






import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class 					_snake_case						(			_A ):
	_A          =   (DDPMScheduler,)






	def    lowerCAmelCase_							(		self					,**UpperCamelCase       )						->    List[Any]:
					snake_case__      :Any         =   {
					    "num_train_timesteps": 1_000,
					    "beta_start": 0.0001,
					    "beta_end": 0.02,
					    "beta_schedule": "linear",
					    "variance_type": "fixed_small",
					    "clip_sample": True,
					}

					config.update(**UpperCamelCase       )
					return config






	def    lowerCAmelCase_							(		self       )						->    Dict:
					for timesteps in [1, 5, 100, 1_000]:
									self.check_over_configs(num_train_timesteps=UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    Union[str, Any]:
					for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]					,[0.002, 0.02, 0.2, 2]       ):
									self.check_over_configs(beta_start=UpperCamelCase					,beta_end=UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					for schedule in ["linear", "squaredcos_cap_v2"]:
									self.check_over_configs(beta_schedule=UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					for variance in ["fixed_small", "fixed_large", "other"]:
									self.check_over_configs(variance_type=UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    Dict:
					for clip_sample in [True, False]:
									self.check_over_configs(clip_sample=UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					self.check_over_configs(thresholding=UpperCamelCase       )
					for threshold in [0.5, 1.0, 2.0]:
									for prediction_type in ["epsilon", "sample", "v_prediction"]:
													self.check_over_configs(
													    thresholding=UpperCamelCase					,prediction_type=UpperCamelCase					,sample_max_value=UpperCamelCase					,)






	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					for prediction_type in ["epsilon", "sample", "v_prediction"]:
									self.check_over_configs(prediction_type=UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    List[str]:
					for t in [0, 500, 999]:
									self.check_over_forward(time_step=UpperCamelCase       )






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

					assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 0.0       )       ) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(487       ) - 0.00979       )       ) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(999       ) - 0.02       )       ) < 1E-5






	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					snake_case__      :str         =   self.scheduler_classes[0]
					snake_case__      :Optional[Any]         =   self.get_scheduler_config()
					snake_case__      :str         =   scheduler_class(**UpperCamelCase       )

					snake_case__      :Any         =   len(UpperCamelCase       )

					snake_case__      :Union[str, Any]         =   self.dummy_model()
					snake_case__      :Optional[Any]         =   self.dummy_sample_deter
					snake_case__      :int         =   torch.manual_seed(0       )

					for t in reversed(range(UpperCamelCase       )       ):
									# 1. predict noise residual
									snake_case__      :Optional[int]         =   model(UpperCamelCase					,UpperCamelCase       )

									# 2. predict previous mean of sample x_t-1
									snake_case__      :Union[str, Any]         =   scheduler.step(UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,generator=UpperCamelCase       ).prev_sample

									# if t > 0:
									#     noise = self.dummy_sample_deter
									#     variance = scheduler.get_variance(t) ** (0.5) * noise
									#
									# sample = pred_prev_sample + variance
									snake_case__      :Union[str, Any]         =   pred_prev_sample

					snake_case__      :int         =   torch.sum(torch.abs(UpperCamelCase       )       )
					snake_case__      :List[Any]         =   torch.mean(torch.abs(UpperCamelCase       )       )

					assert abs(result_sum.item() - 258.9606       ) < 1E-2
					assert abs(result_mean.item() - 0.3372       ) < 1E-3






	def    lowerCAmelCase_							(		self       )						->    str:
					snake_case__      :List[Any]         =   self.scheduler_classes[0]
					snake_case__      :Tuple         =   self.get_scheduler_config(prediction_type="v_prediction"       )
					snake_case__      :int         =   scheduler_class(**UpperCamelCase       )

					snake_case__      :List[Any]         =   len(UpperCamelCase       )

					snake_case__      :int         =   self.dummy_model()
					snake_case__      :Any         =   self.dummy_sample_deter
					snake_case__      :List[str]         =   torch.manual_seed(0       )

					for t in reversed(range(UpperCamelCase       )       ):
									# 1. predict noise residual
									snake_case__      :Optional[Any]         =   model(UpperCamelCase					,UpperCamelCase       )

									# 2. predict previous mean of sample x_t-1
									snake_case__      :str         =   scheduler.step(UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,generator=UpperCamelCase       ).prev_sample

