



'''simple docstring'''

import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 						a_		(     snake_case    ,   unittest.TestCase    ):
							UpperCAmelCase			:		Optional[Any]        =				GPTaTokenizer
							UpperCAmelCase			:		Optional[Any]        =				GPTaTokenizerFast
							UpperCAmelCase			:		Optional[int]        =				True
							UpperCAmelCase			:		Tuple        =				{"""add_prefix_space""": True}
							UpperCAmelCase			:		Optional[Any]        =				False


							def    UpperCamelCase     (				self   :						Union[str, Any]			)			->					Optional[Any]:
										super().setUp()

										# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
										snake_case:    Tuple      							=[
										    'l',
										    'o',
										    'w',
										    'e',
										    'r',
										    's',
										    't',
										    'i',
										    'd',
										    'n',
										    '\u0120',
										    '\u0120l',
										    '\u0120n',
										    '\u0120lo',
										    '\u0120low',
										    'er',
										    '\u0120lowest',
										    '\u0120newer',
										    '\u0120wider',
										    '<unk>',
										    '<|endoftext|>',
										]
										snake_case:    int      							=dict(zip(a_      ,						range(len(a_			)			)			)			)
										snake_case:    List[str]      							=['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
										snake_case:    Tuple      							={'unk_token': '<unk>'}

										snake_case:    Optional[int]      							=os.path.join(self.tmpdirname      ,						VOCAB_FILES_NAMES['vocab_file']			)
										snake_case:    Union[str, Any]      							=os.path.join(self.tmpdirname      ,						VOCAB_FILES_NAMES['merges_file']			)
										with open(self.vocab_file      ,						'w'      ,						encoding='utf-8'			) as fp:
													fp.write(json.dumps(a_			) + '\n'			)
										with open(self.merges_file      ,						'w'      ,						encoding='utf-8'			) as fp:
													fp.write('\n'.join(a_			)			)


							def    UpperCamelCase     (				self   :						Dict      ,						**a_   :						int			)			->					Optional[int]:
										kwargs.update(self.special_tokens_map			)
										return GPTaTokenizer.from_pretrained(self.tmpdirname      ,						**a_			)


							def    UpperCamelCase     (				self   :						List[str]      ,						**a_   :						str			)			->					Union[str, Any]:
										kwargs.update(self.special_tokens_map			)
										return GPTaTokenizerFast.from_pretrained(self.tmpdirname      ,						**a_			)


							def    UpperCamelCase     (				self   :						Any      ,						a_   :						Any			)			->					Optional[int]:
										snake_case:    Dict      							='lower newer'
										snake_case:    Dict      							='lower newer'
										return input_text, output_text


							def    UpperCamelCase     (				self   :						Tuple			)			->					List[Any]:
										snake_case:    List[Any]      							=GPTaTokenizer(self.vocab_file      ,						self.merges_file      ,						**self.special_tokens_map			)
										snake_case:    Tuple      							='lower newer'
										snake_case:    Optional[Any]      							=['\u0120low', 'er', '\u0120', 'n', 'e', 'w', 'er']
										snake_case:    List[Any]      							=tokenizer.tokenize(a_      ,						add_prefix_space=a_			)
										self.assertListEqual(a_      ,						a_			)

										snake_case:    Tuple      							=tokens + [tokenizer.unk_token]
										snake_case:    Union[str, Any]      							=[1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(a_			)      ,						a_			)


							def    UpperCamelCase     (				self   :						Dict			)			->					Optional[int]:
										if not self.test_rust_tokenizer:
													return

										snake_case:    int      							=self.get_tokenizer()
										snake_case:    Union[str, Any]      							=self.get_rust_tokenizer(add_prefix_space=a_			)

										snake_case:    Dict      							='lower newer'

										# Testing tokenization
										snake_case:    int      							=tokenizer.tokenize(a_      ,						add_prefix_space=a_			)
										snake_case:    int      							=rust_tokenizer.tokenize(a_			)
										self.assertListEqual(a_      ,						a_			)

