    bool = True							,       )				->Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:


									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     snake_case__(					self			:    List[str]							,       _UpperCamelCase			:    torch.FloatTensor							,       _UpperCamelCase			:    torch.FloatTensor							,       _UpperCamelCase			:    torch.FloatTensor							,       )				->torch.FloatTensor:
									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			:    str							)				->str:
									return self.config.num_train_timesteps


import concurrent.futures
import json
import os
import shutil
import tempfile
import unittest

from transformers import AutoTokenizer, PreTrainedTokenizerFast
from transformers.testing_utils import require_tokenizers

from ..test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class      snake_case_       (		__A     ,	unittest.TestCase ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE					:       str						      =    PreTrainedTokenizerFast
			SCREAMING_SNAKE_CASE					:       List[str]						      =    False
			SCREAMING_SNAKE_CASE					:       Tuple						      =    True
			SCREAMING_SNAKE_CASE					:       str						      =    "tokenizer_file"







			def     snake_case__(					self			:    Dict							)				->int:
									snake_case_       =     False  # because we don't have pretrained_vocab_files_map
									super().setUp()
									snake_case_       =     True

									snake_case_       =     ['''robot-test/dummy-tokenizer-fast''', '''robot-test/dummy-tokenizer-wordlevel''']
									snake_case_       =     '''SaulLu/dummy-tokenizer-bytelevel-bpe'''

									# Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
									snake_case_       =     [(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]

									snake_case_       =     PreTrainedTokenizerFast.from_pretrained(model_paths[0]							)
									tokenizer.save_pretrained(self.tmpdirname							)







			def     snake_case__(					self			:    Union[str, Any]							)				->Dict:
									# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
									# model
									pass







			def     snake_case__(					self			:    str							)				->Any:
									# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
									# model
									pass







			def     snake_case__(					self			:    List[Any]							)				->str:
									# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
									# model
									pass







			def     snake_case__(					self			:    str							)				->List[Any]:
									# PreTrainedTokenizerFast doesn't have tokenizer_file in its signature
									pass







			def     snake_case__(					self			:    Any							)				->Union[str, Any]:
									snake_case_       =     self.tmpdirname
									# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
																					try:
																											snake_case_       =     tempfile.mkdtemp()
																											snake_case_       =     self.rust_tokenizer_class.from_pretrained(_UpperCamelCase							,       **_UpperCamelCase							)

																											tokenizer.save_pretrained(self.tmpdirname							)
																											super().test_training_new_tokenizer()
																					finally:
																											# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
																											# is restored
																											shutil.rmtree(self.tmpdirname							)
																											snake_case_       =     tmpdirname_orig







			def     snake_case__(					self			:    List[str]							)				->List[Any]:
									snake_case_       =     self.tmpdirname
									# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
																					try:
																											snake_case_       =     tempfile.mkdtemp()
																											snake_case_       =     self.rust_tokenizer_class.from_pretrained(_UpperCamelCase							,       **_UpperCamelCase							)

																											tokenizer.save_pretrained(self.tmpdirname							)
																											super().test_training_new_tokenizer_with_special_tokens_change()
																					finally:
																											# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
																											# is restored
																											shutil.rmtree(self.tmpdirname							)
																											snake_case_       =     tmpdirname_orig







			def     snake_case__(					self			:    Optional[int]							)				->Optional[Any]:
									snake_case_       =     self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name							)

									snake_case_       =     ('''a''' for _ in range(1_0_0_0							))
									snake_case_       =     tokenizer.train_new_from_iterator(text_iterator=_UpperCamelCase							,       length=1_0_0_0							,       vocab_size=5_0							)

									snake_case_       =     new_tokenizer.encode('''a🤗'''							)
									self.assertEqual(_UpperCamelCase							,       [6_4, 1_7_2, 2_5_3, 9_7, 2_4_5]							)






@require_tokenizers
class      snake_case_       (		unittest.TestCase ):







