

import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path

import pytest

import transformers
from transformers import (
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AutoTokenizer,
    BertConfig,
    BertTokenizer,
    BertTokenizerFast,
    CTRLTokenizer,
    GPTaTokenizer,
    GPTaTokenizerFast,
    PreTrainedTokenizerFast,
    RobertaTokenizer,
    RobertaTokenizerFast,
    is_tokenizers_available,
)
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.auto.tokenization_auto import (
    TOKENIZER_MAPPING,
    get_tokenizer_config,
    tokenizer_class_from_name,
)
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import (
    DUMMY_DIFF_TOKENIZER_IDENTIFIER,
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tokenizers,
    slow,
)


sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
 from test_module.custom_tokenization_fast import CustomTokenizerFast







class   __lowercase      (unittest.TestCase ):
 def 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[Any]					=					0
 @slow
 def 	UpperCamelCase__  (							self					)					->Optional[Any]:



       '''simple docstring'''





       for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
             __lowerCAmelCase					:						List[str]					=					AutoTokenizer.from_pretrained(A_					)
             self.assertIsNotNone(A_					)
             self.assertIsInstance(A_      ,							(BertTokenizer, BertTokenizerFast)					)
             self.assertGreater(len(A_					)      ,							0					)

       for model_name in GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys():
             __lowerCAmelCase					:						str					=					AutoTokenizer.from_pretrained(A_					)
             self.assertIsNotNone(A_					)
             self.assertIsInstance(A_      ,							(GPTaTokenizer, GPTaTokenizerFast)					)
             self.assertGreater(len(A_					)      ,							0					)
 def 	UpperCamelCase__  (							self					)					->Dict:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[int]					=					AutoTokenizer.from_pretrained(A_					)
       self.assertIsInstance(A_      ,							(BertTokenizer, BertTokenizerFast)					)
       self.assertEqual(tokenizer.vocab_size      ,							12					)
 def 	UpperCamelCase__  (							self					)					->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						Any					=					AutoTokenizer.from_pretrained(A_					)
       self.assertIsInstance(A_      ,							(RobertaTokenizer, RobertaTokenizerFast)					)
       self.assertEqual(tokenizer.vocab_size      ,							20					)
 def 	UpperCamelCase__  (							self					)					->Dict:



       '''simple docstring'''





       __lowerCAmelCase					:						Dict					=					AutoConfig.from_pretrained(A_					)
       self.assertIsInstance(A_      ,							A_					)
       # Check that tokenizer_type ≠ model_type
       __lowerCAmelCase					:						Optional[int]					=					AutoTokenizer.from_pretrained(A_      ,							config=A_					)
       self.assertIsInstance(A_      ,							(BertTokenizer, BertTokenizerFast)					)
       self.assertEqual(tokenizer.vocab_size      ,							12					)
 def 	UpperCamelCase__  (							self					)					->Optional[Any]:



       '''simple docstring'''





       with tempfile.TemporaryDirectory() as tmp_dir:
             shutil.copy('''./tests/fixtures/vocab.txt'''      ,							os.path.join(A_      ,							'''vocab.txt'''					)					)

             __lowerCAmelCase					:						int					=					AutoTokenizer.from_pretrained(A_      ,							tokenizer_type='''bert'''      ,							use_fast=A_					)
             self.assertIsInstance(A_      ,							A_					)

       with tempfile.TemporaryDirectory() as tmp_dir:
             shutil.copy('''./tests/fixtures/vocab.json'''      ,							os.path.join(A_      ,							'''vocab.json'''					)					)
             shutil.copy('''./tests/fixtures/merges.txt'''      ,							os.path.join(A_      ,							'''merges.txt'''					)					)

             __lowerCAmelCase					:						List[Any]					=					AutoTokenizer.from_pretrained(A_      ,							tokenizer_type='''gpt2'''      ,							use_fast=A_					)
             self.assertIsInstance(A_      ,							A_					)
 @require_tokenizers
 def 	UpperCamelCase__  (							self					)					->Optional[int]:



       '''simple docstring'''





       with tempfile.TemporaryDirectory() as tmp_dir:
             shutil.copy('''./tests/fixtures/vocab.txt'''      ,							os.path.join(A_      ,							'''vocab.txt'''					)					)

