


from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
   import torch

_SCREAMING_SNAKE_CASE :					Tuple			=	logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
   from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):







   def 						_lowercase       (     cls			:					Union[str, Any]			)  ->   Any:





        '''simple docstring'''





        return cls







def 						_lowercase       (     __lowerCamelCase			:					Union[Tuple, List]		,__lowerCamelCase			:					Optional[Union[List["torch.Generator"], "torch.Generator"]] = None		,__lowerCamelCase			:					Optional["torch.device"] = None		,__lowerCamelCase			:					Optional["torch.dtype"] = None		,__lowerCamelCase			:					Optional["torch.layout"] = None		,)  ->   Tuple:





     '''simple docstring'''





     UpperCamelCase__							:  Tuple								=     device
     UpperCamelCase__							:  Optional[int]								=     shape[0]

     UpperCamelCase__							:  List[str]								=     layout or torch.strided
     UpperCamelCase__							:  str								=     device or torch.device('''cpu'''			)

     if generator is not None:
          UpperCamelCase__							:  Optional[int]								=     generator.device.type if not isinstance(__lowerCamelCase		,__lowerCamelCase			) else generator[0].device.type
          if gen_device_type != device.type and gen_device_type == "cpu":
               UpperCamelCase__							:  Optional[Any]								=     '''cpu'''
               if device != "mps":
                    logger.info(
                        F'The passed generator was created on \'cpu\' even though a tensor on {device} was expected.'
                        F' Tensors will be created on \'cpu\' and then moved to {device}. Note that one can probably'
                        F' slighly speed up this function by passing a generator that was created on the {device} device.'			)
          elif gen_device_type != device.type and gen_device_type == "cuda":
               raise ValueError(F'Cannot generate a {device} tensor from a generator of type {gen_device_type}.'			)

     if isinstance(__lowerCamelCase		,__lowerCamelCase			):
          UpperCamelCase__							:  Any								=     (1,) + shape[1:]
          UpperCamelCase__							:  int								=     [
              torch.randn(__lowerCamelCase		,generator=generator[i]		,device=__lowerCamelCase		,dtype=__lowerCamelCase		,layout=__lowerCamelCase			)
              for i in range(__lowerCamelCase			)
          ]
          UpperCamelCase__							:  Optional[int]								=     torch.cat(__lowerCamelCase		,dim=0			).to(__lowerCamelCase			)
     else:
          UpperCamelCase__							:  Optional[Any]								=     torch.randn(__lowerCamelCase		,generator=__lowerCamelCase		,device=__lowerCamelCase		,dtype=__lowerCamelCase		,layout=__lowerCamelCase			).to(__lowerCamelCase			)

     return latents







def 						_lowercase       (     __lowerCamelCase			:					Union[str, Any]			)  ->   Optional[int]:





     '''simple docstring'''





     if is_torch_version('''<'''		,'''2.0.0'''			) or not hasattr(__lowerCamelCase		,'''_dynamo'''			):
          return False
     return isinstance(__lowerCamelCase		,torch._dynamo.eval_frame.OptimizedModule			)








import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property

class  UpperCamelCase__ ( unittest.TestCase			):






      @cached_property
      def 	__lowercase(     self					:   List[Any]   )  -> int:
           return BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''   )






      @cached_property
      def 	__lowercase(     self					:   List[Any]   )  -> Tuple:
           return BlenderbotTokenizerFast.from_pretrained('''facebook/blenderbot-3B'''   )






      def 	__lowercase(     self					:   List[str]   )  -> Optional[int]:
           UpperCamelCase__							:  Optional[Any]								=     self.tokenizer_ab
           UpperCamelCase__							:  str								=     ''' I am a small frog.'''
           UpperCamelCase__							:  Dict								=     tok([src_text],		padding=__lowerCamelCase,		truncation=__lowerCamelCase   )['''input_ids''']
           UpperCamelCase__							:  List[str]								=     tok.batch_decode(__lowerCamelCase,		skip_special_tokens=__lowerCamelCase,		clean_up_tokenization_spaces=__lowerCamelCase   )[0]
           assert src_text == decoded






      def 	__lowercase(     self					:   int   )  -> Any:
           UpperCamelCase__							:  str								=     self.rust_tokenizer_ab
           UpperCamelCase__							:  Optional[int]								=     ''' I am a small frog.'''
           UpperCamelCase__							:  List[str]								=     tok([src_text],		padding=__lowerCamelCase,		truncation=__lowerCamelCase   )['''input_ids''']
           UpperCamelCase__							:  Optional[int]								=     tok.batch_decode(__lowerCamelCase,		skip_special_tokens=__lowerCamelCase,		clean_up_tokenization_spaces=__lowerCamelCase   )[0]
           assert src_text == decoded






      def 	__lowercase(     self					:   Optional[int]   )  -> Optional[Any]:
           assert self.tokenizer_ab.add_prefix_space
           assert self.tokenizer_ab([''' Sam''', '''Sam''']   ).input_ids == [[55_02, 2], [55_02, 2]]






      def 	__lowercase(     self					:   Union[str, Any]   )  -> Optional[int]:
           assert self.rust_tokenizer_ab.add_prefix_space
           assert self.rust_tokenizer_ab([''' Sam''', '''Sam''']   ).input_ids == [[55_02, 2], [55_02, 2]]




