

'''simple docstring'''





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       ( lowercase__	,       unittest.TestCase			):
				lowerCamelCase_							=						LEDTokenizer
				lowerCamelCase_							=						LEDTokenizerFast
				lowerCamelCase_							=						True







				def    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										super().setUp()
										lowercase   :       Optional[int]								=[
										    '''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[int]								=['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
										lowercase   :       Union[str, 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    lowerCamelCase_  (					self    :		List[Any]		,				**UpperCAmelCase__    :		Dict      ):
										'''simple docstring'''
										kwargs.update(self.special_tokens_map      )
										return self.tokenizer_class.from_pretrained(self.tmpdirname		,				**UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Optional[int]		,				**UpperCAmelCase__    :		List[str]      ):
										'''simple docstring'''
										kwargs.update(self.special_tokens_map      )
										return self.rust_tokenizer_class.from_pretrained(self.tmpdirname		,				**UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Dict		,				UpperCAmelCase__    :		int      ):
										'''simple docstring'''
										return "lower newer", "lower newer"







				@cached_property
				def    lowerCamelCase_  (					self    :		Optional[Any]      ):
										'''simple docstring'''
										return LEDTokenizer.from_pretrained('''allenai/led-base-16384'''      )







				@cached_property
				def    lowerCamelCase_  (					self    :		Optional[Any]      ):
										'''simple docstring'''
										return LEDTokenizerFast.from_pretrained('''allenai/led-base-16384'''      )







				@require_torch
				def    lowerCamelCase_  (					self    :		Any      ):
										'''simple docstring'''
										lowercase   :       Union[str, Any]								=['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
										lowercase   :       Optional[int]								=[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																lowercase   :       Dict								=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   :       List[str]								=batch.input_ids.tolist()[0]
																self.assertListEqual(UpperCAmelCase__		,				UpperCAmelCase__      )







				@require_torch
				def    lowerCamelCase_  (					self    :		List[Any]      ):
										'''simple docstring'''
										lowercase   :       str								=['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																lowercase   :       Any								=tokenizer(UpperCAmelCase__		,				padding=UpperCAmelCase__		,				return_tensors='''pt'''      )
																self.assertIn('''input_ids'''		,				UpperCAmelCase__      )
																self.assertIn('''attention_mask'''		,				UpperCAmelCase__      )
																self.assertNotIn('''labels'''		,				UpperCAmelCase__      )
																self.assertNotIn('''decoder_attention_mask'''		,				UpperCAmelCase__      )







				@require_torch
				def    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										lowercase   :       Optional[int]								=[
										    '''Summary of the text.''',
										    '''Another summary.''',
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																lowercase   :       str								=tokenizer(text_target=UpperCAmelCase__		,				max_length=32		,				padding='''max_length'''		,				return_tensors='''pt'''      )
																self.assertEqual(32		,				targets['''input_ids'''].shape[1]      )







				@require_torch
				def    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																lowercase   :       Tuple								=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, 5122)      )







				@require_torch
				def    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										lowercase   :       Optional[int]								=['''A long paragraph for summarization.''']
										lowercase   :       Any								=[
										    '''Summary of the text.''',
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																lowercase   :       List[Any]								=tokenizer(UpperCAmelCase__		,				return_tensors='''pt'''      )
																lowercase   :       Optional[Any]								=tokenizer(text_target=UpperCAmelCase__		,				return_tensors='''pt'''      )
																lowercase   :       Any								=inputs['''input_ids''']
																lowercase   :       List[Any]								=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    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																lowercase   :       Any								=['''Summary of the text.''', '''Another summary.''']
																lowercase   :       Optional[int]								=[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

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







				def    lowerCamelCase_  (					self    :		Optional[Any]      ):
										'''simple docstring'''
										pass







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

																						lowercase   :       Optional[int]								=tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']      )
																						lowercase   :       str								=tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']      )
																						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>''']      )








'''simple docstring'''





import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
							from ... import PreTrainedTokenizerBase, TensorType

