


'''simple docstring'''

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 _lowerCamelCase	(						unittest.TestCase							):



		'''simple docstring'''







		def        __lowerCAmelCase		( self		:						Dict      )  ->			Any:
			__magic_name__	:   List[str]					     =					0







		@slow
		def        __lowerCAmelCase		( self		:						List[str]      )  ->			Optional[Any]:
			for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
				__magic_name__	:   List[Any]					     =					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():
				__magic_name__	:   Dict					     =					AutoTokenizer.from_pretrained(_A      )
				self.assertIsNotNone(_A      )
				self.assertIsInstance(_A  ,					(GPTaTokenizer, GPTaTokenizerFast)      )
				self.assertGreater(len(_A      )  ,					0      )







		def        __lowerCAmelCase		( self		:						Tuple      )  ->			Union[str, Any]:
			__magic_name__	:   Tuple					     =					AutoTokenizer.from_pretrained(_A      )
			self.assertIsInstance(_A  ,					(BertTokenizer, BertTokenizerFast)      )
			self.assertEqual(tokenizer.vocab_size  ,					12      )







		def        __lowerCAmelCase		( self		:						Union[str, Any]      )  ->			Tuple:
			__magic_name__	:   Dict					     =					AutoTokenizer.from_pretrained(_A      )
			self.assertIsInstance(_A  ,					(RobertaTokenizer, RobertaTokenizerFast)      )
			self.assertEqual(tokenizer.vocab_size  ,					20      )







		def        __lowerCAmelCase		( self		:						Union[str, Any]      )  ->			List[Any]:
			__magic_name__	:   List[Any]					     =					AutoConfig.from_pretrained(_A      )
			self.assertIsInstance(_A  ,					_A      )
			# Check that tokenizer_type ≠ model_type
			__magic_name__	:   int					     =					AutoTokenizer.from_pretrained(_A  ,					config=_A      )
			self.assertIsInstance(_A  ,					(BertTokenizer, BertTokenizerFast)      )
			self.assertEqual(tokenizer.vocab_size  ,					12      )







		def        __lowerCAmelCase		( self		:						List[str]      )  ->			Tuple:
			with tempfile.TemporaryDirectory() as tmp_dir:
				shutil.copy('./tests/fixtures/vocab.txt'  ,					os.path.join(_A  ,					'vocab.txt'      )      )

				__magic_name__	:   List[Any]					     =					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'      )      )

				__magic_name__	:   int					     =					AutoTokenizer.from_pretrained(_A  ,					tokenizer_type='gpt2'  ,					use_fast=_A      )
				self.assertIsInstance(_A  ,					_A      )







		@require_tokenizers
		def        __lowerCAmelCase		( self		:						int      )  ->			Any:
			with tempfile.TemporaryDirectory() as tmp_dir:
				shutil.copy('./tests/fixtures/vocab.txt'  ,					os.path.join(_A  ,					'vocab.txt'      )      )

				__magic_name__	:   Union[str, Any]					     =					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'      )      )

				__magic_name__	:   Union[str, Any]					     =					AutoTokenizer.from_pretrained(_A  ,					tokenizer_type='gpt2'      )
				self.assertIsInstance(_A  ,					_A      )







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			List[str]:
			with pytest.raises(_A      ):
				AutoTokenizer.from_pretrained('./'  ,					tokenizer_type='xxx'      )







		@require_tokenizers
		def        __lowerCAmelCase		( self		:						Optional[int]      )  ->			Optional[Any]:
			for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
				__magic_name__	:   List[str]					     =					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        __lowerCAmelCase		( self		:						Tuple      )  ->			Dict:
			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'  ,					):
					__magic_name__	:   Optional[int]					     =					tokenizer_class.from_pretrained('julien-c/herlolip-not-exists'      )







		def        __lowerCAmelCase		( self		:						str      )  ->			Any:
			# tests: https://github.com/huggingface/transformers/pull/13251
			# 1. models with `-`, e.g. xlm-roberta -> xlm_roberta
			# 2. models that don't remap 1-1 from model-name to model file, e.g., openai-gpt -> openai
			__magic_name__	:   List[Any]					     =					TOKENIZER_MAPPING.values()
			__magic_name__	:   Union[str, Any]					     =					[]

