import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


A_      =      logging.get_logger(__name__)


A_      =      {"vocab_file": "vocab.json", "merges_file": "merges.txt"}

# See all BART models at https://huggingface.co/models?filter=bart
A_      =      {
    "vocab_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
    },
    "merges_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
    },
}

A_      =      {
    "facebook/bart-base": 1024,
    "facebook/bart-large": 1024,
    "facebook/bart-large-mnli": 1024,
    "facebook/bart-large-cnn": 1024,
    "facebook/bart-large-xsum": 1024,
    "yjernite/bart_eli5": 1024,
}






@lru_cache()
def        __UpperCAmelCase    (       )->       Optional[int]:



			"""simple docstring"""







			lowercase							=		(
			    list(range(ord('''!'''       ),		ord('''~'''       ) + 1       )       ) + list(range(ord('''¡'''       ),		ord('''¬'''       ) + 1       )       ) + list(range(ord('''®'''       ),		ord('''ÿ'''       ) + 1       )       )
			)
			lowercase							=		bs[:]
			lowercase							=		0
			for b in range(2**8       ):
						if b not in bs:
									bs.append(UpperCAmelCase       )
									cs.append(2**8 + n       )
									n += 1
			lowercase							=		[chr(UpperCAmelCase       ) for n in cs]
			return dict(zip(UpperCAmelCase,		UpperCAmelCase       )       )






def        __UpperCAmelCase    (       UpperCAmelCase       )->       str:



			"""simple docstring"""







			lowercase							=		set()
			lowercase							=		word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)       )
						lowercase							=		char
			return pairs






class      __lowercase					(    _A    ):
						lowercase					 =     VOCAB_FILES_NAMES
						lowercase					 =     PRETRAINED_VOCAB_FILES_MAP
						lowercase					 =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						lowercase					 =     ['input_ids', 'attention_mask']

						def __init__(	self				:      Optional[int]							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Union[str, Any]="replace"							,							__lowerCamelCase				:      Optional[Any]="<s>"							,							__lowerCamelCase				:      int="</s>"							,							__lowerCamelCase				:      int="</s>"							,							__lowerCamelCase				:      Optional[Any]="<s>"							,							__lowerCamelCase				:      Optional[int]="<unk>"							,							__lowerCamelCase				:      List[str]="<pad>"							,							__lowerCamelCase				:      Union[str, Any]="<mask>"							,							__lowerCamelCase				:      str=False							,							**__lowerCamelCase				:      List[str]							,							)							->		Dict:


									'''simple docstring'''

									lowercase							=		AddedToken(__lowerCamelCase							,							lstrip=__lowerCamelCase							,							rstrip=__lowerCamelCase   ) if isinstance(__lowerCamelCase							,							__lowerCamelCase   ) else bos_token
									lowercase							=		AddedToken(__lowerCamelCase							,							lstrip=__lowerCamelCase							,							rstrip=__lowerCamelCase   ) if isinstance(__lowerCamelCase							,							__lowerCamelCase   ) else eos_token
									lowercase							=		AddedToken(__lowerCamelCase							,							lstrip=__lowerCamelCase							,							rstrip=__lowerCamelCase   ) if isinstance(__lowerCamelCase							,							__lowerCamelCase   ) else sep_token
									lowercase							=		AddedToken(__lowerCamelCase							,							lstrip=__lowerCamelCase							,							rstrip=__lowerCamelCase   ) if isinstance(__lowerCamelCase							,							__lowerCamelCase   ) else cls_token
									lowercase							=		AddedToken(__lowerCamelCase							,							lstrip=__lowerCamelCase							,							rstrip=__lowerCamelCase   ) if isinstance(__lowerCamelCase							,							__lowerCamelCase   ) else unk_token
									lowercase							=		AddedToken(__lowerCamelCase							,							lstrip=__lowerCamelCase							,							rstrip=__lowerCamelCase   ) if isinstance(__lowerCamelCase							,							__lowerCamelCase   ) else pad_token