										# Testing conversion to ids without special tokens
										snake_case:    Optional[int]      							=tokenizer.encode(a_      ,						add_special_tokens=a_      ,						add_prefix_space=a_			)
										snake_case:    Optional[int]      							=rust_tokenizer.encode(a_      ,						add_special_tokens=a_			)
										self.assertListEqual(a_      ,						a_			)

										# Testing conversion to ids with special tokens
										snake_case:    int      							=self.get_rust_tokenizer(add_prefix_space=a_			)
										snake_case:    List[Any]      							=tokenizer.encode(a_      ,						add_prefix_space=a_			)
										snake_case:    Optional[int]      							=rust_tokenizer.encode(a_			)
										self.assertListEqual(a_      ,						a_			)

										# Testing the unknown token
										snake_case:    Any      							=tokens + [rust_tokenizer.unk_token]
										snake_case:    Any      							=[1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
										self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(a_			)      ,						a_			)


							def    UpperCamelCase     (				self   :						List[str]      ,						*a_   :						Union[str, Any]      ,						**a_   :						List[Any]			)			->					Optional[int]:
										# It's very difficult to mix/test pretokenization with byte-level
										# And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
										pass


							def    UpperCamelCase     (				self   :						Optional[Any]      ,						a_   :						Union[str, Any]=1_5			)			->					Optional[Any]:
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''			):
																snake_case:    List[str]      							=self.rust_tokenizer_class.from_pretrained(a_      ,						**a_			)

																# Simple input
																snake_case:    int      							='This is a simple input'
																snake_case:    str      							=['This is a simple input 1', 'This is a simple input 2']
																snake_case:    Dict      							=('This is a simple input', 'This is a pair')
																snake_case:    Optional[int]      							=[
																    ('This is a simple input 1', 'This is a simple input 2'),
																    ('This is a simple pair 1', 'This is a simple pair 2'),
																]

																# Simple input tests
																self.assertRaises(a_      ,						tokenizer_r.encode      ,						a_      ,						max_length=a_      ,						padding='max_length'			)

																# Simple input
																self.assertRaises(a_      ,						tokenizer_r.encode_plus      ,						a_      ,						max_length=a_      ,						padding='max_length'			)

																# Simple input
																self.assertRaises(
																    a_      ,						tokenizer_r.batch_encode_plus      ,						a_      ,						max_length=a_      ,						padding='max_length'      ,						)

																# Pair input
																self.assertRaises(a_      ,						tokenizer_r.encode      ,						a_      ,						max_length=a_      ,						padding='max_length'			)

																# Pair input
																self.assertRaises(a_      ,						tokenizer_r.encode_plus      ,						a_      ,						max_length=a_      ,						padding='max_length'			)

																# Pair input
																self.assertRaises(
																    a_      ,						tokenizer_r.batch_encode_plus      ,						a_      ,						max_length=a_      ,						padding='max_length'      ,						)


							def    UpperCamelCase     (				self   :						Any			)			->					int:
										snake_case:    Dict      							=GPTaTokenizer.from_pretrained(self.tmpdirname      ,						pad_token='<pad>'			)

										# Simple input
										snake_case:    Optional[int]      							='This is a simple input'
										snake_case:    Union[str, Any]      							=['This is a simple input looooooooong', 'This is a simple input']
										snake_case:    str      							=('This is a simple input', 'This is a pair')
										snake_case:    str      							=[
										    ('This is a simple input loooooong', 'This is a simple input'),
										    ('This is a simple pair loooooong', 'This is a simple pair'),
										]

										snake_case:    Optional[int]      							=tokenizer.pad_token_id

										snake_case:    str      							=tokenizer(a_      ,						padding='max_length'      ,						max_length=3_0      ,						return_tensors='np'			)
										snake_case:    str      							=tokenizer(a_      ,						padding=a_      ,						truncate=a_      ,						return_tensors='np'			)
										snake_case:    Dict      							=tokenizer(*a_      ,						padding='max_length'      ,						max_length=6_0      ,						return_tensors='np'			)
										snake_case:    str      							=tokenizer(a_      ,						padding=a_      ,						truncate=a_      ,						return_tensors='np'			)

										# s
										# test single string max_length padding
										self.assertEqual(out_s['input_ids'].shape[-1]      ,						3_0			)
										self.assertTrue(pad_token_id in out_s['input_ids']			)
										self.assertTrue(0 in out_s['attention_mask']			)

