



"""simple docstring"""

from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import TFLEDForConditionalGeneration, TFLEDModel




@require_tf
class        _UpperCAmelCase       :
 _lowerCAmelCase   :			Tuple							=		LEDConfig
 _lowerCAmelCase   :			List[Any]							=		{}
 _lowerCAmelCase   :			Tuple							=		"""gelu"""

 def __init__(     self		:       Any  ,     lowercase_		:       Optional[int]  ,     lowercase_		:       Tuple=13  ,     lowercase_		:       Union[str, Any]=7  ,     lowercase_		:       str=True  ,     lowercase_		:       Tuple=False  ,     lowercase_		:       List[str]=99  ,     lowercase_		:       str=32  ,     lowercase_		:       Any=2  ,     lowercase_		:       str=4  ,     lowercase_		:       List[str]=37  ,     lowercase_		:       List[str]=0.1  ,     lowercase_		:       List[str]=0.1  ,     lowercase_		:       Dict=20  ,     lowercase_		:       Any=2  ,     lowercase_		:       int=1  ,     lowercase_		:       Union[str, Any]=0  ,     lowercase_		:       Optional[Any]=4  ,     ):
     snake_case_ :     List[str]				=	parent
     snake_case_ :     Union[str, Any]				=	batch_size
     snake_case_ :     Dict				=	seq_length
     snake_case_ :     Optional[Any]				=	is_training
     snake_case_ :     List[Any]				=	use_labels
     snake_case_ :     Any				=	vocab_size
     snake_case_ :     str				=	hidden_size
     snake_case_ :     Union[str, Any]				=	num_hidden_layers
     snake_case_ :     List[Any]				=	num_attention_heads
     snake_case_ :     List[str]				=	intermediate_size

     snake_case_ :     Union[str, Any]				=	hidden_dropout_prob
     snake_case_ :     int				=	attention_probs_dropout_prob
     snake_case_ :     Union[str, Any]				=	max_position_embeddings
     snake_case_ :     List[Any]				=	eos_token_id
     snake_case_ :     Any				=	pad_token_id
     snake_case_ :     Tuple				=	bos_token_id
     snake_case_ :     Union[str, Any]				=	attention_window

     # `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
     # [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
     # returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
     # because its local attention only attends to `self.attention_window` and one before and one after
     snake_case_ :     Optional[int]				=	self.attention_window + 2

     # because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
     # the `test_attention_outputs` and `test_hidden_states_output` tests
     snake_case_ :     int				=	(
         self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
     )

 def 						_snake_case					(     self		:       int				):
     snake_case_ :     Any				=	ids_tensor([self.batch_size, self.seq_length - 1]  ,     self.vocab_size				)
     snake_case_ :     List[str]				=	tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size				)  ,     1				)
     snake_case_ :     Dict				=	tf.concat([input_ids, eos_tensor]  ,     axis=1				)

     snake_case_ :     Dict				=	ids_tensor([self.batch_size, self.seq_length]  ,     self.vocab_size				)

     snake_case_ :     str				=	self.config_cls(
         vocab_size=self.vocab_size  ,     d_model=self.hidden_size  ,     encoder_layers=self.num_hidden_layers  ,     decoder_layers=self.num_hidden_layers  ,     encoder_attention_heads=self.num_attention_heads  ,     decoder_attention_heads=self.num_attention_heads  ,     encoder_ffn_dim=self.intermediate_size  ,     decoder_ffn_dim=self.intermediate_size  ,     dropout=self.hidden_dropout_prob  ,     attention_dropout=self.attention_probs_dropout_prob  ,     max_position_embeddings=self.max_position_embeddings  ,     eos_token_ids=[2]  ,     bos_token_id=self.bos_token_id  ,     pad_token_id=self.pad_token_id  ,     decoder_start_token_id=self.pad_token_id  ,     attention_window=self.attention_window  ,     **self.config_updates  ,     )
     snake_case_ :     int				=	prepare_led_inputs_dict(lowercase_  ,     lowercase_  ,     lowercase_				)
     snake_case_ :     Optional[int]				=	tf.concat(
         [tf.zeros_like(lowercase_				)[:, :-1], tf.ones_like(lowercase_				)[:, -1:]]  ,     axis=-1  ,     )
     snake_case_ :     List[str]				=	global_attention_mask
     return config, inputs_dict






