






'''simple docstring'''


import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE       ,unittest.TestCase	):


			'''simple docstring'''





			__a							:							Tuple					     =					CanineTokenizer
			__a							:							str					     =					False






			def 				A      (   self							:						Optional[int]			)							->    Union[str, Any]:

									'''simple docstring'''







									super().setUp()
									UpperCamelCase__         = CanineTokenizer()
									tokenizer.save_pretrained(self.tmpdirname			)






			@cached_property
			def 				A      (   self							:						str			)							->    str:

									'''simple docstring'''







									return CanineTokenizer.from_pretrained("""google/canine-s"""			)






			def 				A      (   self							:						List[str] ,      **lowercase							:						int			)							->    CanineTokenizer:

									'''simple docstring'''







									UpperCamelCase__         = self.tokenizer_class.from_pretrained(self.tmpdirname ,      **lowercase			)
									UpperCamelCase__         = 1_0_2_4
									return tokenizer






			@require_torch
			def 				A      (   self							:						List[str]			)							->    Union[str, Any]:

									'''simple docstring'''







									UpperCamelCase__         = self.canine_tokenizer
									UpperCamelCase__         = ["""Life is like a box of chocolates.""", """You never know what you're gonna get."""]
									# fmt: off
									UpperCamelCase__         = [5_7_3_4_4, 7_6, 1_0_5, 1_0_2, 1_0_1, 3_2, 1_0_5, 1_1_5, 3_2, 1_0_8, 1_0_5, 1_0_7, 1_0_1, 3_2, 9_7, 3_2, 9_8, 1_1_1, 1_2_0, 3_2, 1_1_1, 1_0_2, 3_2, 9_9, 1_0_4, 1_1_1, 9_9, 1_1_1, 1_0_8, 9_7, 1_1_6, 1_0_1, 1_1_5, 4_6, 5_7_3_4_5, 0, 0, 0, 0]
									# fmt: on
									UpperCamelCase__         = tokenizer(lowercase ,      padding=lowercase ,      return_tensors="""pt"""			)
									self.assertIsInstance(lowercase ,      lowercase			)

									UpperCamelCase__         = list(batch.input_ids.numpy()[0]			)

									self.assertListEqual(lowercase ,      lowercase			)

									self.assertEqual((2, 3_9) ,      batch.input_ids.shape			)
									self.assertEqual((2, 3_9) ,      batch.attention_mask.shape			)






			@require_torch
			def 				A      (   self							:						Optional[int]			)							->    Tuple:

									'''simple docstring'''







									UpperCamelCase__         = self.canine_tokenizer
									UpperCamelCase__         = ["""Once there was a man.""", """He wrote a test in HuggingFace Tranformers."""]
									UpperCamelCase__         = tokenizer(lowercase ,      padding=lowercase ,      return_tensors="""pt"""			)
									# check if input_ids, attention_mask and token_type_ids are returned
									self.assertIn("""input_ids""" ,      lowercase			)
									self.assertIn("""attention_mask""" ,      lowercase			)
									self.assertIn("""token_type_ids""" ,      lowercase			)






			@require_torch
			def 				A      (   self							:						List[str]			)							->    List[Any]:

									'''simple docstring'''







									UpperCamelCase__         = self.canine_tokenizer
									UpperCamelCase__         = [
									    """What's the weater?""",
									    """It's about 25 degrees.""",
									]
									UpperCamelCase__         = tokenizer(
									    text_target=lowercase ,      max_length=3_2 ,      padding="""max_length""" ,      truncation=lowercase ,      return_tensors="""pt"""			)
									self.assertEqual(3_2 ,      targets["""input_ids"""].shape[1]			)






			def 				A      (   self							:						Optional[Any]			)							->    Tuple:

									'''simple docstring'''







									UpperCamelCase__         = self.get_tokenizers()
									for tokenizer in tokenizers:
															with self.subTest(f"{tokenizer.__class__.__name__}"			):
																					self.assertNotEqual(tokenizer.model_max_length ,      4_2			)

        # Now let's start the test
									UpperCamelCase__         = 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
																					UpperCamelCase__         = tempfile.mkdtemp()

																					UpperCamelCase__         = """ He is very happy, UNwant\u00E9d,running"""
																					UpperCamelCase__         = tokenizer.encode(lowercase ,      add_special_tokens=lowercase			)
																					tokenizer.save_pretrained(lowercase			)

