



import unittest

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


class 	a__       (       unittest.TestCase       ):


					"""simple docstring"""



					@cached_property
					def 				__UpperCAmelCase		(		self     :Optional[Any]    ):
										return BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'    )

					@cached_property
					def 				__UpperCAmelCase		(		self     :Any    ):
										return BlenderbotTokenizerFast.from_pretrained('facebook/blenderbot-3B'    )

					def 				__UpperCAmelCase		(		self     :str    ):
										lowercase						   =       self.tokenizer_ab
										lowercase						   =       ' I am a small frog.'
										lowercase						   =       tok([src_text]      ,  padding=lowercase__      ,  truncation=lowercase__    )['input_ids']
										lowercase						   =       tok.batch_decode(lowercase__      ,  skip_special_tokens=lowercase__      ,  clean_up_tokenization_spaces=lowercase__    )[0]
										assert src_text == decoded

					def 				__UpperCAmelCase		(		self     :Union[str, Any]    ):
										lowercase						   =       self.rust_tokenizer_ab
										lowercase						   =       ' I am a small frog.'
										lowercase						   =       tok([src_text]      ,  padding=lowercase__      ,  truncation=lowercase__    )['input_ids']
										lowercase						   =       tok.batch_decode(lowercase__      ,  skip_special_tokens=lowercase__      ,  clean_up_tokenization_spaces=lowercase__    )[0]
										assert src_text == decoded

					def 				__UpperCAmelCase		(		self     :Any    ):
										assert self.tokenizer_ab.add_prefix_space
										assert self.tokenizer_ab([' Sam', 'Sam']    ).input_ids == [[5502, 2], [5502, 2]]



					def 				__UpperCAmelCase		(		self     :Union[str, Any]    ):
										assert self.rust_tokenizer_ab.add_prefix_space
										assert self.rust_tokenizer_ab([' Sam', 'Sam']    ).input_ids == [[5502, 2], [5502, 2]]











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

__magic_name__       =     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 		__snake_case						(    cls				):
							"""simple docstring"""






							return cls





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase = None       ,			_UpperCAmelCase = None       ,			_UpperCAmelCase = None       ,			_UpperCAmelCase = None       ,			):
					"""simple docstring"""






					lowercase						   =       device
					lowercase						   =       shape[0]

					lowercase						   =       layout or torch.strided
					lowercase						   =       device or torch.device('cpu'				)

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

					return latents





def 		__snake_case						(    _UpperCAmelCase				):
					"""simple docstring"""






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