									# if t > 0:
									#     noise = self.dummy_sample_deter
									#     variance = scheduler.get_variance(t) ** (0.5) * noise
									#
									# sample = pred_prev_sample + variance
									snake_case__      :List[Any]         =   pred_prev_sample

					snake_case__      :str         =   torch.sum(torch.abs(UpperCamelCase       )       )
					snake_case__      :int         =   torch.mean(torch.abs(UpperCamelCase       )       )

					assert abs(result_sum.item() - 202.0296       ) < 1E-2
					assert abs(result_mean.item() - 0.2631       ) < 1E-3






	def    lowerCAmelCase_							(		self       )						->    Any:
					snake_case__      :str         =   self.scheduler_classes[0]
					snake_case__      :Dict         =   self.get_scheduler_config()
					snake_case__      :Any         =   scheduler_class(**UpperCamelCase       )

					snake_case__      :Tuple         =   [100, 87, 50, 1, 0]

					scheduler.set_timesteps(timesteps=UpperCamelCase       )

					snake_case__      :List[Any]         =   scheduler.timesteps

					for i, timestep in enumerate(UpperCamelCase       ):
									if i == len(UpperCamelCase       ) - 1:
													snake_case__      :Any         =   -1
									else:
													snake_case__      :Optional[Any]         =   timesteps[i + 1]

									snake_case__      :str         =   scheduler.previous_timestep(UpperCamelCase       )
									snake_case__      :Any         =   prev_t.item()

									self.assertEqual(UpperCamelCase					,UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    Optional[int]:
					snake_case__      :Dict         =   self.scheduler_classes[0]
					snake_case__      :List[str]         =   self.get_scheduler_config()
					snake_case__      :int         =   scheduler_class(**UpperCamelCase       )

					snake_case__      :Optional[int]         =   [100, 87, 50, 51, 0]

					with self.assertRaises(UpperCamelCase					,msg="`custom_timesteps` must be in descending order."       ):
									scheduler.set_timesteps(timesteps=UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    int:
					snake_case__      :Optional[Any]         =   self.scheduler_classes[0]
					snake_case__      :str         =   self.get_scheduler_config()
					snake_case__      :List[Any]         =   scheduler_class(**UpperCamelCase       )

					snake_case__      :Tuple         =   [100, 87, 50, 1, 0]
					snake_case__      :Optional[Any]         =   len(UpperCamelCase       )

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






	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					snake_case__      :Union[str, Any]         =   self.scheduler_classes[0]
					snake_case__      :Any         =   self.get_scheduler_config()
					snake_case__      :List[str]         =   scheduler_class(**UpperCamelCase       )

					snake_case__      :List[str]         =   [scheduler.config.num_train_timesteps]

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







import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError

from transformers import (
    AlbertTokenizer,
    AutoTokenizer,
    BertTokenizer,
    BertTokenizerFast,
    GPTaTokenizerFast,
    is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie


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

from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
	from test_module.custom_tokenization_fast import CustomTokenizerFast


class 					_snake_case						(			unittest.TestCase ):






	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					# A mock response for an HTTP head request to emulate server down
					snake_case__      :Tuple         =   mock.Mock()
					snake_case__      :List[str]         =   500
					snake_case__      :Any         =   {}
					snake_case__      :Union[str, Any]         =   HTTPError
					snake_case__      :Tuple         =   {}

					# Download this model to make sure it's in the cache.
					snake_case__      :Any         =   BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert"       )

					# Under the mock environment we get a 500 error when trying to reach the tokenizer.
					with mock.patch("requests.Session.request"					,return_value=UpperCamelCase       ) as mock_head:
									snake_case__      :Dict         =   BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert"       )
									# This check we did call the fake head request
									mock_head.assert_called()






	@require_tokenizers
	def    lowerCAmelCase_							(		self       )						->    Dict:
					# A mock response for an HTTP head request to emulate server down
					snake_case__      :Union[str, Any]         =   mock.Mock()
					snake_case__      :int         =   500
					snake_case__      :Any         =   {}
					snake_case__      :Dict         =   HTTPError
					snake_case__      :List[Any]         =   {}

					# Download this model to make sure it's in the cache.
					snake_case__      :Optional[int]         =   GPTaTokenizerFast.from_pretrained("gpt2"       )

					# Under the mock environment we get a 500 error when trying to reach the tokenizer.
					with mock.patch("requests.Session.request"					,return_value=UpperCamelCase       ) as mock_head:
									snake_case__      :Any         =   GPTaTokenizerFast.from_pretrained("gpt2"       )
									# This check we did call the fake head request
									mock_head.assert_called()






	def    lowerCAmelCase_							(		self       )						->    int:
					# This test is for deprecated behavior and can be removed in v5
					try:
									snake_case__      :Union[str, Any]         =   tempfile.mktemp()
									with open(UpperCamelCase					,"wb"       ) as f:
													http_get("https://huggingface.co/albert-base-v1/resolve/main/spiece.model"					,UpperCamelCase       )

									snake_case__      :Tuple         =   AlbertTokenizer.from_pretrained(UpperCamelCase       )
					finally:
									os.remove(UpperCamelCase       )

					# Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
					# the current folder and have the right name.
					if os.path.isfile("tokenizer.json"       ):
									# We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
									return
					try:
									with open("tokenizer.json"					,"wb"       ) as f:
													http_get("https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json"					,UpperCamelCase       )
									snake_case__      :Dict         =   AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"       )
									# The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
									self.assertEqual(tokenizer.vocab_size					,1_000       )
									# Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.

					finally:
									os.remove("tokenizer.json"       )






	def    lowerCAmelCase_							(		self       )						->    Union[str, Any]:
					# This test is for deprecated behavior and can be removed in v5
					snake_case__      :Union[str, Any]         =   AlbertTokenizer.from_pretrained("https://huggingface.co/albert-base-v1/resolve/main/spiece.model"       )




@is_staging_test
class 					_snake_case						(			unittest.TestCase ):
	_A          =   ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']






	@classmethod
	def    lowerCAmelCase_							(		cls       )						->    Optional[int]:
					snake_case__      :List[str]         =   TOKEN
					HfFolder.save_token(UpperCamelCase       )






	@classmethod
	def    lowerCAmelCase_							(		cls       )						->    Union[str, Any]:
					try:
									delete_repo(token=cls._token					,repo_id="test-tokenizer"       )
					except HTTPError:
									pass

					try:
									delete_repo(token=cls._token					,repo_id="valid_org/test-tokenizer-org"       )
					except HTTPError:
									pass

					try:
									delete_repo(token=cls._token					,repo_id="test-dynamic-tokenizer"       )
					except HTTPError:
									pass






	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					with tempfile.TemporaryDirectory() as tmp_dir:
									snake_case__      :List[str]         =   os.path.join(UpperCamelCase					,"vocab.txt"       )
									with open(UpperCamelCase					,"w"					,encoding="utf-8"       ) as vocab_writer:
													vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]       )       )
									snake_case__      :str         =   BertTokenizer(UpperCamelCase       )

					tokenizer.push_to_hub("test-tokenizer"					,use_auth_token=self._token       )
					snake_case__      :Dict         =   BertTokenizer.from_pretrained(f'{USER}/test-tokenizer'       )
					self.assertDictEqual(new_tokenizer.vocab					,tokenizer.vocab       )

					# Reset repo
					delete_repo(token=self._token					,repo_id="test-tokenizer"       )

					# Push to hub via save_pretrained
					with tempfile.TemporaryDirectory() as tmp_dir:
									tokenizer.save_pretrained(UpperCamelCase					,repo_id="test-tokenizer"					,push_to_hub=UpperCamelCase					,use_auth_token=self._token       )

					snake_case__      :List[str]         =   BertTokenizer.from_pretrained(f'{USER}/test-tokenizer'       )
					self.assertDictEqual(new_tokenizer.vocab					,tokenizer.vocab       )






	def    lowerCAmelCase_							(		self       )						->    Optional[int]:
					with tempfile.TemporaryDirectory() as tmp_dir:
									snake_case__      :List[Any]         =   os.path.join(UpperCamelCase					,"vocab.txt"       )
									with open(UpperCamelCase					,"w"					,encoding="utf-8"       ) as vocab_writer:
													vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]       )       )
									snake_case__      :Any         =   BertTokenizer(UpperCamelCase       )

					tokenizer.push_to_hub("valid_org/test-tokenizer-org"					,use_auth_token=self._token       )
					snake_case__      :Any         =   BertTokenizer.from_pretrained("valid_org/test-tokenizer-org"       )
					self.assertDictEqual(new_tokenizer.vocab					,tokenizer.vocab       )

					# Reset repo
					delete_repo(token=self._token					,repo_id="valid_org/test-tokenizer-org"       )

					# Push to hub via save_pretrained
					with tempfile.TemporaryDirectory() as tmp_dir:
									tokenizer.save_pretrained(
									    UpperCamelCase					,repo_id="valid_org/test-tokenizer-org"					,push_to_hub=UpperCamelCase					,use_auth_token=self._token       )

					snake_case__      :Union[str, Any]         =   BertTokenizer.from_pretrained("valid_org/test-tokenizer-org"       )
					self.assertDictEqual(new_tokenizer.vocab					,tokenizer.vocab       )






	@require_tokenizers
	def    lowerCAmelCase_							(		self       )						->    Any:
					CustomTokenizer.register_for_auto_class()
					with tempfile.TemporaryDirectory() as tmp_dir:
									snake_case__      :str         =   os.path.join(UpperCamelCase					,"vocab.txt"       )
									with open(UpperCamelCase					,"w"					,encoding="utf-8"       ) as vocab_writer:
													vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]       )       )
									snake_case__      :Optional[int]         =   CustomTokenizer(UpperCamelCase       )

