


import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class      a    (		lowercase__     ,  unittest.TestCase	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			List[Any]							       =       GPTaTokenizer
     SCREAMING_SNAKE_CASE	:			Union[str, Any]							       =       GPTaTokenizerFast
     SCREAMING_SNAKE_CASE	:			Tuple							       =       True
     SCREAMING_SNAKE_CASE	:			str							       =       {'''add_prefix_space''': True}
     SCREAMING_SNAKE_CASE	:			Dict							       =       False

     def 			lowerCamelCase__			(    self					:		List[Any]      )						->							Optional[int]:
          super().setUp()

          # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
          __UpperCAmelCase     :			int  							=      [
              'l',
              'o',
              'w',
              'e',
              'r',
              's',
              't',
              'i',
              'd',
              'n',
              '\u0120',
              '\u0120l',
              '\u0120n',
              '\u0120lo',
              '\u0120low',
              'er',
              '\u0120lowest',
              '\u0120newer',
              '\u0120wider',
              '<unk>',
              '<|endoftext|>',
          ]
          __UpperCAmelCase     :			List[str]  							=      dict(zip(_a							,			range(len(_a      )      )      )      )
          __UpperCAmelCase     :			Union[str, Any]  							=      ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
          __UpperCAmelCase     :			List[str]  							=      {'unk_token': '<unk>'}

          __UpperCAmelCase     :			Optional[Any]  							=      os.path.join(self.tmpdirname							,			VOCAB_FILES_NAMES['''vocab_file''']      )
          __UpperCAmelCase     :			int  							=      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(_a      ) + '''\n'''      )
          with open(self.merges_file							,			'''w'''							,			encoding='''utf-8'''      ) as fp:
               fp.write('''\n'''.join(_a      )      )

     def 			lowerCamelCase__			(    self					:		Dict							,			**snake_case					:		int      )						->							List[str]:
          kwargs.update(self.special_tokens_map      )
          return GPTaTokenizer.from_pretrained(self.tmpdirname							,			**_a      )

     def 			lowerCamelCase__			(    self					:		Any							,			**snake_case					:		Optional[Any]      )						->							int:
          kwargs.update(self.special_tokens_map      )
          return GPTaTokenizerFast.from_pretrained(self.tmpdirname							,			**_a      )

     def 			lowerCamelCase__			(    self					:		Tuple							,			snake_case					:		List[Any]      )						->							Union[str, Any]:
          __UpperCAmelCase     :			List[Any]  							=      'lower newer'
          __UpperCAmelCase     :			int  							=      'lower newer'
          return input_text, output_text

     def 			lowerCamelCase__			(    self					:		Dict      )						->							List[Any]:
          __UpperCAmelCase     :			Union[str, Any]  							=      GPTaTokenizer(self.vocab_file							,			self.merges_file							,			**self.special_tokens_map      )
          __UpperCAmelCase     :			List[Any]  							=      'lower newer'
          __UpperCAmelCase     :			str  							=      ['\u0120low', 'er', '\u0120', 'n', 'e', 'w', 'er']
          __UpperCAmelCase     :			int  							=      tokenizer.tokenize(_a							,			add_prefix_space=_a      )
          self.assertListEqual(_a							,			_a      )

          __UpperCAmelCase     :			List[str]  							=      tokens + [tokenizer.unk_token]
          __UpperCAmelCase     :			Optional[Any]  							=      [14, 15, 10, 9, 3, 2, 15, 19]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(_a      )							,			_a      )

     def 			lowerCamelCase__			(    self					:		str      )						->							Optional[int]:






          if not self.test_rust_tokenizer:
               return

          __UpperCAmelCase     :			Optional[int]  							=      self.get_tokenizer()
          __UpperCAmelCase     :			Optional[Any]  							=      self.get_rust_tokenizer(add_prefix_space=_a      )

          __UpperCAmelCase     :			Union[str, Any]  							=      'lower newer'

          # Testing tokenization
          __UpperCAmelCase     :			Tuple  							=      tokenizer.tokenize(_a							,			add_prefix_space=_a      )
          __UpperCAmelCase     :			int  							=      rust_tokenizer.tokenize(_a      )
          self.assertListEqual(_a							,			_a      )

