



"""simple docstring"""






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 	__snake_case	(     _lowercase  ,					unittest.TestCase):
  snake_case__    :       Optional[int]        =						GPTaTokenizer
  snake_case__    :       Dict        =						GPTaTokenizerFast
  snake_case__    :       Optional[Any]        =						True
  snake_case__    :       Dict        =						{"add_prefix_space": True}
  snake_case__    :       Optional[int]        =						False




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[int]		):






         """simple docstring"""






         super().setUp()

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

         _lowerCamelCase						:    int         =			os.path.join(self.tmpdirname						, VOCAB_FILES_NAMES['''vocab_file''']		)
         _lowerCamelCase						:    str         =			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 						SCREAMING_SNAKE_CASE    (			self					:						Tuple						, **__lowerCAmelCase					:						Any		):






         """simple docstring"""






         kwargs.update(self.special_tokens_map		)
         return GPTaTokenizer.from_pretrained(self.tmpdirname						, **__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[int]						, **__lowerCAmelCase					:						Union[str, Any]		):






         """simple docstring"""






         kwargs.update(self.special_tokens_map		)
         return GPTaTokenizerFast.from_pretrained(self.tmpdirname						, **__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						int						, __lowerCAmelCase					:						Optional[int]		):






         """simple docstring"""






         _lowerCamelCase						:    List[str]         =			'''lower newer'''
         _lowerCamelCase						:    Any         =			'''lower newer'''
         return input_text, output_text




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[int]		):






         """simple docstring"""






         _lowerCamelCase						:    Tuple         =			GPTaTokenizer(self.vocab_file						, self.merges_file						, **self.special_tokens_map		)
         _lowerCamelCase						:    Dict         =			'''lower newer'''
         _lowerCamelCase						:    Dict         =			['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
         _lowerCamelCase						:    Optional[int]         =			tokenizer.tokenize(__lowerCAmelCase						, add_prefix_space=__lowerCAmelCase		)
         self.assertListEqual(__lowerCAmelCase						, __lowerCAmelCase		)

         _lowerCamelCase						:    List[str]         =			tokens + [tokenizer.unk_token]
         _lowerCamelCase						:    List[str]         =			[1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase		)						, __lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]		):






         """simple docstring"""






         if not self.test_rust_tokenizer:
                return

         _lowerCamelCase						:    List[str]         =			self.get_tokenizer()
         _lowerCamelCase						:    Tuple         =			self.get_rust_tokenizer(add_prefix_space=__lowerCAmelCase		)

         _lowerCamelCase						:    Dict         =			'''lower newer'''

         # Testing tokenization
         _lowerCamelCase						:    Dict         =			tokenizer.tokenize(__lowerCAmelCase						, add_prefix_space=__lowerCAmelCase		)
         _lowerCamelCase						:    Dict         =			rust_tokenizer.tokenize(__lowerCAmelCase		)
         self.assertListEqual(__lowerCAmelCase						, __lowerCAmelCase		)

         # Testing conversion to ids without special tokens
         _lowerCamelCase						:    List[str]         =			tokenizer.encode(__lowerCAmelCase						, add_special_tokens=__lowerCAmelCase						, add_prefix_space=__lowerCAmelCase		)
         _lowerCamelCase						:    Optional[Any]         =			rust_tokenizer.encode(__lowerCAmelCase						, add_special_tokens=__lowerCAmelCase		)
         self.assertListEqual(__lowerCAmelCase						, __lowerCAmelCase		)

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

         # Testing the unknown token
         _lowerCamelCase						:    Optional[int]         =			tokens + [rust_tokenizer.unk_token]
         _lowerCamelCase						:    Union[str, Any]         =			[1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
         self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(__lowerCAmelCase		)						, __lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[str]						, *__lowerCAmelCase					:						Tuple						, **__lowerCAmelCase					:						str		):






         """simple docstring"""






         pass




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]						, __lowerCAmelCase					:						str=1_5		):






         """simple docstring"""






         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''		):
                       _lowerCamelCase						:    Tuple         =			self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase						, **__lowerCAmelCase		)

                       # Simple input
                       _lowerCamelCase						:    Tuple         =			'''This is a simple input'''
                       _lowerCamelCase						:    List[Any]         =			['''This is a simple input 1''', '''This is a simple input 2''']
                       _lowerCamelCase						:    List[str]         =			('''This is a simple input''', '''This is a pair''')
                       _lowerCamelCase						:    int         =			[
                           ('''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 						SCREAMING_SNAKE_CASE    (			self					:						Dict		):






         """simple docstring"""






         _lowerCamelCase						:    List[Any]         =			GPTaTokenizer.from_pretrained(self.tmpdirname						, pad_token='''<pad>'''		)

