
import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led 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




@require_tokenizers
class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase							,unittest.TestCase					):





	'''simple docstring'''



	_UpperCAmelCase       :		Optional[Any]       =					LEDTokenizer
	_UpperCAmelCase       :		Any       =					LEDTokenizerFast
	_UpperCAmelCase       :		Tuple       =					True



	def  A							(  self	:    List[Any]	):


				'''simple docstring'''


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

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



	def  A							(  self	:    Dict			,  **lowercase	:    Optional[int]	):


				'''simple docstring'''


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



	def  A							(  self	:    List[str]			,  **lowercase	:    List[str]	):


				'''simple docstring'''


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



	def  A							(  self	:    Union[str, Any]			,  lowercase	:    List[Any]	):


				'''simple docstring'''


				return "lower newer", "lower newer"



	@cached_property
	def  A							(  self	:    List[str]	):


				'''simple docstring'''


				return LEDTokenizer.from_pretrained('allenai/led-base-16384'	)



	@cached_property
	def  A							(  self	:    List[Any]	):


				'''simple docstring'''


				return LEDTokenizerFast.from_pretrained('allenai/led-base-16384'	)



	@require_torch
	def  A							(  self	:    int	):


				'''simple docstring'''


				_snake_case		=			['A long paragraph for summarization.', 'Another paragraph for summarization.']
				_snake_case		=			[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

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

							self.assertEqual((2, 9)			,  batch.input_ids.shape	)
							self.assertEqual((2, 9)			,  batch.attention_mask.shape	)
							_snake_case		=			batch.input_ids.tolist()[0]
							self.assertListEqual(lowercase			,  lowercase	)



	@require_torch
	def  A							(  self	:    Optional[Any]	):


				'''simple docstring'''


				_snake_case		=			['A long paragraph for summarization.', 'Another paragraph for summarization.']
				for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							_snake_case		=			tokenizer(lowercase			,  padding=lowercase			,  return_tensors='pt'	)
							self.assertIn('input_ids'			,  lowercase	)
							self.assertIn('attention_mask'			,  lowercase	)
							self.assertNotIn('labels'			,  lowercase	)
							self.assertNotIn('decoder_attention_mask'			,  lowercase	)



	@require_torch
	def  A							(  self	:    Optional[Any]	):


				'''simple docstring'''


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



	@require_torch
	def  A							(  self	:    List[Any]	):


				'''simple docstring'''


				for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							_snake_case		=			tokenizer(
							    ['I am a small frog' * 1_024, 'I am a small frog']			,  padding=lowercase			,  truncation=lowercase			,  return_tensors='pt'	)
							self.assertIsInstance(lowercase			,  lowercase	)
							self.assertEqual(batch.input_ids.shape			,  (2, 5_122)	)



	@require_torch
	def  A							(  self	:    str	):


				'''simple docstring'''


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



	@require_torch
	def  A							(  self	:    Optional[int]	):


				'''simple docstring'''


				for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							_snake_case		=			['Summary of the text.', 'Another summary.']
							_snake_case		=			[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

							_snake_case		=			tokenizer(lowercase			,  padding=lowercase	)
							_snake_case		=			[[0] * len(lowercase	) for x in encoded_output['input_ids']]
							_snake_case		=			tokenizer.pad(lowercase	)
							self.assertSequenceEqual(outputs['global_attention_mask']			,  lowercase	)



	def  A							(  self	:    int	):


				'''simple docstring'''


				pass





	def  A							(  self	:    Union[str, Any]	):


				'''simple docstring'''


				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
										_snake_case		=			self.rust_tokenizer_class.from_pretrained(lowercase			,  **lowercase	)
										_snake_case		=			self.tokenizer_class.from_pretrained(lowercase			,  **lowercase	)
										_snake_case		=			'A, <mask> AllenNLP sentence.'
										_snake_case		=			tokenizer_r.encode_plus(lowercase			,  add_special_tokens=lowercase			,  return_token_type_ids=lowercase	)
										_snake_case		=			tokenizer_p.encode_plus(lowercase			,  add_special_tokens=lowercase			,  return_token_type_ids=lowercase	)
										self.assertEqual(sum(tokens_r['token_type_ids']	)			,  sum(tokens_p['token_type_ids']	)	)
										self.assertEqual(
										    sum(tokens_r['attention_mask']	) / len(tokens_r['attention_mask']	)			,  sum(tokens_p['attention_mask']	) / len(tokens_p['attention_mask']	)			,  )