										# s2
										# test automatic padding
										self.assertEqual(out_sa['input_ids'].shape[-1]      ,						3_3			)
										# long slice doesn't have padding
										self.assertFalse(pad_token_id in out_sa['input_ids'][0]			)
										self.assertFalse(0 in out_sa['attention_mask'][0]			)
										# short slice does have padding
										self.assertTrue(pad_token_id in out_sa['input_ids'][1]			)
										self.assertTrue(0 in out_sa['attention_mask'][1]			)

										# p
										# test single pair max_length padding
										self.assertEqual(out_p['input_ids'].shape[-1]      ,						6_0			)
										self.assertTrue(pad_token_id in out_p['input_ids']			)
										self.assertTrue(0 in out_p['attention_mask']			)

										# p2
										# test automatic padding pair
										self.assertEqual(out_pa['input_ids'].shape[-1]      ,						5_2			)
										# long slice pair doesn't have padding
										self.assertFalse(pad_token_id in out_pa['input_ids'][0]			)
										self.assertFalse(0 in out_pa['attention_mask'][0]			)
										# short slice pair does have padding
										self.assertTrue(pad_token_id in out_pa['input_ids'][1]			)
										self.assertTrue(0 in out_pa['attention_mask'][1]			)


							def    UpperCamelCase     (				self   :						Dict			)			->					int:
										snake_case:    Any      							='$$$'
										snake_case:    Tuple      							=GPTaTokenizer.from_pretrained(self.tmpdirname      ,						bos_token=a_      ,						add_bos_token=a_			)

										snake_case:    Dict      							='This is a simple input'
										snake_case:    Optional[Any]      							=['This is a simple input 1', 'This is a simple input 2']

										snake_case:    Any      							=tokenizer.bos_token_id

										snake_case:    Dict      							=tokenizer(a_			)
										snake_case:    Optional[int]      							=tokenizer(a_			)

										self.assertEqual(out_s.input_ids[0]      ,						a_			)
										self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids			)			)

										snake_case:    int      							=tokenizer.decode(out_s.input_ids			)
										snake_case:    Union[str, Any]      							=tokenizer.batch_decode(out_sa.input_ids			)

										self.assertEqual(decode_s.split()[0]      ,						a_			)
										self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa			)			)


							def    UpperCamelCase     (				self   :						Union[str, Any]			)			->					List[str]:
										pass




							def    UpperCamelCase     (				self   :						List[str]			)			->					str:
										# TODO: change to self.get_tokenizers() when the fast version is implemented
										snake_case:    Dict      							=[self.get_tokenizer(do_lower_case=a_      ,						add_bos_token=a_			)]
										for tokenizer in tokenizers:
													with self.subTest(F'''{tokenizer.__class__.__name__}'''			):
																snake_case:    List[str]      							='Encode this.'
																snake_case:    Dict      							='This one too please.'
																snake_case:    str      							=tokenizer.encode(a_      ,						add_special_tokens=a_			)
																encoded_sequence += tokenizer.encode(a_      ,						add_special_tokens=a_			)
																snake_case:    Any      							=tokenizer.encode_plus(
																    a_      ,						a_      ,						add_special_tokens=a_      ,						return_special_tokens_mask=a_      ,						)
																snake_case:    Optional[int]      							=encoded_sequence_dict['input_ids']
																snake_case:    List[str]      							=encoded_sequence_dict['special_tokens_mask']
																self.assertEqual(len(a_			)      ,						len(a_			)			)

																snake_case:    List[str]      							=[
																    (x if not special_tokens_mask[i] else None) for i, x in enumerate(a_			)
																]
																snake_case:    Tuple      							=[x for x in filtered_sequence if x is not None]
																self.assertEqual(a_      ,						a_			)






