

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 			A						(							_UpperCAmelCase   ,							unittest.TestCase			):







    """simple docstring"""





    lowerCamelCase		     =							LEDTokenizer
    lowerCamelCase		     =							LEDTokenizerFast
    lowerCamelCase		     =							True


    def 						snake_case__					(				self			:	Any		)->   Optional[int]:





        '''simple docstring'''







        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(lowercase_,range(len(lowercase_		)		)		)		)
        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(lowercase_		) + '\n'		)
        with open(self.merges_file,'w',encoding='utf-8'		) as fp:
            fp.write('\n'.join(lowercase_		)		)


    def 						snake_case__					(				self			:	Optional[int],**lowercase_			:	Tuple		)->   List[str]:





        '''simple docstring'''







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


    def 						snake_case__					(				self			:	Any,**lowercase_			:	Dict		)->   Tuple:





        '''simple docstring'''







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


    def 						snake_case__					(				self			:	str,lowercase_			:	int		)->   Dict:





        '''simple docstring'''







        return "lower newer", "lower newer"


    @cached_property
    def 						snake_case__					(				self			:	List[str]		)->   List[Any]:





        '''simple docstring'''







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


    @cached_property
    def 						snake_case__					(				self			:	str		)->   Optional[Any]:





        '''simple docstring'''







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


    @require_torch
    def 						snake_case__					(				self			:	List[str]		)->   Tuple:





        '''simple docstring'''







        A__   				=       ['A long paragraph for summarization.', 'Another paragraph for summarization.']
        A__   				=       [0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            A__   				=       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		)
            A__   				=       batch.input_ids.tolist()[0]
            self.assertListEqual(lowercase_,lowercase_		)


    @require_torch
    def 						snake_case__					(				self			:	Tuple		)->   List[str]:





        '''simple docstring'''







        A__   				=       ['A long paragraph for summarization.', 'Another paragraph for summarization.']
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            A__   				=       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 						snake_case__					(				self			:	Optional[int]		)->   int:





        '''simple docstring'''







        A__   				=       [
            'Summary of the text.',
            'Another summary.',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            A__   				=       tokenizer(text_target=lowercase_,max_length=3_2,padding='max_length',return_tensors='pt'		)
            self.assertEqual(3_2,targets['input_ids'].shape[1]		)


    @require_torch
    def 						snake_case__					(				self			:	Dict		)->   Any:





        '''simple docstring'''







        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            A__   				=       tokenizer(
                ['I am a small frog' * 1_0_2_4, 'I am a small frog'],padding=lowercase_,truncation=lowercase_,return_tensors='pt'		)
            self.assertIsInstance(lowercase_,lowercase_		)
            self.assertEqual(batch.input_ids.shape,(2, 5_1_2_2)		)


    @require_torch
    def 						snake_case__					(				self			:	List[Any]		)->   Optional[int]:





        '''simple docstring'''







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


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





        '''simple docstring'''







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

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


    def 						snake_case__					(				self			:	Tuple		)->   Tuple:





        '''simple docstring'''







        pass


    def 						snake_case__					(				self			:	int		)->   Tuple:





        '''simple docstring'''







        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(lowercase_,**lowercase_		)
                A__   				=       self.tokenizer_class.from_pretrained(lowercase_,**lowercase_		)
                A__   				=       'A, <mask> AllenNLP sentence.'
                A__   				=       tokenizer_r.encode_plus(lowercase_,add_special_tokens=lowercase_,return_token_type_ids=lowercase_		)
                A__   				=       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']		),)

                A__   				=       tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']		)
                A__   				=       tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']		)
                self.assertSequenceEqual(tokens_p['input_ids'],[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]		)
                self.assertSequenceEqual(tokens_r['input_ids'],[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]		)

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




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

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


lowercase_			=  logging.get_logger(__name__)

lowercase_			=  {
    "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/config.json",
    # See all BART models at https://huggingface.co/models?filter=bart
}


class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    lowerCamelCase		     =							'bart'
    lowerCamelCase		     =							['past_key_values']
    lowerCamelCase		     =							{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}


    def __init__(				self			:	Tuple,lowercase_			:	Optional[int]=5_0_2_6_5,lowercase_			:	List[str]=1_0_2_4,lowercase_			:	Any=1_2,lowercase_			:	Optional[Any]=4_0_9_6,lowercase_			:	str=1_6,lowercase_			:	int=1_2,lowercase_			:	Optional[Any]=4_0_9_6,lowercase_			:	Any=1_6,lowercase_			:	Any=0.0,lowercase_			:	str=0.0,lowercase_			:	Optional[Any]="gelu",lowercase_			:	List[str]=1_0_2_4,lowercase_			:	List[Any]=0.1,lowercase_			:	Union[str, Any]=0.0,lowercase_			:	Optional[int]=0.0,lowercase_			:	List[Any]=0.02,lowercase_			:	int=0.0,lowercase_			:	Optional[Any]=False,lowercase_			:	List[Any]=True,lowercase_			:	Union[str, Any]=3,lowercase_			:	int=1,lowercase_			:	int=0,lowercase_			:	List[str]=2,lowercase_			:	Optional[int]=True,lowercase_			:	Tuple=2,lowercase_			:	List[str]=2,**lowercase_			:	Dict,)->   List[Any]:





        '''simple docstring'''







        A__   				=       vocab_size
        A__   				=       max_position_embeddings
        A__   				=       d_model
        A__   				=       encoder_ffn_dim
        A__   				=       encoder_layers
        A__   				=       encoder_attention_heads
        A__   				=       decoder_ffn_dim
        A__   				=       decoder_layers
        A__   				=       decoder_attention_heads
        A__   				=       dropout
        A__   				=       attention_dropout
        A__   				=       activation_dropout
        A__   				=       activation_function
        A__   				=       init_std
        A__   				=       encoder_layerdrop
        A__   				=       decoder_layerdrop
        A__   				=       classifier_dropout
        A__   				=       use_cache
        A__   				=       encoder_layers
        A__   				=       scale_embedding  # scale factor will be sqrt(d_model) if True

        super().__init__(
            num_labels=lowercase_,pad_token_id=lowercase_,bos_token_id=lowercase_,eos_token_id=lowercase_,is_encoder_decoder=lowercase_,decoder_start_token_id=lowercase_,forced_eos_token_id=lowercase_,**lowercase_,)

        # ensure backward compatibility for BART CNN models
        if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated',lowercase_		):
            A__   				=       self.bos_token_id
            warnings.warn(
                F'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
                'The config can simply be saved and uploaded again to be fixed.'		)




class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    @property
    def 						snake_case__					(				self			:	Dict		)->   Mapping[str, Mapping[int, str]]:





        '''simple docstring'''







        if self.task in ["default", "seq2seq-lm"]:
            A__   				=       OrderedDict(
                [
                    ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                    ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                ]		)

            if self.use_past:
                A__   				=       {0: 'batch'}
                A__   				=       {0: 'batch', 1: 'past_decoder_sequence + sequence'}
            else:
                A__   				=       {0: 'batch', 1: 'decoder_sequence'}
                A__   				=       {0: 'batch', 1: 'decoder_sequence'}

            if self.use_past:
                self.fill_with_past_key_values_(lowercase_,direction='inputs'		)
        elif self.task == "causal-lm":
            # TODO: figure this case out.
            A__   				=       OrderedDict(
                [
                    ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                    ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                ]		)
            if self.use_past:
                A__				,						A__   				=       self.num_layers
                for i in range(lowercase_		):
                    A__   				=       {0: 'batch', 2: 'past_sequence + sequence'}
                    A__   				=       {0: 'batch', 2: 'past_sequence + sequence'}
        else:
            A__   				=       OrderedDict(
                [
                    ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                    ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                    ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
                    ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
                ]		)

        return common_inputs


    @property
    def 						snake_case__					(				self			:	Optional[Any]		)->   Mapping[str, Mapping[int, str]]:





        '''simple docstring'''







        if self.task in ["default", "seq2seq-lm"]:
            A__   				=       super().outputs
        else:
            A__   				=       super(lowercase_,self		).outputs
            if self.use_past:
                A__				,						A__   				=       self.num_layers
                for i in range(lowercase_		):
                    A__   				=       {0: 'batch', 2: 'past_sequence + sequence'}
                    A__   				=       {0: 'batch', 2: 'past_sequence + sequence'}
        return common_outputs


    def 						snake_case__					(				self			:	Tuple,lowercase_			:	PreTrainedTokenizer,lowercase_			:	int = -1,lowercase_			:	int = -1,lowercase_			:	bool = False,lowercase_			:	Optional[TensorType] = None,)->   Mapping[str, Any]:





        '''simple docstring'''







        A__   				=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowercase_,lowercase_,lowercase_,lowercase_,lowercase_		)

        # Generate decoder inputs
        A__   				=       seq_length if not self.use_past else 1
        A__   				=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowercase_,lowercase_,lowercase_,lowercase_,lowercase_		)
        A__   				=       {F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
        A__   				=       dict(**lowercase_,**lowercase_		)

