


'''simple docstring'''





import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class A						(     UpperCamelCase_  ):
      UpperCamelCase__							:						Any   =(KDPMaAncestralDiscreteScheduler,)
      UpperCamelCase__							:						Optional[Any]   =10






      def  lowerCamelCase (      self		:  Optional[int]    ,     **lowercase_		:  Any				)      ->		str:
             """simple docstring"""




             _lowerCamelCase :	Union[str, Any]						={
                 'num_train_timesteps': 1100,
                 'beta_start': 0.0001,
                 'beta_end': 0.02,
                 'beta_schedule': 'linear',
             }

             config.update(**lowercase_				)
             return config






      def  lowerCamelCase (      self		:  List[Any]				)      ->		List[Any]:
             """simple docstring"""




             for timesteps in [10, 50, 100, 1000]:
                    self.check_over_configs(num_train_timesteps=lowercase_				)






      def  lowerCamelCase (      self		:  Optional[int]				)      ->		Optional[int]:
             """simple docstring"""




             for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]    ,     [0.0002, 0.002, 0.02]				):
                    self.check_over_configs(beta_start=lowercase_    ,     beta_end=lowercase_				)






      def  lowerCamelCase (      self		:  Optional[int]				)      ->		Dict:
             """simple docstring"""




             for schedule in ["linear", "scaled_linear"]:
                    self.check_over_configs(beta_schedule=lowercase_				)






      def  lowerCamelCase (      self		:  Optional[int]				)      ->		int:
             """simple docstring"""




             if torch_device == "mps":
                    return
             _lowerCamelCase :	List[str]						=self.scheduler_classes[0]
             _lowerCamelCase :	int						=self.get_scheduler_config()
             _lowerCamelCase :	int						=scheduler_class(**lowercase_				)

             scheduler.set_timesteps(self.num_inference_steps				)

             _lowerCamelCase :	str						=torch.manual_seed(0				)

             _lowerCamelCase :	str						=self.dummy_model()
             _lowerCamelCase :	Optional[Any]						=self.dummy_sample_deter * scheduler.init_noise_sigma
             _lowerCamelCase :	Dict						=sample.to(lowercase_				)

             for i, t in enumerate(scheduler.timesteps				):
                    _lowerCamelCase :	List[Any]						=scheduler.scale_model_input(lowercase_    ,     lowercase_				)

                    _lowerCamelCase :	Optional[int]						=model(lowercase_    ,     lowercase_				)

                    _lowerCamelCase :	str						=scheduler.step(lowercase_    ,     lowercase_    ,     lowercase_    ,     generator=lowercase_				)
                    _lowerCamelCase :	str						=output.prev_sample

             _lowerCamelCase :	Union[str, Any]						=torch.sum(torch.abs(lowercase_				)				)
             _lowerCamelCase :	int						=torch.mean(torch.abs(lowercase_				)				)

             assert abs(result_sum.item() - 13849.3877				) < 1E-2
             assert abs(result_mean.item() - 18.0331				) < 5E-3






      def  lowerCamelCase (      self		:  Union[str, Any]				)      ->		int:
             """simple docstring"""




             for prediction_type in ["epsilon", "v_prediction"]:
                    self.check_over_configs(prediction_type=lowercase_				)






      def  lowerCamelCase (      self		:  Tuple				)      ->		int:
             """simple docstring"""




             if torch_device == "mps":
                    return
             _lowerCamelCase :	List[Any]						=self.scheduler_classes[0]
             _lowerCamelCase :	Tuple						=self.get_scheduler_config(prediction_type='v_prediction'				)
             _lowerCamelCase :	Tuple						=scheduler_class(**lowercase_				)

             scheduler.set_timesteps(self.num_inference_steps				)

             _lowerCamelCase :	int						=self.dummy_model()
             _lowerCamelCase :	Dict						=self.dummy_sample_deter * scheduler.init_noise_sigma
             _lowerCamelCase :	List[str]						=sample.to(lowercase_				)

             _lowerCamelCase :	str						=torch.manual_seed(0				)

             for i, t in enumerate(scheduler.timesteps				):
                    _lowerCamelCase :	Optional[Any]						=scheduler.scale_model_input(lowercase_    ,     lowercase_				)

                    _lowerCamelCase :	Union[str, Any]						=model(lowercase_    ,     lowercase_				)

                    _lowerCamelCase :	List[str]						=scheduler.step(lowercase_    ,     lowercase_    ,     lowercase_    ,     generator=lowercase_				)
                    _lowerCamelCase :	List[Any]						=output.prev_sample

             _lowerCamelCase :	Dict						=torch.sum(torch.abs(lowercase_				)				)
             _lowerCamelCase :	Union[str, Any]						=torch.mean(torch.abs(lowercase_				)				)

             assert abs(result_sum.item() - 328.9970				) < 1E-2
             assert abs(result_mean.item() - 0.4284				) < 1E-3






      def  lowerCamelCase (      self		:  List[Any]				)      ->		Optional[int]:
             """simple docstring"""




             if torch_device == "mps":
                    return
             _lowerCamelCase :	List[Any]						=self.scheduler_classes[0]
             _lowerCamelCase :	Optional[Any]						=self.get_scheduler_config()
             _lowerCamelCase :	Dict						=scheduler_class(**lowercase_				)

             scheduler.set_timesteps(self.num_inference_steps    ,     device=lowercase_				)
             _lowerCamelCase :	Any						=torch.manual_seed(0				)

