






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      __SCREAMING_SNAKE_CASE       ( __lowercase		,       unittest.TestCase):
						_SCREAMING_SNAKE_CASE						:							Optional[int]				=       BartTokenizer
						_SCREAMING_SNAKE_CASE						:							List[str]				=       BartTokenizerFast
						_SCREAMING_SNAKE_CASE						:							str				=       True
						_SCREAMING_SNAKE_CASE						:							List[Any]				=       filter_roberta_detectors






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												super().setUp()
												lowerCAmelCase__         =    [
												    'l',
												    'o',
												    'w',
												    'e',
												    'r',
												    's',
												    't',
												    'i',
												    'd',
												    'n',
												    '\u0120',
												    '\u0120l',
												    '\u0120n',
												    '\u0120lo',
												    '\u0120low',
												    'er',
												    '\u0120lowest',
												    '\u0120newer',
												    '\u0120wider',
												    '<unk>',
												]
												lowerCAmelCase__         =    dict(zip(SCREAMING_SNAKE_CASE_  ,    range(len(SCREAMING_SNAKE_CASE_ ) ) ) )
												lowerCAmelCase__         =    ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
												lowerCAmelCase__         =    {'unk_token': '<unk>'}

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






						def    UpperCamelCase__   (						self  ,    **_UpperCamelCase ):






												"""simple docstring"""
												kwargs.update(self.special_tokens_map )
												return self.tokenizer_class.from_pretrained(self.tmpdirname  ,    **SCREAMING_SNAKE_CASE_ )






						def    UpperCamelCase__   (						self  ,    **_UpperCamelCase ):






												"""simple docstring"""
												kwargs.update(self.special_tokens_map )
												return self.rust_tokenizer_class.from_pretrained(self.tmpdirname  ,    **SCREAMING_SNAKE_CASE_ )






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase ):






												"""simple docstring"""
												return "lower newer", "lower newer"






						@cached_property
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												return BartTokenizer.from_pretrained('facebook/bart-large' )






						@cached_property
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												return BartTokenizerFast.from_pretrained('facebook/bart-large' )






						@require_torch
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    ['A long paragraph for summarization.', 'Another paragraph for summarization.']
												lowerCAmelCase__         =    [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		lowerCAmelCase__         =    tokenizer(SCREAMING_SNAKE_CASE_  ,    max_length=len(SCREAMING_SNAKE_CASE_ )  ,    padding=SCREAMING_SNAKE_CASE_  ,    return_tensors='pt' )
																		self.assertIsInstance(SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_ )

																		self.assertEqual((2, 9)  ,    batch.input_ids.shape )
																		self.assertEqual((2, 9)  ,    batch.attention_mask.shape )
																		lowerCAmelCase__         =    batch.input_ids.tolist()[0]
																		self.assertListEqual(SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_ )
																		# Test that special tokens are reset






						@require_torch
						def    UpperCamelCase__   (						self ):






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






						@require_torch
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    [
												    'Summary of the text.',
												    'Another summary.',
												]
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		lowerCAmelCase__         =    tokenizer(text_target=SCREAMING_SNAKE_CASE_  ,    max_length=32  ,    padding='max_length'  ,    return_tensors='pt' )
																		self.assertEqual(32  ,    targets['input_ids'].shape[1] )






						@require_torch
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		lowerCAmelCase__         =    tokenizer(
																		    ['I am a small frog' * 10_24, 'I am a small frog']  ,    padding=SCREAMING_SNAKE_CASE_  ,    truncation=SCREAMING_SNAKE_CASE_  ,    return_tensors='pt' )
																		self.assertIsInstance(SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_ )
																		self.assertEqual(batch.input_ids.shape  ,    (2, 10_24) )






						@require_torch
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    ['A long paragraph for summarization.']
												lowerCAmelCase__         =    [
												    'Summary of the text.',
												]
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		lowerCAmelCase__         =    tokenizer(SCREAMING_SNAKE_CASE_  ,    return_tensors='pt' )
																		lowerCAmelCase__         =    tokenizer(text_target=SCREAMING_SNAKE_CASE_  ,    return_tensors='pt' )
																		lowerCAmelCase__         =    inputs['input_ids']
																		lowerCAmelCase__         =    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 ):






												"""simple docstring"""
												pass







						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
																								lowerCAmelCase__         =    self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_  ,    **SCREAMING_SNAKE_CASE_ )
																								lowerCAmelCase__         =    self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_  ,    **SCREAMING_SNAKE_CASE_ )
																								lowerCAmelCase__         =    'A, <mask> AllenNLP sentence.'
																								lowerCAmelCase__         =    tokenizer_r.encode_plus(SCREAMING_SNAKE_CASE_  ,    add_special_tokens=SCREAMING_SNAKE_CASE_  ,    return_token_type_ids=SCREAMING_SNAKE_CASE_ )
																								lowerCAmelCase__         =    tokenizer_p.encode_plus(SCREAMING_SNAKE_CASE_  ,    add_special_tokens=SCREAMING_SNAKE_CASE_  ,    return_token_type_ids=SCREAMING_SNAKE_CASE_ )

