



'''simple docstring'''







import json
import os
import unittest

from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES, FSMTTokenizer
from transformers.testing_utils import slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


# using a different tiny model than the one used for default params defined in init to ensure proper testing
SCREAMING_SNAKE_CASE__     : Tuple				=   """stas/tiny-wmt19-en-ru"""




class 				__lowerCAmelCase(							lowerCAmelCase__		,			unittest.TestCase      ):
			__snake_case  :		Any		=      FSMTTokenizer
			__snake_case  :		Any		=      False






			def 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







					super().setUp()

					# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      [
					    'l',
					    'o',
					    'w',
					    'e',
					    'r',
					    's',
					    't',
					    'i',
					    'd',
					    'n',
					    'w</w>',
					    'r</w>',
					    't</w>',
					    'lo',
					    'low',
					    'er</w>',
					    'low</w>',
					    'lowest</w>',
					    'newer</w>',
					    'wider</w>',
					    '<unk>',
					]
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      dict(zip(SCREAMING_SNAKE_CASE      ,       range(len(SCREAMING_SNAKE_CASE	)	)	)	)
					SCREAMING_SNAKE_CASE_						:Any										=      ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      ['en', 'ru']
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      {
					    'langs': self.langs,
					    'src_vocab_size': 10,
					    'tgt_vocab_size': 20,
					}

					SCREAMING_SNAKE_CASE_						:Dict										=      os.path.join(self.tmpdirname      ,       VOCAB_FILES_NAMES['src_vocab_file']	)
					SCREAMING_SNAKE_CASE_						:Dict										=      os.path.join(self.tmpdirname      ,       VOCAB_FILES_NAMES['tgt_vocab_file']	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      os.path.join(self.tmpdirname      ,       'tokenizer_config.json'	)
					SCREAMING_SNAKE_CASE_						:Tuple										=      os.path.join(self.tmpdirname      ,       VOCAB_FILES_NAMES['merges_file']	)
					with open(self.src_vocab_file      ,       'w'	) as fp:
							fp.write(json.dumps(SCREAMING_SNAKE_CASE	)	)
					with open(self.tgt_vocab_file      ,       'w'	) as fp:
							fp.write(json.dumps(SCREAMING_SNAKE_CASE	)	)
					with open(self.merges_file      ,       'w'	) as fp:
							fp.write('\n'.join(SCREAMING_SNAKE_CASE	)	)
					with open(SCREAMING_SNAKE_CASE      ,       'w'	) as fp:
							fp.write(json.dumps(SCREAMING_SNAKE_CASE	)	)






			@cached_property
			def 				_lowercase	(   self  :							List[Any]	):




					"""simple docstring"""







					return FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'	)






			@cached_property
			def 				_lowercase	(   self  :							str	):




					"""simple docstring"""







					return FSMTTokenizer.from_pretrained('facebook/wmt19-en-ru'	)






			def 				_lowercase	(   self  :							Optional[int]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:int										=      FSMTTokenizer.from_pretrained(SCREAMING_SNAKE_CASE	)
					self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang]      ,       ['en', 'ru']	)
					self.assertEqual(tokenizer.src_vocab_size      ,       21	)
					self.assertEqual(tokenizer.tgt_vocab_size      ,       21	)






			def 				_lowercase	(   self  :							Dict	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[Any]										=      FSMTTokenizer(self.langs      ,       self.src_vocab_file      ,       self.tgt_vocab_file      ,       self.merges_file	)

					SCREAMING_SNAKE_CASE_						:Optional[int]										=      'lower'
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      ['low', 'er</w>']
					SCREAMING_SNAKE_CASE_						:List[str]										=      tokenizer.tokenize(SCREAMING_SNAKE_CASE	)
					self.assertListEqual(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:List[str]										=      tokens + ['<unk>']
					SCREAMING_SNAKE_CASE_						:str										=      [14, 15, 20]
					self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE	)      ,       SCREAMING_SNAKE_CASE	)






			@slow
			def 				_lowercase	(   self  :							Optional[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Dict										=      self.tokenizer_ru_en

					SCREAMING_SNAKE_CASE_						:List[str]										=      tokenizer.encode('sequence builders'      ,       add_special_tokens=SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Tuple										=      tokenizer.encode('multi-sequence build'      ,       add_special_tokens=SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:int										=      tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)

					assert encoded_sentence == text + [2]
					assert encoded_pair == text + [2] + text_a + [2]






			@slow
			def 				_lowercase	(   self  :							Dict	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:str										=      self.tokenizer_en_ru
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      self.tokenizer_ru_en

					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      [
					    [
					        'Here\'s a little song I wrote. Don\'t worry, be happy.',
					        [2_470, 39, 11, 2_349, 7_222, 70, 5_979, 7, 8_450, 1_050, 13_160, 5, 26, 6_445, 7, 2],
					    ],
					    ['This is it. No more. I\'m done!', [132, 21, 37, 7, 1_434, 86, 7, 70, 6_476, 1_305, 427, 2]],
					]