             _lowerCamelCase :	Tuple						=self.dummy_model()
             _lowerCamelCase :	str						=self.dummy_sample_deter.to(lowercase_				) * scheduler.init_noise_sigma

             for t in scheduler.timesteps:
                    _lowerCamelCase :	Optional[Any]						=scheduler.scale_model_input(lowercase_    ,     lowercase_				)

                    _lowerCamelCase :	int						=model(lowercase_    ,     lowercase_				)

                    _lowerCamelCase :	int						=scheduler.step(lowercase_    ,     lowercase_    ,     lowercase_    ,     generator=lowercase_				)
                    _lowerCamelCase :	Optional[int]						=output.prev_sample

             _lowerCamelCase :	Union[str, Any]						=torch.sum(torch.abs(lowercase_				)				)
             _lowerCamelCase :	List[Any]						=torch.mean(torch.abs(lowercase_				)				)

             assert abs(result_sum.item() - 13849.3818				) < 1E-1
             assert abs(result_mean.item() - 18.0331				) < 1E-3






from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_clip import CLIPTokenizer


lowerCamelCase		      = logging.get_logger(__name__)

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

lowerCamelCase		      = {
    'vocab_file': {
        'openai/clip-vit-base-patch32': 'https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json',
    },
    'merges_file': {
        'openai/clip-vit-base-patch32': 'https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'openai/clip-vit-base-patch32': (
            'https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json'
        ),
    },
}

lowerCamelCase		      = {
    'openai/clip-vit-base-patch32': 77,
}




class A						(     UpperCamelCase_  ):
      UpperCamelCase__							:						Dict   =VOCAB_FILES_NAMES
      UpperCamelCase__							:						Optional[int]   =PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase__							:						Optional[int]   =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase__							:						Optional[Any]   =['input_ids', 'attention_mask']
      UpperCamelCase__							:						str   =CLIPTokenizer






      def __init__(      self		:  str    ,     lowercase_		:  Optional[Any]=None    ,     lowercase_		:  Optional[int]=None    ,     lowercase_		:  int=None    ,     lowercase_		:  Optional[int]="<|endoftext|>"    ,     lowercase_		:  Union[str, Any]="<|startoftext|>"    ,     lowercase_		:  Tuple="<|endoftext|>"    ,     lowercase_		:  int="<|endoftext|>"    ,     **lowercase_		:  Optional[Any]    ,     )      ->		Any:
             """simple docstring"""




             super().__init__(
                 lowercase_    ,     lowercase_    ,     tokenizer_file=lowercase_    ,     unk_token=lowercase_    ,     bos_token=lowercase_    ,     eos_token=lowercase_    ,     pad_token=lowercase_    ,     **lowercase_    ,     )

             if not isinstance(self.backend_tokenizer.pre_tokenizer    ,     pre_tokenizers.Sequence				):
                    raise ValueError(
                        'The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been'
                        ' heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using'
                        ' to be compatible with this version.The easiest way to do so is'
                        ' `CLIPTokenizerFast.from_pretrained("path_to_local_folder_or_hub_repo, from_slow=True)`. If you want'
                        ' to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of'
                        ' transformers.'				)

             self._wrap_decode_method_backend_tokenizer()






      def  lowerCamelCase (      self		:  Any				)      ->		Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase :	int						=self.backend_tokenizer.decode

             def new_decode_method(*lowercase_		:  List[str]    ,     **lowercase_		:  int				):
                    _lowerCamelCase :	Tuple						=orig_decode_method(*lowercase_    ,     **lowercase_				)
                    _lowerCamelCase :	Dict						=text.replace(self.backend_tokenizer.model.end_of_word_suffix    ,     ' '				).strip()
                    return text

             _lowerCamelCase :	List[Any]						=new_decode_method






      def  lowerCamelCase (      self		:  Dict    ,     lowercase_		:  List[int]    ,     lowercase_		:  Optional[List[int]] = None				)      ->		List[int]:
             """simple docstring"""




             _lowerCamelCase :	Union[str, Any]						=[self.bos_token_id]
             _lowerCamelCase :	str						=[self.eos_token_id]

             if token_ids_a is None:
                    return bos_token + token_ids_a + eos_token
             return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token






      def  lowerCamelCase (      self		:  Any    ,     lowercase_		:  List[int]    ,     lowercase_		:  Optional[List[int]] = None				)      ->		List[int]:
             """simple docstring"""




             _lowerCamelCase :	int						=[self.bos_token_id]
             _lowerCamelCase :	Any						=[self.eos_token_id]

             if token_ids_a is None:
                    return len(bos_token + token_ids_a + eos_token				) * [0]
             return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token				) * [0]






      def  lowerCamelCase (      self		:  str    ,     lowercase_		:  str    ,     lowercase_		:  Optional[str] = None				)      ->		Tuple[str]:
             """simple docstring"""




             _lowerCamelCase :	str						=self._tokenizer.model.save(lowercase_    ,     name=lowercase_				)
             return tuple(lowercase_				)