 def 						_snake_case					(     self		:       str  ,     lowercase_		:       Optional[int]  ,     lowercase_		:       int				):
     snake_case_ :     List[str]				=	TFLEDModel(config=lowercase_				).get_decoder()
     snake_case_ :     str				=	inputs_dict['''input_ids''']

     snake_case_ :     Optional[int]				=	input_ids[:1, :]
     snake_case_ :     List[str]				=	inputs_dict['''attention_mask'''][:1, :]
     snake_case_ :     List[Any]				=	1

     # first forward pass
     snake_case_ :     str				=	model(lowercase_  ,     attention_mask=lowercase_  ,     use_cache=lowercase_				)

     snake_case_,	snake_case_ :     Any				=	outputs.to_tuple()

     # create hypothetical next token and extent to next_input_ids
     snake_case_ :     Union[str, Any]				=	ids_tensor((self.batch_size, 3)  ,     config.vocab_size				)
     snake_case_ :     Union[str, Any]				=	tf.cast(ids_tensor((self.batch_size, 3)  ,     2				)  ,     tf.inta				)

     # append to next input_ids and
     snake_case_ :     int				=	tf.concat([input_ids, next_tokens]  ,     axis=-1				)
     snake_case_ :     Any				=	tf.concat([attention_mask, next_attn_mask]  ,     axis=-1				)

     snake_case_ :     int				=	model(lowercase_  ,     attention_mask=lowercase_				)[0]
     snake_case_ :     Any				=	model(lowercase_  ,     attention_mask=lowercase_  ,     past_key_values=lowercase_				)[0]

     self.parent.assertEqual(next_tokens.shape[1]  ,     output_from_past.shape[1]				)

     # select random slice
     snake_case_ :     Any				=	int(ids_tensor((1,)  ,     output_from_past.shape[-1]				)				)
     snake_case_ :     Union[str, Any]				=	output_from_no_past[:, -3:, random_slice_idx]
     snake_case_ :     List[str]				=	output_from_past[:, :, random_slice_idx]

     # test that outputs are equal for slice
     tf.debugging.assert_near(lowercase_  ,     lowercase_  ,     rtol=1E-3				)





def __lowercase			(							_a		,			_a		,			_a		,			_a=None		,			_a=None		,			_a=None		,			_a=None		,			):
    if attention_mask is None:
        snake_case_ :     Dict				=	tf.cast(tf.math.not_equal(_a		,			config.pad_token_id    )		,			tf.inta    )
    if decoder_attention_mask is None:
        snake_case_ :     Optional[int]				=	tf.concat(
            [
                tf.ones(decoder_input_ids[:, :1].shape		,			dtype=tf.inta    ),
                tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]		,			config.pad_token_id    )		,			tf.inta    ),
            ]		,			axis=-1		,			)
    if head_mask is None:
        snake_case_ :     str				=	tf.ones((config.encoder_layers, config.encoder_attention_heads)    )
    if decoder_head_mask is None:
        snake_case_ :     Optional[int]				=	tf.ones((config.decoder_layers, config.decoder_attention_heads)    )
    return {
        "input_ids": input_ids,
        "attention_mask": attention_mask,
        "decoder_input_ids": decoder_input_ids,
        "decoder_attention_mask": decoder_attention_mask,
        "head_mask": head_mask,
        "decoder_head_mask": decoder_head_mask,
    }