@require_tokenizers
class 						a_		(     unittest.TestCase    ):


							def    UpperCamelCase     (				self   :						Tuple			)			->					Any:
										# More context:
										# https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1
										# https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519
										# https://github.com/huggingface/transformers/pull/17088#discussion_r871246439

										snake_case:    str      							=AutoTokenizer.from_pretrained('facebook/opt-350m'      ,						from_slow=a_			)
										snake_case:    Optional[int]      							='A photo of a cat'

										snake_case:    Optional[int]      							=tokenizer.encode(
										    a_      ,						)
										self.assertEqual(a_      ,						[2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]			)
										tokenizer.save_pretrained('test_opt'			)

										snake_case:    str      							=AutoTokenizer.from_pretrained('./test_opt'			)
										snake_case:    Optional[int]      							=tokenizer.encode(
										    a_      ,						)
										self.assertEqual(a_      ,						[2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]			)


							def    UpperCamelCase     (				self   :						int			)			->					Tuple:
										snake_case:    List[Any]      							=AutoTokenizer.from_pretrained('facebook/opt-350m'      ,						use_slow=a_			)
										snake_case:    int      							='A photo of a cat'

										snake_case:    Optional[Any]      							=tokenizer.encode(
										    a_      ,						)
										# Same as above
										self.assertEqual(a_      ,						[2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]			)




							@unittest.skip('This test is failing because of a bug in the fast tokenizer'			)
							def    UpperCamelCase     (				self   :						str			)			->					Optional[int]:
										snake_case:    str      							=AutoTokenizer.from_pretrained('facebook/opt-350m'      ,						from_slow=a_			)

										snake_case:    List[str]      							='bos'
										snake_case:    Dict      							=tokenizer.get_vocab()['bos']

										snake_case:    int      							='A photo of a cat'
										snake_case:    Union[str, Any]      							=tokenizer.encode(
										    a_      ,						)
										# We changed the bos token
										self.assertEqual(a_      ,						[3_1_9_5_7, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]			)
										tokenizer.save_pretrained('./tok'			)
										snake_case:    Optional[Any]      							=AutoTokenizer.from_pretrained('./tok'			)
										self.assertTrue(tokenizer.is_fast			)
										snake_case:    Union[str, Any]      							=tokenizer.encode(
										    a_      ,						)
										self.assertEqual(a_      ,						[3_1_9_5_7, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]			)









'''simple docstring'''

import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest






class 						a_		(     snake_case    ):
							UpperCAmelCase			:		str        =				(CMStochasticIterativeScheduler,)
							UpperCAmelCase			:		int        =				10


							def    UpperCamelCase     (				self   :						Dict      ,						**a_   :						List[str]			)			->					Any:
										snake_case:    Any      							={
										    'num_train_timesteps': 2_0_1,
										    'sigma_min': 0.0_0_2,
										    'sigma_max': 8_0.0,
										}

										config.update(**a_			)
										return config


							def    UpperCamelCase     (				self   :						List[Any]			)			->					List[Any]:
										snake_case:    Any      							=1_0

										snake_case:    List[str]      							=self.get_scheduler_config()
										snake_case:    List[Any]      							=self.scheduler_classes[0](**a_			)

										scheduler.set_timesteps(a_			)

										snake_case:    Dict      							=scheduler.timesteps[0]
										snake_case:    Union[str, Any]      							=scheduler.timesteps[1]

										snake_case:    List[str]      							=self.dummy_sample
										snake_case:    List[str]      							=0.1 * sample

										snake_case:    int      							=scheduler.step(a_      ,						a_      ,						a_			).prev_sample
										snake_case:    Optional[Any]      							=scheduler.step(a_      ,						a_      ,						a_			).prev_sample

										self.assertEqual(output_a.shape      ,						sample.shape			)
										self.assertEqual(output_a.shape      ,						output_a.shape			)


							def    UpperCamelCase     (				self   :						int			)			->					int:
										for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
													self.check_over_configs(num_train_timesteps=a_			)


							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Dict:
										for clip_denoised in [True, False]:
													self.check_over_configs(clip_denoised=a_			)


							def    UpperCamelCase     (				self   :						Tuple			)			->					List[str]:
										snake_case:    List[Any]      							=self.scheduler_classes[0]
										snake_case:    List[Any]      							=self.get_scheduler_config()
										snake_case:    Any      							=scheduler_class(**a_			)

										snake_case:    Dict      							=1
										scheduler.set_timesteps(a_			)
										snake_case:    List[Any]      							=scheduler.timesteps

										snake_case:    Optional[Any]      							=torch.manual_seed(0			)

										snake_case:    Optional[Any]      							=self.dummy_model()
										snake_case:    List[Any]      							=self.dummy_sample_deter * scheduler.init_noise_sigma

										for i, t in enumerate(a_			):
													# 1. scale model input
													snake_case:    Any      							=scheduler.scale_model_input(a_      ,						a_			)

