
import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors







@require_tokenizers
class  _lowerCamelCase			(lowerCamelCase							,					unittest.TestCase					):
     lowercase__				  =			BartTokenizer
     lowercase__				  =			BartTokenizerFast
     lowercase__				  =			True
     lowercase__				  =			filter_roberta_detectors

     def 						__lowerCamelCase				(  self    ):
            super().setUp()
            __snake_case						       =   [
                'l',
                'o',
                'w',
                'e',
                'r',
                's',
                't',
                'i',
                'd',
                'n',
                '\u0120',
                '\u0120l',
                '\u0120n',
                '\u0120lo',
                '\u0120low',
                'er',
                '\u0120lowest',
                '\u0120newer',
                '\u0120wider',
                '<unk>',
            ]
            __snake_case						       =   dict(zip(SCREAMING_SNAKE_CASE_							,       range(len(SCREAMING_SNAKE_CASE_    )    )    )    )
            __snake_case						       =   ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
            __snake_case						       =   {'unk_token': '<unk>'}

            __snake_case						       =   os.path.join(self.tmpdirname							,       VOCAB_FILES_NAMES['vocab_file']    )
            __snake_case						       =   os.path.join(self.tmpdirname							,       VOCAB_FILES_NAMES['merges_file']    )
            with open(self.vocab_file							,       'w'							,       encoding='utf-8'    ) as fp:
                   fp.write(json.dumps(SCREAMING_SNAKE_CASE_    ) + '\n'    )
            with open(self.merges_file							,       'w'							,       encoding='utf-8'    ) as fp:
                   fp.write('\n'.join(SCREAMING_SNAKE_CASE_    )    )

     def 						__lowerCamelCase				(  self							,       **SCREAMING_SNAKE_CASE_    ):
            kwargs.update(self.special_tokens_map    )
            return self.tokenizer_class.from_pretrained(self.tmpdirname							,       **SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self							,       **SCREAMING_SNAKE_CASE_    ):
            kwargs.update(self.special_tokens_map    )
            return self.rust_tokenizer_class.from_pretrained(self.tmpdirname							,       **SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_    ):
            return "lower newer", "lower newer"

     @cached_property
     def 						__lowerCamelCase				(  self    ):
            return BartTokenizer.from_pretrained('facebook/bart-large'    )

     @cached_property
     def 						__lowerCamelCase				(  self    ):
            return BartTokenizerFast.from_pretrained('facebook/bart-large'    )

     @require_torch
     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
            __snake_case						       =   [0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   __snake_case						       =   tokenizer(SCREAMING_SNAKE_CASE_							,       max_length=len(SCREAMING_SNAKE_CASE_    )							,       padding=SCREAMING_SNAKE_CASE_							,       return_tensors='pt'    )
                   self.assertIsInstance(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )

                   self.assertEqual((2, 9)							,       batch.input_ids.shape    )
                   self.assertEqual((2, 9)							,       batch.attention_mask.shape    )
                   __snake_case						       =   batch.input_ids.tolist()[0]
                   self.assertListEqual(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )
                   # Test that special tokens are reset

     @require_torch
     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   __snake_case						       =   tokenizer(SCREAMING_SNAKE_CASE_							,       padding=SCREAMING_SNAKE_CASE_							,       return_tensors='pt'    )
                   # check if input_ids are returned and no labels
                   self.assertIn('input_ids'							,       SCREAMING_SNAKE_CASE_    )
                   self.assertIn('attention_mask'							,       SCREAMING_SNAKE_CASE_    )
                   self.assertNotIn('labels'							,       SCREAMING_SNAKE_CASE_    )
                   self.assertNotIn('decoder_attention_mask'							,       SCREAMING_SNAKE_CASE_    )

     @require_torch
     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   [
                'Summary of the text.',
                'Another summary.',
            ]
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   __snake_case						       =   tokenizer(text_target=SCREAMING_SNAKE_CASE_							,       max_length=32							,       padding='max_length'							,       return_tensors='pt'    )
                   self.assertEqual(32							,       targets['input_ids'].shape[1]    )

     @require_torch
     def 						__lowerCamelCase				(  self    ):
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   __snake_case						       =   tokenizer(
                       ['I am a small frog' * 1_024, 'I am a small frog']							,       padding=SCREAMING_SNAKE_CASE_							,       truncation=SCREAMING_SNAKE_CASE_							,       return_tensors='pt'    )
                   self.assertIsInstance(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )
                   self.assertEqual(batch.input_ids.shape							,       (2, 1_024)    )

     @require_torch
     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   ['A long paragraph for summarization.']
            __snake_case						       =   [
                'Summary of the text.',
            ]
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   __snake_case						       =   tokenizer(SCREAMING_SNAKE_CASE_							,       return_tensors='pt'    )
                   __snake_case						       =   tokenizer(text_target=SCREAMING_SNAKE_CASE_							,       return_tensors='pt'    )
                   __snake_case						       =   inputs['input_ids']
                   __snake_case						       =   targets['input_ids']
                   self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()    )
                   self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()    )
                   self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()    )
                   self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()    )

     def 						__lowerCamelCase				(  self    ):
            pass

     def 						__lowerCamelCase				(  self    ):
            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                   with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''    ):
                          __snake_case						       =   self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_							,       **SCREAMING_SNAKE_CASE_    )
                          __snake_case						       =   self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_							,       **SCREAMING_SNAKE_CASE_    )
                          __snake_case						       =   'A, <mask> AllenNLP sentence.'
                          __snake_case						       =   tokenizer_r.encode_plus(SCREAMING_SNAKE_CASE_							,       add_special_tokens=SCREAMING_SNAKE_CASE_							,       return_token_type_ids=SCREAMING_SNAKE_CASE_    )
                          __snake_case						       =   tokenizer_p.encode_plus(SCREAMING_SNAKE_CASE_							,       add_special_tokens=SCREAMING_SNAKE_CASE_							,       return_token_type_ids=SCREAMING_SNAKE_CASE_    )