			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        __lowerCAmelCase		( self		:						Union[str, Any]      )  ->			List[str]:
			self.assertIsInstance(AutoTokenizer.from_pretrained('bert-base-cased'  ,					use_fast=_A      )  ,					_A      )
			self.assertIsInstance(AutoTokenizer.from_pretrained('bert-base-cased'      )  ,					_A      )







		@require_tokenizers
		def        __lowerCAmelCase		( self		:						str      )  ->			List[str]:
			__magic_name__	:   str					     =					AutoTokenizer.from_pretrained('distilbert-base-uncased'  ,					do_lower_case=_A      )
			__magic_name__	:   List[Any]					     =					'Hello, world. How are you?'
			__magic_name__	:   Tuple					     =					tokenizer.tokenize(_A      )
			self.assertEqual('[UNK]'  ,					tokens[0]      )

			__magic_name__	:   List[str]					     =					AutoTokenizer.from_pretrained('microsoft/mpnet-base'  ,					do_lower_case=_A      )
			__magic_name__	:   str					     =					tokenizer.tokenize(_A      )
			self.assertEqual('[UNK]'  ,					tokens[0]      )







		@require_tokenizers
		def        __lowerCAmelCase		( self		:						Tuple      )  ->			List[Any]:
			__magic_name__	:   str					     =					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  ,					30000      )
			self.assertEqual(tokenizer.unk_token  ,					'[UNK]'      )
			self.assertEqual(tokenizer.padding_side  ,					'right'      )
			self.assertEqual(tokenizer.truncation_side  ,					'right'      )







		def        __lowerCAmelCase		( self		:						Any      )  ->			Optional[Any]:
			__magic_name__	:   Tuple					     =					AutoTokenizer.from_pretrained(_A      )
			self.assertIsInstance(_A  ,					(BertTokenizer, BertTokenizerFast)      )
			with tempfile.TemporaryDirectory() as tmp_dir:
				tokenizer.save_pretrained(_A      )
				__magic_name__	:   List[str]					     =					AutoTokenizer.from_pretrained(_A      )

			self.assertIsInstance(_A  ,					tokenizer.__class__      )
			self.assertEqual(tokenizera.vocab_size  ,					12      )







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







		def        __lowerCAmelCase		( self		:						Optional[int]      )  ->			Any:
			# Check we can load the tokenizer config of an online model.
			__magic_name__	:   List[Any]					     =					get_tokenizer_config('bert-base-cased'      )
			__magic_name__	:   Dict					     =					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.
			__magic_name__	:   Optional[Any]					     =					get_tokenizer_config(_A      )
			self.assertDictEqual(_A  ,					{}      )

			# A tokenizer saved with `save_pretrained` always creates a tokenizer config.
			__magic_name__	:   List[str]					     =					AutoTokenizer.from_pretrained(_A      )
			with tempfile.TemporaryDirectory() as tmp_dir:
				tokenizer.save_pretrained(_A      )
				__magic_name__	:   List[Any]					     =					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        __lowerCAmelCase		( self		:						str      )  ->			List[Any]:
			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      )

				__magic_name__	:   Tuple					     =					CustomTokenizer.from_pretrained(_A      )
				with tempfile.TemporaryDirectory() as tmp_dir:
					tokenizer.save_pretrained(_A      )

					__magic_name__	:   Optional[int]					     =					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        __lowerCAmelCase		( self		:						Any      )  ->			Any:
			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:
					__magic_name__	:   Tuple					     =					BertTokenizerFast.from_pretrained(_A      )
					bert_tokenizer.save_pretrained(_A      )
					__magic_name__	:   Tuple					     =					CustomTokenizerFast.from_pretrained(_A      )

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

					__magic_name__	:   Union[str, Any]					     =					AutoTokenizer.from_pretrained(_A      )
					self.assertIsInstance(_A  ,					_A      )