         # Simple input
         _lowerCamelCase						:    Optional[Any]         =			'''This is a simple input'''
         _lowerCamelCase						:    Union[str, Any]         =			['''This is a simple input looooooooong''', '''This is a simple input''']
         _lowerCamelCase						:    Optional[int]         =			('''This is a simple input''', '''This is a pair''')
         _lowerCamelCase						:    str         =			[
             ('''This is a simple input loooooong''', '''This is a simple input'''),
             ('''This is a simple pair loooooong''', '''This is a simple pair'''),
         ]

         _lowerCamelCase						:    Optional[Any]         =			tokenizer.pad_token_id

         _lowerCamelCase						:    List[str]         =			tokenizer(__lowerCAmelCase						, padding='''max_length'''						, max_length=3_0						, return_tensors='''np'''		)
         _lowerCamelCase						:    Any         =			tokenizer(__lowerCAmelCase						, padding=__lowerCAmelCase						, truncate=__lowerCAmelCase						, return_tensors='''np'''		)
         _lowerCamelCase						:    Tuple         =			tokenizer(*__lowerCAmelCase						, padding='''max_length'''						, max_length=6_0						, return_tensors='''np'''		)
         _lowerCamelCase						:    int         =			tokenizer(__lowerCAmelCase						, padding=__lowerCAmelCase						, truncate=__lowerCAmelCase						, return_tensors='''np'''		)

         # s
         # test single string max_length padding
         self.assertEqual(out_s['''input_ids'''].shape[-1]						, 3_0		)
         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]						, 3_3		)
         # 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]						, 6_0		)
         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]						, 5_2		)
         # 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 						SCREAMING_SNAKE_CASE    (			self					:						Any		):






         """simple docstring"""






         _lowerCamelCase						:    Optional[int]         =			'''$$$'''
         _lowerCamelCase						:    Optional[Any]         =			GPTaTokenizer.from_pretrained(self.tmpdirname						, bos_token=__lowerCAmelCase						, add_bos_token=__lowerCAmelCase		)

         _lowerCamelCase						:    List[Any]         =			'''This is a simple input'''
         _lowerCamelCase						:    int         =			['''This is a simple input 1''', '''This is a simple input 2''']

         _lowerCamelCase						:    Tuple         =			tokenizer.bos_token_id

         _lowerCamelCase						:    List[Any]         =			tokenizer(__lowerCAmelCase		)
         _lowerCamelCase						:    Dict         =			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		)		)

         _lowerCamelCase						:    List[Any]         =			tokenizer.decode(out_s.input_ids		)
         _lowerCamelCase						:    List[str]         =			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		)		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						int		):






         """simple docstring"""






         pass




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]		):






         """simple docstring"""






         _lowerCamelCase						:    Dict         =			[self.get_tokenizer(do_lower_case=__lowerCAmelCase						, add_bos_token=__lowerCAmelCase		)]
         for tokenizer in tokenizers:
                with self.subTest(f'''{tokenizer.__class__.__name__}'''		):
                       _lowerCamelCase						:    List[str]         =			'''Encode this.'''
                       _lowerCamelCase						:    Dict         =			'''This one too please.'''
                       _lowerCamelCase						:    Optional[Any]         =			tokenizer.encode(__lowerCAmelCase						, add_special_tokens=__lowerCAmelCase		)
                       encoded_sequence += tokenizer.encode(__lowerCAmelCase						, add_special_tokens=__lowerCAmelCase		)
                       _lowerCamelCase						:    Any         =			tokenizer.encode_plus(
                           __lowerCAmelCase						, __lowerCAmelCase						, add_special_tokens=__lowerCAmelCase						, return_special_tokens_mask=__lowerCAmelCase						, )
                       _lowerCamelCase						:    List[str]         =			encoded_sequence_dict['''input_ids''']
                       _lowerCamelCase						:    List[str]         =			encoded_sequence_dict['''special_tokens_mask''']
                       self.assertEqual(len(__lowerCAmelCase		)						, len(__lowerCAmelCase		)		)

                       _lowerCamelCase						:    List[Any]         =			[
                           (x if not special_tokens_mask[i] else None) for i, x in enumerate(__lowerCAmelCase		)
                       ]
                       _lowerCamelCase						:    Union[str, Any]         =			[x for x in filtered_sequence if x is not None]
                       self.assertEqual(__lowerCAmelCase						, __lowerCAmelCase		)






@require_tokenizers
class 	__snake_case	(     unittest.TestCase):




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[int]		):






         """simple docstring"""






         _lowerCamelCase						:    Optional[Any]         =			AutoTokenizer.from_pretrained('''facebook/opt-350m'''						, from_slow=__lowerCAmelCase		)
         _lowerCamelCase						:    List[Any]         =			'''A photo of a cat'''

         _lowerCamelCase						:    int         =			tokenizer.encode(
             __lowerCAmelCase						, )
         self.assertEqual(__lowerCAmelCase						, [2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]		)
         tokenizer.save_pretrained('''test_opt'''		)

