import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser


_lowerCamelCase   :				int     			=						logging.getLogger(__name__)
torch.set_grad_enabled(False)
_lowerCamelCase   :				Optional[int]     			=						'''cuda''' if torch.cuda.is_available() else '''cpu'''





def 		_lowerCAmelCase							(UpperCAmelCase__       :							str ,	UpperCAmelCase__       :							Optional[int]=1_0_0 ,	UpperCAmelCase__       :							str=" "					):
					SCREAMING_SNAKE_CASE           =    text.split(UpperCAmelCase__					)
					return [character.join(text[i : i + n]					).strip() for i in range(0 ,	len(UpperCAmelCase__					) ,	UpperCAmelCase__					)]





def 		_lowerCAmelCase							(UpperCAmelCase__       :							dict					):
					SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    [], []
					for title, text in zip(documents["title"] ,	documents["text"]					):
										if text is not None:
															for passage in split_text(UpperCAmelCase__					):
																				titles.append(title if title is not None else ""					)
																				texts.append(UpperCAmelCase__					)
					return {"title": titles, "text": texts}





def 		_lowerCAmelCase							(UpperCAmelCase__       :							dict ,	UpperCAmelCase__       :							DPRContextEncoder ,	UpperCAmelCase__       :							DPRContextEncoderTokenizerFast					):
					SCREAMING_SNAKE_CASE           =    ctx_tokenizer(
					    documents["title"] ,	documents["text"] ,	truncation=UpperCAmelCase__ ,	padding="longest" ,	return_tensors="pt"					)["input_ids"]
					SCREAMING_SNAKE_CASE           =    ctx_encoder(input_ids.to(device=UpperCAmelCase__					) ,	return_dict=UpperCAmelCase__					).pooler_output
					return {"embeddings": embeddings.detach().cpu().numpy()}





def 		_lowerCAmelCase							(UpperCAmelCase__       :							"RagExampleArguments" ,	UpperCAmelCase__       :							"ProcessingArguments" ,	UpperCAmelCase__       :							"IndexHnswArguments" ,	):
					######################################
					logger.info("Step 1 - Create the dataset"					)
					######################################

					# The dataset needed for RAG must have three columns:
					# - title (string): title of the document
					# - text (string): text of a passage of the document
					# - embeddings (array of dimension d): DPR representation of the passage
					# Let's say you have documents in tab-separated csv files with columns "title" and "text"
					assert os.path.isfile(rag_example_args.csv_path					), "Please provide a valid path to a csv file"

					# You can load a Dataset object this way
					SCREAMING_SNAKE_CASE           =    load_dataset(
					    "csv" ,	data_files=[rag_example_args.csv_path] ,	split="train" ,	delimiter="\t" ,	column_names=["title", "text"]					)

					# More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

					# Then split the documents into passages of 100 words
					SCREAMING_SNAKE_CASE           =    dataset.map(UpperCAmelCase__ ,	batched=UpperCAmelCase__ ,	num_proc=processing_args.num_proc					)

					# And compute the embeddings
					SCREAMING_SNAKE_CASE           =    DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					).to(device=UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					)
					SCREAMING_SNAKE_CASE           =    Features(
					    {"text": Value("string"					), "title": Value("string"					), "embeddings": Sequence(Value("float32"					)					)}					)  # optional, save as float32 instead of float64 to save space
					SCREAMING_SNAKE_CASE           =    dataset.map(
					    partial(UpperCAmelCase__ ,	ctx_encoder=UpperCAmelCase__ ,	ctx_tokenizer=UpperCAmelCase__					) ,	batched=UpperCAmelCase__ ,	batch_size=processing_args.batch_size ,	features=UpperCAmelCase__ ,	)

					# And finally save your dataset
					SCREAMING_SNAKE_CASE           =    os.path.join(rag_example_args.output_dir ,	"my_knowledge_dataset"					)
					dataset.save_to_disk(UpperCAmelCase__					)
					# from datasets import load_from_disk
					# dataset = load_from_disk(passages_path)  # to reload the dataset

					######################################
					logger.info("Step 2 - Index the dataset"					)
					######################################

					# Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
					SCREAMING_SNAKE_CASE           =    faiss.IndexHNSWFlat(index_hnsw_args.d ,	index_hnsw_args.m ,	faiss.METRIC_INNER_PRODUCT					)
					dataset.add_faiss_index("embeddings" ,	custom_index=UpperCAmelCase__					)

