
'''simple docstring'''


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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__lowercase				: Any											=  logging.get_logger(__name__)

__lowercase				: List[Any]											=  '''▁'''

__lowercase				: Tuple											=  {'''vocab_file''': '''sentencepiece.bpe.model'''}

__lowercase				: str											=  {
    '''vocab_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
    }
}

__lowercase				: Union[str, Any]											=  {
    '''facebook/xglm-564M''': 2048,
}

class 						__lowercase					(	snake_case__     ):
			lowerCamelCase      :		str						   =      VOCAB_FILES_NAMES
			lowerCamelCase      :		str						   =      PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase      :		Tuple						   =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase      :		Dict						   =      ["""input_ids""", """attention_mask"""]

			def __init__(self      , A      , A="<s>"      , A="</s>"      , A="</s>"      , A="<s>"      , A="<unk>"      , A="<pad>"      , A = None      , **A      , ):
								lowerCamelCase_       :   Tuple  =		{} if sp_model_kwargs is None else sp_model_kwargs

								# Compatibility with the original tokenizer
								lowerCamelCase_       :   List[str]  =		7
								lowerCamelCase_       :   int  =		[F"""<madeupword{i}>""" for i in range(self.num_madeup_words   )]

								lowerCamelCase_       :   Optional[int]  =		kwargs.get('''additional_special_tokens'''      , []   )
								kwargs["additional_special_tokens"] += [
								    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
								]

								super().__init__(
								    bos_token=__lowercase      , eos_token=__lowercase      , unk_token=__lowercase      , sep_token=__lowercase      , cls_token=__lowercase      , pad_token=__lowercase      , sp_model_kwargs=self.sp_model_kwargs      , **__lowercase      , )

								lowerCamelCase_       :   Union[str, Any]  =		spm.SentencePieceProcessor(**self.sp_model_kwargs   )
								self.sp_model.Load(str(__lowercase   )   )
								lowerCamelCase_       :   Union[str, Any]  =		vocab_file

								# Original fairseq vocab and spm vocab must be "aligned":
								# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
								# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
								# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
								# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

								# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
								lowerCamelCase_       :   int  =		1

								# Mimic fairseq token-to-id alignment for the first 4 token
								lowerCamelCase_       :   List[str]  =		{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

								lowerCamelCase_       :   int  =		len(self.sp_model   )
								lowerCamelCase_       :   Dict  =		{F"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words   )}
								self.fairseq_tokens_to_ids.update(__lowercase   )

								lowerCamelCase_       :   Optional[Any]  =		{v: k for k, v in self.fairseq_tokens_to_ids.items()}

			def __getstate__(self   ):
								lowerCamelCase_       :   Optional[int]  =		self.__dict__.copy()
								lowerCamelCase_       :   List[str]  =		None
								lowerCamelCase_       :   List[str]  =		self.sp_model.serialized_model_proto()
								return state

			def __setstate__(self      , A   ):
								lowerCamelCase_       :   Union[str, Any]  =		d

								# for backward compatibility
								if not hasattr(self      , '''sp_model_kwargs'''   ):
													lowerCamelCase_       :   List[Any]  =		{}

								lowerCamelCase_       :   Optional[int]  =		spm.SentencePieceProcessor(**self.sp_model_kwargs   )
								self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )

			def 							UpperCAmelCase__			(self      , A      , A = None   ):

								if token_ids_a is None:
													return [self.sep_token_id] + token_ids_a
								lowerCamelCase_       :   int  =		[self.sep_token_id]
								return sep + token_ids_a + sep + sep + token_ids_a

			def 							UpperCAmelCase__			(self      , A      , A = None      , A = False   ):

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

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

			def 							UpperCAmelCase__			(self      , A      , A = None   ):
								lowerCamelCase_       :   List[str]  =		[self.sep_token_id]

								if token_ids_a is None:
													return len(sep + token_ids_a   ) * [0]
								return len(sep + token_ids_a + sep + sep + token_ids_a   ) * [0]

			@property
			def 							UpperCAmelCase__			(self   ):
								return len(self.sp_model   ) + self.fairseq_offset + self.num_madeup_words

			def 							UpperCAmelCase__			(self   ):
								lowerCamelCase_       :   List[Any]  =		{self.convert_ids_to_tokens(__lowercase   ): i for i in range(self.vocab_size   )}
								vocab.update(self.added_tokens_encoder   )
								return vocab

			def 							UpperCAmelCase__			(self      , A   ):
								return self.sp_model.encode(__lowercase      , out_type=__lowercase   )

			def 							UpperCAmelCase__			(self      , A   ):
								if token in self.fairseq_tokens_to_ids:
													return self.fairseq_tokens_to_ids[token]
								lowerCamelCase_       :   Optional[Any]  =		self.sp_model.PieceToId(__lowercase   )