@require_tf
class        _UpperCAmelCase       (			lowerCAmelCase__    ,     lowerCAmelCase__    ,     unittest.TestCase):
 _lowerCAmelCase   :			Optional[Any]							=		(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
 _lowerCAmelCase   :			Union[str, Any]							=		(TFLEDForConditionalGeneration,) if is_tf_available() else ()
 _lowerCAmelCase   :			Optional[int]							=		(
     {
         """conversational""": TFLEDForConditionalGeneration,
         """feature-extraction""": TFLEDModel,
         """summarization""": TFLEDForConditionalGeneration,
         """text2text-generation""": TFLEDForConditionalGeneration,
         """translation""": TFLEDForConditionalGeneration,
     }
     if is_tf_available()
     else {}
 )
 _lowerCAmelCase   :			Any							=		True
 _lowerCAmelCase   :			Tuple							=		False
 _lowerCAmelCase   :			Optional[Any]							=		False
 _lowerCAmelCase   :			Optional[int]							=		False

 def 						_snake_case					(     self		:       Optional[int]				):
     snake_case_ :     Union[str, Any]				=	TFLEDModelTester(self				)
     snake_case_ :     Tuple				=	ConfigTester(self  ,     config_class=lowercase_				)

 def 						_snake_case					(     self		:       Tuple				):
     self.config_tester.run_common_tests()

 def 						_snake_case					(     self		:       int				):
     snake_case_ :     str				=	self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.check_decoder_model_past_large_inputs(*lowercase_				)

 def 						_snake_case					(     self		:       List[Any]				):
     snake_case_,	snake_case_ :     Dict				=	self.model_tester.prepare_config_and_inputs_for_common()
     snake_case_ :     Union[str, Any]				=	tf.zeros_like(inputs_dict['''attention_mask''']				)
     snake_case_ :     Dict				=	2
     snake_case_ :     List[Any]				=	tf.where(
         tf.range(self.model_tester.seq_length				)[None, :] < num_global_attn_indices  ,     1  ,     inputs_dict['''global_attention_mask''']  ,     )

     snake_case_ :     Tuple				=	True
     snake_case_ :     List[Any]				=	self.model_tester.seq_length
     snake_case_ :     int				=	self.model_tester.encoder_seq_length

     def check_decoder_attentions_output(lowercase_		:       List[str]				):
         snake_case_ :     str				=	outputs.decoder_attentions
         self.assertEqual(len(lowercase_				)  ,     self.model_tester.num_hidden_layers				)
         self.assertListEqual(
             list(decoder_attentions[0].shape[-3:]				)  ,     [self.model_tester.num_attention_heads, seq_length, seq_length]  ,     )

     def check_encoder_attentions_output(lowercase_		:       Dict				):
         snake_case_ :     Any				=	[t.numpy() for t in outputs.encoder_attentions]
         snake_case_ :     Any				=	[t.numpy() for t in outputs.encoder_global_attentions]
         self.assertEqual(len(lowercase_				)  ,     self.model_tester.num_hidden_layers				)
         self.assertEqual(len(lowercase_				)  ,     self.model_tester.num_hidden_layers				)
         self.assertListEqual(
             list(attentions[0].shape[-3:]				)  ,     [self.model_tester.num_attention_heads, seq_length, seq_length]  ,     )
         self.assertListEqual(
             list(global_attentions[0].shape[-3:]				)  ,     [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]  ,     )

     for model_class in self.all_model_classes:
         snake_case_ :     Optional[Any]				=	True
         snake_case_ :     Tuple				=	False
         snake_case_ :     int				=	False
         snake_case_ :     Optional[int]				=	model_class(lowercase_				)
         snake_case_ :     str				=	model(self._prepare_for_class(lowercase_  ,     lowercase_				)				)
         snake_case_ :     Any				=	len(lowercase_				)
         self.assertEqual(config.output_hidden_states  ,     lowercase_				)
         check_encoder_attentions_output(lowercase_				)

         if self.is_encoder_decoder:
             snake_case_ :     str				=	model_class(lowercase_				)
             snake_case_ :     Any				=	model(self._prepare_for_class(lowercase_  ,     lowercase_				)				)
             self.assertEqual(config.output_hidden_states  ,     lowercase_				)
             check_decoder_attentions_output(lowercase_				)

         # Check that output attentions can also be changed via the config
         del inputs_dict["output_attentions"]
         snake_case_ :     List[Any]				=	True
         snake_case_ :     Any				=	model_class(lowercase_				)
         snake_case_ :     Tuple				=	model(self._prepare_for_class(lowercase_  ,     lowercase_				)				)
         self.assertEqual(config.output_hidden_states  ,     lowercase_				)
         check_encoder_attentions_output(lowercase_				)