         _lowerCamelCase						:    Optional[Any]         =			AutoTokenizer.from_pretrained('''./test_opt'''		)
         _lowerCamelCase						:    str         =			tokenizer.encode(
             __lowerCAmelCase						, )
         self.assertEqual(__lowerCAmelCase						, [2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[Any]		):






         """simple docstring"""






         _lowerCamelCase						:    List[str]         =			AutoTokenizer.from_pretrained('''facebook/opt-350m'''						, use_slow=__lowerCAmelCase		)
         _lowerCamelCase						:    str         =			'''A photo of a cat'''

         _lowerCamelCase						:    int         =			tokenizer.encode(
             __lowerCAmelCase						, )
         # Same as above
         self.assertEqual(__lowerCAmelCase						, [2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]		)




  @unittest.skip('''This test is failing because of a bug in the fast tokenizer'''		)
  def 						SCREAMING_SNAKE_CASE    (			self					:						Any		):






         """simple docstring"""






         _lowerCamelCase						:    List[Any]         =			AutoTokenizer.from_pretrained('''facebook/opt-350m'''						, from_slow=__lowerCAmelCase		)

         _lowerCamelCase						:    Dict         =			'''bos'''
         _lowerCamelCase						:    List[str]         =			tokenizer.get_vocab()['''bos''']

         _lowerCamelCase						:    List[Any]         =			'''A photo of a cat'''
         _lowerCamelCase						:    Tuple         =			tokenizer.encode(
             __lowerCAmelCase						, )
         # We changed the bos token
         self.assertEqual(__lowerCAmelCase						, [3_1_9_5_7, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]		)
         tokenizer.save_pretrained('''./tok'''		)
         _lowerCamelCase						:    str         =			AutoTokenizer.from_pretrained('''./tok'''		)
         self.assertTrue(tokenizer.is_fast		)
         _lowerCamelCase						:    Union[str, Any]         =			tokenizer.encode(
             __lowerCAmelCase						, )
         self.assertEqual(__lowerCAmelCase						, [3_1_9_5_7, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]		)







"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__      =    logging.get_logger(__name__)

lowerCAmelCase__      =    {
    '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/config.json''',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}






class 	__snake_case	(     _lowercase):
  snake_case__    :       List[Any]        =						"xglm"
  snake_case__    :       Dict        =						["past_key_values"]

  snake_case__    :       str        =						{
      "num_attention_heads": "attention_heads",
      "hidden_size": "d_model",
      "num_hidden_layers": "num_layers",
  }




  def __init__(			self					:						List[str]						, __lowerCAmelCase					:						List[Any]=2_5_6_0_0_8						, __lowerCAmelCase					:						int=2_0_4_8						, __lowerCAmelCase					:						Dict=1_0_2_4						, __lowerCAmelCase					:						List[str]=4_0_9_6						, __lowerCAmelCase					:						Tuple=2_4						, __lowerCAmelCase					:						Dict=1_6						, __lowerCAmelCase					:						Tuple="gelu"						, __lowerCAmelCase					:						Tuple=0.1						, __lowerCAmelCase					:						Tuple=0.1						, __lowerCAmelCase					:						Optional[Any]=0.0						, __lowerCAmelCase					:						List[Any]=0.0						, __lowerCAmelCase					:						int=0.02						, __lowerCAmelCase					:						Any=True						, __lowerCAmelCase					:						Tuple=True						, __lowerCAmelCase					:						str=2						, __lowerCAmelCase					:						Dict=1						, __lowerCAmelCase					:						Dict=0						, __lowerCAmelCase					:						List[Any]=2						, **__lowerCAmelCase					:						Optional[Any]						, ):






         """simple docstring"""






         _lowerCamelCase						:    List[Any]         =			vocab_size
         _lowerCamelCase						:    List[Any]         =			max_position_embeddings
         _lowerCamelCase						:    int         =			d_model
         _lowerCamelCase						:    Optional[Any]         =			ffn_dim
         _lowerCamelCase						:    Any         =			num_layers
         _lowerCamelCase						:    Union[str, Any]         =			attention_heads
         _lowerCamelCase						:    List[str]         =			activation_function
         _lowerCamelCase						:    Union[str, Any]         =			dropout
         _lowerCamelCase						:    int         =			attention_dropout
         _lowerCamelCase						:    Optional[int]         =			activation_dropout
         _lowerCamelCase						:    Any         =			layerdrop
         _lowerCamelCase						:    List[str]         =			init_std
         _lowerCamelCase						:    Union[str, Any]         =			scale_embedding  # scale factor will be sqrt(d_model) if True
         _lowerCamelCase						:    str         =			use_cache

         super().__init__(
             pad_token_id=__lowerCAmelCase						, bos_token_id=__lowerCAmelCase						, eos_token_id=__lowerCAmelCase						, decoder_start_token_id=__lowerCAmelCase						, **__lowerCAmelCase						, )