																					UpperCamelCase__         = tokenizer.__class__.from_pretrained(lowercase			)
																					UpperCamelCase__         = after_tokenizer.encode(lowercase ,      add_special_tokens=lowercase			)
																					self.assertListEqual(lowercase ,      lowercase			)

																					shutil.rmtree(lowercase			)

									UpperCamelCase__         = self.get_tokenizers(model_max_length=4_2			)
									for tokenizer in tokenizers:
															with self.subTest(f"{tokenizer.__class__.__name__}"			):
																					# Isolate this from the other tests because we save additional tokens/etc
																					UpperCamelCase__         = tempfile.mkdtemp()

																					UpperCamelCase__         = """ He is very happy, UNwant\u00E9d,running"""

																					UpperCamelCase__         = tokenizer.additional_special_tokens

																					# We can add a new special token for Canine as follows:
																					UpperCamelCase__         = chr(0xE007			)
																					additional_special_tokens.append(lowercase			)
																					tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens}			)
																					UpperCamelCase__         = tokenizer.encode(lowercase ,      add_special_tokens=lowercase			)
																					tokenizer.save_pretrained(lowercase			)

																					UpperCamelCase__         = tokenizer.__class__.from_pretrained(lowercase			)
																					UpperCamelCase__         = after_tokenizer.encode(lowercase ,      add_special_tokens=lowercase			)
																					self.assertListEqual(lowercase ,      lowercase			)
																					self.assertIn(lowercase ,      after_tokenizer.additional_special_tokens			)
																					self.assertEqual(after_tokenizer.model_max_length ,      4_2			)

																					UpperCamelCase__         = tokenizer.__class__.from_pretrained(lowercase ,      model_max_length=4_3			)
																					self.assertEqual(tokenizer.model_max_length ,      4_3			)

																					shutil.rmtree(lowercase			)






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

									'''simple docstring'''







									UpperCamelCase__         = self.get_tokenizers(do_lower_case=lowercase			)
									for tokenizer in tokenizers:
															with self.subTest(f"{tokenizer.__class__.__name__}"			):
																					UpperCamelCase__    ,   UpperCamelCase__         = self.get_clean_sequence(lowercase			)

																					# a special token for Canine can be defined as follows:
																					UpperCamelCase__         = 0xE005
																					UpperCamelCase__         = chr(lowercase			)

																					tokenizer.add_special_tokens({"""cls_token""": special_token}			)
																					UpperCamelCase__         = tokenizer.encode(lowercase ,      add_special_tokens=lowercase			)
																					self.assertEqual(len(lowercase			) ,      1			)

																					UpperCamelCase__         = tokenizer.decode(ids + encoded_special_token ,      clean_up_tokenization_spaces=lowercase			)
																					UpperCamelCase__         = tokenizer.encode(lowercase ,      add_special_tokens=lowercase			)

																					UpperCamelCase__         = tokenizer.encode(lowercase ,      add_special_tokens=lowercase			)
																					UpperCamelCase__         = tokenizer.encode(lowercase ,      add_special_tokens=lowercase			)
																					self.assertEqual(lowercase ,      input_encoded + special_token_id			)

																					UpperCamelCase__         = tokenizer.decode(lowercase ,      skip_special_tokens=lowercase			)
																					self.assertTrue(special_token not in decoded			)






			def 				A      (   self							:						str			)							->    Dict:

									'''simple docstring'''







									UpperCamelCase__         = self.get_tokenizers(do_lower_case=lowercase			)
									for tokenizer in tokenizers:
															with self.subTest(f"{tokenizer.__class__.__name__}"			):
																					UpperCamelCase__         = chr(0xE005			)
																					UpperCamelCase__         = chr(0xE006			)

																					# `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
																					tokenizer.add_tokens([SPECIAL_TOKEN_1] ,      special_tokens=lowercase			)
																					# `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
																					# which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
																					tokenizer.add_special_tokens({"""additional_special_tokens""": [SPECIAL_TOKEN_2]}			)

																					UpperCamelCase__         = tokenizer.tokenize(lowercase			)
																					UpperCamelCase__         = tokenizer.tokenize(lowercase			)

																					self.assertEqual(len(lowercase			) ,      1			)
																					self.assertEqual(len(lowercase			) ,      1			)
																					self.assertEqual(token_a[0] ,      lowercase			)
																					self.assertEqual(token_a[0] ,      lowercase			)