										_snake_case		=			tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']	)
										_snake_case		=			tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']	)
										self.assertSequenceEqual(tokens_p['input_ids']			,  [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]	)
										self.assertSequenceEqual(tokens_r['input_ids']			,  [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]	)

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

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

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
			from ...onnx.config import PatchingSpec
			from ...tokenization_utils_base import PreTrainedTokenizerBase


_lowerCamelCase       :				List[Any]       =			logging.get_logger(__name__)

_lowerCamelCase       :				Union[str, Any]       =			{
    '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096''': '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096-finetuned-triviaqa''': (
        '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'''
    ),
    '''allenai/longformer-base-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
    '''allenai/longformer-large-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
}




class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase					):





	'''simple docstring'''



	_UpperCAmelCase       :		Dict       =					"longformer"



	def __init__(  self	:    Optional[Any]			,  lowercase	:    Union[List[int], int] = 512			,  lowercase	:    int = 2			,  lowercase	:    int = 1			,  lowercase	:    int = 0			,  lowercase	:    int = 2			,  lowercase	:    int = 30_522			,  lowercase	:    int = 768			,  lowercase	:    int = 12			,  lowercase	:    int = 12			,  lowercase	:    int = 3_072			,  lowercase	:    str = "gelu"			,  lowercase	:    float = 0.1			,  lowercase	:    float = 0.1			,  lowercase	:    int = 512			,  lowercase	:    int = 2			,  lowercase	:    float = 0.02			,  lowercase	:    float = 1E-12			,  lowercase	:    bool = False			,  **lowercase	:    Optional[Any]			,  ):


				'''simple docstring'''


				super().__init__(pad_token_id=lowercase			,  **lowercase	)

				_snake_case		=			attention_window
				_snake_case		=			sep_token_id
				_snake_case		=			bos_token_id
				_snake_case		=			eos_token_id
				_snake_case		=			vocab_size
				_snake_case		=			hidden_size
				_snake_case		=			num_hidden_layers
				_snake_case		=			num_attention_heads
				_snake_case		=			hidden_act
				_snake_case		=			intermediate_size
				_snake_case		=			hidden_dropout_prob
				_snake_case		=			attention_probs_dropout_prob
				_snake_case		=			max_position_embeddings
				_snake_case		=			type_vocab_size
				_snake_case		=			initializer_range
				_snake_case		=			layer_norm_eps
				_snake_case		=			onnx_export




class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase					):





	'''simple docstring'''



	def __init__(  self	:    int			,  lowercase	:    "PretrainedConfig"			,  lowercase	:    str = "default"			,  lowercase	:    "List[PatchingSpec]" = None	):


				'''simple docstring'''


				super().__init__(lowercase			,  lowercase			,  lowercase	)
				_snake_case		=			True



	@property
	def  A							(  self	:    Union[str, Any]	):


				'''simple docstring'''


				if self.task == "multiple-choice":
							_snake_case		=			{0: 'batch', 1: 'choice', 2: 'sequence'}
				else:
							_snake_case		=			{0: 'batch', 1: 'sequence'}
				return OrderedDict(
				    [
				        ('input_ids', dynamic_axis),
				        ('attention_mask', dynamic_axis),
				        ('global_attention_mask', dynamic_axis),
				    ]	)



	@property
	def  A							(  self	:    int	):


				'''simple docstring'''


				_snake_case		=			super().outputs
				if self.task == "default":
							_snake_case		=			{0: 'batch'}
				return outputs



	@property
	def  A							(  self	:    List[Any]	):


				'''simple docstring'''


				return 1E-4



	@property
	def  A							(  self	:    List[str]	):


				'''simple docstring'''


				return max(super().default_onnx_opset			,  14	)





	def  A							(  self	:    str			,  lowercase	:    "PreTrainedTokenizerBase"			,  lowercase	:    int = -1			,  lowercase	:    int = -1			,  lowercase	:    bool = False			,  lowercase	:    Optional[TensorType] = None			,  ):


				'''simple docstring'''


				_snake_case		=			super().generate_dummy_inputs(
				    preprocessor=lowercase			,  batch_size=lowercase			,  seq_length=lowercase			,  is_pair=lowercase			,  framework=lowercase	)
				import torch

				# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
				# makes the export fail randomly
				_snake_case		=			torch.zeros_like(inputs['input_ids']	)
				# make every second token global
				_snake_case		=			1

				return inputs