



import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


SCREAMING_SNAKE_CASE_:Any 			=		False






class SCREAMING_SNAKE_CASE__	(						unittest.TestCase       ):




    '''simple docstring'''






    def      _lowerCAmelCase   (			self,						lowerCamelCase__=32						):
        set_seed(0						)
        A					:			List[str]		=  UNetaDModel(sample_size=__UpperCAmelCase,						in_channels=3,						out_channels=3						)
        A					:			Tuple		=  torch.optim.SGD(model.parameters(),						lr=0.0001						)
        return model, optimizer






    @slow
    def      _lowerCAmelCase   (			self						):
        A					:			Tuple		=  "cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
        A					:			Optional[int]		=  DDPMScheduler(
            num_train_timesteps=1000,						beta_start=0.0001,						beta_end=0.02,						beta_schedule="""linear""",						clip_sample=__UpperCAmelCase,						)
        A					:			str		=  DDIMScheduler(
            num_train_timesteps=1000,						beta_start=0.0001,						beta_end=0.02,						beta_schedule="""linear""",						clip_sample=__UpperCAmelCase,						)

        assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

        # shared batches for DDPM and DDIM
        set_seed(0						)
        A					:			List[Any]		=  [torch.randn((4, 3, 32, 32)						).clip(-1,						1						).to(__UpperCAmelCase						) for _ in range(4						)]
        A					:			Dict		=  [torch.randn((4, 3, 32, 32)						).to(__UpperCAmelCase						) for _ in range(4						)]
        A					:			List[str]		=  [torch.randint(0,						1000,						(4,)						).long().to(__UpperCAmelCase						) for _ in range(4						)]

        # train with a DDPM scheduler
        A					:			Any		=  self.get_model_optimizer(resolution=32						)
        model.train().to(__UpperCAmelCase						)
        for i in range(4						):
            optimizer.zero_grad()
            A					:			List[Any]		=  ddpm_scheduler.add_noise(clean_images[i],						noise[i],						timesteps[i]						)
            A					:			List[str]		=  model(__UpperCAmelCase,						timesteps[i]						).sample
            A					:			str		=  torch.nn.functional.mse_loss(__UpperCAmelCase,						noise[i]						)
            loss.backward()
            optimizer.step()
        del model, optimizer

        # recreate the model and optimizer, and retry with DDIM
        A					:			List[Any]		=  self.get_model_optimizer(resolution=32						)
        model.train().to(__UpperCAmelCase						)
        for i in range(4						):
            optimizer.zero_grad()
            A					:			Optional[int]		=  ddim_scheduler.add_noise(clean_images[i],						noise[i],						timesteps[i]						)
            A					:			Optional[Any]		=  model(__UpperCAmelCase,						timesteps[i]						).sample
            A					:			Tuple		=  torch.nn.functional.mse_loss(__UpperCAmelCase,						noise[i]						)
            loss.backward()
            optimizer.step()
        del model, optimizer

        self.assertTrue(torch.allclose(__UpperCAmelCase,						__UpperCAmelCase,						atol=1e-5						)						)
        self.assertTrue(torch.allclose(__UpperCAmelCase,						__UpperCAmelCase,						atol=1e-5						)						)










import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
			UpperCAmelCase__      			=						'''pt'''
elif is_tf_available():
			UpperCAmelCase__      			=						'''tf'''
else:
			UpperCAmelCase__      			=						'''jax'''




class 							snake_case_       (    __UpperCamelCase		,							unittest.TestCase      ):

						"""simple docstring"""



						snake_case__        =      ByTaTokenizer
						snake_case__        =      False



						def        UpperCAmelCase__					(self:					int   )    ->     str:




											'''simple docstring'''



											super().setUp()
											__a					:						int      =					ByTaTokenizer()
											tokenizer.save_pretrained(self.tmpdirname   )



						@cached_property
						def        UpperCAmelCase__					(self:					Any   )    ->     Optional[int]:




											'''simple docstring'''



											return ByTaTokenizer.from_pretrained("google/byt5-small"   )



						def        UpperCAmelCase__					(self:					List[str] ,					**__UpperCAmelCase:					Dict   )    ->     ByTaTokenizer:




											'''simple docstring'''



											return self.tokenizer_class.from_pretrained(self.tmpdirname ,					**__UpperCAmelCase   )



						def        UpperCAmelCase__					(self:					int ,					__UpperCAmelCase:					Tuple ,					__UpperCAmelCase:					Optional[int]=False ,					__UpperCAmelCase:					Optional[Any]=20 ,					__UpperCAmelCase:					Tuple=5   )    ->     Tuple[str, list]:




											'''simple docstring'''



											__a					:						Tuple      =					[]
											for i in range(len(__UpperCAmelCase   )   ):
																try:
																					__a					:						Union[str, Any]      =					tokenizer.decode([i] ,					clean_up_tokenization_spaces=__UpperCAmelCase   )
																except UnicodeDecodeError:
																					pass
																toks.append((i, tok)   )

											__a					:						Any      =					list(filter(lambda __UpperCAmelCase   : re.match(R"^[ a-zA-Z]+$" ,					t[1]   ) ,					__UpperCAmelCase   )   )
											__a					:						int      =					list(filter(lambda __UpperCAmelCase   : [t[0]] == tokenizer.encode(t[1] ,					add_special_tokens=__UpperCAmelCase   ) ,					__UpperCAmelCase   )   )
											if max_length is not None and len(__UpperCAmelCase   ) > max_length:
																__a					:						str      =					toks[:max_length]
											if min_length is not None and len(__UpperCAmelCase   ) < min_length and len(__UpperCAmelCase   ) > 0:
																while len(__UpperCAmelCase   ) < min_length:
																					__a					:						List[str]      =					toks + toks
        # toks_str = [t[1] for t in toks]
											__a					:						Optional[int]      =					[t[0] for t in toks]

											# Ensure consistency
											__a					:						Any      =					tokenizer.decode(__UpperCAmelCase ,					clean_up_tokenization_spaces=__UpperCAmelCase   )
											if " " not in output_txt and len(__UpperCAmelCase   ) > 1:
																__a					:						Union[str, Any]      =					(
																    tokenizer.decode([toks_ids[0]] ,					clean_up_tokenization_spaces=__UpperCAmelCase   )
																    + " "
																    + tokenizer.decode(toks_ids[1:] ,					clean_up_tokenization_spaces=__UpperCAmelCase   )
																)
											if with_prefix_space:
																__a					:						Tuple      =					" " + output_txt
											__a					:						Tuple      =					tokenizer.encode(__UpperCAmelCase ,					add_special_tokens=__UpperCAmelCase   )
											return output_txt, output_ids



						def        UpperCAmelCase__					(self:					Tuple   )    ->     List[Any]:




											'''simple docstring'''



											__a					:						Any      =					self.ta_base_tokenizer
											__a					:						str      =					tokenizer(["hi</s>", "I went to the gym</s>", "</s>"]   )
											__a					:						int      =					tokenizer(["hi", "I went to the gym", ""]   )
											self.assertListEqual(batch_with_eos_added["input_ids"] ,					batch_without_eos_added["input_ids"]   )



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




											'''simple docstring'''



											__a					:						Any      =					self.ta_base_tokenizer
											__a					:						int      =					"Unicode €."
											__a					:						int      =					tokenizer(__UpperCAmelCase   )
											__a					:						List[Any]      =					[88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
											self.assertEqual(encoded["input_ids"] ,					__UpperCAmelCase   )

											# decoding
											__a					:						List[str]      =					tokenizer.decode(__UpperCAmelCase   )
											self.assertEqual(__UpperCAmelCase ,					"Unicode €.</s>"   )

											__a					:						Optional[int]      =					tokenizer("e è é ê ë"   )
											__a					:						List[str]      =					[104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
											self.assertEqual(encoded["input_ids"] ,					__UpperCAmelCase   )
											# decoding
											__a					:						Optional[Any]      =					tokenizer.decode(__UpperCAmelCase   )
											self.assertEqual(__UpperCAmelCase ,					"e è é ê ë</s>"   )

											# encode/decode, but with `encode` instead of `__call__`
											self.assertEqual(tokenizer.decode(tokenizer.encode("e è é ê ë"   )   ) ,					"e è é ê ë</s>"   )



						def        UpperCAmelCase__					(self:					List[str]   )    ->     Optional[int]:




											'''simple docstring'''



