






import unittest

import torch

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


lowerCAmelCase_           = False
class      UpperCamelCase (				unittest.TestCase ):







		"""simple docstring"""



		def       A(  self		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       str=3_2		)    ->   Dict:


						'''simple docstring'''
						set_seed(0		)
						A	 =      UNetaDModel(sample_size=_SCREAMING_SNAKE_CASE						,in_channels=3						,out_channels=3		)
						A	 =      torch.optim.SGD(model.parameters()						,lr=0.0001		)
						return model, optimizer







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


						'''simple docstring'''
						A	 =      'cpu'  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
						A	 =      DDPMScheduler(
						    num_train_timesteps=1_0_0_0						,beta_start=0.0001						,beta_end=0.02						,beta_schedule='linear'						,clip_sample=_SCREAMING_SNAKE_CASE						,)
						A	 =      DDIMScheduler(
						    num_train_timesteps=1_0_0_0						,beta_start=0.0001						,beta_end=0.02						,beta_schedule='linear'						,clip_sample=_SCREAMING_SNAKE_CASE						,)

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

						# shared batches for DDPM and DDIM
						set_seed(0		)
						A	 =      [torch.randn((4, 3, 3_2, 3_2)		).clip(-1						,1		).to(_SCREAMING_SNAKE_CASE		) for _ in range(4		)]
						A	 =      [torch.randn((4, 3, 3_2, 3_2)		).to(_SCREAMING_SNAKE_CASE		) for _ in range(4		)]
						A	 =      [torch.randint(0						,1_0_0_0						,(4,)		).long().to(_SCREAMING_SNAKE_CASE		) for _ in range(4		)]

						# train with a DDPM scheduler
						A ,		A	 =      self.get_model_optimizer(resolution=3_2		)
						model.train().to(_SCREAMING_SNAKE_CASE		)
						for i in range(4		):
										optimizer.zero_grad()
										A	 =      ddpm_scheduler.add_noise(clean_images[i]						,noise[i]						,timesteps[i]		)
										A	 =      model(_SCREAMING_SNAKE_CASE						,timesteps[i]		).sample
										A	 =      torch.nn.functional.mse_loss(_SCREAMING_SNAKE_CASE						,noise[i]		)
										loss.backward()
										optimizer.step()
						del model, optimizer

						# recreate the model and optimizer, and retry with DDIM
						A ,		A	 =      self.get_model_optimizer(resolution=3_2		)
						model.train().to(_SCREAMING_SNAKE_CASE		)
						for i in range(4		):
										optimizer.zero_grad()
										A	 =      ddim_scheduler.add_noise(clean_images[i]						,noise[i]						,timesteps[i]		)
										A	 =      model(_SCREAMING_SNAKE_CASE						,timesteps[i]		).sample
										A	 =      torch.nn.functional.mse_loss(_SCREAMING_SNAKE_CASE						,noise[i]		)
										loss.backward()
										optimizer.step()
						del model, optimizer

						self.assertTrue(torch.allclose(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,atol=1E-5		)		)
						self.assertTrue(torch.allclose(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,atol=1E-5		)		)













import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


lowerCAmelCase_           = logging.get_logger(__name__)

lowerCAmelCase_           = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase_           = {
    'tokenizer_file': {
        'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
    },
}

lowerCAmelCase_           = {
    'gpt-neox-20b': 2_048,
}
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		snake_case    =   VOCAB_FILES_NAMES
		snake_case    =   PRETRAINED_VOCAB_FILES_MAP
		snake_case    =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		snake_case    =   ["input_ids", "attention_mask"]
		def __init__(  self		:       str						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]=None						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]=None						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]=None						,_SCREAMING_SNAKE_CASE		:       Dict="<|endoftext|>"						,_SCREAMING_SNAKE_CASE		:       int="<|endoftext|>"						,_SCREAMING_SNAKE_CASE		:       Optional[int]="<|endoftext|>"						,_SCREAMING_SNAKE_CASE		:       List[Any]=False						,**_SCREAMING_SNAKE_CASE		:       Union[str, Any]						,)    ->   int:


						'''simple docstring'''
						super().__init__(
						    _SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,tokenizer_file=_SCREAMING_SNAKE_CASE						,unk_token=_SCREAMING_SNAKE_CASE						,bos_token=_SCREAMING_SNAKE_CASE						,eos_token=_SCREAMING_SNAKE_CASE						,add_prefix_space=_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE						,)

						A	 =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
						if pre_tok_state.get('add_prefix_space'						,_SCREAMING_SNAKE_CASE		) != add_prefix_space:
										A	 =      getattr(_SCREAMING_SNAKE_CASE						,pre_tok_state.pop('type'		)		)
										A	 =      add_prefix_space
										A	 =      pre_tok_class(**_SCREAMING_SNAKE_CASE		)

						A	 =      add_prefix_space
		def       A(  self		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       str						,_SCREAMING_SNAKE_CASE		:       Optional[str] = None		)    ->   Tuple[str]:


						'''simple docstring'''
						A	 =      self._tokenizer.model.save(_SCREAMING_SNAKE_CASE						,name=_SCREAMING_SNAKE_CASE		)
						return tuple(_SCREAMING_SNAKE_CASE		)







		def       A(  self		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       "Conversation"		)    ->   List[int]:


						'''simple docstring'''
						A	 =      []
						for is_user, text in conversation.iter_texts():
										input_ids.extend(self.encode(_SCREAMING_SNAKE_CASE						,add_special_tokens=_SCREAMING_SNAKE_CASE		) + [self.eos_token_id]		)

						if len(_SCREAMING_SNAKE_CASE		) > self.model_max_length:
										A	 =      input_ids[-self.model_max_length :]
						return input_ids