									# Mask token behave like a normal word, i.e. include the space before it
									lowercase							=		AddedToken(__lowerCamelCase							,							lstrip=__lowerCamelCase							,							rstrip=__lowerCamelCase   ) if isinstance(__lowerCamelCase							,							__lowerCamelCase   ) else mask_token

									super().__init__(
									    errors=__lowerCamelCase							,							bos_token=__lowerCamelCase							,							eos_token=__lowerCamelCase							,							unk_token=__lowerCamelCase							,							sep_token=__lowerCamelCase							,							cls_token=__lowerCamelCase							,							pad_token=__lowerCamelCase							,							mask_token=__lowerCamelCase							,							add_prefix_space=__lowerCamelCase							,							**__lowerCamelCase							,							)

									with open(__lowerCamelCase							,							encoding='''utf-8'''   ) as vocab_handle:
												lowercase							=		json.load(__lowerCamelCase   )
									lowercase							=		{v: k for k, v in self.encoder.items()}
									lowercase							=		errors  # how to handle errors in decoding
									lowercase							=		bytes_to_unicode()
									lowercase							=		{v: k for k, v in self.byte_encoder.items()}
									with open(__lowerCamelCase							,							encoding='''utf-8'''   ) as merges_handle:
												lowercase							=		merges_handle.read().split('''\n'''   )[1:-1]
									lowercase							=		[tuple(merge.split()   ) for merge in bpe_merges]
									lowercase							=		dict(zip(__lowerCamelCase							,							range(len(__lowerCamelCase   )   )   )   )
									lowercase							=		{}
									lowercase							=		add_prefix_space

									# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
									lowercase							=		re.compile(r'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''   )

						@property
						def        __a		(	self				:      int   )							->		Any:


									'''simple docstring'''

									return len(self.encoder   )

						def        __a		(	self				:      List[str]   )							->		Any:


									'''simple docstring'''

									return dict(self.encoder							,							**self.added_tokens_encoder   )

						def        __a		(	self				:      List[str]							,							__lowerCamelCase				:      Any   )							->		List[str]:


									'''simple docstring'''

									if token in self.cache:
												return self.cache[token]
									lowercase							=		tuple(__lowerCamelCase   )
									lowercase							=		get_pairs(__lowerCamelCase   )

									if not pairs:
												return token

									while True:
												lowercase							=		min(__lowerCamelCase							,							key=lambda __lowerCamelCase   : self.bpe_ranks.get(__lowerCamelCase							,							float('''inf'''   )   )   )
												if bigram not in self.bpe_ranks:
															break
												lowercase		,lowercase							=		bigram
												lowercase							=		[]
												lowercase							=		0
												while i < len(__lowerCamelCase   ):
															try:
																		lowercase							=		word.index(__lowerCamelCase							,							__lowerCamelCase   )
															except ValueError:
																		new_word.extend(word[i:]   )
																		break
															else:
																		new_word.extend(word[i:j]   )
																		lowercase							=		j

															if word[i] == first and i < len(__lowerCamelCase   ) - 1 and word[i + 1] == second:
																		new_word.append(first + second   )
																		i += 2
															else:
																		new_word.append(word[i]   )
																		i += 1
												lowercase							=		tuple(__lowerCamelCase   )
												lowercase							=		new_word
												if len(__lowerCamelCase   ) == 1:
															break
												else:
															lowercase							=		get_pairs(__lowerCamelCase   )
									lowercase							=		''' '''.join(__lowerCamelCase   )
									lowercase							=		word
									return word

						def        __a		(	self				:      List[str]							,							__lowerCamelCase				:      Union[str, Any]   )							->		List[str]:


									'''simple docstring'''

									lowercase							=		[]
									for token in re.findall(self.pat							,							__lowerCamelCase   ):
												lowercase							=		''''''.join(
												    self.byte_encoder[b] for b in token.encode('''utf-8'''   )   )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
												bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__lowerCamelCase   ).split(''' '''   )   )
									return bpe_tokens

						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      Optional[int]   )							->		List[str]:


									'''simple docstring'''

									return self.encoder.get(__lowerCamelCase							,							self.encoder.get(self.unk_token   )   )

						def        __a		(	self				:      List[str]							,							__lowerCamelCase				:      Union[str, Any]   )							->		Any:


									'''simple docstring'''

									return self.decoder.get(__lowerCamelCase   )

						def        __a		(	self				:      str							,							__lowerCamelCase				:      List[Any]   )							->		Any:


									'''simple docstring'''

									lowercase							=		''''''.join(__lowerCamelCase   )
									lowercase							=		bytearray([self.byte_decoder[c] for c in text]   ).decode('''utf-8'''							,							errors=self.errors   )
									return text

						def        __a		(	self				:      Dict							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Optional[str] = None   )							->		Tuple[str]:


									'''simple docstring'''

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

									with open(__lowerCamelCase							,							'''w'''							,							encoding='''utf-8'''   ) as f:
												f.write(json.dumps(self.encoder							,							indent=2							,							sort_keys=__lowerCamelCase							,							ensure_ascii=__lowerCamelCase   ) + '''\n'''   )

									lowercase							=		0
									with open(__lowerCamelCase							,							'''w'''							,							encoding='''utf-8'''   ) as writer:
												writer.write('''#version: 0.2\n'''   )
												for bpe_tokens, token_index in sorted(self.bpe_ranks.items()							,							key=lambda __lowerCamelCase   : kv[1]   ):
															if index != token_index:
																		logger.warning(
																		    f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
																		    ''' Please check that the tokenizer is not corrupted!'''   )
																		lowercase							=		token_index
															writer.write(''' '''.join(__lowerCamelCase   ) + '''\n'''   )
															index += 1

									return vocab_file, merge_file

						def        __a		(	self				:      List[Any]							,							__lowerCamelCase				:      List[int]							,							__lowerCamelCase				:      Optional[List[int]] = None   )							->		List[int]:


									'''simple docstring'''

									if token_ids_a is None:
												return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									lowercase							=		[self.cls_token_id]
									lowercase							=		[self.sep_token_id]
									return cls + token_ids_a + sep + sep + token_ids_a + sep

						def        __a		(	self				:      str							,							__lowerCamelCase				:      List[int]							,							__lowerCamelCase				:      Optional[List[int]] = None							,							__lowerCamelCase				:      bool = False   )							->		List[int]:


									'''simple docstring'''

									if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=__lowerCamelCase							,							token_ids_a=__lowerCamelCase							,							already_has_special_tokens=__lowerCamelCase   )

									if token_ids_a is None:
												return [1] + ([0] * len(__lowerCamelCase   )) + [1]
									return [1] + ([0] * len(__lowerCamelCase   )) + [1, 1] + ([0] * len(__lowerCamelCase   )) + [1]

						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      List[int]							,							__lowerCamelCase				:      Optional[List[int]] = None   )							->		List[int]:


									'''simple docstring'''

									lowercase							=		[self.sep_token_id]
									lowercase							=		[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 + sep + token_ids_a + sep   ) * [0]



						def        __a		(	self				:      Union[str, Any]							,							__lowerCamelCase				:      List[Any]							,							__lowerCamelCase				:      Optional[Any]=False							,							**__lowerCamelCase				:      List[str]   )							->		int:


									'''simple docstring'''

									lowercase							=		kwargs.pop('''add_prefix_space'''							,							self.add_prefix_space   )
									if (is_split_into_words or add_prefix_space) and (len(__lowerCamelCase   ) > 0 and not text[0].isspace()):
												lowercase							=		''' ''' + text
									return (text, kwargs)






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      __lowercase					:
						lowercase					 =     LEDConfig
						lowercase					 =     {}
						lowercase					 =     'gelu'

						def __init__(	self				:      List[str]							,							__lowerCamelCase				:      Union[str, Any]							,							__lowerCamelCase				:      Dict=13							,							__lowerCamelCase				:      Optional[Any]=7							,							__lowerCamelCase				:      Any=True							,							__lowerCamelCase				:      List[str]=False							,							__lowerCamelCase				:      Any=99							,							__lowerCamelCase				:      Any=32							,							__lowerCamelCase				:      str=2							,							__lowerCamelCase				:      Optional[Any]=4							,							__lowerCamelCase				:      Union[str, Any]=37							,							__lowerCamelCase				:      str=0.1							,							__lowerCamelCase				:      Any=0.1							,							__lowerCamelCase				:      Optional[int]=20							,							__lowerCamelCase				:      Optional[Any]=2							,							__lowerCamelCase				:      Any=1							,							__lowerCamelCase				:      Optional[int]=0							,							__lowerCamelCase				:      Any=4							,							)							->		str:


									'''simple docstring'''

									lowercase							=		parent
									lowercase							=		batch_size
									lowercase							=		seq_length
									lowercase							=		is_training
									lowercase							=		use_labels
									lowercase							=		vocab_size
									lowercase							=		hidden_size
									lowercase							=		num_hidden_layers
									lowercase							=		num_attention_heads
									lowercase							=		intermediate_size

									lowercase							=		hidden_dropout_prob
									lowercase							=		attention_probs_dropout_prob
									lowercase							=		max_position_embeddings
									lowercase							=		eos_token_id
									lowercase							=		pad_token_id
									lowercase							=		bos_token_id
									lowercase							=		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
									lowercase							=		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
									lowercase							=		(
									    self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
									)

						def        __a		(	self				:      Optional[int]   )							->		Tuple:


									'''simple docstring'''

									lowercase							=		ids_tensor([self.batch_size, self.seq_length - 1]							,							self.vocab_size   )
									lowercase							=		tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   )							,							1   )
									lowercase							=		tf.concat([input_ids, eos_tensor]							,							axis=1   )

									lowercase							=		ids_tensor([self.batch_size, self.seq_length]							,							self.vocab_size   )

									lowercase							=		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							,							)
									lowercase							=		prepare_led_inputs_dict(__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase   )
									lowercase							=		tf.concat(
									    [tf.zeros_like(__lowerCamelCase   )[:, :-1], tf.ones_like(__lowerCamelCase   )[:, -1:]]							,							axis=-1							,							)
									lowercase							=		global_attention_mask
									return config, inputs_dict



						def        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      Dict							,							__lowerCamelCase				:      Dict   )							->		Optional[Any]:


									'''simple docstring'''

									lowercase							=		TFLEDModel(config=__lowerCamelCase   ).get_decoder()
									lowercase							=		inputs_dict['''input_ids''']

									lowercase							=		input_ids[:1, :]
									lowercase							=		inputs_dict['''attention_mask'''][:1, :]
									lowercase							=		1

									# first forward pass
									lowercase							=		model(__lowerCamelCase							,							attention_mask=__lowerCamelCase							,							use_cache=__lowerCamelCase   )

									lowercase		,lowercase							=		outputs.to_tuple()

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

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

									lowercase							=		model(__lowerCamelCase							,							attention_mask=__lowerCamelCase   )[0]
									lowercase							=		model(__lowerCamelCase							,							attention_mask=__lowerCamelCase							,							past_key_values=__lowerCamelCase   )[0]

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

									# select random slice
									lowercase							=		int(ids_tensor((1,)							,							output_from_past.shape[-1]   )   )
									lowercase							=		output_from_no_past[:, -3:, random_slice_idx]
									lowercase							=		output_from_past[:, :, random_slice_idx]

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






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase=None,		UpperCAmelCase=None,		UpperCAmelCase=None,		UpperCAmelCase=None,		)->       str:



			"""simple docstring"""







			if attention_mask is None:
						lowercase							=		tf.cast(tf.math.not_equal(UpperCAmelCase,		config.pad_token_id       ),		tf.inta       )
			if decoder_attention_mask is None:
						lowercase							=		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:
						lowercase							=		tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
			if decoder_head_mask is None:
						lowercase							=		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      __lowercase					(    _A       ,     _A       ,     unittest.TestCase    ):
						lowercase					 =     (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
						lowercase					 =     (TFLEDForConditionalGeneration,) if is_tf_available() else ()
						lowercase					 =     (
						    {
						        'conversational': TFLEDForConditionalGeneration,
						        'feature-extraction': TFLEDModel,
						        'summarization': TFLEDForConditionalGeneration,
						        'text2text-generation': TFLEDForConditionalGeneration,
						        'translation': TFLEDForConditionalGeneration,
						    }
						    if is_tf_available()
						    else {}
						)
						lowercase					 =     True
						lowercase					 =     False
						lowercase					 =     False
						lowercase					 =     False

						def        __a		(	self				:      Dict   )							->		int:


									'''simple docstring'''

									lowercase							=		TFLEDModelTester(self   )
									lowercase							=		ConfigTester(self							,							config_class=__lowerCamelCase   )

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


									'''simple docstring'''

									self.config_tester.run_common_tests()

						def        __a		(	self				:      List[str]   )							->		Any:


									'''simple docstring'''

									lowercase							=		self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.check_decoder_model_past_large_inputs(*__lowerCamelCase   )

						def        __a		(	self				:      List[Any]   )							->		Tuple:


									'''simple docstring'''

									lowercase		,lowercase							=		self.model_tester.prepare_config_and_inputs_for_common()
									lowercase							=		tf.zeros_like(inputs_dict['''attention_mask''']   )
									lowercase							=		2
									lowercase							=		tf.where(
									    tf.range(self.model_tester.seq_length   )[None, :] < num_global_attn_indices							,							1							,							inputs_dict['''global_attention_mask''']							,							)

									lowercase							=		True
									lowercase							=		self.model_tester.seq_length
									lowercase							=		self.model_tester.encoder_seq_length

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

												if self.is_encoder_decoder:
															lowercase							=		model_class(__lowerCamelCase   )
															lowercase							=		model(self._prepare_for_class(__lowerCamelCase							,							__lowerCamelCase   )   )
															self.assertEqual(config.output_hidden_states							,							__lowerCamelCase   )
															check_decoder_attentions_output(__lowerCamelCase   )

												# Check that output attentions can also be changed via the config
												del inputs_dict["output_attentions"]
												lowercase							=		True
												lowercase							=		model_class(__lowerCamelCase   )
												lowercase							=		model(self._prepare_for_class(__lowerCamelCase							,							__lowerCamelCase   )   )
												self.assertEqual(config.output_hidden_states							,							__lowerCamelCase   )
												check_encoder_attentions_output(__lowerCamelCase   )

												# Check attention is always last and order is fine
												lowercase							=		True
												lowercase							=		True
												lowercase							=		model_class(__lowerCamelCase   )
												lowercase							=		model(self._prepare_for_class(__lowerCamelCase							,							__lowerCamelCase   )   )

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

						@unittest.skip('''LED keeps using potentially symbolic tensors in conditionals and breaks tracing.'''   )
						def        __a		(	self				:      Dict   )							->		str:


									'''simple docstring'''

									pass



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


									'''simple docstring'''

									pass






def        __UpperCAmelCase    (       UpperCAmelCase       )->       Optional[Any]:



			"""simple docstring"""







			return tf.constant(UpperCAmelCase,		dtype=tf.intaa       )


A_      =      1e-4






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

						def        __a		(	self				:      List[Any]   )							->		str:


									'''simple docstring'''

									lowercase							=		TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''   ).led

									# change to intended input here
									lowercase							=		_long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]   )
									lowercase							=		_long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]   )
									lowercase							=		prepare_led_inputs_dict(model.config							,							__lowerCamelCase							,							__lowerCamelCase   )
									lowercase							=		model(**__lowerCamelCase   )[0]
									lowercase							=		(1, 10_24, 7_68)
									self.assertEqual(output.shape							,							__lowerCamelCase   )
									# change to expected output here
									lowercase							=		tf.convert_to_tensor(
									    [[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]]							,							)
									tf.debugging.assert_near(output[:, :3, :3]							,							__lowerCamelCase							,							atol=1E-3   )



						def        __a		(	self				:      str   )							->		Optional[Any]:


									'''simple docstring'''

									lowercase							=		TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''   )

									# change to intended input here
									lowercase							=		_long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]   )
									lowercase							=		_long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]   )
									lowercase							=		prepare_led_inputs_dict(model.config							,							__lowerCamelCase							,							__lowerCamelCase   )
									lowercase							=		model(**__lowerCamelCase   )[0]
									lowercase							=		(1, 10_24, model.config.vocab_size)
									self.assertEqual(output.shape							,							__lowerCamelCase   )
									# change to expected output here
									lowercase							=		tf.convert_to_tensor(
									    [[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]]							,							)
									tf.debugging.assert_near(output[:, :3, :3]							,							__lowerCamelCase							,							atol=1E-3							,							rtol=1E-3   )