          # Testing conversion to ids without special tokens
          __UpperCAmelCase     :			str  							=      tokenizer.encode(_a							,			add_special_tokens=_a							,			add_prefix_space=_a      )
          __UpperCAmelCase     :			str  							=      rust_tokenizer.encode(_a							,			add_special_tokens=_a      )
          self.assertListEqual(_a							,			_a      )

          # Testing conversion to ids with special tokens
          __UpperCAmelCase     :			List[Any]  							=      self.get_rust_tokenizer(add_prefix_space=_a      )
          __UpperCAmelCase     :			List[Any]  							=      tokenizer.encode(_a							,			add_prefix_space=_a      )
          __UpperCAmelCase     :			Any  							=      rust_tokenizer.encode(_a      )
          self.assertListEqual(_a							,			_a      )

          # Testing the unknown token
          __UpperCAmelCase     :			Union[str, Any]  							=      tokens + [rust_tokenizer.unk_token]
          __UpperCAmelCase     :			List[str]  							=      [14, 15, 10, 9, 3, 2, 15, 19]
          self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(_a      )							,			_a      )

     def 			lowerCamelCase__			(    self					:		Dict							,			*snake_case					:		Optional[int]							,			**snake_case					:		Any      )						->							List[str]:
          pass

     def 			lowerCamelCase__			(    self					:		List[Any]							,			snake_case					:		Dict=15      )						->							Optional[int]:






          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'      ):
                    __UpperCAmelCase     :			List[Any]  							=      self.rust_tokenizer_class.from_pretrained(_a							,			**_a      )

                    # Simple input
                    __UpperCAmelCase     :			List[Any]  							=      'This is a simple input'
                    __UpperCAmelCase     :			List[Any]  							=      ['This is a simple input 1', 'This is a simple input 2']
                    __UpperCAmelCase     :			List[Any]  							=      ('This is a simple input', 'This is a pair')
                    __UpperCAmelCase     :			Tuple  							=      [
                        ('This is a simple input 1', 'This is a simple input 2'),
                        ('This is a simple pair 1', 'This is a simple pair 2'),
                    ]

                    # Simple input tests
                    self.assertRaises(_a							,			tokenizer_r.encode							,			_a							,			max_length=_a							,			padding='''max_length'''      )

                    # Simple input
                    self.assertRaises(_a							,			tokenizer_r.encode_plus							,			_a							,			max_length=_a							,			padding='''max_length'''      )

                    # Simple input
                    self.assertRaises(
                        _a							,			tokenizer_r.batch_encode_plus							,			_a							,			max_length=_a							,			padding='''max_length'''							,			)

                    # Pair input
                    self.assertRaises(_a							,			tokenizer_r.encode							,			_a							,			max_length=_a							,			padding='''max_length'''      )

                    # Pair input
                    self.assertRaises(_a							,			tokenizer_r.encode_plus							,			_a							,			max_length=_a							,			padding='''max_length'''      )

                    # Pair input
                    self.assertRaises(
                        _a							,			tokenizer_r.batch_encode_plus							,			_a							,			max_length=_a							,			padding='''max_length'''							,			)

     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							str:
          __UpperCAmelCase     :			Tuple  							=      GPTaTokenizer.from_pretrained(self.tmpdirname							,			pad_token='''<pad>'''      )

          # Simple input
          __UpperCAmelCase     :			int  							=      'This is a simple input'
          __UpperCAmelCase     :			Optional[int]  							=      ['This is a simple input looooooooong', 'This is a simple input']
          __UpperCAmelCase     :			Dict  							=      ('This is a simple input', 'This is a pair')
          __UpperCAmelCase     :			List[Any]  							=      [
              ('This is a simple input loooooong', 'This is a simple input'),
              ('This is a simple pair loooooong', 'This is a simple pair'),
          ]

          __UpperCAmelCase     :			Union[str, Any]  							=      tokenizer.pad_token_id

          __UpperCAmelCase     :			int  							=      tokenizer(_a							,			padding='''max_length'''							,			max_length=30							,			return_tensors='''np'''      )
          __UpperCAmelCase     :			List[str]  							=      tokenizer(_a							,			padding=_a							,			truncate=_a							,			return_tensors='''np'''      )
          __UpperCAmelCase     :			Optional[Any]  							=      tokenizer(*_a							,			padding='''max_length'''							,			max_length=60							,			return_tensors='''np'''      )
          __UpperCAmelCase     :			Union[str, Any]  							=      tokenizer(_a							,			padding=_a							,			truncate=_a							,			return_tensors='''np'''      )