																								# 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'] )  ,    )

																								lowerCAmelCase__         =    tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'] )
																								lowerCAmelCase__         =    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, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
																								self.assertSequenceEqual(tokens_r['input_ids']  ,    [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )

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







from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


lowerCamelCase_				:							Union[str, Any]       =    logging.get_logger(__name__)

lowerCamelCase_				:							Optional[Any]       =    {
    "google/long-t5-local-base": "https://huggingface.co/google/long-t5-local-base/blob/main/config.json",
    "google/long-t5-local-large": "https://huggingface.co/google/long-t5-local-large/blob/main/config.json",
    "google/long-t5-tglobal-base": "https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json",
    "google/long-t5-tglobal-large": "https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json",
}







class  _lowerCamelCase			(lowerCamelCase					):
     lowercase__				  =			"""longt5"""
     lowercase__				  =			["""past_key_values"""]
     lowercase__				  =			{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}

     def __init__(  self							,       SCREAMING_SNAKE_CASE_=32_128							,       SCREAMING_SNAKE_CASE_=512							,       SCREAMING_SNAKE_CASE_=64							,       SCREAMING_SNAKE_CASE_=2_048							,       SCREAMING_SNAKE_CASE_=6							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_=8							,       SCREAMING_SNAKE_CASE_=127							,       SCREAMING_SNAKE_CASE_=16							,       SCREAMING_SNAKE_CASE_=32							,       SCREAMING_SNAKE_CASE_=128							,       SCREAMING_SNAKE_CASE_=0.1							,       SCREAMING_SNAKE_CASE_=1E-6							,       SCREAMING_SNAKE_CASE_=1.0							,       SCREAMING_SNAKE_CASE_="relu"							,       SCREAMING_SNAKE_CASE_=True							,       SCREAMING_SNAKE_CASE_="local"							,       SCREAMING_SNAKE_CASE_=True							,       SCREAMING_SNAKE_CASE_=0							,       SCREAMING_SNAKE_CASE_=1							,       **SCREAMING_SNAKE_CASE_							,       ):
            __snake_case						       =   vocab_size
            __snake_case						       =   d_model
            __snake_case						       =   d_kv
            __snake_case						       =   d_ff
            __snake_case						       =   num_layers
            # default = symmetry
            __snake_case						       =   num_decoder_layers if num_decoder_layers is not None else self.num_layers
            __snake_case						       =   num_heads
            __snake_case						       =   local_radius
            __snake_case						       =   global_block_size
            __snake_case						       =   relative_attention_num_buckets
            __snake_case						       =   relative_attention_max_distance
            __snake_case						       =   dropout_rate
            __snake_case						       =   layer_norm_epsilon
            __snake_case						       =   initializer_factor
            __snake_case						       =   feed_forward_proj
            __snake_case						       =   encoder_attention_type
            __snake_case						       =   use_cache

            __snake_case						       =   self.feed_forward_proj.split('-'    )
            __snake_case						       =   act_info[-1]
            __snake_case						       =   act_info[0] == 'gated'

            if len(SCREAMING_SNAKE_CASE_    ) > 1 and act_info[0] != "gated" or len(SCREAMING_SNAKE_CASE_    ) > 2:
                   raise ValueError(
                       f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
                       'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
                       '\'gated-gelu\' or \'relu\''    )

            # for backwards compatibility
            if feed_forward_proj == "gated-gelu":
                   __snake_case						       =   'gelu_new'

            super().__init__(
                pad_token_id=SCREAMING_SNAKE_CASE_							,       eos_token_id=SCREAMING_SNAKE_CASE_							,       is_encoder_decoder=SCREAMING_SNAKE_CASE_							,       **SCREAMING_SNAKE_CASE_							,       )







class  _lowerCamelCase			(lowerCamelCase					):

     @property
     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   {
                'input_ids': {0: 'batch', 1: 'encoder_sequence'},
                'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
            }
            if self.use_past:
                   __snake_case						       =   'past_encoder_sequence + sequence'
                   __snake_case						       =   {0: 'batch'}
                   __snake_case						       =   {0: 'batch', 1: 'past_decoder_sequence + sequence'}
            else:
                   __snake_case						       =   {0: 'batch', 1: 'decoder_sequence'}
                   __snake_case						       =   {0: 'batch', 1: 'decoder_sequence'}

            if self.use_past:
                   self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_							,       direction='inputs'    )

            return common_inputs

     @property
     def 						__lowerCamelCase				(  self    ):
            return 13
