






'''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


SCREAMING_SNAKE_CASE							=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							=						{
    '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 UpperCAmelCase_       (     __A					):




      """simple docstring"""




      UpperCamelCase_             =      '''gpt_neo'''
      UpperCamelCase_             =      ['''past_key_values''']
      UpperCamelCase_             =      {'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}






      def __init__(			self      :		Dict  ,   UpperCAmelCase      :		int=5_0257  ,   UpperCAmelCase      :		Optional[Any]=2048  ,   UpperCAmelCase      :		str=2048  ,   UpperCAmelCase      :		str=24  ,   UpperCAmelCase      :		Tuple=[[["global", "local"], 12]]  ,   UpperCAmelCase      :		int=16  ,   UpperCAmelCase      :		Union[str, Any]=None  ,   UpperCAmelCase      :		Optional[int]=256  ,   UpperCAmelCase      :		List[str]="gelu_new"  ,   UpperCAmelCase      :		Optional[int]=0.0  ,   UpperCAmelCase      :		Dict=0.0  ,   UpperCAmelCase      :		int=0.0  ,   UpperCAmelCase      :		Optional[Any]=0.1  ,   UpperCAmelCase      :		Optional[Any]=1e-5  ,   UpperCAmelCase      :		Dict=0.0_2  ,   UpperCAmelCase      :		Dict=True  ,   UpperCAmelCase      :		int=5_0256  ,   UpperCAmelCase      :		List[str]=5_0256  ,   **UpperCAmelCase      :		List[str]  ,   )	->	Union[str, Any]:


       '''simple docstring'''

       lowercase       : Tuple						=vocab_size
       lowercase       : Optional[int]						=max_position_embeddings
       lowercase       : Tuple						=hidden_size
       lowercase       : str						=num_layers
       lowercase       : Optional[Any]						=num_heads
       lowercase       : List[Any]						=intermediate_size
       lowercase       : Union[str, Any]						=window_size
       lowercase       : Optional[Any]						=activation_function
       lowercase       : Union[str, Any]						=resid_dropout
       lowercase       : List[str]						=embed_dropout
       lowercase       : int						=attention_dropout
       lowercase       : List[str]						=classifier_dropout
       lowercase       : List[Any]						=layer_norm_epsilon
       lowercase       : int						=initializer_range
       lowercase       : List[Any]						=use_cache

       lowercase       : Union[str, Any]						=bos_token_id
       lowercase       : Optional[int]						=eos_token_id

       lowercase       : Tuple						=attention_types
       lowercase       : List[str]						=self.expand_attention_types_params(UpperCAmelCase		)

       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=UpperCAmelCase  ,   eos_token_id=UpperCAmelCase  ,   **UpperCAmelCase		)






      @staticmethod
      def 					A__   (			UpperCAmelCase      :		Dict		)	->	int:


       '''simple docstring'''

       lowercase       : int						=[]
       for item in attention_types:
        for _ in range(item[1]		):
         attentions.extend(item[0]		)
       return attentions





def 				lowercase_					(     __A			:							Any       ,   __A			:							Tuple       ,   __A			:							Tuple       ,   __A			:							Dict      )							->   List[str]:



 """simple docstring"""




 import torch

 lowercase       : str						=input.size()
 lowercase       : List[str]						=len(__A      )
 lowercase       : Optional[Any]						=shape[dimension]

 lowercase       : Optional[Any]						=torch.arange(0       ,   __A       ,   __A      )
 lowercase       : List[str]						=torch.div(sizedim - size       ,   __A       ,   rounding_mode='''floor'''      ) + 1
 lowercase       : Optional[int]						=torch.arange(__A      ) + low_indices[:min_length][:, None]

 lowercase       : List[Any]						=[slice(__A      )] * rank
 lowercase       : Dict						=indices
 lowercase       : str						=input[s]

 lowercase       : str						=list(range(0       ,   rank + 1      )      )
 perm.append(perm.pop(dimension + 1      )      )

 return sliced.permute(__A      )


def 				lowercase_					(     __A			:							Tuple       ,   __A			:							int      )							->   List[str]:



 """simple docstring"""




 import torch

 lowercase       : Any						=torch.arange(1       ,   __A      )
 lowercase       : Union[str, Any]						=torch.remainder(__A       ,   __A      )
 lowercase       : Any						=remainders == 0
 lowercase       : List[str]						=candidates[divisor_indices]
 lowercase       : Optional[int]						=torch.max(__A      )
 return largest_divisor, torch.div(__A       ,   __A       ,   rounding_mode='''floor'''      )





class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      @property
      def 					A__   (			self      :		List[str]		)	->	Mapping[str, Mapping[int, str]]:


       '''simple docstring'''

       lowercase       : Union[str, Any]						=OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}		)
       if self.use_past:
        self.fill_with_past_key_values_(UpperCAmelCase  ,   direction='''inputs'''		)
        lowercase       : List[str]						={0: '''batch''', 1: '''past_sequence + sequence'''}
       else:
        lowercase       : Dict						={0: '''batch''', 1: '''sequence'''}

       return common_inputs






      @property
      def 					A__   (			self      :		int		)	->	int:


       '''simple docstring'''

       return self._config.num_heads






      def 					A__   (			self      :		List[str]  ,   UpperCAmelCase      :		PreTrainedTokenizer  ,   UpperCAmelCase      :		int = -1  ,   UpperCAmelCase      :		int = -1  ,   UpperCAmelCase      :		bool = False  ,   UpperCAmelCase      :		Optional[TensorType] = None  ,   )	->	Mapping[str, Any]:


       '''simple docstring'''

       lowercase       : Union[str, Any]						=super(UpperCAmelCase  ,   self		).generate_dummy_inputs(
           UpperCAmelCase  ,   batch_size=UpperCAmelCase  ,   seq_length=UpperCAmelCase  ,   is_pair=UpperCAmelCase  ,   framework=UpperCAmelCase		)

       # We need to order the input in the way they appears in the forward()
       lowercase       : List[Any]						=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

         lowercase	,		lowercase       : Tuple						=common_inputs['''input_ids'''].shape
         # Not using the same length for past_key_values
         lowercase       : Optional[Any]						=seqlen + 2
         lowercase       : List[str]						=(
             batch,
             self.num_attention_heads,
             past_key_values_length,
             self._config.hidden_size // self.num_attention_heads,
         )
         lowercase       : Optional[Any]						=[
             (torch.zeros(UpperCAmelCase		), torch.zeros(UpperCAmelCase		)) for _ in range(self.num_layers		)
         ]

       lowercase       : Any						=common_inputs['''attention_mask''']
       if self.use_past:
        lowercase       : List[Any]						=ordered_inputs['''attention_mask'''].dtype
        lowercase       : List[str]						=torch.cat(
            [ordered_inputs['''attention_mask'''], torch.ones(UpperCAmelCase  ,   UpperCAmelCase  ,   dtype=UpperCAmelCase		)]  ,   dim=1		)

       return ordered_inputs






      @property
      def 					A__   (			self      :		str		)	->	int:


       '''simple docstring'''

       return 13











'''simple docstring'''



import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
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 UpperCAmelCase_       (     __A					,							unittest.TestCase					):




      """simple docstring"""




      UpperCamelCase_             =      MvpTokenizer
      UpperCamelCase_             =      MvpTokenizerFast
      UpperCamelCase_             =      True
      UpperCamelCase_             =      filter_roberta_detectors






      def 					A__   (			self      :		Union[str, Any]		)	->	List[str]:


       '''simple docstring'''

       super().setUp()
       lowercase       : Dict						=[
           '''l''',
           '''o''',
           '''w''',
           '''e''',
           '''r''',
           '''s''',
           '''t''',
           '''i''',
           '''d''',
           '''n''',
           '''\u0120''',
           '''\u0120l''',
           '''\u0120n''',
           '''\u0120lo''',
           '''\u0120low''',
           '''er''',
           '''\u0120lowest''',
           '''\u0120newer''',
           '''\u0120wider''',
           '''<unk>''',
       ]
       lowercase       : Tuple						=dict(zip(UpperCAmelCase  ,   range(len(UpperCAmelCase		)		)		)		)
       lowercase       : Optional[Any]						=['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
       lowercase       : List[Any]						={'''unk_token''': '''<unk>'''}

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






      def 					A__   (			self      :		Union[str, Any]  ,   **UpperCAmelCase      :		Dict		)	->	Tuple:


       '''simple docstring'''

       kwargs.update(self.special_tokens_map		)
       return self.tokenizer_class.from_pretrained(self.tmpdirname  ,   **UpperCAmelCase		)






      def 					A__   (			self      :		List[str]  ,   **UpperCAmelCase      :		Any		)	->	Tuple:


       '''simple docstring'''

       kwargs.update(self.special_tokens_map		)
       return self.rust_tokenizer_class.from_pretrained(self.tmpdirname  ,   **UpperCAmelCase		)






      def 					A__   (			self      :		Tuple  ,   UpperCAmelCase      :		Any		)	->	Union[str, Any]:


       '''simple docstring'''

       return "lower newer", "lower newer"






      @cached_property
      def 					A__   (			self      :		Tuple		)	->	List[Any]:


       '''simple docstring'''

       return MvpTokenizer.from_pretrained('''RUCAIBox/mvp'''		)






      @cached_property
      def 					A__   (			self      :		Any		)	->	int:


       '''simple docstring'''

       return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp'''		)






      @require_torch
      def 					A__   (			self      :		Union[str, Any]		)	->	Optional[int]:


       '''simple docstring'''

       lowercase       : Optional[int]						=['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
       lowercase       : List[str]						=[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        lowercase       : Union[str, Any]						=tokenizer(UpperCAmelCase  ,   max_length=len(UpperCAmelCase		)  ,   padding=UpperCAmelCase  ,   return_tensors='''pt'''		)
        self.assertIsInstance(UpperCAmelCase  ,   UpperCAmelCase		)

        self.assertEqual((2, 9)  ,   batch.input_ids.shape		)
        self.assertEqual((2, 9)  ,   batch.attention_mask.shape		)
        lowercase       : Union[str, Any]						=batch.input_ids.tolist()[0]
        self.assertListEqual(UpperCAmelCase  ,   UpperCAmelCase		)
        # Test that special tokens are reset






      @require_torch
      def 					A__   (			self      :		Tuple		)	->	Any:


       '''simple docstring'''

       lowercase       : Any						=['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        lowercase       : Dict						=tokenizer(UpperCAmelCase  ,   padding=UpperCAmelCase  ,   return_tensors='''pt'''		)
        # check if input_ids are returned and no labels
        self.assertIn('''input_ids'''  ,   UpperCAmelCase		)
        self.assertIn('''attention_mask'''  ,   UpperCAmelCase		)
        self.assertNotIn('''labels'''  ,   UpperCAmelCase		)
        self.assertNotIn('''decoder_attention_mask'''  ,   UpperCAmelCase		)






      @require_torch
      def 					A__   (			self      :		Dict		)	->	int:


       '''simple docstring'''

       lowercase       : int						=[
           '''Summary of the text.''',
           '''Another summary.''',
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        lowercase       : Optional[Any]						=tokenizer(text_target=UpperCAmelCase  ,   max_length=32  ,   padding='''max_length'''  ,   return_tensors='''pt'''		)
        self.assertEqual(32  ,   targets['''input_ids'''].shape[1]		)






      @require_torch
      def 					A__   (			self      :		Union[str, Any]		)	->	Any:


       '''simple docstring'''

       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        lowercase       : Union[str, Any]						=tokenizer(
            ['''I am a small frog''' * 1024, '''I am a small frog''']  ,   padding=UpperCAmelCase  ,   truncation=UpperCAmelCase  ,   return_tensors='''pt'''		)
        self.assertIsInstance(UpperCAmelCase  ,   UpperCAmelCase		)
        self.assertEqual(batch.input_ids.shape  ,   (2, 1024)		)






      @require_torch
      def 					A__   (			self      :		str		)	->	List[Any]:


       '''simple docstring'''

       lowercase       : Optional[Any]						=['''A long paragraph for summarization.''']
       lowercase       : List[Any]						=[
           '''Summary of the text.''',
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        lowercase       : List[str]						=tokenizer(UpperCAmelCase  ,   text_target=UpperCAmelCase  ,   return_tensors='''pt'''		)
        lowercase       : Optional[int]						=inputs['''input_ids''']
        lowercase       : Optional[Any]						=inputs['''labels''']
        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 					A__   (			self      :		List[Any]		)	->	List[Any]:


       '''simple docstring'''

       pass






      def 					A__   (			self      :		List[str]		)	->	Tuple:


       '''simple docstring'''

       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'		):
         lowercase       : Union[str, Any]						=self.rust_tokenizer_class.from_pretrained(UpperCAmelCase  ,   **UpperCAmelCase		)
         lowercase       : Tuple						=self.tokenizer_class.from_pretrained(UpperCAmelCase  ,   **UpperCAmelCase		)
         lowercase       : Optional[Any]						='''A, <mask> AllenNLP sentence.'''
         lowercase       : int						=tokenizer_r.encode_plus(UpperCAmelCase  ,   add_special_tokens=UpperCAmelCase  ,   return_token_type_ids=UpperCAmelCase		)
         lowercase       : List[Any]						=tokenizer_p.encode_plus(UpperCAmelCase  ,   add_special_tokens=UpperCAmelCase  ,   return_token_type_ids=UpperCAmelCase		)

         # 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''']		)  ,   )

         lowercase       : Any						=tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']		)
         lowercase       : str						=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, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]		)
         self.assertSequenceEqual(tokens_r['''input_ids''']  ,   [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]		)

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