          # s
          # test single string max_length padding
          self.assertEqual(out_s['''input_ids'''].shape[-1]							,			30      )
          self.assertTrue(pad_token_id in out_s['''input_ids''']      )
          self.assertTrue(0 in out_s['''attention_mask''']      )

          # s2
          # test automatic padding
          self.assertEqual(out_sa['''input_ids'''].shape[-1]							,			33      )
          # long slice doesn't have padding
          self.assertFalse(pad_token_id in out_sa['''input_ids'''][0]      )
          self.assertFalse(0 in out_sa['''attention_mask'''][0]      )
          # short slice does have padding
          self.assertTrue(pad_token_id in out_sa['''input_ids'''][1]      )
          self.assertTrue(0 in out_sa['''attention_mask'''][1]      )

          # p
          # test single pair max_length padding
          self.assertEqual(out_p['''input_ids'''].shape[-1]							,			60      )
          self.assertTrue(pad_token_id in out_p['''input_ids''']      )
          self.assertTrue(0 in out_p['''attention_mask''']      )

          # p2
          # test automatic padding pair
          self.assertEqual(out_pa['''input_ids'''].shape[-1]							,			52      )
          # long slice pair doesn't have padding
          self.assertFalse(pad_token_id in out_pa['''input_ids'''][0]      )
          self.assertFalse(0 in out_pa['''attention_mask'''][0]      )
          # short slice pair does have padding
          self.assertTrue(pad_token_id in out_pa['''input_ids'''][1]      )
          self.assertTrue(0 in out_pa['''attention_mask'''][1]      )

     def 			lowerCamelCase__			(    self					:		Tuple      )						->							Optional[int]:
          __UpperCAmelCase     :			Optional[Any]  							=      '$$$'
          __UpperCAmelCase     :			Dict  							=      GPTaTokenizer.from_pretrained(self.tmpdirname							,			bos_token=_a							,			add_bos_token=_a      )

          __UpperCAmelCase     :			str  							=      'This is a simple input'
          __UpperCAmelCase     :			Tuple  							=      ['This is a simple input 1', 'This is a simple input 2']

          __UpperCAmelCase     :			Optional[Any]  							=      tokenizer.bos_token_id

          __UpperCAmelCase     :			List[str]  							=      tokenizer(_a      )
          __UpperCAmelCase     :			Dict  							=      tokenizer(_a      )

          self.assertEqual(out_s.input_ids[0]							,			_a      )
          self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids      )      )

          __UpperCAmelCase     :			int  							=      tokenizer.decode(out_s.input_ids      )
          __UpperCAmelCase     :			str  							=      tokenizer.batch_decode(out_sa.input_ids      )

          self.assertEqual(decode_s.split()[0]							,			_a      )
          self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa      )      )

     def 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							Union[str, Any]:
          pass






     def 			lowerCamelCase__			(    self					:		Tuple      )						->							Optional[int]:
          __UpperCAmelCase     :			str  							=      [self.get_tokenizer(do_lower_case=_a							,			add_bos_token=_a      )]
          for tokenizer in tokenizers:
               with self.subTest(f'{tokenizer.__class__.__name__}'      ):
                    __UpperCAmelCase     :			str  							=      'Encode this.'
                    __UpperCAmelCase     :			Union[str, Any]  							=      'This one too please.'
                    __UpperCAmelCase     :			Any  							=      tokenizer.encode(_a							,			add_special_tokens=_a      )
                    encoded_sequence += tokenizer.encode(_a							,			add_special_tokens=_a      )
                    __UpperCAmelCase     :			Optional[int]  							=      tokenizer.encode_plus(
                        _a							,			_a							,			add_special_tokens=_a							,			return_special_tokens_mask=_a							,			)
                    __UpperCAmelCase     :			Optional[int]  							=      encoded_sequence_dict['input_ids']
                    __UpperCAmelCase     :			str  							=      encoded_sequence_dict['special_tokens_mask']
                    self.assertEqual(len(_a      )							,			len(_a      )      )

