





'''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     (    __lowerCamelCase			,       unittest.TestCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					List[Any]					=GPTaTokenizer
 SCREAMING_SNAKE_CASE_	:					int					=GPTaTokenizerFast
 SCREAMING_SNAKE_CASE_	:					Union[str, Any]					=True
 SCREAMING_SNAKE_CASE_	:					Optional[int]					={"add_prefix_space": True}
 SCREAMING_SNAKE_CASE_	:					Optional[Any]					=False






 def        _lowerCamelCase					(       self       :							Any	):
        super().setUp()

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

        __UpperCamelCase       =  os.path.join(self.tmpdirname							,	VOCAB_FILES_NAMES['vocab_file']	)
        __UpperCamelCase       =  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       :							Tuple							,	**__A       :							Any	):
        kwargs.update(self.special_tokens_map	)
        return GPTaTokenizer.from_pretrained(self.tmpdirname							,	**__A	)






 def        _lowerCamelCase					(       self       :							Union[str, Any]							,	**__A       :							List[str]	):
        kwargs.update(self.special_tokens_map	)
        return GPTaTokenizerFast.from_pretrained(self.tmpdirname							,	**__A	)






 def        _lowerCamelCase					(       self       :							Optional[Any]							,	__A       :							str	):
        __UpperCamelCase       =  'lower newer'
        __UpperCamelCase       =  'lower newer'
        return input_text, output_text






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

        __UpperCamelCase       =  tokens + [tokenizer.unk_token]
        __UpperCamelCase       =  [1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__A	)							,	__A	)






 def        _lowerCamelCase					(       self       :							List[Any]	):
        if not self.test_rust_tokenizer:
               return

        __UpperCamelCase       =  self.get_tokenizer()
        __UpperCamelCase       =  self.get_rust_tokenizer(add_prefix_space=__A	)

        __UpperCamelCase       =  'lower newer'

        # Testing tokenization
        __UpperCamelCase       =  tokenizer.tokenize(__A							,	add_prefix_space=__A	)
        __UpperCamelCase       =  rust_tokenizer.tokenize(__A	)
        self.assertListEqual(__A							,	__A	)

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

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

        # Testing the unknown token
        __UpperCamelCase       =  tokens + [rust_tokenizer.unk_token]
        __UpperCamelCase       =  [1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
        self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(__A	)							,	__A	)






 def        _lowerCamelCase					(       self       :							Any							,	*__A       :							Union[str, Any]							,	**__A       :							Optional[Any]	):
        # It's very difficult to mix/test pretokenization with byte-level
        # And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
        pass






 def        _lowerCamelCase					(       self       :							List[str]							,	__A       :							Any=1_5	):
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
                      __UpperCamelCase       =  self.rust_tokenizer_class.from_pretrained(__A							,	**__A	)

                      # Simple input
                      __UpperCamelCase       =  'This is a simple input'
                      __UpperCamelCase       =  ['This is a simple input 1', 'This is a simple input 2']
                      __UpperCamelCase       =  ('This is a simple input', 'This is a pair')
                      __UpperCamelCase       =  [
                          ('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       :							List[Any]	):
        __UpperCamelCase       =  GPTaTokenizer.from_pretrained(self.tmpdirname							,	pad_token='<pad>'	)

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

        __UpperCamelCase       =  tokenizer.pad_token_id

        __UpperCamelCase       =  tokenizer(__A							,	padding='max_length'							,	max_length=3_0							,	return_tensors='np'	)
        __UpperCamelCase       =  tokenizer(__A							,	padding=__A							,	truncate=__A							,	return_tensors='np'	)
        __UpperCamelCase       =  tokenizer(*__A							,	padding='max_length'							,	max_length=6_0							,	return_tensors='np'	)
        __UpperCamelCase       =  tokenizer(__A							,	padding=__A							,	truncate=__A							,	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        _lowerCamelCase					(       self       :							Any	):
        __UpperCamelCase       =  '$$$'
        __UpperCamelCase       =  GPTaTokenizer.from_pretrained(self.tmpdirname							,	bos_token=__A							,	add_bos_token=__A	)

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

        __UpperCamelCase       =  tokenizer.bos_token_id

        __UpperCamelCase       =  tokenizer(__A	)
        __UpperCamelCase       =  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       =  tokenizer.decode(out_s.input_ids	)
        __UpperCamelCase       =  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       :							int	):
        pass






 def        _lowerCamelCase					(       self       :							int	):
        # TODO: change to self.get_tokenizers() when the fast version is implemented
        __UpperCamelCase       =  [self.get_tokenizer(do_lower_case=__A							,	add_bos_token=__A	)]
        for tokenizer in tokenizers:
               with self.subTest(f'''{tokenizer.__class__.__name__}'''	):
                      __UpperCamelCase       =  'Encode this.'
                      __UpperCamelCase       =  'This one too please.'
                      __UpperCamelCase       =  tokenizer.encode(__A							,	add_special_tokens=__A	)
                      encoded_sequence += tokenizer.encode(__A							,	add_special_tokens=__A	)
                      __UpperCamelCase       =  tokenizer.encode_plus(
                          __A							,	__A							,	add_special_tokens=__A							,	return_special_tokens_mask=__A							,	)
                      __UpperCamelCase       =  encoded_sequence_dict['input_ids']
                      __UpperCamelCase       =  encoded_sequence_dict['special_tokens_mask']
                      self.assertEqual(len(__A	)							,	len(__A	)	)

