

from collections import OrderedDict
from typing import Any, List, Mapping, Optional

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


_UpperCAmelCase    	=			logging.get_logger(__name__)

_UpperCAmelCase    	=			{
    "gpt2": "https://huggingface.co/gpt2/resolve/main/config.json",
    "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/config.json",
    "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/config.json",
    "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/config.json",
    "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/config.json",
}



class        _UpperCAmelCase (   __lowercase			):
  '''simple docstring'''







  SCREAMING_SNAKE_CASE							:				Any					      =				'''gpt2'''
  SCREAMING_SNAKE_CASE							:				Dict					      =				['''past_key_values''']
  SCREAMING_SNAKE_CASE							:				Optional[int]					      =				{
      '''hidden_size''': '''n_embd''',
      '''max_position_embeddings''': '''n_positions''',
      '''num_attention_heads''': '''n_head''',
      '''num_hidden_layers''': '''n_layer''',
  }







  def __init__( self	: List[str]		,						UpperCamelCase__	: Any=50257		,						UpperCamelCase__	: Dict=1024		,						UpperCamelCase__	: List[str]=768		,						UpperCamelCase__	: List[Any]=12		,						UpperCamelCase__	: Optional[Any]=12		,						UpperCamelCase__	: List[Any]=None		,						UpperCamelCase__	: Optional[Any]="gelu_new"		,						UpperCamelCase__	: Any=0.1		,						UpperCamelCase__	: Union[str, Any]=0.1		,						UpperCamelCase__	: int=0.1		,						UpperCamelCase__	: Union[str, Any]=1e-5		,						UpperCamelCase__	: Tuple=0.02		,						UpperCamelCase__	: Optional[Any]="cls_index"		,						UpperCamelCase__	: List[str]=True		,						UpperCamelCase__	: str=None		,						UpperCamelCase__	: List[str]=True		,						UpperCamelCase__	: Optional[Any]=0.1		,						UpperCamelCase__	: List[str]=True		,						UpperCamelCase__	: List[Any]=True		,						UpperCamelCase__	: Any=50256		,						UpperCamelCase__	: Optional[int]=50256		,						UpperCamelCase__	: List[Any]=False		,						UpperCamelCase__	: int=False		,						**UpperCamelCase__	: Tuple		,						):
        A    =				vocab_size
        A    =				n_positions
        A    =				n_embd
        A    =				n_layer
        A    =				n_head
        A    =				n_inner
        A    =				activation_function
        A    =				resid_pdrop
        A    =				embd_pdrop
        A    =				attn_pdrop
        A    =				layer_norm_epsilon
        A    =				initializer_range
        A    =				summary_type
        A    =				summary_use_proj
        A    =				summary_activation
        A    =				summary_first_dropout
        A    =				summary_proj_to_labels
        A    =				scale_attn_weights
        A    =				use_cache
        A    =				scale_attn_by_inverse_layer_idx
        A    =				reorder_and_upcast_attn

        A    =				bos_token_id
        A    =				eos_token_id

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



class        _UpperCAmelCase (   __lowercase			):
  '''simple docstring'''







  def __init__( self	: List[Any]		,						UpperCamelCase__	: PretrainedConfig		,						UpperCamelCase__	: str = "default"		,						UpperCamelCase__	: List[PatchingSpec] = None		,						UpperCamelCase__	: bool = False		,						):
        super().__init__(UpperCamelCase__		,						task=UpperCamelCase__		,						patching_specs=UpperCamelCase__		,						use_past=UpperCamelCase__ )
        if not getattr(self._config		,						'pad_token_id'		,						UpperCamelCase__ ):
              # TODO: how to do that better?
              A    =				0







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

        return common_inputs







  @property
  def 	UpperCamelCase       ( self	: List[str] ):
        return self._config.n_layer







  @property
  def 	UpperCamelCase       ( self	: Union[str, Any] ):
        return self._config.n_head







  def 	UpperCamelCase       ( self	: Dict		,						UpperCamelCase__	: PreTrainedTokenizer		,						UpperCamelCase__	: int = -1		,						UpperCamelCase__	: int = -1		,						UpperCamelCase__	: bool = False		,						UpperCamelCase__	: Optional[TensorType] = None		,						):
        A    =				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()
        A    =				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

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

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

        return ordered_inputs







  @property
  def 	UpperCamelCase       ( self	: int ):
        return 13






import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
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 (   __lowercase     ,				unittest.TestCase			):
  '''simple docstring'''







