




import os
import tempfile
import unittest
import uuid
from pathlib import Path

from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available


if is_torch_available():
					import torch

if is_soundfile_availble():
					import soundfile as sf

if is_vision_available():
					from PIL import Image







def 			lowerCamelCase_		(       UpperCamelCase__				:      int=""      )     ->       Any:





				"""simple docstring"""


				__lowerCamelCase		       =    tempfile.mkdtemp()
				return os.path.join(lowerCAmelCase_ ,     str(uuid.uuida()      ) + suffix      )







@require_soundfile
@require_torch
class     __lowerCAmelCase (       unittest.TestCase ):

			"""simple docstring"""







			def       lowercase_    (      self							)    ->      Dict:



							'''simple docstring'''
							__lowerCamelCase		       =    torch.rand(12			,						dtype=torch.floataa							) - 0.5
							__lowerCamelCase		       =    AgentAudio(__a							)
							__lowerCamelCase		       =    str(agent_type.to_string()							)

							# Ensure that the tensor and the agent_type's tensor are the same
							self.assertTrue(torch.allclose(__a			,						agent_type.to_raw()			,						atol=1e-4							)							)

							del agent_type

							# Ensure the path remains even after the object deletion
							self.assertTrue(os.path.exists(__a							)							)

							# Ensure that the file contains the same value as the original tensor
							__lowerCamelCase		       =    sf.read(__a							)
							self.assertTrue(torch.allclose(__a			,						torch.tensor(__a							)			,						atol=1e-4							)							)






			def       lowercase_    (      self							)    ->      Tuple:



							'''simple docstring'''
							__lowerCamelCase		       =    torch.rand(12			,						dtype=torch.floataa							) - 0.5
							__lowerCamelCase		       =    get_new_path(suffix='.wav'							)
							sf.write(__a			,						__a			,						16_000							)

							__lowerCamelCase		       =    AgentAudio(__a							)

							self.assertTrue(torch.allclose(__a			,						agent_type.to_raw()			,						atol=1e-4							)							)
							self.assertEqual(agent_type.to_string()			,						__a							)







@require_vision
@require_torch
class     __lowerCAmelCase (       unittest.TestCase ):

			"""simple docstring"""







			def       lowercase_    (      self							)    ->      Any:



							'''simple docstring'''
							__lowerCamelCase		       =    torch.randint(0			,						256			,						(64, 64, 3)							)
							__lowerCamelCase		       =    AgentImage(__a							)
							__lowerCamelCase		       =    str(agent_type.to_string()							)

							# Ensure that the tensor and the agent_type's tensor are the same
							self.assertTrue(torch.allclose(__a			,						agent_type._tensor			,						atol=1e-4							)							)

							self.assertIsInstance(agent_type.to_raw()			,						Image.Image							)

							# Ensure the path remains even after the object deletion
							del agent_type
							self.assertTrue(os.path.exists(__a							)							)


			def       lowercase_    (      self							)    ->      List[str]:



							'''simple docstring'''
							__lowerCamelCase		       =    Path(get_tests_dir('fixtures/tests_samples/COCO'							)							) / """000000039769.png"""
							__lowerCamelCase		       =    Image.open(__a							)
							__lowerCamelCase		       =    AgentImage(__a							)

							self.assertTrue(path.samefile(agent_type.to_string()							)							)
							self.assertTrue(image == agent_type.to_raw()							)

							# Ensure the path remains even after the object deletion
							del agent_type
							self.assertTrue(os.path.exists(__a							)							)






			def       lowercase_    (      self							)    ->      Optional[int]:



							'''simple docstring'''
							__lowerCamelCase		       =    Path(get_tests_dir('fixtures/tests_samples/COCO'							)							) / """000000039769.png"""
							__lowerCamelCase		       =    Image.open(__a							)
							__lowerCamelCase		       =    AgentImage(__a							)

							self.assertFalse(path.samefile(agent_type.to_string()							)							)
							self.assertTrue(image == agent_type.to_raw()							)

							# Ensure the path remains even after the object deletion
							del agent_type
							self.assertTrue(os.path.exists(__a							)							)







class     __lowerCAmelCase (       unittest.TestCase ):

			"""simple docstring"""







			def       lowercase_    (      self							)    ->      List[str]:



							'''simple docstring'''
							__lowerCamelCase		       =    """Hey!"""
							__lowerCamelCase		       =    AgentText(__a							)

							self.assertEqual(__a			,						agent_type.to_string()							)
							self.assertEqual(__a			,						agent_type.to_raw()							)
							self.assertEqual(__a			,						__a							)










import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCamelCase       :       int        =				'''▁'''

lowerCamelCase       :       Optional[int]        =				{'''vocab_file''': '''spiece.model'''}

lowerCamelCase       :       str        =				{
    '''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''}
}

lowerCamelCase       :       str        =				{
    '''google/pegasus-xsum''': 5_12,
}


lowerCamelCase       :       Optional[Any]        =				logging.get_logger(__name__)

class 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

					_A  :	Tuple     =					VOCAB_FILES_NAMES

					_A  :	Tuple     =					VOCAB_FILES_NAMES
					_A  :	Optional[int]     =					PRETRAINED_VOCAB_FILES_MAP
					_A  :	str     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_A  :	Optional[int]     =					['''input_ids''', '''attention_mask''']



					def __init__(     self :		Union[str, Any]						,   __a :		int						,   __a :		Any="<pad>"						,   __a :		Optional[int]="</s>"						,   __a :		Union[str, Any]="<unk>"						,   __a :		Optional[int]="<mask_2>"						,   __a :		Optional[int]="<mask_1>"						,   __a :		Dict=None						,   __a :		List[str]=103						,   __a :		Optional[Dict[str, Any]] = None						,   **__a :		List[Any]						,   )	->     None:


												"""simple docstring"""

												__lowercase					:							Tuple			=   offset
												if additional_special_tokens is not None:
																			if not isinstance(__a						,   __a							):
																										raise TypeError(
																										    F"additional_special_tokens should be of type {type(__a							)}, but is"
																										    F" {type(__a							)}"							)

																			__lowercase					:							Dict			=   (
																			    ([mask_token_sent] + additional_special_tokens)
																			    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
																			    else additional_special_tokens
																			)
																			# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
																			additional_special_tokens_extended += [
																			    F"<unk_{i}>" for i in range(len(__a							)						,   self.offset - 1							)
																			]

																			if len(set(__a							)							) != len(__a							):
																										raise ValueError(
																										    """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
																										    F" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."							)
																			__lowercase					:							Optional[Any]			=   additional_special_tokens_extended
												else:
																			__lowercase					:							int			=   [mask_token_sent] if mask_token_sent is not None else []
																			additional_special_tokens += [F"<unk_{i}>" for i in range(2						,   self.offset							)]

												__lowercase					:							str			=   {} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    eos_token=__a						,   unk_token=__a						,   mask_token=__a						,   pad_token=__a						,   mask_token_sent=__a						,   offset=__a						,   additional_special_tokens=__a						,   sp_model_kwargs=self.sp_model_kwargs						,   **__a						,   )
												__lowercase					:							Optional[Any]			=   mask_token_sent
												__lowercase					:							Dict			=   vocab_file
												__lowercase					:							Optional[Any]			=   spm.SentencePieceProcessor(**self.sp_model_kwargs							)
												self.sp_model.Load(__a							)

												# add special tokens to encoder dict
												__lowercase					:							Dict[int, str]			=   {
												    0: self.pad_token,
												    1: self.eos_token,
												}

												if self.mask_token_sent is not None:
																			self.encoder.update(
																			    {
																			        2: self.mask_token_sent,
																			        3: self.mask_token,
																			    }							)

												if self.offset > 0:
																			# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
																			# mask_token_sent is already added to list -> so start at 1
																			self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1						,   self.offset - 1							)}							)

												__lowercase					:							Dict[str, int]			=   {v: k for k, v in self.encoder.items()}



					@property
					def 					lowerCAmelCase (     self :		Dict							)	->     int:


												"""simple docstring"""

												return len(self.sp_model							) + self.offset



					def 					lowerCAmelCase (     self :		int							)	->     Dict[str, int]:


												"""simple docstring"""

												__lowercase					:							Any			=   {self.convert_ids_to_tokens(__a							): i for i in range(self.vocab_size							)}
												vocab.update(self.added_tokens_encoder							)
												return vocab



					def __getstate__(     self :		List[str]							)	->     List[str]:


												"""simple docstring"""

