






'''simple docstring'''





import unittest

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





class 		SCREAMING_SNAKE_CASE__   (     unittest.TestCase    ):



		@cached_property
		def _lowerCAmelCase     (    self:    Dict)  ->str:






								'''simple docstring'''


								return BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B")



		@cached_property
		def _lowerCAmelCase     (    self:    Any)  ->Dict:






								'''simple docstring'''


								return BlenderbotTokenizerFast.from_pretrained("facebook/blenderbot-3B")



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






								'''simple docstring'''


								a_				     =   self.tokenizer_ab
								a_				     =   " I am a small frog."
								a_				     =   tok([src_text]				,   padding=a				,   truncation=a)["input_ids"]
								a_				     =   tok.batch_decode(a				,   skip_special_tokens=a				,   clean_up_tokenization_spaces=a)[0]
								assert src_text == decoded



		def _lowerCAmelCase     (    self:    Dict)  ->Tuple:






								'''simple docstring'''


								a_				     =   self.rust_tokenizer_ab
								a_				     =   " I am a small frog."
								a_				     =   tok([src_text]				,   padding=a				,   truncation=a)["input_ids"]
								a_				     =   tok.batch_decode(a				,   skip_special_tokens=a				,   clean_up_tokenization_spaces=a)[0]
								assert src_text == decoded



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






								'''simple docstring'''


								assert self.tokenizer_ab.add_prefix_space
								assert self.tokenizer_ab([" Sam", "Sam"]).input_ids == [[55_02, 2], [55_02, 2]]




		def _lowerCAmelCase     (    self:    Optional[Any])  ->Optional[Any]:






								'''simple docstring'''


								assert self.rust_tokenizer_ab.add_prefix_space
								assert self.rust_tokenizer_ab([" Sam", "Sam"]).input_ids == [[55_02, 2], [55_02, 2]]











'''simple docstring'''





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

a_      = 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 			__UpperCAmelCase				(cls				)		->				Optional[Any]:

													'''simple docstring'''


													return cls



def 			__UpperCAmelCase				(lowercase__	,lowercase__ = None	,lowercase__ = None	,lowercase__ = None	,lowercase__ = None	,)		->				Union[str, Any]:

						'''simple docstring'''


						a_				     =   device
						a_				     =   shape[0]

						a_				     =   layout or torch.strided
						a_				     =   device or torch.device("cpu"				)

						if generator is not None:
												a_				     =   generator.device.type if not isinstance(lowercase__	,lowercase__				) else generator[0].device.type
												if gen_device_type != device.type and gen_device_type == "cpu":
																		a_				     =   "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(lowercase__	,lowercase__				):
												a_				     =   (1,) + shape[1:]
												a_				     =   [
												    torch.randn(lowercase__	,generator=generator[i]	,device=lowercase__	,dtype=lowercase__	,layout=lowercase__				)
												    for i in range(lowercase__				)
												]
												a_				     =   torch.cat(lowercase__	,dim=0				).to(lowercase__				)
						else:
												a_				     =   torch.randn(lowercase__	,generator=lowercase__	,device=lowercase__	,dtype=lowercase__	,layout=lowercase__				).to(lowercase__				)

						return latents



def 			__UpperCAmelCase				(lowercase__				)		->				Tuple:

						'''simple docstring'''


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



