



import json
import os
import re
import unittest

from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 	lowerCamelCase__	(       _A							,  unittest.TestCase):



 """simple docstring"""

 a__					: str =  CodeGenTokenizer
 a__					: Tuple =  CodeGenTokenizerFast
 a__					: List[str] =  True
 a__					: str =  {"add_prefix_space": True}
 a__					: Tuple =  False

 def     snake_case_      (    self					:					Optional[int]      )   ->	Tuple:
    super().setUp()

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

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

 def     snake_case_      (    self					:					Union[str, Any]			,						**__lowerCAmelCase					:					List[Any]      )   ->	int:
    kwargs.update(self.special_tokens_map      )
    return CodeGenTokenizer.from_pretrained(self.tmpdirname			,						**__lowerCAmelCase      )

 def     snake_case_      (    self					:					int			,						**__lowerCAmelCase					:					int      )   ->	str:
    kwargs.update(self.special_tokens_map      )
    return CodeGenTokenizerFast.from_pretrained(self.tmpdirname			,						**__lowerCAmelCase      )

 def     snake_case_      (    self					:					List[str]			,						__lowerCAmelCase					:					List[str]      )   ->	List[Any]:
    _A      =       '''lower newer'''
    _A      =       '''lower newer'''
    return input_text, output_text

 def     snake_case_      (    self					:					Any      )   ->	Union[str, Any]:
    _A      =       CodeGenTokenizer(self.vocab_file			,						self.merges_file			,						**self.special_tokens_map      )
    _A      =       '''lower newer'''
    _A      =       ['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
    _A      =       tokenizer.tokenize(__lowerCAmelCase			,						add_prefix_space=__lowerCAmelCase      )
    self.assertListEqual(__lowerCAmelCase			,						__lowerCAmelCase      )

    _A      =       tokens + [tokenizer.unk_token]
    _A      =       [14, 15, 10, 9, 3, 2, 15, 19]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase      )			,						__lowerCAmelCase      )

 def     snake_case_      (    self					:					Tuple      )   ->	Union[str, Any]:
    if not self.test_rust_tokenizer:
       return

    _A      =       self.get_tokenizer()
    _A      =       self.get_rust_tokenizer(add_prefix_space=__lowerCAmelCase      )

    _A      =       '''lower newer'''

    # Testing tokenization
    _A      =       tokenizer.tokenize(__lowerCAmelCase			,						add_prefix_space=__lowerCAmelCase      )
    _A      =       rust_tokenizer.tokenize(__lowerCAmelCase      )
    self.assertListEqual(__lowerCAmelCase			,						__lowerCAmelCase      )

    # Testing conversion to ids without special tokens
    _A      =       tokenizer.encode(__lowerCAmelCase			,						add_special_tokens=__lowerCAmelCase			,						add_prefix_space=__lowerCAmelCase      )
    _A      =       rust_tokenizer.encode(__lowerCAmelCase			,						add_special_tokens=__lowerCAmelCase      )
    self.assertListEqual(__lowerCAmelCase			,						__lowerCAmelCase      )

    # Testing conversion to ids with special tokens
    _A      =       self.get_rust_tokenizer(add_prefix_space=__lowerCAmelCase      )
    _A      =       tokenizer.encode(__lowerCAmelCase			,						add_prefix_space=__lowerCAmelCase      )
    _A      =       rust_tokenizer.encode(__lowerCAmelCase      )
    self.assertListEqual(__lowerCAmelCase			,						__lowerCAmelCase      )

    # Testing the unknown token
    _A      =       tokens + [rust_tokenizer.unk_token]
    _A      =       [14, 15, 10, 9, 3, 2, 15, 19]
    self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(__lowerCAmelCase      )			,						__lowerCAmelCase      )

 def     snake_case_      (    self					:					str			,						*__lowerCAmelCase					:					Dict			,						**__lowerCAmelCase					:					str      )   ->	Any:
    # It's very difficult to mix/test pretokenization with byte-level
    # And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
    pass

 def     snake_case_      (    self					:					Dict			,						__lowerCAmelCase					:					int=15      )   ->	int:
    for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
       with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''      ):
          _A      =       self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase			,						**__lowerCAmelCase      )