			'''simple docstring'''

			def     snake_case__(					self			:    Optional[int]							)				->int:
									snake_case_       =     AutoTokenizer.from_pretrained('''bert-base-cased'''							)
									snake_case_       =     json.loads(tokenizer._tokenizer.to_str()							)
									snake_case_       =     len(_UpperCamelCase							)

									with tempfile.TemporaryDirectory() as tmp_dir:
															# Hack to save this in the tokenizer_config.json
															snake_case_       =     ['''tokenizer.4.0.0.json''']
															tokenizer.save_pretrained(_UpperCamelCase							)
															json.dump(_UpperCamelCase							,       open(os.path.join(_UpperCamelCase							,       '''tokenizer.4.0.0.json'''							)							,       '''w'''							)							)

															# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
															snake_case_       =     AutoTokenizer.from_pretrained(_UpperCamelCase							)
															self.assertEqual(len(_UpperCamelCase							)							,       len(_UpperCamelCase							) + 1							)
															snake_case_       =     json.loads(new_tokenizer._tokenizer.to_str()							)
															self.assertIn('''huggingface'''							,       json_tokenizer['''model''']['''vocab''']							)

															# Will need to be adjusted if we reach v42 and this test is still here.
															# Should pick the old tokenizer file as the version of Transformers is < 4.0.0
															shutil.move(os.path.join(_UpperCamelCase							,       '''tokenizer.4.0.0.json'''							)							,       os.path.join(_UpperCamelCase							,       '''tokenizer.42.0.0.json'''							)							)
															snake_case_       =     ['''tokenizer.42.0.0.json''']
															tokenizer.save_pretrained(_UpperCamelCase							)
															snake_case_       =     AutoTokenizer.from_pretrained(_UpperCamelCase							)
															self.assertEqual(len(_UpperCamelCase							)							,       len(_UpperCamelCase							)							)
															snake_case_       =     json.loads(new_tokenizer._tokenizer.to_str()							)
															self.assertNotIn('''huggingface'''							,       json_tokenizer['''model''']['''vocab''']							)







			def     snake_case__(					self			:    List[str]							)				->Optional[Any]:
									# This repo has two tokenizer files, one for v4.0.0 and above with an added token, one for versions lower.
									snake_case_       =     '''hf-internal-testing/test-two-tokenizers'''

									# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
									snake_case_       =     AutoTokenizer.from_pretrained(_UpperCamelCase							)
									self.assertEqual(len(_UpperCamelCase							)							,       2_8_9_9_7							)
									snake_case_       =     json.loads(tokenizer._tokenizer.to_str()							)
									self.assertIn('''huggingface'''							,       json_tokenizer['''model''']['''vocab''']							)

									# Testing an older version by monkey-patching the version in the module it's used.
									import transformers as old_transformers

									snake_case_       =     '''3.0.0'''
									snake_case_       =     old_transformers.models.auto.AutoTokenizer.from_pretrained(_UpperCamelCase							)
									self.assertEqual(len(_UpperCamelCase							)							,       2_8_9_9_6							)
									snake_case_       =     json.loads(old_tokenizer._tokenizer.to_str()							)
									self.assertNotIn('''huggingface'''							,       json_tokenizer['''model''']['''vocab''']							)






@require_tokenizers
class      snake_case_       (		unittest.TestCase ):







			'''simple docstring'''

			def     snake_case__(					self			:    str							)				->Optional[int]:
									# See https://github.com/huggingface/transformers/pull/12550
									# and https://github.com/huggingface/tokenizers/issues/537
									snake_case_       =     PreTrainedTokenizerFast.from_pretrained('''robot-test/dummy-tokenizer-wordlevel'''							)
									snake_case_       =     '''The Matrix is a 1999 science fiction action film.'''

									with concurrent.futures.ThreadPoolExecutor() as executor:
															snake_case_       =     [executor.submit(self.fetch							,       _UpperCamelCase							,       _UpperCamelCase							) for i in range(1_0							)]
															snake_case_       =     [future.result() for future in futures]
															self.assertEqual(_UpperCamelCase							,       [[1, 1_0, 0, 8, 0, 1_8, 0, 0, 0, 2] for i in range(1_0							)]							)







			def     snake_case__(					self			:    Optional[int]							,       _UpperCamelCase			:    Dict							,       _UpperCamelCase			:    str							)				->Tuple:
									return tokenizer.encode(_UpperCamelCase							,       truncation='''longest_first'''							,       padding='''longest'''							)