												__lowercase					:							Union[str, Any]			=   self.__dict__.copy()
												__lowercase					:							Optional[Any]			=   None
												return state



					def __setstate__(     self :		Tuple						,   __a :		Any							)	->     Tuple:


												"""simple docstring"""

												__lowercase					:							List[str]			=   d

												# for backward compatibility
												if not hasattr(self						,   """sp_model_kwargs"""							):
																			__lowercase					:							List[str]			=   {}

												__lowercase					:							Optional[int]			=   spm.SentencePieceProcessor(**self.sp_model_kwargs							)
												self.sp_model.Load(self.vocab_file							)



					def 					lowerCAmelCase (     self :		Dict						,   __a :		str							)	->     List[str]:


												"""simple docstring"""

												return self.sp_model.encode(__a						,   out_type=__a							)



					def 					lowerCAmelCase (     self :		List[str]						,   __a :		str							)	->     int:


												"""simple docstring"""

												if token in self.decoder:
																			return self.decoder[token]
												elif token in self.added_tokens_decoder:
																			return self.added_tokens_decoder[token]
												__lowercase					:							Union[str, Any]			=   self.sp_model.piece_to_id(__a							)
												return sp_id + self.offset



					def 					lowerCAmelCase (     self :		Dict						,   __a :		int							)	->     str:


												"""simple docstring"""

												if index in self.encoder:
																			return self.encoder[index]
												elif index in self.added_tokens_encoder:
																			return self.added_tokens_encoder[index]
												else:
																			__lowercase					:							List[Any]			=   self.sp_model.IdToPiece(index - self.offset							)
												return token



					def 					lowerCAmelCase (     self :		Union[str, Any]						,   __a :		int							)	->     Dict:


												"""simple docstring"""

												__lowercase					:							Optional[int]			=   []
												__lowercase					:							Tuple			=   """"""
												for token in tokens:
																			# make sure that special tokens are not decoded using sentencepiece model
																			if token in self.all_special_tokens:
																										out_string += self.sp_model.decode(__a							) + token
																										__lowercase					:							str			=   []
																			else:
																										current_sub_tokens.append(__a							)
												out_string += self.sp_model.decode(__a							)
												return out_string.strip()



					def 					lowerCAmelCase (     self :		int						,   __a :		Optional[Any]=False							)	->     int:


												"""simple docstring"""

												return 1



					def 					lowerCAmelCase (     self :		Optional[int]						,   __a :		List[str]							)	->     Optional[Any]:


												"""simple docstring"""

												__lowercase					:							List[Any]			=   set(self.all_special_ids							)  # call it once instead of inside list comp
												all_special_ids.remove(self.unk_token_id							)  # <unk> is only sometimes special

												return [1 if x in all_special_ids else 0 for x in seq]



					def 					lowerCAmelCase (     self :		Union[str, Any]						,   __a :		List						,   __a :		Optional[List] = None						,   __a :		bool = False							)	->     List[int]:


												"""simple docstring"""

												if already_has_special_tokens:
																			return self._special_token_mask(__a							)
												elif token_ids_a is None:
																			return self._special_token_mask(__a							) + [1]
												else:
																			return self._special_token_mask(token_ids_a + token_ids_a							) + [1]



					def 					lowerCAmelCase (     self :		Optional[int]						,   __a :		Dict						,   __a :		Tuple=None							)	->     List[int]:


												"""simple docstring"""

												if token_ids_a is None:
																			return token_ids_a + [self.eos_token_id]
												# We don't expect to process pairs, but leave the pair logic for API consistency
												return token_ids_a + token_ids_a + [self.eos_token_id]



					def 					lowerCAmelCase (     self :		Tuple						,   __a :		str						,   __a :		Optional[str] = None							)	->     Tuple[str]:


												"""simple docstring"""

												if not os.path.isdir(__a							):
																			logger.error(F"Vocabulary path ({save_directory}) should be a directory"							)
																			return
												__lowercase					:							Optional[int]			=   os.path.join(
												    __a						,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

												if os.path.abspath(self.vocab_file							) != os.path.abspath(__a							) and os.path.isfile(self.vocab_file							):
																			copyfile(self.vocab_file						,   __a							)
												elif not os.path.isfile(self.vocab_file							):
																			with open(__a						,   """wb"""							) as fi:
																										__lowercase					:							Any			=   self.sp_model.serialized_model_proto()
																										fi.write(__a							)

												return (out_vocab_file,)