             __lowerCAmelCase					:						int					=					AutoTokenizer.from_pretrained(A_      ,							tokenizer_type='''bert'''					)
             self.assertIsInstance(A_      ,							A_					)

       with tempfile.TemporaryDirectory() as tmp_dir:
             shutil.copy('''./tests/fixtures/vocab.json'''      ,							os.path.join(A_      ,							'''vocab.json'''					)					)
             shutil.copy('''./tests/fixtures/merges.txt'''      ,							os.path.join(A_      ,							'''merges.txt'''					)					)

             __lowerCAmelCase					:						List[Any]					=					AutoTokenizer.from_pretrained(A_      ,							tokenizer_type='''gpt2'''					)
             self.assertIsInstance(A_      ,							A_					)
 def 	UpperCamelCase__  (							self					)					->Dict:



       '''simple docstring'''





       with pytest.raises(A_					):
             AutoTokenizer.from_pretrained('''./'''      ,							tokenizer_type='''xxx'''					)
 @require_tokenizers
 def 	UpperCamelCase__  (							self					)					->List[str]:



       '''simple docstring'''





       for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
             __lowerCAmelCase					:						Any					=					tokenizer_class.from_pretrained('''wietsedv/bert-base-dutch-cased'''					)
             self.assertIsInstance(A_      ,							(BertTokenizer, BertTokenizerFast)					)

             if isinstance(A_      ,							A_					):
                   self.assertEqual(tokenizer.basic_tokenizer.do_lower_case      ,							A_					)
             else:
                   self.assertEqual(tokenizer.do_lower_case      ,							A_					)

             self.assertEqual(tokenizer.model_max_length      ,							512					)
 @require_tokenizers
 def 	UpperCamelCase__  (							self					)					->str:



       '''simple docstring'''





       for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
             with self.assertRaisesRegex(
                 A_      ,							'''julien-c/herlolip-not-exists is not a local folder and is not a valid model identifier'''      ,							):
                   __lowerCAmelCase					:						Tuple					=					tokenizer_class.from_pretrained('''julien-c/herlolip-not-exists'''					)
 def 	UpperCamelCase__  (							self					)					->List[str]:



       '''simple docstring'''





       __lowerCAmelCase					:						Tuple					=					TOKENIZER_MAPPING.values()
       __lowerCAmelCase					:						Dict					=					[]

       for slow_tok, fast_tok in tokenizers:
             if slow_tok is not None:
                   tokenizer_names.append(slow_tok.__name__					)

             if fast_tok is not None:
                   tokenizer_names.append(fast_tok.__name__					)

       for tokenizer_name in tokenizer_names:
             # must find the right class
             tokenizer_class_from_name(A_					)
 @require_tokenizers
 def 	UpperCamelCase__  (							self					)					->Optional[int]:



       '''simple docstring'''





       self.assertIsInstance(AutoTokenizer.from_pretrained('''bert-base-cased'''      ,							use_fast=A_					)      ,							A_					)
       self.assertIsInstance(AutoTokenizer.from_pretrained('''bert-base-cased'''					)      ,							A_					)
 @require_tokenizers
 def 	UpperCamelCase__  (							self					)					->List[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[Any]					=					AutoTokenizer.from_pretrained('''distilbert-base-uncased'''      ,							do_lower_case=A_					)
       __lowerCAmelCase					:						Optional[Any]					=					'''Hello, world. How are you?'''
       __lowerCAmelCase					:						Any					=					tokenizer.tokenize(A_					)
       self.assertEqual('''[UNK]'''      ,							tokens[0]					)

       __lowerCAmelCase					:						str					=					AutoTokenizer.from_pretrained('''microsoft/mpnet-base'''      ,							do_lower_case=A_					)
       __lowerCAmelCase					:						List[str]					=					tokenizer.tokenize(A_					)
       self.assertEqual('''[UNK]'''      ,							tokens[0]					)
 @require_tokenizers
 def 	UpperCamelCase__  (							self					)					->List[str]:



       '''simple docstring'''





