



'''simple docstring'''






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer


UpperCamelCase__:     List[str]										=    logging.get_logger(__name__)

UpperCamelCase__:     Tuple										=    {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

UpperCamelCase__:     Dict										=    {
    "vocab_file": {
        "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt",
        "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt",
        "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/vocab.txt",
        "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/vocab.txt",
        "bert-base-multilingual-uncased": (
            "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt"
        ),
        "bert-base-multilingual-cased": "https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt",
        "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt",
        "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt",
        "bert-large-uncased-whole-word-masking": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt"
        ),
        "bert-large-cased-whole-word-masking": (
            "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt"
        ),
        "bert-large-uncased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"
        ),
        "bert-large-cased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"
        ),
        "bert-base-cased-finetuned-mrpc": (
            "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt"
        ),
        "bert-base-german-dbmdz-cased": "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt",
        "bert-base-german-dbmdz-uncased": (
            "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt"
        ),
        "TurkuNLP/bert-base-finnish-cased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt"
        ),
        "TurkuNLP/bert-base-finnish-uncased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt"
        ),
        "wietsedv/bert-base-dutch-cased": (
            "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json",
        "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json",
        "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json",
        "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json",
        "bert-base-multilingual-uncased": (
            "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json"
        ),
        "bert-base-multilingual-cased": (
            "https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json"
        ),
        "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json",
        "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json",
        "bert-large-uncased-whole-word-masking": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json"
        ),
        "bert-large-cased-whole-word-masking": (
            "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json"
        ),
        "bert-large-uncased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"
        ),
        "bert-large-cased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"
        ),
        "bert-base-cased-finetuned-mrpc": (
            "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json"
        ),
        "bert-base-german-dbmdz-cased": (
            "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json"
        ),
        "bert-base-german-dbmdz-uncased": (
            "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json"
        ),
        "TurkuNLP/bert-base-finnish-cased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json"
        ),
        "TurkuNLP/bert-base-finnish-uncased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json"
        ),
        "wietsedv/bert-base-dutch-cased": (
            "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json"
        ),
    },
}

UpperCamelCase__:     List[Any]										=    {
    "bert-base-uncased": 512,
    "bert-large-uncased": 512,
    "bert-base-cased": 512,
    "bert-large-cased": 512,
    "bert-base-multilingual-uncased": 512,
    "bert-base-multilingual-cased": 512,
    "bert-base-chinese": 512,
    "bert-base-german-cased": 512,
    "bert-large-uncased-whole-word-masking": 512,
    "bert-large-cased-whole-word-masking": 512,
    "bert-large-uncased-whole-word-masking-finetuned-squad": 512,
    "bert-large-cased-whole-word-masking-finetuned-squad": 512,
    "bert-base-cased-finetuned-mrpc": 512,
    "bert-base-german-dbmdz-cased": 512,
    "bert-base-german-dbmdz-uncased": 512,
    "TurkuNLP/bert-base-finnish-cased-v1": 512,
    "TurkuNLP/bert-base-finnish-uncased-v1": 512,
    "wietsedv/bert-base-dutch-cased": 512,
}

UpperCamelCase__:     int										=    {
    "bert-base-uncased": {"do_lower_case": True},
    "bert-large-uncased": {"do_lower_case": True},
    "bert-base-cased": {"do_lower_case": False},
    "bert-large-cased": {"do_lower_case": False},
    "bert-base-multilingual-uncased": {"do_lower_case": True},
    "bert-base-multilingual-cased": {"do_lower_case": False},
    "bert-base-chinese": {"do_lower_case": False},
    "bert-base-german-cased": {"do_lower_case": False},
    "bert-large-uncased-whole-word-masking": {"do_lower_case": True},
    "bert-large-cased-whole-word-masking": {"do_lower_case": False},
    "bert-large-uncased-whole-word-masking-finetuned-squad": {"do_lower_case": True},
    "bert-large-cased-whole-word-masking-finetuned-squad": {"do_lower_case": False},
    "bert-base-cased-finetuned-mrpc": {"do_lower_case": False},
    "bert-base-german-dbmdz-cased": {"do_lower_case": False},
    "bert-base-german-dbmdz-uncased": {"do_lower_case": True},
    "TurkuNLP/bert-base-finnish-cased-v1": {"do_lower_case": False},
    "TurkuNLP/bert-base-finnish-uncased-v1": {"do_lower_case": True},
    "wietsedv/bert-base-dutch-cased": {"do_lower_case": False},
}