                      __UpperCamelCase       =  [
                          (x if not special_tokens_mask[i] else None) for i, x in enumerate(__A	)
                      ]
                      __UpperCamelCase       =  [x for x in filtered_sequence if x is not None]
                      self.assertEqual(__A							,	__A	)






@require_tokenizers
class      snake_case     (    unittest.TestCase       ):




 """simple docstring"""



 def        _lowerCamelCase					(       self       :							Tuple	):
        # More context:
        # https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1
        # https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519
        # https://github.com/huggingface/transformers/pull/17088#discussion_r871246439

        __UpperCamelCase       =  AutoTokenizer.from_pretrained('facebook/opt-350m'							,	from_slow=__A	)
        __UpperCamelCase       =  'A photo of a cat'

        __UpperCamelCase       =  tokenizer.encode(
            __A							,	)
        self.assertEqual(__A							,	[2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]	)
        tokenizer.save_pretrained('test_opt'	)

        __UpperCamelCase       =  AutoTokenizer.from_pretrained('./test_opt'	)
        __UpperCamelCase       =  tokenizer.encode(
            __A							,	)
        self.assertEqual(__A							,	[2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]	)






 def        _lowerCamelCase					(       self       :							List[Any]	):
        __UpperCamelCase       =  AutoTokenizer.from_pretrained('facebook/opt-350m'							,	use_slow=__A	)
        __UpperCamelCase       =  'A photo of a cat'

        __UpperCamelCase       =  tokenizer.encode(
            __A							,	)
        # Same as above
        self.assertEqual(__A							,	[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        _lowerCamelCase					(       self       :							Optional[Any]	):
        __UpperCamelCase       =  AutoTokenizer.from_pretrained('facebook/opt-350m'							,	from_slow=__A	)

        __UpperCamelCase       =  'bos'
        __UpperCamelCase       =  tokenizer.get_vocab()['bos']

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







'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


a__							:							Any =logging.get_logger(__name__)

a__							:							Optional[Any] ={
    '''EleutherAI/gpt-neo-1.3B''': '''https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json''',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Dict					="gpt_neo"
 SCREAMING_SNAKE_CASE_	:					Optional[int]					=["past_key_values"]
 SCREAMING_SNAKE_CASE_	:					List[Any]					={"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}






 def __init__(       self       :							Union[str, Any]							,	__A       :							Union[str, Any]=5_0_2_5_7							,	__A       :							Any=2_0_4_8							,	__A       :							Optional[Any]=2_0_4_8							,	__A       :							Any=2_4							,	__A       :							Union[str, Any]=[[["global", "local"], 1_2]]							,	__A       :							str=1_6							,	__A       :							Optional[int]=None							,	__A       :							Union[str, Any]=2_5_6							,	__A       :							Any="gelu_new"							,	__A       :							Dict=0.0							,	__A       :							Optional[int]=0.0							,	__A       :							int=0.0							,	__A       :							List[str]=0.1							,	__A       :							Any=1e-5							,	__A       :							int=0.02							,	__A       :							List[str]=True							,	__A       :							Tuple=5_0_2_5_6							,	__A       :							Optional[Any]=5_0_2_5_6							,	**__A       :							Optional[Any]							,	):
        __UpperCamelCase       =  vocab_size
        __UpperCamelCase       =  max_position_embeddings
        __UpperCamelCase       =  hidden_size
        __UpperCamelCase       =  num_layers
        __UpperCamelCase       =  num_heads
        __UpperCamelCase       =  intermediate_size
        __UpperCamelCase       =  window_size
        __UpperCamelCase       =  activation_function
        __UpperCamelCase       =  resid_dropout
        __UpperCamelCase       =  embed_dropout
        __UpperCamelCase       =  attention_dropout
        __UpperCamelCase       =  classifier_dropout
        __UpperCamelCase       =  layer_norm_epsilon
        __UpperCamelCase       =  initializer_range
        __UpperCamelCase       =  use_cache