        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
            A__   				=       common_inputs['decoder_input_ids'].shape[1]
            A__				,						A__   				=       self.num_attention_heads
            A__   				=       (
                batch,
                num_encoder_attention_heads,
                encoder_seq_length,
                self._config.hidden_size // num_encoder_attention_heads,
            )
            A__   				=       decoder_seq_length + 3
            A__   				=       (
                batch,
                num_decoder_attention_heads,
                decoder_past_length,
                self._config.hidden_size // num_decoder_attention_heads,
            )

            A__   				=       torch.cat(
                [common_inputs['decoder_attention_mask'], torch.ones(lowercase_,lowercase_		)],dim=1		)

            A__   				=       []
            # If the number of encoder and decoder layers are present in the model configuration, both are considered
            A__				,						A__   				=       self.num_layers
            A__   				=       min(lowercase_,lowercase_		)
            A__   				=       max(lowercase_,lowercase_		) - min_num_layers
            A__   				=       'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

            for _ in range(lowercase_		):
                common_inputs["past_key_values"].append(
                    (
                        torch.zeros(lowercase_		),
                        torch.zeros(lowercase_		),
                        torch.zeros(lowercase_		),
                        torch.zeros(lowercase_		),
                    )		)
            # TODO: test this.
            A__   				=       encoder_shape if remaining_side_name == 'encoder' else decoder_shape
            for _ in range(lowercase_,lowercase_		):
                common_inputs["past_key_values"].append((torch.zeros(lowercase_		), torch.zeros(lowercase_		))		)
        return common_inputs


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





        '''simple docstring'''







        A__   				=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowercase_,lowercase_,lowercase_,lowercase_,lowercase_		)

        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__				,						A__   				=       self.num_layers
            A__				,						A__   				=       self.num_attention_heads
            A__   				=       (
                batch,
                num_encoder_attention_heads,
                past_key_values_length,
                self._config.hidden_size // num_encoder_attention_heads,
            )

            A__   				=       common_inputs['attention_mask'].dtype
            A__   				=       torch.cat(
                [common_inputs['attention_mask'], torch.ones(lowercase_,lowercase_,dtype=lowercase_		)],dim=1		)
            A__   				=       [
                (torch.zeros(lowercase_		), torch.zeros(lowercase_		)) for _ in range(lowercase_		)
            ]
        return common_inputs


    def 						snake_case__					(				self			:	Union[str, Any],lowercase_			:	PreTrainedTokenizer,lowercase_			:	int = -1,lowercase_			:	int = -1,lowercase_			:	bool = False,lowercase_			:	Optional[TensorType] = None,)->   Mapping[str, Any]:





        '''simple docstring'''







        A__   				=       compute_effective_axis_dimension(
            lowercase_,fixed_dimension=OnnxConfig.default_fixed_batch,num_token_to_add=0		)

        # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
        A__   				=       tokenizer.num_special_tokens_to_add(lowercase_		)
        A__   				=       compute_effective_axis_dimension(
            lowercase_,fixed_dimension=OnnxConfig.default_fixed_sequence,num_token_to_add=lowercase_		)

        # Generate dummy inputs according to compute batch and sequence
        A__   				=       [' '.join([tokenizer.unk_token]		) * seq_length] * batch_size
        A__   				=       dict(tokenizer(lowercase_,return_tensors=lowercase_		)		)
        return common_inputs


    def 						snake_case__					(				self			:	Union[str, Any],lowercase_			:	PreTrainedTokenizer,lowercase_			:	int = -1,lowercase_			:	int = -1,lowercase_			:	bool = False,lowercase_			:	Optional[TensorType] = None,)->   Mapping[str, Any]:





        '''simple docstring'''







        if self.task in ["default", "seq2seq-lm"]:
            A__   				=       self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                lowercase_,batch_size=lowercase_,seq_length=lowercase_,is_pair=lowercase_,framework=lowercase_		)

        elif self.task == "causal-lm":
            A__   				=       self._generate_dummy_inputs_for_causal_lm(
                lowercase_,batch_size=lowercase_,seq_length=lowercase_,is_pair=lowercase_,framework=lowercase_		)
        else:
            A__   				=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                lowercase_,batch_size=lowercase_,seq_length=lowercase_,is_pair=lowercase_,framework=lowercase_		)

        return common_inputs


    def 						snake_case__					(				self			:	int,lowercase_			:	Tuple,lowercase_			:	int,lowercase_			:	int,lowercase_			:	str		)->   str:





        '''simple docstring'''







        if self.task in ["default", "seq2seq-lm"]:
            A__   				=       super()._flatten_past_key_values_(lowercase_,lowercase_,lowercase_,lowercase_		)
        else:
            A__   				=       super(lowercase_,self		)._flatten_past_key_values_(
                lowercase_,lowercase_,lowercase_,lowercase_		)