			@require_tokenizers
			def 				A      (   self							:						str			)							->    List[str]:

									'''simple docstring'''







									UpperCamelCase__         = self.get_tokenizers(do_lower_case=lowercase			)
									for tokenizer in tokenizers:
															with self.subTest(f"{tokenizer.__class__.__name__}"			):
																					# a special token for Canine can be defined as follows:
																					UpperCamelCase__         = 0xE006
																					UpperCamelCase__         = chr(lowercase			)

																					UpperCamelCase__         = AddedToken(lowercase ,      lstrip=lowercase			)
																					tokenizer.add_special_tokens({"""additional_special_tokens""": [new_token]}			)

																					with tempfile.TemporaryDirectory() as tmp_dir_name:
																											tokenizer.save_pretrained(lowercase			)
																											tokenizer.from_pretrained(lowercase			)






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

									'''simple docstring'''







									UpperCamelCase__         = []
									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(lowercase			)

																					with open(os.path.join(lowercase ,      """special_tokens_map.json"""			) ,      encoding="""utf-8"""			) as json_file:
																											UpperCamelCase__         = json.load(lowercase			)

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

																					# a special token for Canine can be defined as follows:
																					UpperCamelCase__         = 0xE006
																					UpperCamelCase__         = chr(lowercase			)

																					UpperCamelCase__         = [new_token_a]
																					UpperCamelCase__         = [new_token_a]

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

																					# 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
																					UpperCamelCase__         = tokenizer_class.from_pretrained(lowercase ,      extra_ids=0			)
																					self.assertIn(lowercase ,      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(
																					    [new_token_a] ,      tokenizer_without_change_in_init.convert_ids_to_tokens(
																					        tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a]			)			) ,      )

																					UpperCamelCase__         = 0xE007
																					UpperCamelCase__         = chr(lowercase			)
																					# Now we test that we can change the value of additional_special_tokens in the from_pretrained
																					UpperCamelCase__         = [AddedToken(lowercase ,      lstrip=lowercase			)]
																					UpperCamelCase__         = tokenizer_class.from_pretrained(
																					    lowercase ,      additional_special_tokens=lowercase ,      extra_ids=0			)

																					self.assertIn(lowercase ,      tokenizer.additional_special_tokens			)
																					# self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
																					self.assertEqual(
																					    [new_token_a] ,      tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a]			)			)			)






			@require_tokenizers
			def 				A      (   self							:						Optional[int]			)							->    Optional[int]:

									'''simple docstring'''







									UpperCamelCase__         = self.get_tokenizers(do_lower_case=lowercase			)
									for tokenizer in tokenizers:
															with self.subTest(f"{tokenizer.__class__.__name__}"			):
																					UpperCamelCase__         = """hello world"""
																					if self.space_between_special_tokens:
																											UpperCamelCase__         = """[CLS] hello world [SEP]"""
																					else:
																											UpperCamelCase__         = input
																					UpperCamelCase__         = tokenizer.encode(lowercase ,      add_special_tokens=lowercase			)
																					UpperCamelCase__         = tokenizer.decode(lowercase ,      spaces_between_special_tokens=self.space_between_special_tokens			)
																					self.assertIn(lowercase ,      [output, output.lower()]			)






			def 				A      (   self							:						Optional[Any]			)							->    Optional[int]:

									'''simple docstring'''







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

									UpperCamelCase__         = """a"""
									UpperCamelCase__         = ord(lowercase			)

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

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

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

									UpperCamelCase__         = 0xE006
									UpperCamelCase__         = chr(lowercase			)
									setattr(lowercase ,      """additional_special_tokens_ids""" ,      [additional_special_token_id]			)
									self.assertListEqual(getattr(lowercase ,      """additional_special_tokens"""			) ,      [additional_special_token]			)
									self.assertListEqual(getattr(lowercase ,      """additional_special_tokens_ids"""			) ,      [additional_special_token_id]			)






			def 				A      (   self							:						Optional[int]			)							->    int:

									'''simple docstring'''







									pass






			def 				A      (   self							:						int			)							->    Tuple:

									'''simple docstring'''







									pass






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

									'''simple docstring'''







									pass






			def 				A      (   self							:						Dict			)							->    Dict:

									'''simple docstring'''







									pass






			def 				A      (   self							:						Any			)							->    Tuple:

									'''simple docstring'''







									pass






			def 				A      (   self							:						Optional[Any]			)							->    str:

									'''simple docstring'''







									pass






			def 				A      (   self							:						str			)							->    List[Any]:

									'''simple docstring'''







									pass







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

									'''simple docstring'''







									pass








'''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_				: str    	=							transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)







def 	__magic_name__( _A					):
						'''simple docstring'''





						if isinstance(_A						,      torch.Tensor					):
												return image
						elif isinstance(_A						,      PIL.Image.Image					):
												UpperCamelCase__         = [image]

						UpperCamelCase__         = [trans(img.convert("""RGB"""					)					) for img in image]
						UpperCamelCase__         = torch.stack(_A					)
						return image







class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			def __init__(   self							:						Tuple ,      lowercase							:						Dict ,      lowercase							:						Optional[Any]			)							->    Any:

									'''simple docstring'''







									super().__init__()

									# make sure scheduler can always be converted to DDIM
									UpperCamelCase__         = DDIMScheduler.from_config(scheduler.config			)

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






			def 				A      (   self							:						Tuple ,      lowercase							:						Tuple			)							->    Any:

									'''simple docstring'''







									if strength < 0 or strength > 1:
															raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}"			)






			def 				A      (   self							:						str ,      lowercase							:						List[Any] ,      lowercase							:						Dict ,      lowercase							:						List[str]			)							->    str:

									'''simple docstring'''







									UpperCamelCase__         = min(int(num_inference_steps * strength			) ,      lowercase			)

									UpperCamelCase__         = max(num_inference_steps - init_timestep ,      0			)
									UpperCamelCase__         = self.scheduler.timesteps[t_start:]

									return timesteps, num_inference_steps - t_start






			def 				A      (   self							:						Union[str, Any] ,      lowercase							:						Optional[Any] ,      lowercase							:						Any ,      lowercase							:						Union[str, Any] ,      lowercase							:						Dict ,      lowercase							:						Tuple ,      lowercase							:						int=None			)							->    int:

									'''simple docstring'''







									if not isinstance(lowercase ,      (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(lowercase			)}"			)

									UpperCamelCase__         = image.to(device=lowercase ,      dtype=lowercase			)

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

									UpperCamelCase__         = init_latents.shape
									UpperCamelCase__         = randn_tensor(lowercase ,      generator=lowercase ,      device=lowercase ,      dtype=lowercase			)

									# get latents
									print("""add noise to latents at timestep""" ,      lowercase			)
									UpperCamelCase__         = self.scheduler.add_noise(lowercase ,      lowercase ,      lowercase			)
									UpperCamelCase__         = init_latents

									return latents







			@torch.no_grad()
			def __call__(   self							:						Any ,      lowercase							:						Union[torch.FloatTensor, PIL.Image.Image] = None ,      lowercase							:						float = 0.8 ,      lowercase							:						int = 1 ,      lowercase							:						Optional[Union[torch.Generator, List[torch.Generator]]] = None ,      lowercase							:						float = 0.0 ,      lowercase							:						int = 5_0 ,      lowercase							:						Optional[bool] = None ,      lowercase							:						Optional[str] = "pil" ,      lowercase							:						bool = True ,      )							->    Union[ImagePipelineOutput, Tuple]:

									'''simple docstring'''







									self.check_inputs(lowercase			)

									# 2. Preprocess image
									UpperCamelCase__         = preprocess(lowercase			)

									# 3. set timesteps
									self.scheduler.set_timesteps(lowercase ,      device=self.device			)
									UpperCamelCase__    ,   UpperCamelCase__         = self.get_timesteps(lowercase ,      lowercase ,      self.device			)
									UpperCamelCase__         = timesteps[:1].repeat(lowercase			)

									# 4. Prepare latent variables
									UpperCamelCase__         = self.prepare_latents(lowercase ,      lowercase ,      lowercase ,      self.unet.dtype ,      self.device ,      lowercase			)
									UpperCamelCase__         = latents

									# 5. Denoising loop
									for t in self.progress_bar(lowercase			):
															# 1. predict noise model_output
															UpperCamelCase__         = self.unet(lowercase ,      lowercase			).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
															UpperCamelCase__         = self.scheduler.step(
															    lowercase ,      lowercase ,      lowercase ,      eta=lowercase ,      use_clipped_model_output=lowercase ,      generator=lowercase ,      ).prev_sample

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

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

									return ImagePipelineOutput(images=lowercase			)