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	lowerCamelCase__    				= VOCAB_FILES_NAMES
	lowerCamelCase__    				= PRETRAINED_VOCAB_FILES_MAP
	lowerCamelCase__    				= PRETRAINED_INIT_CONFIGURATION
	lowerCamelCase__    				= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCamelCase__    				= BertTokenizer







	def __init__(		self    :  Any	,    __snake_case    :  Tuple=None	,    __snake_case    :  int=None	,    __snake_case    :  Optional[Any]=True	,    __snake_case    :  Optional[Any]="[UNK]"	,    __snake_case    :  Tuple="[SEP]"	,    __snake_case    :  Union[str, Any]="[PAD]"	,    __snake_case    :  str="[CLS]"	,    __snake_case    :  Dict="[MASK]"	,    __snake_case    :  str=True	,    __snake_case    :  Tuple=None	,    **__snake_case    :  Optional[Any]	,    )    ->	str:
								super().__init__(
								    __snake_case	,    tokenizer_file=__snake_case	,    do_lower_case=__snake_case	,    unk_token=__snake_case	,    sep_token=__snake_case	,    pad_token=__snake_case	,    cls_token=__snake_case	,    mask_token=__snake_case	,    tokenize_chinese_chars=__snake_case	,    strip_accents=__snake_case	,    **__snake_case	,    )

								UpperCAmelCase				: int											=	json.loads(self.backend_tokenizer.normalizer.__getstate__()	)
								if (
								    normalizer_state.get('''lowercase'''	,    __snake_case	) != do_lower_case
								    or normalizer_state.get('''strip_accents'''	,    __snake_case	) != strip_accents
								    or normalizer_state.get('''handle_chinese_chars'''	,    __snake_case	) != tokenize_chinese_chars
								):
															UpperCAmelCase				: Optional[Any]											=	getattr(__snake_case	,    normalizer_state.pop('''type'''	)	)
															UpperCAmelCase				: Union[str, Any]											=	do_lower_case
															UpperCAmelCase				: str											=	strip_accents
															UpperCAmelCase				: List[Any]											=	tokenize_chinese_chars
															UpperCAmelCase				: Dict											=	normalizer_class(**__snake_case	)

								UpperCAmelCase				: str											=	do_lower_case







	def   A			(		self    :  List[str]	,    __snake_case    :  List[Any]	,    __snake_case    :  Optional[Any]=None	)    ->	int:
								UpperCAmelCase				: int											=	[self.cls_token_id] + token_ids_a + [self.sep_token_id]

								if token_ids_a:
															output += token_ids_a + [self.sep_token_id]

								return output







	def   A			(		self    :  str	,    __snake_case    :  List[int]	,    __snake_case    :  Optional[List[int]] = None	)    ->	List[int]:
								UpperCAmelCase				: Dict											=	[self.sep_token_id]
								UpperCAmelCase				: Optional[Any]											=	[self.cls_token_id]
								if token_ids_a is None:
															return len(cls + token_ids_a + sep	) * [0]
								return len(cls + token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1]







	def   A			(		self    :  int	,    __snake_case    :  str	,    __snake_case    :  Optional[str] = None	)    ->	Tuple[str]:
								UpperCAmelCase				: Union[str, Any]											=	self._tokenizer.model.save(__snake_case	,    name=__snake_case	)
								return tuple(__snake_case	)










'''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        SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	lowerCamelCase__    				= LEDConfig
	lowerCamelCase__    				= {}
	lowerCamelCase__    				= """gelu"""







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

								UpperCAmelCase				: Any											=	hidden_dropout_prob
								UpperCAmelCase				: int											=	attention_probs_dropout_prob
								UpperCAmelCase				: Union[str, Any]											=	max_position_embeddings
								UpperCAmelCase				: List[Any]											=	eos_token_id
								UpperCAmelCase				: Dict											=	pad_token_id
								UpperCAmelCase				: List[str]											=	bos_token_id
								UpperCAmelCase				: Tuple											=	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
								UpperCAmelCase				: 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
								UpperCAmelCase				: Union[str, Any]											=	(
								    self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
								)







	def   A			(		self    :  int	)    ->	Optional[int]:
								UpperCAmelCase				: Dict											=	ids_tensor([self.batch_size, self.seq_length - 1]	,    self.vocab_size	)
								UpperCAmelCase				: int											=	tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size	)	,    1	)
								UpperCAmelCase				: Any											=	tf.concat([input_ids, eos_tensor]	,    axis=1	)

								UpperCAmelCase				: Tuple											=	ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size	)

								UpperCAmelCase				: int											=	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	,    )
								UpperCAmelCase				: Optional[Any]											=	prepare_led_inputs_dict(__snake_case	,    __snake_case	,    __snake_case	)
								UpperCAmelCase				: Any											=	tf.concat(
								    [tf.zeros_like(__snake_case	)[:, :-1], tf.ones_like(__snake_case	)[:, -1:]]	,    axis=-1	,    )
								UpperCAmelCase				: Any											=	global_attention_mask
								return config, inputs_dict







	def   A			(		self    :  Optional[int]	,    __snake_case    :  Union[str, Any]	,    __snake_case    :  List[Any]	)    ->	Any:
								UpperCAmelCase				: Optional[int]											=	TFLEDModel(config=__snake_case	).get_decoder()
								UpperCAmelCase				: List[str]											=	inputs_dict['''input_ids''']