       __lowerCAmelCase					:						int					=					AutoTokenizer.from_pretrained('''robot-test/dummy-tokenizer-fast-with-model-config'''					)
       self.assertEqual(type(A_					)      ,							A_					)
       self.assertEqual(tokenizer.model_max_length      ,							512					)
       self.assertEqual(tokenizer.vocab_size      ,							3_0000					)
       self.assertEqual(tokenizer.unk_token      ,							'''[UNK]'''					)
       self.assertEqual(tokenizer.padding_side      ,							'''right'''					)
       self.assertEqual(tokenizer.truncation_side      ,							'''right'''					)
 def 	UpperCamelCase__  (							self					)					->Any:



       '''simple docstring'''





       __lowerCAmelCase					:						Union[str, Any]					=					AutoTokenizer.from_pretrained(A_					)
       self.assertIsInstance(A_      ,							(BertTokenizer, BertTokenizerFast)					)
       with tempfile.TemporaryDirectory() as tmp_dir:
             tokenizer.save_pretrained(A_					)
             __lowerCAmelCase					:						Optional[Any]					=					AutoTokenizer.from_pretrained(A_					)

       self.assertIsInstance(A_      ,							tokenizer.__class__					)
       self.assertEqual(tokenizera.vocab_size      ,							12					)
 def 	UpperCamelCase__  (							self					)					->Union[str, Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						List[str]					=					AutoTokenizer.from_pretrained('''ctrl'''					)
       # There is no fast CTRL so this always gives us a slow tokenizer.
       self.assertIsInstance(A_      ,							A_					)
 def 	UpperCamelCase__  (							self					)					->str:



       '''simple docstring'''





       __lowerCAmelCase					:						Union[str, Any]					=					get_tokenizer_config('''bert-base-cased'''					)
       __lowerCAmelCase					:						Optional[Any]					=					config.pop('''_commit_hash'''      ,							A_					)
       # If we ever update bert-base-cased tokenizer config, this dict here will need to be updated.
       self.assertEqual(A_      ,							{'''do_lower_case''': False}					)

       # This model does not have a tokenizer_config so we get back an empty dict.
       __lowerCAmelCase					:						Any					=					get_tokenizer_config(A_					)
       self.assertDictEqual(A_      ,							{}					)

       # A tokenizer saved with `save_pretrained` always creates a tokenizer config.
       __lowerCAmelCase					:						Any					=					AutoTokenizer.from_pretrained(A_					)
       with tempfile.TemporaryDirectory() as tmp_dir:
             tokenizer.save_pretrained(A_					)
             __lowerCAmelCase					:						str					=					get_tokenizer_config(A_					)

       # Check the class of the tokenizer was properly saved (note that it always saves the slow class).
       self.assertEqual(config['''tokenizer_class''']      ,							'''BertTokenizer'''					)
 def 	UpperCamelCase__  (							self					)					->List[str]:



       '''simple docstring'''





       try:
             AutoConfig.register('''custom'''      ,							A_					)

             AutoTokenizer.register(A_      ,							slow_tokenizer_class=A_					)
             # Trying to register something existing in the Transformers library will raise an error
             with self.assertRaises(A_					):
                   AutoTokenizer.register(A_      ,							slow_tokenizer_class=A_					)

             __lowerCAmelCase					:						int					=					CustomTokenizer.from_pretrained(A_					)
             with tempfile.TemporaryDirectory() as tmp_dir:
                   tokenizer.save_pretrained(A_					)

                   __lowerCAmelCase					:						Any					=					AutoTokenizer.from_pretrained(A_					)
                   self.assertIsInstance(A_      ,							A_					)

       finally:
             if "custom" in CONFIG_MAPPING._extra_content:
                   del CONFIG_MAPPING._extra_content["custom"]
             if CustomConfig in TOKENIZER_MAPPING._extra_content:
                   del TOKENIZER_MAPPING._extra_content[CustomConfig]
 @require_tokenizers
 def 	UpperCamelCase__  (							self					)					->List[str]:



       '''simple docstring'''





       try:
             AutoConfig.register('''custom'''      ,							A_					)

             # Can register in two steps
             AutoTokenizer.register(A_      ,							slow_tokenizer_class=A_					)
             self.assertEqual(TOKENIZER_MAPPING[CustomConfig]      ,							(CustomTokenizer, None)					)
             AutoTokenizer.register(A_      ,							fast_tokenizer_class=A_					)
             self.assertEqual(TOKENIZER_MAPPING[CustomConfig]      ,							(CustomTokenizer, CustomTokenizerFast)					)

             del TOKENIZER_MAPPING._extra_content[CustomConfig]
             # Can register in one step
             AutoTokenizer.register(
                 A_      ,							slow_tokenizer_class=A_      ,							fast_tokenizer_class=A_					)
             self.assertEqual(TOKENIZER_MAPPING[CustomConfig]      ,							(CustomTokenizer, CustomTokenizerFast)					)