UpperCamelCase_							=					logging.get_logger(__name__)





class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):
				lowerCamelCase_							=						'vision-encoder-decoder'
				lowerCamelCase_							=						True







				def __init__(					self    :		Optional[int]		,				**UpperCAmelCase__    :		Tuple      ):
										'''simple docstring'''
										super().__init__(**UpperCAmelCase__      )
										if "encoder" not in kwargs or "decoder" not in kwargs:
																raise ValueError(
																    F'''A configuraton of type {self.model_type} cannot be instantiated because '''
																    F'''not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}'''      )

										lowercase   :       Optional[Any]								=kwargs.pop('''encoder'''      )
										lowercase   :       List[Any]								=encoder_config.pop('''model_type'''      )
										lowercase   :       List[str]								=kwargs.pop('''decoder'''      )
										lowercase   :       Dict								=decoder_config.pop('''model_type'''      )

										lowercase   :       Union[str, Any]								=AutoConfig.for_model(UpperCAmelCase__		,				**UpperCAmelCase__      )
										lowercase   :       List[str]								=AutoConfig.for_model(UpperCAmelCase__		,				**UpperCAmelCase__      )
										lowercase   :       str								=True







				@classmethod
				def    lowerCamelCase_  (					cls    :		List[str]		,				UpperCAmelCase__    :		PretrainedConfig		,				UpperCAmelCase__    :		PretrainedConfig		,				**UpperCAmelCase__    :		Optional[int]      ):
										'''simple docstring'''
										logger.info('''Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''      )
										lowercase   :       int								=True
										lowercase   :       Optional[Any]								=True

										return cls(encoder=encoder_config.to_dict()		,				decoder=decoder_config.to_dict()		,				**UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										lowercase   :       int								=copy.deepcopy(self.__dict__      )
										lowercase   :       Union[str, Any]								=self.encoder.to_dict()
										lowercase   :       Union[str, Any]								=self.decoder.to_dict()
										lowercase   :       int								=self.__class__.model_type
										return output





class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):
				lowerCamelCase_							=						version.parse('1.11'			)







				@property
				def    lowerCamelCase_  (					self    :		Dict      ):
										'''simple docstring'''
										return OrderedDict(
										    [
										        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
										    ]      )







				@property
				def    lowerCamelCase_  (					self    :		str      ):
										'''simple docstring'''
										return 1E-4







				@property
				def    lowerCamelCase_  (					self    :		List[str]      ):
										'''simple docstring'''
										return OrderedDict({'''last_hidden_state''': {0: '''batch''', 1: '''encoder_sequence'''}}      )





class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):







				@property
				def    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										lowercase   :       List[str]								=OrderedDict()
										lowercase   :       Tuple								={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
										lowercase   :       Optional[int]								={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
										lowercase   :       int								={0: '''batch''', 1: '''encoder_sequence'''}

										return common_inputs







				def    lowerCamelCase_  (					self    :		Optional[int]		,				UpperCAmelCase__    :		"PreTrainedTokenizerBase"		,				UpperCAmelCase__    :		int = -1		,				UpperCAmelCase__    :		int = -1		,				UpperCAmelCase__    :		bool = False		,				UpperCAmelCase__    :		Optional["TensorType"] = None		,				):
										'''simple docstring'''
										import torch

										lowercase   :       Optional[Any]								=OrderedDict()

										lowercase   :       List[Any]								=super().generate_dummy_inputs(
										    UpperCAmelCase__		,				batch_size=UpperCAmelCase__		,				seq_length=UpperCAmelCase__		,				is_pair=UpperCAmelCase__		,				framework=UpperCAmelCase__      )

										lowercase		,  lowercase   :       Optional[int]								=dummy_input['''input_ids'''].shape
										lowercase   :       Union[str, Any]								=(batch, encoder_sequence, self._config.encoder_hidden_size)
										lowercase   :       List[str]								=dummy_input.pop('''input_ids'''      )
										lowercase   :       Tuple								=dummy_input.pop('''attention_mask'''      )
										lowercase   :       Union[str, Any]								=torch.zeros(UpperCAmelCase__      )

										return common_inputs





class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):







				@property
				def    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										pass







				def    lowerCamelCase_  (					self    :		List[Any]		,				UpperCAmelCase__    :		PretrainedConfig      ):
										'''simple docstring'''
										return VisionEncoderDecoderEncoderOnnxConfig(UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Optional[int]		,				UpperCAmelCase__    :		PretrainedConfig		,				UpperCAmelCase__    :		PretrainedConfig		,				UpperCAmelCase__    :		str = "default"      ):
										'''simple docstring'''
										lowercase   :       List[Any]								=encoder_config.hidden_size
										return VisionEncoderDecoderDecoderOnnxConfig(UpperCAmelCase__		,				UpperCAmelCase__      )