					__magic_name__	:   List[str]					     =					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        __lowerCAmelCase		( self		:						Union[str, Any]      )  ->			Optional[int]:
			# If remote code is not set, we will time out when asking whether to load the model.
			with self.assertRaises(_A      ):
				__magic_name__	:   str					     =					AutoTokenizer.from_pretrained('hf-internal-testing/test_dynamic_tokenizer'      )
			# If remote code is disabled, we can't load this config.
			with self.assertRaises(_A      ):
				__magic_name__	:   Optional[int]					     =					AutoTokenizer.from_pretrained(
				    'hf-internal-testing/test_dynamic_tokenizer'  ,					trust_remote_code=_A      )

			__magic_name__	:   Optional[Any]					     =					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      )
				__magic_name__	:   int					     =					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
				__magic_name__	:   List[Any]					     =					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      )
					__magic_name__	:   Tuple					     =					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        __lowerCAmelCase		( self		:						List[Any]      )  ->			Union[str, Any]:
			class _lowerCamelCase	(						lowercase__							):



					'''simple docstring'''







					A_     :      Union[str, Any]				   =    False







			class _lowerCamelCase	(						lowercase__							):



					'''simple docstring'''







					A_     :      str				   =    NewTokenizer
					A_     :      str				   =    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
				__magic_name__	:   Any					     =					AutoTokenizer.from_pretrained('hf-internal-testing/test_dynamic_tokenizer'      )
				self.assertEqual(tokenizer.__class__.__name__  ,					'NewTokenizerFast'      )
				self.assertFalse(tokenizer.special_attribute_present      )
				__magic_name__	:   List[str]					     =					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.
				__magic_name__	:   List[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      )
				__magic_name__	:   Tuple					     =					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
				__magic_name__	:   Dict					     =					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      )
				__magic_name__	:   List[str]					     =					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        __lowerCAmelCase		( self		:						List[str]      )  ->			List[Any]:
			__magic_name__	:   Dict					     =					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
				__magic_name__	:   int					     =					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        __lowerCAmelCase		( self		:						Union[str, Any]      )  ->			Union[str, Any]:
			with self.assertRaisesRegex(
			    _A  ,					'bert-base is not a local folder and is not a valid model identifier'      ):
				__magic_name__	:   str					     =					AutoTokenizer.from_pretrained('bert-base'      )







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







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			List[str]:
			# Make sure we have cached the tokenizer.
			__magic_name__	:   str					     =					AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert'      )
			with RequestCounter() as counter:
				__magic_name__	:   str					     =					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      )



'''simple docstring'''

from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


lowerCAmelCase				:Optional[Any]        =      transforms.Compose(
    [
        transforms.Resize((2_5_6, 2_5_6)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)





def lowerCamelCase      (      lowerCAmelCase    :     Tuple		):





	"""simple docstring"""







	if isinstance(lowerCAmelCase		,		torch.Tensor		):
		return image
	elif isinstance(lowerCAmelCase		,		PIL.Image.Image		):
		__magic_name__	:   List[Any]					     =					[image]

	__magic_name__	:   List[Any]					     =					[trans(img.convert('RGB'		)		) for img in image]
	__magic_name__	:   Dict					     =					torch.stack(lowerCAmelCase		)
	return image





class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		def __init__( self		:						Optional[Any]  ,					_A		:						str  ,					_A		:						int      )  ->			Dict:
			super().__init__()

			# make sure scheduler can always be converted to DDIM
			__magic_name__	:   Optional[int]					     =					DDIMScheduler.from_config(scheduler.config      )

			self.register_modules(unet=_A  ,					scheduler=_A      )







		def        __lowerCAmelCase		( self		:						Union[str, Any]  ,					_A		:						Union[str, Any]      )  ->			Optional[int]:
			if strength < 0 or strength > 1:
				raise ValueError(F'The value of strength should in [0.0, 1.0] but is {strength}'      )







		def        __lowerCAmelCase		( self		:						Any  ,					_A		:						List[str]  ,					_A		:						Optional[Any]  ,					_A		:						int      )  ->			List[Any]:
			# get the original timestep using init_timestep
			__magic_name__	:   Tuple					     =					min(int(num_inference_steps * strength      )  ,					_A      )