          # Simple input
          _A      =       '''This is a simple input'''
          _A      =       ['''This is a simple input 1''', '''This is a simple input 2''']
          _A      =       ('''This is a simple input''', '''This is a pair''')
          _A      =       [
              ('''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(__lowerCAmelCase			,						tokenizer_r.encode			,						__lowerCAmelCase			,						max_length=__lowerCAmelCase			,						padding='''max_length'''      )

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

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

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

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

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

 def     snake_case_      (    self					:					List[Any]      )   ->	Optional[int]:
    _A      =       CodeGenTokenizer.from_pretrained(self.tmpdirname			,						pad_token='''<pad>'''      )

    # Simple input
    _A      =       '''This is a simple input'''
    _A      =       ['''This is a simple input looooooooong''', '''This is a simple input''']
    _A      =       ('''This is a simple input''', '''This is a pair''')
    _A      =       [
        ('''This is a simple input loooooong''', '''This is a simple input'''),
        ('''This is a simple pair loooooong''', '''This is a simple pair'''),
    ]

    _A      =       tokenizer.pad_token_id

    _A      =       tokenizer(__lowerCAmelCase			,						padding='''max_length'''			,						max_length=30			,						return_tensors='''np'''      )
    _A      =       tokenizer(__lowerCAmelCase			,						padding=__lowerCAmelCase			,						truncate=__lowerCAmelCase			,						return_tensors='''np'''      )
    _A      =       tokenizer(*__lowerCAmelCase			,						padding='''max_length'''			,						max_length=60			,						return_tensors='''np'''      )
    _A      =       tokenizer(__lowerCAmelCase			,						padding=__lowerCAmelCase			,						truncate=__lowerCAmelCase			,						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     snake_case_      (    self					:					List[Any]      )   ->	int:
    _A      =       '''$$$'''
    _A      =       CodeGenTokenizer.from_pretrained(self.tmpdirname			,						bos_token=__lowerCAmelCase			,						add_bos_token=__lowerCAmelCase      )

    _A      =       '''This is a simple input'''
    _A      =       ['''This is a simple input 1''', '''This is a simple input 2''']

    _A      =       tokenizer.bos_token_id

    _A      =       tokenizer(__lowerCAmelCase      )
    _A      =       tokenizer(__lowerCAmelCase      )

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

    _A      =       tokenizer.decode(out_s.input_ids      )
    _A      =       tokenizer.batch_decode(out_sa.input_ids      )

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

 @slow
 def     snake_case_      (    self					:					Tuple      )   ->	Any:
    _A      =       CodeGenTokenizer.from_pretrained('''Salesforce/codegen-350M-mono'''      )

    _A      =       '''\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#'''
    _A      =       '''\nif len_a > len_b:      result = a\nelse:      result = b'''

    _A      =       tokenizer.encode(__lowerCAmelCase      )
    _A      =       ['''^#''', re.escape('''<|endoftext|>'''      ), '''^\'\'\'''', '''^"""''', '''\n\n\n''']
    _A      =       tokenizer.decode(__lowerCAmelCase			,						truncate_before_pattern=__lowerCAmelCase      )
    self.assertEqual(__lowerCAmelCase			,						__lowerCAmelCase      )

 def     snake_case_      (    self					:					Any      )   ->	Dict:
    pass










"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCamelCase__						= {
    "configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig", "ResNetOnnxConfig"]
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__						= [
      "RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "ResNetForImageClassification",
      "ResNetModel",
      "ResNetPreTrainedModel",
      "ResNetBackbone",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__						= [
      "TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFResNetForImageClassification",
      "TFResNetModel",
      "TFResNetPreTrainedModel",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__						= [
      "FlaxResNetForImageClassification",
      "FlaxResNetModel",
      "FlaxResNetPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_resnet import (
        RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        ResNetBackbone,
        ResNetForImageClassification,
        ResNetModel,
        ResNetPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_resnet import (
        TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFResNetForImageClassification,
        TFResNetModel,
        TFResNetPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
  import sys

  lowerCamelCase__						= _LazyModule(__name__, globals()["__file__"], _import_structure)