											__a					:						Optional[Any]      =					self.ta_base_tokenizer
											__a					:						Any      =					["A long paragraph for summarization.", "Another paragraph for summarization."]
											# fmt: off
											__a					:						Optional[int]      =					[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 1, 0]
											# fmt: on
											__a					:						Optional[Any]      =					tokenizer(__UpperCAmelCase ,					padding=__UpperCAmelCase ,					return_tensors=__UpperCAmelCase   )
											self.assertIsInstance(__UpperCAmelCase ,					__UpperCAmelCase   )

											if FRAMEWORK != "jax":
																__a					:						Optional[int]      =					list(batch.input_ids.numpy()[0]   )
											else:
																__a					:						List[Any]      =					list(batch.input_ids.tolist()[0]   )

											self.assertListEqual(__UpperCAmelCase ,					__UpperCAmelCase   )

											self.assertEqual((2, 37) ,					batch.input_ids.shape   )
											self.assertEqual((2, 37) ,					batch.attention_mask.shape   )



						def        UpperCAmelCase__					(self:					Union[str, Any]   )    ->     Union[str, Any]:




											'''simple docstring'''



											__a					:						int      =					self.ta_base_tokenizer
											__a					:						str      =					["A long paragraph for summarization.", "Another paragraph for summarization."]
											__a					:						Optional[Any]      =					tokenizer(__UpperCAmelCase ,					padding=__UpperCAmelCase ,					return_tensors=__UpperCAmelCase   )
											# check if input_ids are returned and no decoder_input_ids
											self.assertIn("input_ids" ,					__UpperCAmelCase   )
											self.assertIn("attention_mask" ,					__UpperCAmelCase   )
											self.assertNotIn("decoder_input_ids" ,					__UpperCAmelCase   )
											self.assertNotIn("decoder_attention_mask" ,					__UpperCAmelCase   )



						def        UpperCAmelCase__					(self:					Union[str, Any]   )    ->     str:




											'''simple docstring'''



											__a					:						List[str]      =					self.ta_base_tokenizer
											__a					:						Optional[int]      =					[
											    "Summary of the text.",
											    "Another summary.",
											]
											__a					:						Union[str, Any]      =					tokenizer(
											    text_target=__UpperCAmelCase ,					max_length=32 ,					padding="max_length" ,					truncation=__UpperCAmelCase ,					return_tensors=__UpperCAmelCase   )
											self.assertEqual(32 ,					targets["input_ids"].shape[1]   )



						def        UpperCAmelCase__					(self:					str   )    ->     str:




											'''simple docstring'''



											__a					:						Union[str, Any]      =					self.ta_base_tokenizer
											__a					:						Union[str, Any]      =					["A long paragraph for summarization. </s>"]
											__a					:						Dict      =					["Summary of the text. </s>"]
											# fmt: off
											__a					:						str      =					[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 35, 1]
											__a					:						Union[str, Any]      =					[86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1]
											# fmt: on

											__a					:						int      =					tokenizer(__UpperCAmelCase ,					text_target=__UpperCAmelCase   )

											self.assertEqual(__UpperCAmelCase ,					batch["input_ids"][0]   )
											self.assertEqual(__UpperCAmelCase ,					batch["labels"][0]   )



						def        UpperCAmelCase__					(self:					Union[str, Any]   )    ->     Tuple:




											'''simple docstring'''



											__a					:						Optional[Any]      =					self.get_tokenizers()
											for tokenizer in tokenizers:
																with self.subTest(f'{tokenizer.__class__.__name__}'   ):
																					self.assertNotEqual(tokenizer.model_max_length ,					42   )

        # Now let's start the test
											__a					:						str      =					self.get_tokenizers()
											for tokenizer in tokenizers:
																with self.subTest(f'{tokenizer.__class__.__name__}'   ):
																					# Isolate this from the other tests because we save additional tokens/etc
																					__a					:						int      =					tempfile.mkdtemp()

																					__a					:						Optional[int]      =					" He is very happy, UNwant\u00E9d,running"
																					__a					:						str      =					tokenizer.encode(__UpperCAmelCase ,					add_special_tokens=__UpperCAmelCase   )
																					tokenizer.save_pretrained(__UpperCAmelCase   )

																					__a					:						int      =					tokenizer.__class__.from_pretrained(__UpperCAmelCase   )
																					__a					:						Tuple      =					after_tokenizer.encode(__UpperCAmelCase ,					add_special_tokens=__UpperCAmelCase   )
																					self.assertListEqual(__UpperCAmelCase ,					__UpperCAmelCase   )

																					shutil.rmtree(__UpperCAmelCase   )