					# if data needs to be recreated or added, run:
					# import torch
					# model = torch.hub.load("pytorch/fairseq", "transformer.wmt19.en-ru", checkpoint_file="model4.pt", tokenizer="moses", bpe="fastbpe")
					# for src_text, _ in targets: print(f"""[\n"{src_text}",\n {model.encode(src_text).tolist()}\n],""")

					for src_text, tgt_input_ids in targets:
							SCREAMING_SNAKE_CASE_						:List[Any]										=      tokenizer_enc.encode(SCREAMING_SNAKE_CASE      ,       return_tensors=SCREAMING_SNAKE_CASE	)
							self.assertListEqual(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)

							# and decode backward, using the reversed languages model
							SCREAMING_SNAKE_CASE_						:int										=      tokenizer_dec.decode(SCREAMING_SNAKE_CASE      ,       skip_special_tokens=SCREAMING_SNAKE_CASE	)
							self.assertEqual(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)






			@slow
			def 				_lowercase	(   self  :							str	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[Any]										=      FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'      ,       do_lower_case=SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Dict										=      tokenizer.tokenize('USA is United States of America'	)
					SCREAMING_SNAKE_CASE_						:Dict										=      ['us', 'a</w>', 'is</w>', 'un', 'i', 'ted</w>', 'st', 'ates</w>', 'of</w>', 'am', 'er', 'ica</w>']
					self.assertListEqual(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)






			@unittest.skip('FSMTConfig.__init__  requires non-optional args'	)
			def 				_lowercase	(   self  :							Optional[Any]	):




					"""simple docstring"""







					pass






			@unittest.skip('FSMTConfig.__init__  requires non-optional args'	)
			def 				_lowercase	(   self  :							List[Any]	):




					"""simple docstring"""







					pass







import os
import pathlib
import tempfile
import uuid

import numpy as np

from ..utils import is_soundfile_availble, is_torch_available, is_vision_available, logging


__lowercase				    =      logging.get_logger(__name__)

if is_vision_available():
      import PIL.Image
      from PIL import Image
      from PIL.Image import Image as ImageType
else:
      __lowercase				    =      object

if is_torch_available():
      import torch

if is_soundfile_availble():
      import soundfile as sf

class 	__snake_case					:






    '''simple docstring'''





    def __init__( self    :		Optional[int]      ,    snake_case    :		int				):

      """simple docstring"""





      __lowerCAmelCase								=					value





    def __str__( self    :		List[Any]				):

      """simple docstring"""





      return self.to_string()





    def 						A		( self    :		int				):

      """simple docstring"""





      logger.error(
          '''This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'''				)
      return self._value





    def 						A		( self    :		Optional[int]				):

      """simple docstring"""





      logger.error(
          '''This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'''				)
      return str(self._value				)


class 	__snake_case					(      _a							,					_a ):






    '''simple docstring'''





    def 						A		( self    :		int				):

      """simple docstring"""





      return self._value





    def 						A		( self    :		List[str]				):

      """simple docstring"""





      return self._value


class 	__snake_case					(      _a							,					_a ):






    '''simple docstring'''





    def __init__( self    :		int      ,    snake_case    :		Optional[int]				):

      """simple docstring"""





      super().__init__(snake_case				)

      if not is_vision_available():
        raise ImportError('''PIL must be installed in order to handle images.'''				)

      __lowerCAmelCase								=					None
      __lowerCAmelCase								=					None
      __lowerCAmelCase								=					None

      if isinstance(snake_case      ,    snake_case				):
        __lowerCAmelCase								=					value
      elif isinstance(snake_case      ,    (str, pathlib.Path)				):
        __lowerCAmelCase								=					value
      elif isinstance(snake_case      ,    torch.Tensor				):
        __lowerCAmelCase								=					value
      else:
        raise ValueError(f'''Unsupported type for {self.__class__.__name__}: {type(snake_case				)}'''				)





    def 						A		( self    :		Tuple      ,    snake_case    :		Union[str, Any]=None      ,    snake_case    :		int=None				):

      """simple docstring"""





      from IPython.display import Image, display

      display(Image(self.to_string()				)				)





    def 						A		( self    :		Optional[Any]				):

      """simple docstring"""





      if self._raw is not None:
        return self._raw

      if self._path is not None:
        __lowerCAmelCase								=					Image.open(self._path				)
        return self._raw





    def 						A		( self    :		List[Any]				):

      """simple docstring"""





      if self._path is not None:
        return self._path

      if self._raw is not None:
        __lowerCAmelCase								=					tempfile.mkdtemp()
        __lowerCAmelCase								=					os.path.join(snake_case      ,    str(uuid.uuida()				) + '''.png'''				)
        self._raw.save(self._path				)

        return self._path

      if self._tensor is not None:
        __lowerCAmelCase								=					self._tensor.cpu().detach().numpy()

        # There is likely simpler than load into image into save
        __lowerCAmelCase								=					Image.fromarray((array * 255).astype(np.uinta				)				)