        __UpperCamelCase       =  bos_token_id
        __UpperCamelCase       =  eos_token_id

        __UpperCamelCase       =  attention_types
        __UpperCamelCase       =  self.expand_attention_types_params(__A	)

        if len(self.attention_layers	) != self.num_layers:
               raise ValueError(
                   'Configuration for convolutional module is incorrect. '
                   'It is required that `len(config.attention_layers)` == `config.num_layers` '
                   f'''but is `len(config.attention_layers) = {len(self.attention_layers	)}`, '''
                   f'''`config.num_layers = {self.num_layers}`. '''
                   '`config.attention_layers` is prepared using `config.attention_types`. '
                   'Please verify the value of `config.attention_types` argument.'	)

        super().__init__(bos_token_id=__A							,	eos_token_id=__A							,	**__A	)






 @staticmethod
 def        _lowerCamelCase					(       __A       :							Tuple	):
        __UpperCamelCase       =  []
        for item in attention_types:
               for _ in range(item[1]	):
                      attentions.extend(item[0]	)
        return attentions






def 				lowercase__   (   __lowercase						:  Tuple				,  __lowercase						:  Any				,  __lowercase						:  Union[str, Any]				,  __lowercase						:  List[str]     ) ->      Any:



       """simple docstring"""






       import torch

       __UpperCamelCase       =  input.size()
       __UpperCamelCase       =  len(__lowercase     )
       __UpperCamelCase       =  shape[dimension]

       __UpperCamelCase       =  torch.arange(0				,  __lowercase				,  __lowercase     )
       __UpperCamelCase       =  torch.div(sizedim - size				,  __lowercase				,  rounding_mode='floor'     ) + 1
       __UpperCamelCase       =  torch.arange(__lowercase     ) + low_indices[:min_length][:, None]

       __UpperCamelCase       =  [slice(__lowercase     )] * rank
       __UpperCamelCase       =  indices
       __UpperCamelCase       =  input[s]

       __UpperCamelCase       =  list(range(0				,  rank + 1     )     )
       perm.append(perm.pop(dimension + 1     )     )

       return sliced.permute(__lowercase     )
def 				lowercase__   (   __lowercase						:  Union[str, Any]				,  __lowercase						:  Optional[int]     ) ->      Optional[int]:



       """simple docstring"""






       import torch

       __UpperCamelCase       =  torch.arange(1				,  __lowercase     )
       __UpperCamelCase       =  torch.remainder(__lowercase				,  __lowercase     )
       __UpperCamelCase       =  remainders == 0
       __UpperCamelCase       =  candidates[divisor_indices]
       __UpperCamelCase       =  torch.max(__lowercase     )
       return largest_divisor, torch.div(__lowercase				,  __lowercase				,  rounding_mode='floor'     )


class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 @property
 def        _lowerCamelCase					(       self       :							Tuple	):
        __UpperCamelCase       =  OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}	)
        if self.use_past:
               self.fill_with_past_key_values_(__A							,	direction='inputs'	)
               __UpperCamelCase       =  {0: 'batch', 1: 'past_sequence + sequence'}
        else:
               __UpperCamelCase       =  {0: 'batch', 1: 'sequence'}

        return common_inputs






 @property
 def        _lowerCamelCase					(       self       :							int	):
        return self._config.num_heads






 def        _lowerCamelCase					(       self       :							List[str]							,	__A       :							PreTrainedTokenizer							,	__A       :							int = -1							,	__A       :							int = -1							,	__A       :							bool = False							,	__A       :							Optional[TensorType] = None							,	):
        __UpperCamelCase       =  super(__A							,	self	).generate_dummy_inputs(
            __A							,	batch_size=__A							,	seq_length=__A							,	is_pair=__A							,	framework=__A	)

        # We need to order the input in the way they appears in the forward()
        __UpperCamelCase       =  OrderedDict({'input_ids': common_inputs['input_ids']}	)

        # Need to add the past_keys
        if self.use_past:
               if not is_torch_available():
                      raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'	)
               else:
                      import torch

                      __UpperCamelCase       ,						__UpperCamelCase       =  common_inputs['input_ids'].shape
                      # Not using the same length for past_key_values
                      __UpperCamelCase       =  seqlen + 2
                      __UpperCamelCase       =  (
                          batch,
                          self.num_attention_heads,
                          past_key_values_length,
                          self._config.hidden_size // self.num_attention_heads,
                      )
                      __UpperCamelCase       =  [
                          (torch.zeros(__A	), torch.zeros(__A	)) for _ in range(self.num_layers	)
                      ]

        __UpperCamelCase       =  common_inputs['attention_mask']
        if self.use_past:
               __UpperCamelCase       =  ordered_inputs['attention_mask'].dtype
               __UpperCamelCase       =  torch.cat(
                   [ordered_inputs['attention_mask'], torch.ones(__A							,	__A							,	dtype=__A	)]							,	dim=1	)

        return ordered_inputs






 @property
 def        _lowerCamelCase					(       self       :							Dict	):
        return 1_3