                          # token_type_ids should put 0 everywhere
                          self.assertEqual(sum(tokens_r['token_type_ids']    )							,       sum(tokens_p['token_type_ids']    )    )

                          # attention_mask should put 1 everywhere, so sum over length should be 1
                          self.assertEqual(
                              sum(tokens_r['attention_mask']    ) / len(tokens_r['attention_mask']    )							,       sum(tokens_p['attention_mask']    ) / len(tokens_p['attention_mask']    )							,       )

                          __snake_case						       =   tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']    )
                          __snake_case						       =   tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']    )

                          # Rust correctly handles the space before the mask while python doesnt
                          self.assertSequenceEqual(tokens_p['input_ids']							,       [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]    )
                          self.assertSequenceEqual(tokens_r['input_ids']							,       [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]    )

                          self.assertSequenceEqual(
                              SCREAMING_SNAKE_CASE_							,       ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']    )
                          self.assertSequenceEqual(
                              SCREAMING_SNAKE_CASE_							,       ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']    )


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


lowerCamelCase_				:							Any       =    logging.get_logger(__name__)

if is_vision_available():
 import PIL.Image
 from PIL import Image
 from PIL.Image import Image as ImageType
else:
 lowerCamelCase_				:							Union[str, Any]       =    object

if is_torch_available():
 import torch

if is_soundfile_availble():
 import soundfile as sf







class  _lowerCamelCase			:

     def __init__(  self							,       SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   value

     def __str__(  self    ):
            return self.to_string()

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

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







class  _lowerCamelCase			(lowerCamelCase							,					lowerCamelCase					):

     def 						__lowerCamelCase				(  self    ):
            return self._value

     def 						__lowerCamelCase				(  self    ):
            return self._value







class  _lowerCamelCase			(lowerCamelCase							,					lowerCamelCase					):

     def __init__(  self							,       SCREAMING_SNAKE_CASE_    ):
            super().__init__(SCREAMING_SNAKE_CASE_    )

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

            __snake_case						       =   None
            __snake_case						       =   None
            __snake_case						       =   None

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

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_=None    ):
            from IPython.display import Image, display

            display(Image(self.to_string()    )    )

     def 						__lowerCamelCase				(  self    ):
            if self._raw is not None:
                   return self._raw

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

     def 						__lowerCamelCase				(  self    ):
            if self._path is not None:
                   return self._path

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

                   return self._path

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

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

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

                   img.save(self._path    )

                   return self._path







class  _lowerCamelCase			(lowerCamelCase					):

     def __init__(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_=16_000    ):
            super().__init__(SCREAMING_SNAKE_CASE_    )

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

            __snake_case						       =   None
            __snake_case						       =   None

            __snake_case						       =   samplerate

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

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_=None    ):
            from IPython.display import Audio, display

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

     def 						__lowerCamelCase				(  self    ):
            if self._tensor is not None:
                   return self._tensor

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

     def 						__lowerCamelCase				(  self    ):
            if self._path is not None:
                   return self._path

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





lowerCamelCase_				:							Optional[Any]       =    {"text": AgentText, "image": AgentImage, "audio": AgentAudio}
lowerCamelCase_				:							Any       =    {str: AgentText}

if is_vision_available():
 lowerCamelCase_				:							Optional[int]       =    AgentImage
def        __lowercase(							*__snake_case				:		Optional[Any]    ,**__snake_case				:		str					)       ->   Tuple:
       __snake_case						       =   [(arg.to_raw() if isinstance(__snake_case    ,__snake_case					) else arg) for arg in args]
       __snake_case						       =   {k: (v.to_raw() if isinstance(__snake_case    ,__snake_case					) else v) for k, v in kwargs.items()}
       return args, kwargs
def        __lowercase(							__snake_case				:		Optional[int]    ,__snake_case				:		List[str]=None					)       ->   Tuple:
       if isinstance(__snake_case    ,__snake_case					):
              __snake_case						       =   {}
              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:
                                   __snake_case						       =   AGENT_TYPE_MAPPING[output_types[i]](__snake_case					)
                            else:
                                   __snake_case						       =   AgentType(__snake_case					)

                     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(__snake_case    ,_k					):
                                          __snake_case						       =   _v(__snake_case					)
                            if k not in decoded_outputs:
                                   __snake_case						       =   AgentType[v]

       elif isinstance(__snake_case    ,(list, tuple)					):
              __snake_case						       =   type(__snake_case					)()
              for i, v in enumerate(__snake_case					):
                     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]](__snake_case					)					)
                            else:
                                   decoded_outputs.append(AgentType(__snake_case					)					)
                     else:
                            # If the class does not have defined output, then we map according to the type
                            __snake_case						       =   False
                            for _k, _v in INSTANCE_TYPE_MAPPING.items():
                                   if isinstance(__snake_case    ,_k					):
                                          decoded_outputs.append(_v(__snake_case					)					)
                                          __snake_case						       =   True

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

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

              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(__snake_case    ,_k					):
                                   return _v(__snake_case					)
                     return AgentType(__snake_case					)

       return decoded_outputs