								# Need to return unknown token if the SP model returned 0
								return spm_id + self.fairseq_offset if spm_id else self.unk_token_id

			def 							UpperCAmelCase__			(self      , A   ):
								if index in self.fairseq_ids_to_tokens:
													return self.fairseq_ids_to_tokens[index]
								return self.sp_model.IdToPiece(index - self.fairseq_offset   )

			def 							UpperCAmelCase__			(self      , A   ):
								lowerCamelCase_       :   Tuple  =		''''''.join(__lowercase   ).replace(__lowercase      , ''' '''   ).strip()
								return out_string






			def 							UpperCAmelCase__			(self      , A      , A = None   ):
								if not os.path.isdir(__lowercase   ):
													logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
													return
								lowerCamelCase_       :   Optional[Any]  =		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   ) and os.path.isfile(self.vocab_file   ):
													copyfile(self.vocab_file      , __lowercase   )
								elif not os.path.isfile(self.vocab_file   ):
													with open(__lowercase      , '''wb'''   ) as fi:
																		lowerCamelCase_       :   Optional[Any]  =		self.sp_model.serialized_model_proto()
																		fi.write(__lowercase   )

								return (out_vocab_file,)









"""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"""
				__snake_case :							int									=		LEDConfig
				__snake_case :							int									=		{}
				__snake_case :							Any									=		"""gelu"""




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

											__lowerCamelCase   :   Optional[Any]       =hidden_dropout_prob
											__lowerCamelCase   :   Optional[Any]       =attention_probs_dropout_prob
											__lowerCamelCase   :   Optional[Any]       =max_position_embeddings
											__lowerCamelCase   :   Optional[Any]       =eos_token_id
											__lowerCamelCase   :   str       =pad_token_id
											__lowerCamelCase   :   Any       =bos_token_id
											__lowerCamelCase   :   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
											__lowerCamelCase   :   List[Any]       =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
											__lowerCamelCase   :   Optional[Any]       =(
											    self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
											)




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

											__lowerCamelCase   :   Optional[int]       =ids_tensor([self.batch_size, self.seq_length]    ,     self.vocab_size      )

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




				def        __lowercase	(  self	:Union[str, Any]    ,     __lowercase	:List[str]    ,     __lowercase	:List[str]      ):
											__lowerCamelCase   :   int       =TFLEDModel(config=__lowercase      ).get_decoder()
											__lowerCamelCase   :   Dict       =inputs_dict['''input_ids''']

											__lowerCamelCase   :   Optional[Any]       =input_ids[:1, :]
											__lowerCamelCase   :   List[Any]       =inputs_dict['''attention_mask'''][:1, :]
											__lowerCamelCase   :   List[Any]       =1

											# first forward pass
											__lowerCamelCase   :   List[Any]       =model(__lowercase    ,     attention_mask=__lowercase    ,     use_cache=__lowercase      )

											__lowerCamelCase     ,    __lowerCamelCase   :   Any       =outputs.to_tuple()

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

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

											__lowerCamelCase   :   str       =model(__lowercase    ,     attention_mask=__lowercase      )[0]
											__lowerCamelCase   :   List[str]       =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
											__lowerCamelCase   :   List[str]       =int(ids_tensor((1,)    ,     output_from_past.shape[-1]      )      )
											__lowerCamelCase   :   Dict       =output_from_no_past[:, -3:, random_slice_idx]
											__lowerCamelCase   :   int       =output_from_past[:, :, random_slice_idx]

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



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				str     ,    SCREAMING_SNAKE_CASE							:				Any     ,    SCREAMING_SNAKE_CASE							:				List[Any]     ,    SCREAMING_SNAKE_CASE							:				Optional[Any]=None     ,    SCREAMING_SNAKE_CASE							:				Union[str, Any]=None     ,    SCREAMING_SNAKE_CASE							:				Union[str, Any]=None     ,    SCREAMING_SNAKE_CASE							:				Tuple=None     ,    ):


							'''simple docstring'''







							if attention_mask is None:
														__lowerCamelCase   :   Optional[int]       =tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE     ,    config.pad_token_id )     ,    tf.inta )
							if decoder_attention_mask is None:
														__lowerCamelCase   :   str       =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:
														__lowerCamelCase   :   Dict       =tf.ones((config.encoder_layers, config.encoder_attention_heads) )
							if decoder_head_mask is None:
														__lowerCamelCase   :   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_				(     snake_case__							,	snake_case__							,	unittest.TestCase ):
				"""simple docstring"""
				__snake_case :							str									=		(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
				__snake_case :							Tuple									=		(TFLEDForConditionalGeneration,) if is_tf_available() else ()
				__snake_case :							Optional[Any]									=		(
				    {
				        """conversational""": TFLEDForConditionalGeneration,
				        """feature-extraction""": TFLEDModel,
				        """summarization""": TFLEDForConditionalGeneration,
				        """text2text-generation""": TFLEDForConditionalGeneration,
				        """translation""": TFLEDForConditionalGeneration,
				    }
				    if is_tf_available()
				    else {}
				)
				__snake_case :							int									=		True
				__snake_case :							int									=		False
				__snake_case :							Optional[int]									=		False
				__snake_case :							Optional[int]									=		False