        __lowerCAmelCase								=					tempfile.mkdtemp()
        __lowerCAmelCase								=					os.path.join(snake_case      ,    str(uuid.uuida()				) + '''.png'''				)

        img.save(self._path				)

        return self._path


class 	__snake_case					(      _a ):






    '''simple docstring'''





    def __init__( self    :		List[Any]      ,    snake_case    :		int      ,    snake_case    :		Any=16_000				):

      """simple docstring"""





      super().__init__(snake_case				)

      if not is_soundfile_availble():
        raise ImportError('''soundfile must be installed in order to handle audio.'''				)

      __lowerCAmelCase								=					None
      __lowerCAmelCase								=					None

      __lowerCAmelCase								=					samplerate

      if isinstance(snake_case      ,    (str, pathlib.Path)				):
        __lowerCAmelCase								=					value
      elif isinstance(snake_case      ,    torch.Tensor				):
        __lowerCAmelCase								=					value
      else:
        raise ValueError(f'''Unsupported audio type: {type(snake_case				)}'''				)





    def 						A		( self    :		Optional[int]      ,    snake_case    :		Tuple=None      ,    snake_case    :		Tuple=None				):

      """simple docstring"""





      from IPython.display import Audio, display

      display(Audio(self.to_string()      ,    rate=self.samplerate				)				)





    def 						A		( self    :		Optional[int]				):

      """simple docstring"""





      if self._tensor is not None:
        return self._tensor

      if self._path is not None:
        __lowerCAmelCase						,    __lowerCAmelCase								=					sf.read(self._path				)
        __lowerCAmelCase								=					torch.tensor(snake_case				)
        return self._tensor





    def 						A		( self    :		Optional[Any]				):

      """simple docstring"""





      if self._path is not None:
        return self._path

      if self._tensor is not None:
        __lowerCAmelCase								=					tempfile.mkdtemp()
        __lowerCAmelCase								=					os.path.join(snake_case      ,    str(uuid.uuida()				) + '''.wav'''				)
        sf.write(self._path      ,    self._tensor      ,    samplerate=self.samplerate				)
        return self._path


__lowercase				    =      {"""text""": AgentText, """image""": AgentImage, """audio""": AgentAudio}
__lowercase				    =      {str: AgentText}

if is_vision_available():
      __lowercase				    =      AgentImage


def        __SCREAMING_SNAKE_CASE	(				*__lowercase:		List[Any]  ,				**__lowercase:		Tuple):



  '''simple docstring'''






  __lowerCAmelCase								=					[(arg.to_raw() if isinstance(__lowercase  ,				__lowercase) else arg) for arg in args]
  __lowerCAmelCase								=					{k: (v.to_raw() if isinstance(__lowercase  ,				__lowercase) else v) for k, v in kwargs.items()}
  return args, kwargs




def        __SCREAMING_SNAKE_CASE	(				__lowercase:		List[Any]  ,				__lowercase:		List[Any]=None):



  '''simple docstring'''






  if isinstance(__lowercase  ,				__lowercase):
    __lowerCAmelCase								=					{}
    for i, (k, v) in enumerate(outputs.items()):
      if output_types is not None:
        # If the class has defined outputs, we can map directly according to the class definition
        if output_types[i] in AGENT_TYPE_MAPPING:
          __lowerCAmelCase								=					AGENT_TYPE_MAPPING[output_types[i]](__lowercase)
        else:
          __lowerCAmelCase								=					AgentType(__lowercase)

      else:
        # If the class does not have defined output, then we map according to the type
        for _k, _v in INSTANCE_TYPE_MAPPING.items():
          if isinstance(__lowercase  ,				_k):
            __lowerCAmelCase								=					_v(__lowercase)
        if k not in decoded_outputs:
          __lowerCAmelCase								=					AgentType[v]

  elif isinstance(__lowercase  ,				(list, tuple)):
    __lowerCAmelCase								=					type(__lowercase)()
    for i, v in enumerate(__lowercase):
      if output_types is not None:
        # If the class has defined outputs, we can map directly according to the class definition
        if output_types[i] in AGENT_TYPE_MAPPING:
          decoded_outputs.append(AGENT_TYPE_MAPPING[output_types[i]](__lowercase))
        else:
          decoded_outputs.append(AgentType(__lowercase))
      else:
        # If the class does not have defined output, then we map according to the type
        __lowerCAmelCase								=					False
        for _k, _v in INSTANCE_TYPE_MAPPING.items():
          if isinstance(__lowercase  ,				_k):
            decoded_outputs.append(_v(__lowercase))
            __lowerCAmelCase								=					True

        if not found:
          decoded_outputs.append(AgentType(__lowercase))

  else:
    if output_types[0] in AGENT_TYPE_MAPPING:
      # If the class has defined outputs, we can map directly according to the class definition
      __lowerCAmelCase								=					AGENT_TYPE_MAPPING[output_types[0]](__lowercase)

    else:
      # If the class does not have defined output, then we map according to the type
      for _k, _v in INSTANCE_TYPE_MAPPING.items():
        if isinstance(__lowercase  ,				_k):
          return _v(__lowercase)
      return AgentType(__lowercase)

  return decoded_outputs