											__a					:						List[str]      =					self.get_tokenizers(model_max_length=42   )
											for tokenizer in tokenizers:
																with self.subTest(f'{tokenizer.__class__.__name__}'   ):
																					# Isolate this from the other tests because we save additional tokens/etc
																					__a					:						Optional[int]      =					tempfile.mkdtemp()

																					__a					:						Any      =					" He is very happy, UNwant\u00E9d,running"
																					tokenizer.add_tokens(["bim", "bambam"]   )
																					__a					:						List[str]      =					tokenizer.additional_special_tokens
																					additional_special_tokens.append("new_additional_special_token"   )
																					tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}   )
																					__a					:						Tuple      =					tokenizer.encode(__UpperCAmelCase ,					add_special_tokens=__UpperCAmelCase   )
																					tokenizer.save_pretrained(__UpperCAmelCase   )

																					__a					:						Optional[Any]      =					tokenizer.__class__.from_pretrained(__UpperCAmelCase   )
																					__a					:						List[str]      =					after_tokenizer.encode(__UpperCAmelCase ,					add_special_tokens=__UpperCAmelCase   )
																					self.assertListEqual(__UpperCAmelCase ,					__UpperCAmelCase   )
																					self.assertIn("new_additional_special_token" ,					after_tokenizer.additional_special_tokens   )
																					self.assertEqual(after_tokenizer.model_max_length ,					42   )

																					__a					:						List[Any]      =					tokenizer.__class__.from_pretrained(__UpperCAmelCase ,					model_max_length=43   )
																					self.assertEqual(tokenizer.model_max_length ,					43   )

																					shutil.rmtree(__UpperCAmelCase   )



						def        UpperCAmelCase__					(self:					Optional[int]   )    ->     Tuple:




											'''simple docstring'''



											__a					:						Tuple      =					[]
											if self.test_slow_tokenizer:
																tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())   )

											if self.test_rust_tokenizer:
																tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())   )

											for tokenizer_class, tokenizer_utils in tokenizer_list:
																with tempfile.TemporaryDirectory() as tmp_dir:
																					tokenizer_utils.save_pretrained(__UpperCAmelCase   )

																					with open(os.path.join(__UpperCAmelCase ,					"special_tokens_map.json"   ) ,					encoding="utf-8"   ) as json_file:
																										__a					:						Optional[int]      =					json.load(__UpperCAmelCase   )

																					with open(os.path.join(__UpperCAmelCase ,					"tokenizer_config.json"   ) ,					encoding="utf-8"   ) as json_file:
																										__a					:						Any      =					json.load(__UpperCAmelCase   )

																					__a					:						Optional[Any]      =					[f'<extra_id_{i}>' for i in range(125   )]

																					__a					:						Any      =					added_tokens_extra_ids + [
																					    "an_additional_special_token"
																					]
																					__a					:						Union[str, Any]      =					added_tokens_extra_ids + [
																					    "an_additional_special_token"
																					]

																					with open(os.path.join(__UpperCAmelCase ,					"special_tokens_map.json"   ) ,					"w" ,					encoding="utf-8"   ) as outfile:
																										json.dump(__UpperCAmelCase ,					__UpperCAmelCase   )
																					with open(os.path.join(__UpperCAmelCase ,					"tokenizer_config.json"   ) ,					"w" ,					encoding="utf-8"   ) as outfile:
																										json.dump(__UpperCAmelCase ,					__UpperCAmelCase   )

																					# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
																					# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
																					# "special_tokens_map.json" files
																					__a					:						Union[str, Any]      =					tokenizer_class.from_pretrained(
																					    __UpperCAmelCase ,					)
																					self.assertIn(
																					    "an_additional_special_token" ,					tokenizer_without_change_in_init.additional_special_tokens   )
																					# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
																					self.assertEqual(
																					    ["an_additional_special_token"] ,					tokenizer_without_change_in_init.convert_ids_to_tokens(
																					        tokenizer_without_change_in_init.convert_tokens_to_ids(["an_additional_special_token"]   )   ) ,					)

																					# Now we test that we can change the value of additional_special_tokens in the from_pretrained
																					__a					:						Union[str, Any]      =					added_tokens_extra_ids + [AddedToken("a_new_additional_special_token" ,					lstrip=__UpperCAmelCase   )]
																					__a					:						int      =					tokenizer_class.from_pretrained(
																					    __UpperCAmelCase ,					additional_special_tokens=__UpperCAmelCase ,					)

																					self.assertIn("a_new_additional_special_token" ,					tokenizer.additional_special_tokens   )
																					self.assertEqual(
																					    ["a_new_additional_special_token"] ,					tokenizer.convert_ids_to_tokens(
																					        tokenizer.convert_tokens_to_ids(["a_new_additional_special_token"]   )   ) ,					)



						def        UpperCAmelCase__					(self:					List[str]   )    ->     List[str]:




											'''simple docstring'''



											__a					:						Optional[int]      =					[]
											if self.test_slow_tokenizer:
																tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())   )

											if self.test_rust_tokenizer:
																tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())   )

											for tokenizer_class, tokenizer_utils in tokenizer_list:
																with tempfile.TemporaryDirectory() as tmp_dir:
																					tokenizer_utils.save_pretrained(__UpperCAmelCase   )

																					__a					:						Tuple      =					tokenizer_class.from_pretrained(__UpperCAmelCase   )

																					self.assertTrue(tokenizer.decode([255]   ) == ""   )



						def        UpperCAmelCase__					(self:					Optional[int]   )    ->     str:




											'''simple docstring'''



											pass



						def        UpperCAmelCase__					(self:					str   )    ->     str:




											'''simple docstring'''



											pass



						def        UpperCAmelCase__					(self:					List[Any]   )    ->     Any:




											'''simple docstring'''



											pass



						def        UpperCAmelCase__					(self:					Dict   )    ->     Union[str, Any]:




											'''simple docstring'''



											pass



						def        UpperCAmelCase__					(self:					Dict   )    ->     int:




											'''simple docstring'''



											__a					:						Optional[int]      =					self.get_tokenizers(fast=__UpperCAmelCase ,					do_lower_case=__UpperCAmelCase   )
											for tokenizer in tokenizers:
																with self.subTest(f'{tokenizer.__class__.__name__}'   ):
																					__a					:						List[str]      =					["t", "h", "i", "s", " ", "i", "s", " ", "a", " ", "t", "e", "x", "t", "</s>"]
																					__a					:						Any      =					tokenizer.convert_tokens_to_string(__UpperCAmelCase   )

																					self.assertIsInstance(__UpperCAmelCase ,					__UpperCAmelCase   )



						def        UpperCAmelCase__					(self:					List[Any]   )    ->     Any:




											'''simple docstring'''



											__a					:						Union[str, Any]      =					self.get_tokenizers()
											for tokenizer in tokenizers:
																with self.subTest(f'{tokenizer.__class__.__name__}'   ):
																					__a					:						Tuple      =					[
																					    "bos_token",
																					    "eos_token",
																					    "unk_token",
																					    "sep_token",
																					    "pad_token",
																					    "cls_token",
																					    "mask_token",
																					]

																					__a					:						int      =					0
																					__a					:						Optional[Any]      =					tokenizer.convert_ids_to_tokens(
																					    __UpperCAmelCase ,					skip_special_tokens=__UpperCAmelCase   )

																					for attr in attributes_list:
																										setattr(__UpperCAmelCase ,					attr + "_id" ,					__UpperCAmelCase   )
																										self.assertEqual(getattr(__UpperCAmelCase ,					__UpperCAmelCase   ) ,					__UpperCAmelCase   )
																										self.assertEqual(getattr(__UpperCAmelCase ,					attr + "_id"   ) ,					__UpperCAmelCase   )

																										setattr(__UpperCAmelCase ,					attr + "_id" ,					__UpperCAmelCase   )
																										self.assertEqual(getattr(__UpperCAmelCase ,					__UpperCAmelCase   ) ,					__UpperCAmelCase   )
																										self.assertEqual(getattr(__UpperCAmelCase ,					attr + "_id"   ) ,					__UpperCAmelCase   )

																					setattr(__UpperCAmelCase ,					"additional_special_tokens_ids" ,					[]   )
																					self.assertListEqual(getattr(__UpperCAmelCase ,					"additional_special_tokens"   ) ,					[]   )
																					self.assertListEqual(getattr(__UpperCAmelCase ,					"additional_special_tokens_ids"   ) ,					[]   )

																					setattr(__UpperCAmelCase ,					"additional_special_tokens_ids" ,					[token_id_to_test_setters]   )
																					self.assertListEqual(getattr(__UpperCAmelCase ,					"additional_special_tokens"   ) ,					[token_to_test_setters]   )
																					self.assertListEqual(getattr(__UpperCAmelCase ,					"additional_special_tokens_ids"   ) ,					[token_id_to_test_setters]   )