			__magic_name__	:   Any					     =					max(num_inference_steps - init_timestep  ,					0      )
			__magic_name__	:   List[str]					     =					self.scheduler.timesteps[t_start:]

			return timesteps, num_inference_steps - t_start







		def        __lowerCAmelCase		( self		:						Any  ,					_A		:						str  ,					_A		:						Optional[int]  ,					_A		:						Tuple  ,					_A		:						List[str]  ,					_A		:						str  ,					_A		:						Optional[int]=None      )  ->			Dict:
			if not isinstance(_A  ,					(torch.Tensor, PIL.Image.Image, list)      ):
				raise ValueError(
				    F'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_A      )}'      )

			__magic_name__	:   Union[str, Any]					     =					image.to(device=_A  ,					dtype=_A      )

			if isinstance(_A  ,					_A      ) and len(_A      ) != batch_size:
				raise ValueError(
				    F'You have passed a list of generators of length {len(_A      )}, but requested an effective batch'
				    F' size of {batch_size}. Make sure the batch size matches the length of the generators.'      )

			__magic_name__	:   Tuple					     =					init_latents.shape
			__magic_name__	:   Any					     =					randn_tensor(_A  ,					generator=_A  ,					device=_A  ,					dtype=_A      )

			# get latents
			print('add noise to latents at timestep'  ,					_A      )
			__magic_name__	:   List[str]					     =					self.scheduler.add_noise(_A  ,					_A  ,					_A      )
			__magic_name__	:   List[str]					     =					init_latents

			return latents







		@torch.no_grad()
		def __call__( self		:						Tuple  ,					_A		:						Union[torch.FloatTensor, PIL.Image.Image] = None  ,					_A		:						float = 0.8  ,					_A		:						int = 1  ,					_A		:						Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,					_A		:						float = 0.0  ,					_A		:						int = 50  ,					_A		:						Optional[bool] = None  ,					_A		:						Optional[str] = "pil"  ,					_A		:						bool = True  ,					)  ->			Union[ImagePipelineOutput, Tuple]:
			self.check_inputs(_A      )

			# 2. Preprocess image
			__magic_name__	:   int					     =					preprocess(_A      )

			# 3. set timesteps
			self.scheduler.set_timesteps(_A  ,					device=self.device      )
			__magic_name__     ,    __magic_name__	:   Dict					     =					self.get_timesteps(_A  ,					_A  ,					self.device      )
			__magic_name__	:   Dict					     =					timesteps[:1].repeat(_A      )

			# 4. Prepare latent variables
			__magic_name__	:   Optional[Any]					     =					self.prepare_latents(_A  ,					_A  ,					_A  ,					self.unet.dtype  ,					self.device  ,					_A      )
			__magic_name__	:   Optional[Any]					     =					latents

			# 5. Denoising loop
			for t in self.progress_bar(_A      ):
				# 1. predict noise model_output
				__magic_name__	:   Dict					     =					self.unet(_A  ,					_A      ).sample

				# 2. predict previous mean of image x_t-1 and add variance depending on eta
				# eta corresponds to η in paper and should be between [0, 1]
				# do x_t -> x_t-1
				__magic_name__	:   List[Any]					     =					self.scheduler.step(
				    _A  ,					_A  ,					_A  ,					eta=_A  ,					use_clipped_model_output=_A  ,					generator=_A  ,					).prev_sample

			__magic_name__	:   Union[str, Any]					     =					(image / 2 + 0.5).clamp(0  ,					1      )
			__magic_name__	:   List[Any]					     =					image.cpu().permute(0  ,					2  ,					3  ,					1      ).numpy()
			if output_type == "pil":
				__magic_name__	:   Dict					     =					self.numpy_to_pil(_A      )

			if not return_dict:
				return (image, latent_timestep.item())

			return ImagePipelineOutput(images=_A      )