				def        __lowercase	(  self	:Optional[int]      ):
											__lowerCamelCase   :   int       =TFLEDModelTester(self      )
											__lowerCamelCase   :   Any       =ConfigTester(self    ,     config_class=__lowercase      )




				def        __lowercase	(  self	:Dict      ):
											self.config_tester.run_common_tests()




				def        __lowercase	(  self	:str      ):
											__lowerCamelCase   :   Any       =self.model_tester.prepare_config_and_inputs_for_common()
											self.model_tester.check_decoder_model_past_large_inputs(*__lowercase      )




				def        __lowercase	(  self	:Optional[Any]      ):
											__lowerCamelCase     ,    __lowerCamelCase   :   List[str]       =self.model_tester.prepare_config_and_inputs_for_common()
											__lowerCamelCase   :   int       =tf.zeros_like(inputs_dict['''attention_mask''']      )
											__lowerCamelCase   :   List[Any]       =2
											__lowerCamelCase   :   Optional[Any]       =tf.where(
											    tf.range(self.model_tester.seq_length      )[None, :] < num_global_attn_indices    ,     1    ,     inputs_dict['''global_attention_mask''']    ,     )

											__lowerCamelCase   :   Tuple       =True
											__lowerCamelCase   :   Optional[Any]       =self.model_tester.seq_length
											__lowerCamelCase   :   List[str]       =self.model_tester.encoder_seq_length

											def check_decoder_attentions_output(__lowercase	:Any      ):
																		__lowerCamelCase   :   Union[str, Any]       =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	:List[str]      ):
																		__lowerCamelCase   :   Any       =[t.numpy() for t in outputs.encoder_attentions]
																		__lowerCamelCase   :   Optional[int]       =[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:
																		__lowerCamelCase   :   List[str]       =True
																		__lowerCamelCase   :   int       =False
																		__lowerCamelCase   :   Optional[Any]       =False
																		__lowerCamelCase   :   str       =model_class(__lowercase      )
																		__lowerCamelCase   :   Any       =model(self._prepare_for_class(__lowercase    ,     __lowercase      )      )
																		__lowerCamelCase   :   int       =len(__lowercase      )
																		self.assertEqual(config.output_hidden_states    ,     __lowercase      )
																		check_encoder_attentions_output(__lowercase      )

																		if self.is_encoder_decoder:
																									__lowerCamelCase   :   Optional[int]       =model_class(__lowercase      )
																									__lowerCamelCase   :   Union[str, 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"]
																		__lowerCamelCase   :   Any       =True
																		__lowerCamelCase   :   Union[str, Any]       =model_class(__lowercase      )
																		__lowerCamelCase   :   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
																		__lowerCamelCase   :   Optional[Any]       =True
																		__lowerCamelCase   :   Optional[int]       =True
																		__lowerCamelCase   :   Dict       =model_class(__lowercase      )
																		__lowerCamelCase   :   Union[str, Any]       =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        __lowercase	(  self	:int      ):
											pass




				def        __lowercase	(  self	:int      ):
											# TODO: Head-masking not yet implement
											pass



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Union[str, Any] ):


							'''simple docstring'''







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


_UpperCamelCase					      =   1e-4
@slow
@require_tf
class 		SCREAMING_SNAKE_CASE_				(     unittest.TestCase ):
				"""simple docstring"""




				def        __lowercase	(  self	:List[str]      ):
											__lowerCamelCase   :   Dict       =TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''      ).led

											# change to intended input here
											__lowerCamelCase   :   List[str]       =_long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]      )
											__lowerCamelCase   :   Dict       =_long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]      )
											__lowerCamelCase   :   Optional[int]       =prepare_led_inputs_dict(model.config    ,     __lowercase    ,     __lowercase      )
											__lowerCamelCase   :   Dict       =model(**__lowercase      )[0]
											__lowerCamelCase   :   Optional[int]       =(1, 1024, 768)
											self.assertEqual(output.shape    ,     __lowercase      )
											# change to expected output here
											__lowerCamelCase   :   Dict       =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]    ,     __lowercase    ,     atol=1e-3      )




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

											# change to intended input here
											__lowerCamelCase   :   Union[str, Any]       =_long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]      )
											__lowerCamelCase   :   Any       =_long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]      )
											__lowerCamelCase   :   Tuple       =prepare_led_inputs_dict(model.config    ,     __lowercase    ,     __lowercase      )
											__lowerCamelCase   :   List[Any]       =model(**__lowercase      )[0]
											__lowerCamelCase   :   str       =(1, 1024, model.config.vocab_size)
											self.assertEqual(output.shape    ,     __lowercase      )
											# change to expected output here
											__lowerCamelCase   :   List[str]       =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]    ,     __lowercase    ,     atol=1e-3    ,     rtol=1e-3      )