								UpperCAmelCase				: Any											=	input_ids[:1, :]
								UpperCAmelCase				: Tuple											=	inputs_dict['''attention_mask'''][:1, :]
								UpperCAmelCase				: str											=	1

								# first forward pass
								UpperCAmelCase				: Dict											=	model(__snake_case	,    attention_mask=__snake_case	,    use_cache=__snake_case	)

								UpperCAmelCase							,	UpperCAmelCase				: str											=	outputs.to_tuple()

								# create hypothetical next token and extent to next_input_ids
								UpperCAmelCase				: Dict											=	ids_tensor((self.batch_size, 3)	,    config.vocab_size	)
								UpperCAmelCase				: Any											=	tf.cast(ids_tensor((self.batch_size, 3)	,    2	)	,    tf.inta	)

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

								UpperCAmelCase				: Optional[Any]											=	model(__snake_case	,    attention_mask=__snake_case	)[0]
								UpperCAmelCase				: List[str]											=	model(__snake_case	,    attention_mask=__snake_case	,    past_key_values=__snake_case	)[0]

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

								# select random slice
								UpperCAmelCase				: Dict											=	int(ids_tensor((1,)	,    output_from_past.shape[-1]	)	)
								UpperCAmelCase				: Optional[Any]											=	output_from_no_past[:, -3:, random_slice_idx]
								UpperCAmelCase				: Tuple											=	output_from_past[:, :, random_slice_idx]

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


def 				snake_case_  (      _lowerCAmelCase						:		Optional[Any]     ,   _lowerCAmelCase						:		Optional[int]     ,   _lowerCAmelCase						:		Optional[int]     ,   _lowerCAmelCase						:		List[Any]=None     ,   _lowerCAmelCase						:		List[str]=None     ,   _lowerCAmelCase						:		Tuple=None     ,   _lowerCAmelCase						:		Dict=None     ,   )   ->     Optional[int]:
							if attention_mask is None:
														UpperCAmelCase				: Dict											=	tf.cast(tf.math.not_equal(_lowerCAmelCase     ,   config.pad_token_id						)     ,   tf.inta						)
							if decoder_attention_mask is None:
														UpperCAmelCase				: 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:
														UpperCAmelCase				: Tuple											=	tf.ones((config.encoder_layers, config.encoder_attention_heads)						)
							if decoder_head_mask is None:
														UpperCAmelCase				: List[str]											=	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        SCREAMING_SNAKE_CASE(							A__							,	A__							,	unittest.TestCase    ):







	"""simple docstring"""






	lowerCamelCase__    				= (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
	lowerCamelCase__    				= (TFLEDForConditionalGeneration,) if is_tf_available() else ()
	lowerCamelCase__    				= (
	    {
	        """conversational""": TFLEDForConditionalGeneration,
	        """feature-extraction""": TFLEDModel,
	        """summarization""": TFLEDForConditionalGeneration,
	        """text2text-generation""": TFLEDForConditionalGeneration,
	        """translation""": TFLEDForConditionalGeneration,
	    }
	    if is_tf_available()
	    else {}
	)
	lowerCamelCase__    				= True
	lowerCamelCase__    				= False
	lowerCamelCase__    				= False
	lowerCamelCase__    				= False







	def   A			(		self    :  str	)    ->	Tuple:
								UpperCAmelCase				: Optional[Any]											=	TFLEDModelTester(self	)
								UpperCAmelCase				: Tuple											=	ConfigTester(self	,    config_class=__snake_case	)







	def   A			(		self    :  Any	)    ->	Tuple:
								self.config_tester.run_common_tests()







	def   A			(		self    :  int	)    ->	List[str]:
								UpperCAmelCase				: Tuple											=	self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*__snake_case	)







	def   A			(		self    :  Any	)    ->	List[Any]:
								UpperCAmelCase							,	UpperCAmelCase				: Any											=	self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase				: Optional[Any]											=	tf.zeros_like(inputs_dict['''attention_mask''']	)
								UpperCAmelCase				: Optional[int]											=	2
								UpperCAmelCase				: Dict											=	tf.where(
								    tf.range(self.model_tester.seq_length	)[None, :] < num_global_attn_indices	,    1	,    inputs_dict['''global_attention_mask''']	,    )