													# 2. predict noise residual
													snake_case:    List[str]      							=model(a_      ,						a_			)

													# 3. predict previous sample x_t-1
													snake_case:    Dict      							=scheduler.step(a_      ,						a_      ,						a_      ,						generator=a_			).prev_sample

													snake_case:    List[Any]      							=pred_prev_sample

										snake_case:    Optional[Any]      							=torch.sum(torch.abs(a_			)			)
										snake_case:    Optional[Any]      							=torch.mean(torch.abs(a_			)			)

										assert abs(result_sum.item() - 1_9_2.7_6_1_4			) < 1E-2
										assert abs(result_mean.item() - 0.2_5_1_0			) < 1E-3


							def    UpperCamelCase     (				self   :						Dict			)			->					Union[str, Any]:
										snake_case:    Dict      							=self.scheduler_classes[0]
										snake_case:    Tuple      							=self.get_scheduler_config()
										snake_case:    str      							=scheduler_class(**a_			)

										snake_case:    List[Any]      							=[1_0_6, 0]
										scheduler.set_timesteps(timesteps=a_			)
										snake_case:    Optional[Any]      							=scheduler.timesteps

										snake_case:    Dict      							=torch.manual_seed(0			)

										snake_case:    Optional[int]      							=self.dummy_model()
										snake_case:    Any      							=self.dummy_sample_deter * scheduler.init_noise_sigma

										for t in timesteps:
													# 1. scale model input
													snake_case:    List[Any]      							=scheduler.scale_model_input(a_      ,						a_			)

													# 2. predict noise residual
													snake_case:    Any      							=model(a_      ,						a_			)

													# 3. predict previous sample x_t-1
													snake_case:    List[str]      							=scheduler.step(a_      ,						a_      ,						a_      ,						generator=a_			).prev_sample

													snake_case:    Optional[Any]      							=pred_prev_sample

										snake_case:    Union[str, Any]      							=torch.sum(torch.abs(a_			)			)
										snake_case:    Tuple      							=torch.mean(torch.abs(a_			)			)

										assert abs(result_sum.item() - 3_4_7.6_3_5_7			) < 1E-2
										assert abs(result_mean.item() - 0.4_5_2_7			) < 1E-3


							def    UpperCamelCase     (				self   :						int			)			->					Tuple:
										snake_case:    List[Any]      							=self.scheduler_classes[0]
										snake_case:    Union[str, Any]      							=self.get_scheduler_config()
										snake_case:    str      							=scheduler_class(**a_			)

										snake_case:    str      							=[3_9, 3_0, 1_2, 1_5, 0]

										with self.assertRaises(a_      ,						msg='`timesteps` must be in descending order.'			):
													scheduler.set_timesteps(timesteps=a_			)


							def    UpperCamelCase     (				self   :						Dict			)			->					Optional[int]:
										snake_case:    Optional[Any]      							=self.scheduler_classes[0]
										snake_case:    Dict      							=self.get_scheduler_config()
										snake_case:    str      							=scheduler_class(**a_			)

										snake_case:    Any      							=[3_9, 3_0, 1_2, 1, 0]
										snake_case:    List[Any]      							=len(a_			)

										with self.assertRaises(a_      ,						msg='Can only pass one of `num_inference_steps` or `timesteps`.'			):
													scheduler.set_timesteps(num_inference_steps=a_      ,						timesteps=a_			)




							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Tuple:
										snake_case:    Any      							=self.scheduler_classes[0]
										snake_case:    int      							=self.get_scheduler_config()
										snake_case:    Optional[Any]      							=scheduler_class(**a_			)

										snake_case:    List[Any]      							=[scheduler.config.num_train_timesteps]

										with self.assertRaises(
										    a_      ,						msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'      ,						):
													scheduler.set_timesteps(timesteps=a_			)