					# And save the index
					SCREAMING_SNAKE_CASE           =    os.path.join(rag_example_args.output_dir ,	"my_knowledge_dataset_hnsw_index.faiss"					)
					dataset.get_index("embeddings"					).save(UpperCAmelCase__					)
					# dataset.load_faiss_index("embeddings", index_path)  # to reload the index


@dataclass
class   lowercase      :
							lowercase__  :    str									=							field(
							    default=str(Path(a       ).parent / """test_run""" / """dummy-kb""" / """my_knowledge_dataset.csv"""       )			,    metadata={"""help""": """Path to a tab-separated csv file with columns 'title' and 'text'"""}			,    )
							lowercase__  :    Optional[str]									=							field(
							    default=a			,    metadata={"""help""": """Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."""}			,    )
							lowercase__  :    str									=							field(
							    default="""facebook/rag-sequence-nq"""			,    metadata={"""help""": """The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"""}			,    )
							lowercase__  :    str									=							field(
							    default="""facebook/dpr-ctx_encoder-multiset-base"""			,    metadata={
							        """help""": (
							            """The DPR context encoder model to use. Either 'facebook/dpr-ctx_encoder-single-nq-base' or"""
							            """ 'facebook/dpr-ctx_encoder-multiset-base'"""
							        )
							    }			,    )
							lowercase__  :    Optional[str]									=							field(
							    default=str(Path(a       ).parent / """test_run""" / """dummy-kb"""       )			,    metadata={"""help""": """Path to a directory where the dataset passages and the index will be saved"""}			,    )



@dataclass
class   lowercase      :
							lowercase__  :    Optional[int]									=							field(
							    default=a			,    metadata={
							        """help""": """The number of processes to use to split the documents into passages. Default is single process."""
							    }			,    )
							lowercase__  :    int									=							field(
							    default=16			,    metadata={
							        """help""": """The batch size to use when computing the passages embeddings using the DPR context encoder."""
							    }			,    )



@dataclass
class   lowercase      :
							lowercase__  :    int									=							field(
							    default=768			,    metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""}			,    )
							lowercase__  :    int									=							field(
							    default=128			,    metadata={
							        """help""": (
							            """The number of bi-directional links created for every new element during the HNSW index construction."""
							        )
							    }			,    )


if __name__ == "__main__":
							logging.basicConfig(level=logging.WARNING)
							logger.setLevel(logging.INFO)

							_lowerCamelCase   :				Optional[int]     			=						HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
							_lowerCamelCase   :				str     			=						parser.parse_args_into_dataclasses()
							with TemporaryDirectory() as tmp_dir:
														_lowerCamelCase   :				Optional[Any]     			=						rag_example_args.output_dir or tmp_dir
														main(rag_example_args, processing_args, index_hnsw_args)





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__  :    Dict									=							LEDConfig
							lowercase__  :    List[str]									=							{}
							lowercase__  :    Union[str, Any]									=							"""gelu"""




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



												'''simple docstring'''

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

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




							def 		__snake_case(  self				:   int )   ->			Tuple:



												'''simple docstring'''

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

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

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





							def 		__snake_case(  self				:   Any		,							_UpperCamelCase				:   Optional[int]		,							_UpperCamelCase				:   Tuple )   ->			int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    TFLEDModel(config=_UpperCamelCase ).get_decoder()
												SCREAMING_SNAKE_CASE           =    inputs_dict["input_ids"]

												SCREAMING_SNAKE_CASE           =    input_ids[:1, :]
												SCREAMING_SNAKE_CASE           =    inputs_dict["attention_mask"][:1, :]
												SCREAMING_SNAKE_CASE           =    1

												# first forward pass
												SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase		,							attention_mask=_UpperCamelCase		,							use_cache=_UpperCamelCase )

												SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    outputs.to_tuple()

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

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

												SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase		,							attention_mask=_UpperCamelCase )[0]
												SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase		,							attention_mask=_UpperCamelCase		,							past_key_values=_UpperCamelCase )[0]