								UpperCAmelCase				: Optional[int]											=	True
								UpperCAmelCase				: str											=	self.model_tester.seq_length
								UpperCAmelCase				: Optional[Any]											=	self.model_tester.encoder_seq_length

								def check_decoder_attentions_output(__snake_case    :  List[Any]	):
															UpperCAmelCase				: List[str]											=	outputs.decoder_attentions
															self.assertEqual(len(__snake_case	)	,    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(__snake_case    :  int	):
															UpperCAmelCase				: List[Any]											=	[t.numpy() for t in outputs.encoder_attentions]
															UpperCAmelCase				: str											=	[t.numpy() for t in outputs.encoder_global_attentions]
															self.assertEqual(len(__snake_case	)	,    self.model_tester.num_hidden_layers	)
															self.assertEqual(len(__snake_case	)	,    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:
															UpperCAmelCase				: Tuple											=	True
															UpperCAmelCase				: Optional[int]											=	False
															UpperCAmelCase				: int											=	False
															UpperCAmelCase				: Tuple											=	model_class(__snake_case	)
															UpperCAmelCase				: Tuple											=	model(self._prepare_for_class(__snake_case	,    __snake_case	)	)
															UpperCAmelCase				: Optional[int]											=	len(__snake_case	)
															self.assertEqual(config.output_hidden_states	,    __snake_case	)
															check_encoder_attentions_output(__snake_case	)

															if self.is_encoder_decoder:
																						UpperCAmelCase				: Optional[Any]											=	model_class(__snake_case	)
																						UpperCAmelCase				: Optional[int]											=	model(self._prepare_for_class(__snake_case	,    __snake_case	)	)
																						self.assertEqual(config.output_hidden_states	,    __snake_case	)
																						check_decoder_attentions_output(__snake_case	)

															# Check that output attentions can also be changed via the config
															del inputs_dict["output_attentions"]
															UpperCAmelCase				: Optional[int]											=	True
															UpperCAmelCase				: Union[str, Any]											=	model_class(__snake_case	)
															UpperCAmelCase				: Optional[Any]											=	model(self._prepare_for_class(__snake_case	,    __snake_case	)	)
															self.assertEqual(config.output_hidden_states	,    __snake_case	)
															check_encoder_attentions_output(__snake_case	)

															# Check attention is always last and order is fine
															UpperCAmelCase				: Any											=	True
															UpperCAmelCase				: Any											=	True
															UpperCAmelCase				: List[str]											=	model_class(__snake_case	)
															UpperCAmelCase				: Dict											=	model(self._prepare_for_class(__snake_case	,    __snake_case	)	)

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







	@unittest.skip('''LED keeps using potentially symbolic tensors in conditionals and breaks tracing.'''	)
	def   A			(		self    :  Any	)    ->	Union[str, Any]:
								pass







	def   A			(		self    :  str	)    ->	Any:
								# TODO: Head-masking not yet implement
								pass


def 				snake_case_  (      _lowerCAmelCase						:		str						)   ->     Tuple:
							return tf.constant(_lowerCAmelCase     ,   dtype=tf.intaa						)


UpperCamelCase__:     Tuple										=    1E-4




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







	"""simple docstring"""






	def   A			(		self    :  Any	)    ->	Optional[int]:
								UpperCAmelCase				: Optional[Any]											=	TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''	).led

								# change to intended input here
								UpperCAmelCase				: Optional[int]											=	_long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]	)
								UpperCAmelCase				: Dict											=	_long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]	)
								UpperCAmelCase				: Dict											=	prepare_led_inputs_dict(model.config	,    __snake_case	,    __snake_case	)
								UpperCAmelCase				: Optional[Any]											=	model(**__snake_case	)[0]
								UpperCAmelCase				: Optional[int]											=	(1, 1024, 768)
								self.assertEqual(output.shape	,    __snake_case	)
								# change to expected output here
								UpperCAmelCase				: List[str]											=	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]	,    __snake_case	,    atol=1E-3	)







	def   A			(		self    :  Dict	)    ->	Union[str, Any]:
								UpperCAmelCase				: Dict											=	TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''	)

								# change to intended input here
								UpperCAmelCase				: Optional[int]											=	_long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]	)
								UpperCAmelCase				: Optional[Any]											=	_long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]	)
								UpperCAmelCase				: Optional[int]											=	prepare_led_inputs_dict(model.config	,    __snake_case	,    __snake_case	)
								UpperCAmelCase				: Any											=	model(**__snake_case	)[0]
								UpperCAmelCase				: Union[str, Any]											=	(1, 1024, model.config.vocab_size)
								self.assertEqual(output.shape	,    __snake_case	)
								# change to expected output here
								UpperCAmelCase				: Dict											=	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]	,    __snake_case	,    atol=1E-3	,    rtol=1E-3	)