					# No fast custom tokenizer
					tokenizer.push_to_hub("test-dynamic-tokenizer"					,use_auth_token=self._token       )

					snake_case__      :Union[str, Any]         =   AutoTokenizer.from_pretrained(f'{USER}/test-dynamic-tokenizer'					,trust_remote_code=UpperCamelCase       )
					# Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
					self.assertEqual(tokenizer.__class__.__name__					,"CustomTokenizer"       )

					# Fast and slow custom tokenizer
					CustomTokenizerFast.register_for_auto_class()
					with tempfile.TemporaryDirectory() as tmp_dir:
									snake_case__      :int         =   os.path.join(UpperCamelCase					,"vocab.txt"       )
									with open(UpperCamelCase					,"w"					,encoding="utf-8"       ) as vocab_writer:
													vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]       )       )

									snake_case__      :Tuple         =   BertTokenizerFast.from_pretrained(UpperCamelCase       )
									bert_tokenizer.save_pretrained(UpperCamelCase       )
									snake_case__      :List[Any]         =   CustomTokenizerFast.from_pretrained(UpperCamelCase       )

					tokenizer.push_to_hub("test-dynamic-tokenizer"					,use_auth_token=self._token       )

					snake_case__      :List[Any]         =   AutoTokenizer.from_pretrained(f'{USER}/test-dynamic-tokenizer'					,trust_remote_code=UpperCamelCase       )
					# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
					self.assertEqual(tokenizer.__class__.__name__					,"CustomTokenizerFast"       )
					snake_case__      :List[str]         =   AutoTokenizer.from_pretrained(
					    f'{USER}/test-dynamic-tokenizer'					,use_fast=UpperCamelCase					,trust_remote_code=UpperCamelCase       )
					# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
					self.assertEqual(tokenizer.__class__.__name__					,"CustomTokenizer"       )




class 					_snake_case						(			unittest.TestCase ):






	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					snake_case__      :int         =   Trie()
					trie.add("Hello 友達"       )
					self.assertEqual(trie.data					,{"H": {"e": {"l": {"l": {"o": {" ": {"友": {"達": {"": 1}}}}}}}}}       )
					trie.add("Hello"       )
					trie.data
					self.assertEqual(trie.data					,{"H": {"e": {"l": {"l": {"o": {"": 1, " ": {"友": {"達": {"": 1}}}}}}}}}       )






	def    lowerCAmelCase_							(		self       )						->    int:
					snake_case__      :List[str]         =   Trie()
					self.assertEqual(trie.split("[CLS] This is a extra_id_100"       )					,["[CLS] This is a extra_id_100"]       )
					trie.add("[CLS]"       )
					trie.add("extra_id_1"       )
					trie.add("extra_id_100"       )
					self.assertEqual(trie.split("[CLS] This is a extra_id_100"       )					,["[CLS]", " This is a ", "extra_id_100"]       )






	def    lowerCAmelCase_							(		self       )						->    str:
					snake_case__      :Optional[Any]         =   Trie()
					trie.add("A"       )
					self.assertEqual(trie.split("ABC"       )					,["A", "BC"]       )
					self.assertEqual(trie.split("BCA"       )					,["BC", "A"]       )






	def    lowerCAmelCase_							(		self       )						->    Dict:
					snake_case__      :Any         =   Trie()
					trie.add("TOKEN]"       )
					trie.add("[SPECIAL_TOKEN]"       )
					self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]"       )					,["This is something ", "[SPECIAL_TOKEN]"]       )






	def    lowerCAmelCase_							(		self       )						->    Tuple:
					snake_case__      :List[Any]         =   Trie()
					trie.add("A"       )
					trie.add("P"       )
					trie.add("[SPECIAL_TOKEN]"       )
					self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]"       )					,["This is something ", "[SPECIAL_TOKEN]"]       )






	def    lowerCAmelCase_							(		self       )						->    Tuple:
					snake_case__      :str         =   Trie()
					trie.add("AB"       )
					trie.add("B"       )
					trie.add("C"       )
					self.assertEqual(trie.split("ABC"       )					,["AB", "C"]       )






	def    lowerCAmelCase_							(		self       )						->    Union[str, Any]:
					snake_case__      :Dict         =   Trie()
					trie.add("ABC"       )
					trie.add("B"       )
					trie.add("CD"       )
					self.assertEqual(trie.split("ABCD"       )					,["ABC", "D"]       )






	def    lowerCAmelCase_							(		self       )						->    int:
					# Even if the offsets are wrong, we necessarily output correct string
					# parts.
					snake_case__      :Optional[int]         =   Trie()
					snake_case__      :Union[str, Any]         =   trie.cut_text("ABC"					,[0, 0, 2, 1, 2, 3]       )
					self.assertEqual(UpperCamelCase					,["AB", "C"]       )