                    __UpperCAmelCase     :			Tuple  							=      [
                        (x if not special_tokens_mask[i] else None) for i, x in enumerate(_a      )
                    ]
                    __UpperCAmelCase     :			Tuple  							=      [x for x in filtered_sequence if x is not None]
                    self.assertEqual(_a							,			_a      )



@require_tokenizers
class      a    (		unittest.TestCase	):


     """simple docstring"""






     def 			lowerCamelCase__			(    self					:		Tuple      )						->							Any:
          __UpperCAmelCase     :			str  							=      AutoTokenizer.from_pretrained('''facebook/opt-350m'''							,			from_slow=_a      )
          __UpperCAmelCase     :			Dict  							=      'A photo of a cat'

          __UpperCAmelCase     :			int  							=      tokenizer.encode(
              _a							,			)
          self.assertEqual(_a							,			[2, 250, 1345, 9, 10, 4758]      )
          tokenizer.save_pretrained('''test_opt'''      )

          __UpperCAmelCase     :			List[Any]  							=      AutoTokenizer.from_pretrained('''./test_opt'''      )
          __UpperCAmelCase     :			int  							=      tokenizer.encode(
              _a							,			)
          self.assertEqual(_a							,			[2, 250, 1345, 9, 10, 4758]      )

     def 			lowerCamelCase__			(    self					:		int      )						->							Dict:
          __UpperCAmelCase     :			Optional[Any]  							=      AutoTokenizer.from_pretrained('''facebook/opt-350m'''							,			use_slow=_a      )
          __UpperCAmelCase     :			Union[str, Any]  							=      'A photo of a cat'

          __UpperCAmelCase     :			List[Any]  							=      tokenizer.encode(
              _a							,			)
          # Same as above
          self.assertEqual(_a							,			[2, 250, 1345, 9, 10, 4758]      )






     @unittest.skip('''This test is failing because of a bug in the fast tokenizer'''      )
     def 			lowerCamelCase__			(    self					:		int      )						->							Any:
          __UpperCAmelCase     :			Any  							=      AutoTokenizer.from_pretrained('''facebook/opt-350m'''							,			from_slow=_a      )

          __UpperCAmelCase     :			Union[str, Any]  							=      'bos'
          __UpperCAmelCase     :			Dict  							=      tokenizer.get_vocab()['bos']

          __UpperCAmelCase     :			Optional[Any]  							=      'A photo of a cat'
          __UpperCAmelCase     :			Any  							=      tokenizer.encode(
              _a							,			)
          # We changed the bos token
          self.assertEqual(_a							,			[3_1957, 250, 1345, 9, 10, 4758]      )
          tokenizer.save_pretrained('''./tok'''      )
          __UpperCAmelCase     :			List[str]  							=      AutoTokenizer.from_pretrained('''./tok'''      )
          self.assertTrue(tokenizer.is_fast      )
          __UpperCAmelCase     :			Optional[Any]  							=      tokenizer.encode(
              _a							,			)
          self.assertEqual(_a							,			[3_1957, 250, 1345, 9, 10, 4758]      )



'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__UpperCAmelCase      :Union[str, Any]			    =       {
    "configuration_rag": ["RagConfig"],
    "retrieval_rag": ["RagRetriever"],
    "tokenization_rag": ["RagTokenizer"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase      :Dict			    =       [
          "RagModel",
          "RagPreTrainedModel",
          "RagSequenceForGeneration",
          "RagTokenForGeneration",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase      :Dict			    =       [
          "TFRagModel",
          "TFRagPreTrainedModel",
          "TFRagSequenceForGeneration",
          "TFRagTokenForGeneration",
      ]


if TYPE_CHECKING:
      from .configuration_rag import RagConfig
      from .retrieval_rag import RagRetriever
      from .tokenization_rag import RagTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_rag import (
                TFRagModel,
                TFRagPreTrainedModel,
                TFRagSequenceForGeneration,
                TFRagTokenForGeneration,
            )

else:
      import sys

      __UpperCAmelCase      :Any			    =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)