  SCREAMING_SNAKE_CASE							:				List[Any]					      =				BartTokenizer
  SCREAMING_SNAKE_CASE							:				List[str]					      =				BartTokenizerFast
  SCREAMING_SNAKE_CASE							:				Any					      =				True
  SCREAMING_SNAKE_CASE							:				Union[str, Any]					      =				filter_roberta_detectors







  def 	UpperCamelCase       ( self	: str ):
        super().setUp()
        A    =				[
            'l',
            'o',
            'w',
            'e',
            'r',
            's',
            't',
            'i',
            'd',
            'n',
            '\u0120',
            '\u0120l',
            '\u0120n',
            '\u0120lo',
            '\u0120low',
            'er',
            '\u0120lowest',
            '\u0120newer',
            '\u0120wider',
            '<unk>',
        ]
        A    =				dict(zip(UpperCamelCase__		,						range(len(UpperCamelCase__ ) ) ) )
        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(UpperCamelCase__ ) + '\n' )
        with open(self.merges_file		,						'w'		,						encoding='utf-8' ) as fp:
              fp.write('\n'.join(UpperCamelCase__ ) )







  def 	UpperCamelCase       ( self	: Dict		,						**UpperCamelCase__	: List[Any] ):
        kwargs.update(self.special_tokens_map )
        return self.tokenizer_class.from_pretrained(self.tmpdirname		,						**UpperCamelCase__ )







  def 	UpperCamelCase       ( self	: Any		,						**UpperCamelCase__	: Union[str, Any] ):
        kwargs.update(self.special_tokens_map )
        return self.rust_tokenizer_class.from_pretrained(self.tmpdirname		,						**UpperCamelCase__ )







  def 	UpperCamelCase       ( self	: int		,						UpperCamelCase__	: str ):
        return "lower newer", "lower newer"







  @cached_property
  def 	UpperCamelCase       ( self	: Optional[Any] ):
        return BartTokenizer.from_pretrained('facebook/bart-large' )







  @cached_property
  def 	UpperCamelCase       ( self	: str ):
        return BartTokenizerFast.from_pretrained('facebook/bart-large' )







  @require_torch
  def 	UpperCamelCase       ( self	: Dict ):
        A    =				['A long paragraph for summarization.', 'Another paragraph for summarization.']
        A    =				[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              A    =				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 )
              A    =				batch.input_ids.tolist()[0]
              self.assertListEqual(UpperCamelCase__		,						UpperCamelCase__ )
              # Test that special tokens are reset







  @require_torch
  def 	UpperCamelCase       ( self	: Tuple ):
        A    =				['A long paragraph for summarization.', 'Another paragraph for summarization.']
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              A    =				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 	UpperCamelCase       ( self	: Dict ):
        A    =				[
            'Summary of the text.',
            'Another summary.',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              A    =				tokenizer(text_target=UpperCamelCase__		,						max_length=32		,						padding='max_length'		,						return_tensors='pt' )
              self.assertEqual(32		,						targets['input_ids'].shape[1] )







  @require_torch
  def 	UpperCamelCase       ( self	: int ):
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              A    =				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 	UpperCamelCase       ( self	: Optional[Any] ):
        A    =				['A long paragraph for summarization.']
        A    =				[
            'Summary of the text.',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              A    =				tokenizer(UpperCamelCase__		,						return_tensors='pt' )
              A    =				tokenizer(text_target=UpperCamelCase__		,						return_tensors='pt' )
              A    =				inputs['input_ids']
              A    =				targets['input_ids']
              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 	UpperCamelCase       ( self	: List[str] ):
        pass







  def 	UpperCamelCase       ( self	: Tuple ):
        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(UpperCamelCase__		,						**UpperCamelCase__ )
                    A    =				self.tokenizer_class.from_pretrained(UpperCamelCase__		,						**UpperCamelCase__ )
                    A    =				'A, <mask> AllenNLP sentence.'
                    A    =				tokenizer_r.encode_plus(UpperCamelCase__		,						add_special_tokens=UpperCamelCase__		,						return_token_type_ids=UpperCamelCase__ )
                    A    =				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'] )		,						)

                    A    =				tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'] )
                    A    =				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, 50264, 3823, 487, 21992, 3645, 4, 2] )
                    self.assertSequenceEqual(tokens_r['input_ids']		,						[0, 250, 6, 50264, 3823, 487, 21992, 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>'] )