         # Check attention is always last and order is fine
         snake_case_ :     Optional[Any]				=	True
         snake_case_ :     List[str]				=	True
         snake_case_ :     int				=	model_class(lowercase_				)
         snake_case_ :     str				=	model(self._prepare_for_class(lowercase_  ,     lowercase_				)				)

         self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)  ,     len(lowercase_				)				)
         self.assertEqual(model.config.output_hidden_states  ,     lowercase_				)
         check_encoder_attentions_output(lowercase_				)

 @unittest.skip('''LED keeps using potentially symbolic tensors in conditionals and breaks tracing.'''				)
 def 						_snake_case					(     self		:       List[str]				):
     pass






 def 						_snake_case					(     self		:       Union[str, Any]				):
     # TODO: Head-masking not yet implement
     pass





def __lowercase			(							_a    ):
    return tf.constant(_a		,			dtype=tf.intaa    )


lowercase__    :  Optional[int]							    =	1e-4






@slow
@require_tf
class        _UpperCAmelCase       (			unittest.TestCase):

 def 						_snake_case					(     self		:       Any				):
     snake_case_ :     Tuple				=	TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''				).led

     # change to intended input here
     snake_case_ :     str				=	_long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]				)
     snake_case_ :     str				=	_long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]				)
     snake_case_ :     Optional[Any]				=	prepare_led_inputs_dict(model.config  ,     lowercase_  ,     lowercase_				)
     snake_case_ :     Optional[int]				=	model(**lowercase_				)[0]
     snake_case_ :     Optional[Any]				=	(1, 1024, 768)
     self.assertEqual(output.shape  ,     lowercase_				)
     # change to expected output here
     snake_case_ :     Optional[Any]				=	tf.convert_to_tensor(
         [[2.30_50, 2.82_79, 0.65_31], [-1.84_57, -0.14_55, -3.56_61], [-1.01_86, 0.45_86, -2.20_43]]  ,     )
     tf.debugging.assert_near(output[:, :3, :3]  ,     lowercase_  ,     atol=1E-3				)






 def 						_snake_case					(     self		:       Optional[int]				):
     snake_case_ :     int				=	TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''				)

     # change to intended input here
     snake_case_ :     Any				=	_long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]				)
     snake_case_ :     Optional[int]				=	_long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]				)
     snake_case_ :     Optional[int]				=	prepare_led_inputs_dict(model.config  ,     lowercase_  ,     lowercase_				)
     snake_case_ :     Tuple				=	model(**lowercase_				)[0]
     snake_case_ :     Optional[int]				=	(1, 1024, model.config.vocab_size)
     self.assertEqual(output.shape  ,     lowercase_				)
     # change to expected output here
     snake_case_ :     Any				=	tf.convert_to_tensor(
         [[33.65_07, 6.45_72, 16.80_89], [5.87_39, -2.42_38, 11.29_02], [-3.21_39, -4.31_49, 4.27_83]]  ,     )
     tf.debugging.assert_near(output[:, :3, :3]  ,     lowercase_  ,     atol=1E-3  ,     rtol=1E-3				)










"""simple docstring"""

import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
     from .tokenization_pegasus import PegasusTokenizer
else:
     lowercase__    :  int							    =	None


lowercase__    :  Any							    =	logging.get_logger(__name__)


lowercase__    :  List[str]							    =	'''▁'''

lowercase__    :  Optional[int]							    =	{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowercase__    :  str							    =	{
    '''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''},
    '''tokenizer_file''': {
        '''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'''
    },
}

lowercase__    :  List[Any]							    =	{
    '''google/pegasus-xsum''': 5_12,
}




class        _UpperCAmelCase       (			lowerCAmelCase__):
 _lowerCAmelCase   :			List[str]							=		VOCAB_FILES_NAMES
 _lowerCAmelCase   :			List[str]							=		PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase   :			Any							=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase   :			Tuple							=		PegasusTokenizer
 _lowerCAmelCase   :			str							=		["""input_ids""", """attention_mask"""]