             # Trying to register something existing in the Transformers library will raise an error
             with self.assertRaises(A_					):
                   AutoTokenizer.register(A_      ,							fast_tokenizer_class=A_					)

             # We pass through a bert tokenizer fast cause there is no converter slow to fast for our new toknizer
             # and that model does not have a tokenizer.json
             with tempfile.TemporaryDirectory() as tmp_dir:
                   __lowerCAmelCase					:						Tuple					=					BertTokenizerFast.from_pretrained(A_					)
                   bert_tokenizer.save_pretrained(A_					)
                   __lowerCAmelCase					:						Tuple					=					CustomTokenizerFast.from_pretrained(A_					)

             with tempfile.TemporaryDirectory() as tmp_dir:
                   tokenizer.save_pretrained(A_					)

                   __lowerCAmelCase					:						Dict					=					AutoTokenizer.from_pretrained(A_					)
                   self.assertIsInstance(A_      ,							A_					)

                   __lowerCAmelCase					:						Any					=					AutoTokenizer.from_pretrained(A_      ,							use_fast=A_					)
                   self.assertIsInstance(A_      ,							A_					)

       finally:
             if "custom" in CONFIG_MAPPING._extra_content:
                   del CONFIG_MAPPING._extra_content["custom"]
             if CustomConfig in TOKENIZER_MAPPING._extra_content:
                   del TOKENIZER_MAPPING._extra_content[CustomConfig]
 def 	UpperCamelCase__  (							self					)					->List[str]:



       '''simple docstring'''





       with self.assertRaises(A_					):
             __lowerCAmelCase					:						Any					=					AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer'''					)
       # If remote code is disabled, we can't load this config.
       with self.assertRaises(A_					):
             __lowerCAmelCase					:						Dict					=					AutoTokenizer.from_pretrained(
                 '''hf-internal-testing/test_dynamic_tokenizer'''      ,							trust_remote_code=A_					)

       __lowerCAmelCase					:						str					=					AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer'''      ,							trust_remote_code=A_					)
       self.assertTrue(tokenizer.special_attribute_present					)
       # Test tokenizer can be reloaded.
       with tempfile.TemporaryDirectory() as tmp_dir:
             tokenizer.save_pretrained(A_					)
             __lowerCAmelCase					:						str					=					AutoTokenizer.from_pretrained(A_      ,							trust_remote_code=A_					)
       self.assertTrue(reloaded_tokenizer.special_attribute_present					)

       if is_tokenizers_available():
             self.assertEqual(tokenizer.__class__.__name__      ,							'''NewTokenizerFast'''					)
             self.assertEqual(reloaded_tokenizer.__class__.__name__      ,							'''NewTokenizerFast'''					)

             # Test we can also load the slow version
             __lowerCAmelCase					:						Tuple					=					AutoTokenizer.from_pretrained(
                 '''hf-internal-testing/test_dynamic_tokenizer'''      ,							trust_remote_code=A_      ,							use_fast=A_					)
             self.assertTrue(tokenizer.special_attribute_present					)
             self.assertEqual(tokenizer.__class__.__name__      ,							'''NewTokenizer'''					)
             # Test tokenizer can be reloaded.
             with tempfile.TemporaryDirectory() as tmp_dir:
                   tokenizer.save_pretrained(A_					)
                   __lowerCAmelCase					:						Dict					=					AutoTokenizer.from_pretrained(A_      ,							trust_remote_code=A_      ,							use_fast=A_					)
             self.assertEqual(reloaded_tokenizer.__class__.__name__      ,							'''NewTokenizer'''					)
             self.assertTrue(reloaded_tokenizer.special_attribute_present					)
       else:
             self.assertEqual(tokenizer.__class__.__name__      ,							'''NewTokenizer'''					)
             self.assertEqual(reloaded_tokenizer.__class__.__name__      ,							'''NewTokenizer'''					)
 @require_tokenizers
 def 	UpperCamelCase__  (							self					)					->Any:



       '''simple docstring'''





       class   __lowercase      (_UpperCAmelCase ):
        _UpperCamelCase			  =  False







       class   __lowercase      (_UpperCAmelCase ):
        _UpperCamelCase			  =  NewTokenizer
        _UpperCamelCase			  =  False

       try:
             AutoConfig.register('''custom'''      ,							A_					)
             AutoTokenizer.register(A_      ,							slow_tokenizer_class=A_					)
             AutoTokenizer.register(A_      ,							fast_tokenizer_class=A_					)
             # If remote code is not set, the default is to use local
             __lowerCAmelCase					:						Optional[Any]					=					AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer'''					)
             self.assertEqual(tokenizer.__class__.__name__      ,							'''NewTokenizerFast'''					)
             self.assertFalse(tokenizer.special_attribute_present					)
             __lowerCAmelCase					:						Optional[Any]					=					AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer'''      ,							use_fast=A_					)
             self.assertEqual(tokenizer.__class__.__name__      ,							'''NewTokenizer'''					)
             self.assertFalse(tokenizer.special_attribute_present					)

             # If remote code is disabled, we load the local one.
             __lowerCAmelCase					:						str					=					AutoTokenizer.from_pretrained(
                 '''hf-internal-testing/test_dynamic_tokenizer'''      ,							trust_remote_code=A_					)
             self.assertEqual(tokenizer.__class__.__name__      ,							'''NewTokenizerFast'''					)
             self.assertFalse(tokenizer.special_attribute_present					)
             __lowerCAmelCase					:						Optional[int]					=					AutoTokenizer.from_pretrained(
                 '''hf-internal-testing/test_dynamic_tokenizer'''      ,							trust_remote_code=A_      ,							use_fast=A_					)
             self.assertEqual(tokenizer.__class__.__name__      ,							'''NewTokenizer'''					)
             self.assertFalse(tokenizer.special_attribute_present					)

             # If remote is enabled, we load from the Hub
             __lowerCAmelCase					:						int					=					AutoTokenizer.from_pretrained(
                 '''hf-internal-testing/test_dynamic_tokenizer'''      ,							trust_remote_code=A_					)
             self.assertEqual(tokenizer.__class__.__name__      ,							'''NewTokenizerFast'''					)
             self.assertTrue(tokenizer.special_attribute_present					)
             __lowerCAmelCase					:						Dict					=					AutoTokenizer.from_pretrained(
                 '''hf-internal-testing/test_dynamic_tokenizer'''      ,							trust_remote_code=A_      ,							use_fast=A_					)
             self.assertEqual(tokenizer.__class__.__name__      ,							'''NewTokenizer'''					)
             self.assertTrue(tokenizer.special_attribute_present					)

       finally:
             if "custom" in CONFIG_MAPPING._extra_content:
                   del CONFIG_MAPPING._extra_content["custom"]
             if CustomConfig in TOKENIZER_MAPPING._extra_content:
                   del TOKENIZER_MAPPING._extra_content[CustomConfig]
 def 	UpperCamelCase__  (							self					)					->Any:



       '''simple docstring'''





       __lowerCAmelCase					:						Union[str, Any]					=					AutoTokenizer.from_pretrained(
           '''hf-internal-testing/test_dynamic_tokenizer_legacy'''      ,							trust_remote_code=A_					)
       self.assertTrue(tokenizer.special_attribute_present					)
       if is_tokenizers_available():
             self.assertEqual(tokenizer.__class__.__name__      ,							'''NewTokenizerFast'''					)

             # Test we can also load the slow version
             __lowerCAmelCase					:						Tuple					=					AutoTokenizer.from_pretrained(
                 '''hf-internal-testing/test_dynamic_tokenizer_legacy'''      ,							trust_remote_code=A_      ,							use_fast=A_					)
             self.assertTrue(tokenizer.special_attribute_present					)
             self.assertEqual(tokenizer.__class__.__name__      ,							'''NewTokenizer'''					)
       else:
             self.assertEqual(tokenizer.__class__.__name__      ,							'''NewTokenizer'''					)
 def 	UpperCamelCase__  (							self					)					->Union[str, Any]:



       '''simple docstring'''





       with self.assertRaisesRegex(
           A_      ,							'''bert-base is not a local folder and is not a valid model identifier'''					):
             __lowerCAmelCase					:						List[str]					=					AutoTokenizer.from_pretrained('''bert-base'''					)
 def 	UpperCamelCase__  (							self					)					->Optional[int]:



       '''simple docstring'''





       with self.assertRaisesRegex(
           A_      ,							R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''					):
             __lowerCAmelCase					:						List[Any]					=					AutoTokenizer.from_pretrained(A_      ,							revision='''aaaaaa'''					)





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



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[Any]					=					AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bert'''					)
       with RequestCounter() as counter:
             __lowerCAmelCase					:						Optional[Any]					=					AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bert'''					)
             self.assertEqual(counter.get_request_count      ,							0					)
             self.assertEqual(counter.head_request_count      ,							1					)
             self.assertEqual(counter.other_request_count      ,							0					)





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

import numpy as np
import torch

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







@dataclass
class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  42
 _UpperCamelCase			  =  42
 _UpperCamelCase			  =  None







class   __lowercase      (_UpperCAmelCase		,   _UpperCAmelCase ):
 _UpperCamelCase			  =  2
 @register_to_config
 def __init__(							self      ,							A_ = 0.02      ,							A_ = 100      ,							A_ = 1.007      ,							A_ = 80      ,							A_ = 0.05      ,							A_ = 50      ,							)					->int:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[int]					=					sigma_max

       # setable values
       __lowerCAmelCase					:						int					=					None
       __lowerCAmelCase					:						np.IntTensor					=					None
       __lowerCAmelCase					:						torch.FloatTensor					=					None  # sigma(t_i)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_ = None					)					->torch.FloatTensor:



       '''simple docstring'''





       return sample
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_ = None					)					->List[str]:



       '''simple docstring'''





       __lowerCAmelCase					:						str					=					num_inference_steps
       __lowerCAmelCase					:						Dict					=					np.arange(0      ,							self.num_inference_steps					)[::-1].copy()
       __lowerCAmelCase					:						Optional[Any]					=					torch.from_numpy(A_					).to(A_					)
       __lowerCAmelCase					:						Tuple					=					[
           (
               self.config.sigma_max**2
               * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
           )
           for i in self.timesteps
       ]
       __lowerCAmelCase					:						Optional[int]					=					torch.tensor(A_      ,							dtype=torch.floataa      ,							device=A_					)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_ = None					)					->Tuple[torch.FloatTensor, float]:



       '''simple docstring'''





       if self.config.s_min <= sigma <= self.config.s_max:
             __lowerCAmelCase					:						List[str]					=					min(self.config.s_churn / self.num_inference_steps      ,							2**0.5 - 1					)
       else:
             __lowerCAmelCase					:						List[str]					=					0

       # sample eps ~ N(0, S_noise^2 * I)
       __lowerCAmelCase					:						int					=					self.config.s_noise * randn_tensor(sample.shape      ,							generator=A_					).to(sample.device					)
       __lowerCAmelCase					:						str					=					sigma + gamma * sigma
       __lowerCAmelCase					:						Any					=					sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

       return sample_hat, sigma_hat
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_ = True      ,							)					->Union[KarrasVeOutput, Tuple]:



       '''simple docstring'''





       __lowerCAmelCase					:						Union[str, Any]					=					sample_hat + sigma_hat * model_output
       __lowerCAmelCase					:						int					=					(sample_hat - pred_original_sample) / sigma_hat
       __lowerCAmelCase					:						Tuple					=					sample_hat + (sigma_prev - sigma_hat) * derivative

       if not return_dict:
             return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=A_      ,							derivative=A_      ,							pred_original_sample=A_					)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_ = True      ,							)					->Union[KarrasVeOutput, Tuple]:



       '''simple docstring'''





       __lowerCAmelCase					:						str					=					sample_prev + sigma_prev * model_output
       __lowerCAmelCase					:						List[Any]					=					(sample_prev - pred_original_sample) / sigma_prev
       __lowerCAmelCase					:						Any					=					sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

       if not return_dict:
             return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=A_      ,							derivative=A_      ,							pred_original_sample=A_					)





 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_					)					->Any:



       '''simple docstring'''





       raise NotImplementedError()


