






'''simple docstring'''
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   (				__lowerCamelCase     ,					unittest.TestCase					):

					"""simple docstring"""





					_UpperCamelCase		 =      PreTrainedTokenizerFast
					_UpperCamelCase		 =      False
					_UpperCamelCase		 =      True
					_UpperCamelCase		 =      'tokenizer_file'




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	str:
										a_									=   False  # because we don't have pretrained_vocab_files_map
										super().setUp()
										a_									=   True

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

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

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




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Any:
										pass




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	str:
										pass




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Optional[Any]:
										pass




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Optional[Any]:
										pass




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	int:
										a_									=   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:
																									a_									=   tempfile.mkdtemp()
																									a_									=   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     )
																									a_									=   tmpdirname_orig




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	str:
										a_									=   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:
																									a_									=   tempfile.mkdtemp()
																									a_									=   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     )
																									a_									=   tmpdirname_orig




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Any:
										a_									=   self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name     )

										a_									=   ('a' for _ in range(1000     ))
										a_									=   tokenizer.train_new_from_iterator(text_iterator=UpperCAmelCase__   , length=1000   , vocab_size=50     )

										a_									=   new_tokenizer.encode('a🤗'     )
										self.assertEqual(UpperCAmelCase__   , [64, 172, 253, 97, 245]     )



@require_tokenizers
class    _snake_case   (				unittest.TestCase					):

					"""simple docstring"""





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

										with tempfile.TemporaryDirectory() as tmp_dir:
															# Hack to save this in the tokenizer_config.json
															a_									=   ['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
															a_									=   AutoTokenizer.from_pretrained(UpperCAmelCase__     )
															self.assertEqual(len(UpperCAmelCase__     )   , len(UpperCAmelCase__     ) + 1     )
															a_									=   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'     )     )
															a_									=   ['tokenizer.42.0.0.json']
															tokenizer.save_pretrained(UpperCAmelCase__     )
															a_									=   AutoTokenizer.from_pretrained(UpperCAmelCase__     )
															self.assertEqual(len(UpperCAmelCase__     )   , len(UpperCAmelCase__     )     )
															a_									=   json.loads(new_tokenizer._tokenizer.to_str()     )
															self.assertNotIn('huggingface'   , json_tokenizer['model']['vocab']     )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Tuple:
										a_									=   'hf-internal-testing/test-two-tokenizers'

										# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
										a_									=   AutoTokenizer.from_pretrained(UpperCAmelCase__     )
										self.assertEqual(len(UpperCAmelCase__     )   , 2_8997     )
										a_									=   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

										a_									=   '3.0.0'
										a_									=   old_transformers.models.auto.AutoTokenizer.from_pretrained(UpperCAmelCase__     )
										self.assertEqual(len(UpperCAmelCase__     )   , 2_8996     )
										a_									=   json.loads(old_tokenizer._tokenizer.to_str()     )
										self.assertNotIn('huggingface'   , json_tokenizer['model']['vocab']     )



@require_tokenizers
class    _snake_case   (				unittest.TestCase					):

					"""simple docstring"""





					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Any:
										a_									=   PreTrainedTokenizerFast.from_pretrained('robot-test/dummy-tokenizer-wordlevel'     )
										a_									=   'The Matrix is a 1999 science fiction action film.'

										with concurrent.futures.ThreadPoolExecutor() as executor:
															a_									=   [executor.submit(self.fetch   , UpperCAmelCase__   , UpperCAmelCase__     ) for i in range(10     )]
															a_									=   [future.result() for future in futures]
															self.assertEqual(UpperCAmelCase__   , [[1, 10, 0, 8, 0, 18, 0, 0, 0, 2] for i in range(10     )]     )




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__   , UpperCAmelCase__     )      ->	Optional[int]:
										return tokenizer.encode(UpperCAmelCase__   , truncation='longest_first'   , padding='longest'     )









'''simple docstring'''
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class    _snake_case   (				snake_case					):

					"""simple docstring"""





					_UpperCamelCase		 =      42
					_UpperCamelCase		 =      42




					def __init__(     self   , UpperCAmelCase__   , UpperCAmelCase__     )      ->	int:
										super().__init__()

										a_									=   PNDMScheduler.from_config(scheduler.config     )

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




					@torch.no_grad()
					def __call__(     self   , UpperCAmelCase__ = 1   , UpperCAmelCase__ = 50   , UpperCAmelCase__ = None   , UpperCAmelCase__ = "pil"   , UpperCAmelCase__ = True   , **UpperCAmelCase__   , )      ->	Union[ImagePipelineOutput, Tuple]:
										a_									=   randn_tensor(
										    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   , generator=UpperCAmelCase__   , device=self.device   , )

										self.scheduler.set_timesteps(UpperCAmelCase__     )
										for t in self.progress_bar(self.scheduler.timesteps     ):
															a_									=   self.unet(UpperCAmelCase__   , UpperCAmelCase__     ).sample

															a_									=   self.scheduler.step(UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__     ).prev_sample

										a_									=   (image / 2 + 0.5).clamp(0   , 1     )
										a_									=   image.cpu().permute(0   , 2   , 3   , 1     ).numpy()
										if output_type == "pil":
															a_									=   self.numpy_to_pil(UpperCAmelCase__     )

										if not return_dict:
															return (image,)

										return ImagePipelineOutput(images=UpperCAmelCase__     )