 def __init__(     self		:       Any  ,     lowercase_		:       Optional[Any]=None  ,     lowercase_		:       int=None  ,     lowercase_		:       Tuple="<pad>"  ,     lowercase_		:       int="</s>"  ,     lowercase_		:       Tuple="<unk>"  ,     lowercase_		:       str="<mask_2>"  ,     lowercase_		:       Optional[Any]="<mask_1>"  ,     lowercase_		:       str=None  ,     lowercase_		:       List[str]=103  ,     **lowercase_		:       List[Any]  ,     ):
     snake_case_ :     Dict				=	offset

     if additional_special_tokens is not None:
         if not isinstance(lowercase_  ,     lowercase_				):
             raise TypeError(
                 f"additional_special_tokens should be of type {type(lowercase_				)}, but is"
                 f" {type(lowercase_				)}"				)

         snake_case_ :     str				=	(
             ([mask_token_sent] + additional_special_tokens)
             if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
             else additional_special_tokens
         )
         # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
         additional_special_tokens_extended += [
             f"<unk_{i}>" for i in range(len(lowercase_				)  ,     self.offset - 1				)
         ]

         if len(set(lowercase_				)				) != len(lowercase_				):
             raise ValueError(
                 '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
                 f" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."				)
         snake_case_ :     Union[str, Any]				=	additional_special_tokens_extended
     else:
         snake_case_ :     Dict				=	[mask_token_sent] if mask_token_sent is not None else []
         additional_special_tokens += [f"<unk_{i}>" for i in range(2  ,     self.offset				)]

     super().__init__(
         lowercase_  ,     tokenizer_file=lowercase_  ,     pad_token=lowercase_  ,     eos_token=lowercase_  ,     unk_token=lowercase_  ,     mask_token=lowercase_  ,     mask_token_sent=lowercase_  ,     offset=lowercase_  ,     additional_special_tokens=lowercase_  ,     **lowercase_  ,     )
     snake_case_ :     List[Any]				=	vocab_file
     snake_case_ :     List[Any]				=	False if not self.vocab_file else True

 def 						_snake_case					(     self		:       str  ,     lowercase_		:       Union[str, Any]				):
     snake_case_ :     Any				=	set(self.all_special_ids				)  # call it once instead of inside list comp
     all_special_ids.remove(self.unk_token_id				)  # <unk> is only sometimes special

     if all_special_ids != set(range(len(self.additional_special_tokens				) + 3				)				):
         raise ValueError(
             '''There should be 3 special tokens: mask_token, pad_token, and eos_token +'''
             f" {len(self.additional_special_tokens				)} additional_special_tokens, but got {all_special_ids}"				)

     return [1 if x in all_special_ids else 0 for x in seq]

 def 						_snake_case					(     self		:       int  ,     lowercase_		:       List  ,     lowercase_		:       Optional[List] = None  ,     lowercase_		:       bool = False				):
     if already_has_special_tokens:
         return self._special_token_mask(lowercase_				)
     elif token_ids_a is None:
         return self._special_token_mask(lowercase_				) + [1]
     else:
         return self._special_token_mask(token_ids_a + token_ids_a				) + [1]

 def 						_snake_case					(     self		:       List[Any]  ,     lowercase_		:       Optional[int]  ,     lowercase_		:       str=None				):
     if token_ids_a is None:
         return token_ids_a + [self.eos_token_id]
     # We don't expect to process pairs, but leave the pair logic for API consistency
     return token_ids_a + token_ids_a + [self.eos_token_id]






 def 						_snake_case					(     self		:       Optional[Any]  ,     lowercase_		:       str  ,     lowercase_		:       Optional[str] = None				):
     if not self.can_save_slow_tokenizer:
         raise ValueError(
             '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
             '''tokenizer.'''				)

     if not os.path.isdir(lowercase_				):
         logger.error(f"Vocabulary path ({save_directory}) should be a directory"				)
         return
     snake_case_ :     Dict				=	os.path.join(
         lowercase_  ,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)

     if os.path.abspath(self.vocab_file				) != os.path.abspath(lowercase_				):
         copyfile(self.vocab_file  ,     lowercase_				)

     return (out_vocab_file,)