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

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

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





def 		__lowerCamelCase							(UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							List[str] ,	UpperCAmelCase__       :							Dict ,	UpperCAmelCase__       :							List[str]=None ,	UpperCAmelCase__       :							Union[str, Any]=None ,	UpperCAmelCase__       :							Optional[int]=None ,	UpperCAmelCase__       :							Union[str, Any]=None ,	):
					if attention_mask is None:
										SCREAMING_SNAKE_CASE           =    tf.cast(tf.math.not_equal(UpperCAmelCase__ ,	config.pad_token_id					) ,	tf.inta					)
					if decoder_attention_mask is None:
										SCREAMING_SNAKE_CASE           =    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:
										SCREAMING_SNAKE_CASE           =    tf.ones((config.encoder_layers, config.encoder_attention_heads)					)
					if decoder_head_mask is None:
										SCREAMING_SNAKE_CASE           =    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__  :    Optional[int]									=							(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
							lowercase__  :    List[Any]									=							(TFLEDForConditionalGeneration,) if is_tf_available() else ()
							lowercase__  :    int									=							(
							    {
							        """conversational""": TFLEDForConditionalGeneration,
							        """feature-extraction""": TFLEDModel,
							        """summarization""": TFLEDForConditionalGeneration,
							        """text2text-generation""": TFLEDForConditionalGeneration,
							        """translation""": TFLEDForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							lowercase__  :    List[Any]									=							True
							lowercase__  :    List[str]									=							False
							lowercase__  :    List[str]									=							False
							lowercase__  :    Union[str, Any]									=							False




							def 		__snake_case(  self				:   Tuple )   ->			Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    TFLEDModelTester(self )
												SCREAMING_SNAKE_CASE           =    ConfigTester(self		,							config_class=_UpperCamelCase )




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



												'''simple docstring'''

												self.config_tester.run_common_tests()




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs_for_common()
												self.model_tester.check_decoder_model_past_large_inputs(*_UpperCamelCase )




							def 		__snake_case(  self				:   Dict )   ->			str:



												'''simple docstring'''

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

												SCREAMING_SNAKE_CASE           =    True
												SCREAMING_SNAKE_CASE           =    self.model_tester.seq_length
												SCREAMING_SNAKE_CASE           =    self.model_tester.encoder_seq_length

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

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

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

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

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




							@unittest.skip("LED keeps using potentially symbolic tensors in conditionals and breaks tracing." )
							def 		__snake_case(  self				:   Optional[Any] )   ->			Tuple:



												'''simple docstring'''

												pass





							def 		__snake_case(  self				:   str )   ->			str:



												'''simple docstring'''

												pass





def 		__lowerCamelCase							(UpperCAmelCase__       :							Optional[int]					):
					return tf.constant(UpperCAmelCase__ ,	dtype=tf.intaa					)


_lowerCamelCase   :				str     			=						1e-4


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




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384" ).led

												# change to intended input here
												SCREAMING_SNAKE_CASE           =    _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
												SCREAMING_SNAKE_CASE           =    _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
												SCREAMING_SNAKE_CASE           =    prepare_led_inputs_dict(model.config		,							_UpperCamelCase		,							_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    model(**_UpperCamelCase )[0]
												SCREAMING_SNAKE_CASE           =    (1, 1_024, 768)
												self.assertEqual(output.shape		,							_UpperCamelCase )
												# change to expected output here
												SCREAMING_SNAKE_CASE           =    tf.convert_to_tensor(
												    [[2.3_0_5_0, 2.8_2_7_9, 0.6_5_3_1], [-1.8_4_5_7, -0.1_4_5_5, -3.5_6_6_1], [-1.0_1_8_6, 0.4_5_8_6, -2.2_0_4_3]]		,							)
												tf.debugging.assert_near(output[:, :3, :3]		,							_UpperCamelCase		,							atol=1e-3 )





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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384" )

												# change to intended input here
												SCREAMING_SNAKE_CASE           =    _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
												SCREAMING_SNAKE_CASE           =    _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
												SCREAMING_SNAKE_CASE           =    prepare_led_inputs_dict(model.config		,							_UpperCamelCase		,							_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    model(**_UpperCamelCase )[0]
												SCREAMING_SNAKE_CASE           =    (1, 1_024, model.config.vocab_size)
												self.assertEqual(output.shape		,							_UpperCamelCase )
												# change to expected output here
												SCREAMING_SNAKE_CASE           =    tf.convert_to_tensor(
												    [[3_3.6_5_0_7, 6.4_5_7_2, 1_6.8_0_8_9], [5.8_7_3_9, -2.4_2_3_8, 1_1.2_9_0_2], [-3.2_1_3_9, -4.3_1_4_9, 4.2_7_8_3]]		,							)
												tf.debugging.assert_near(output[:, :3, :3]		,							_UpperCamelCase		,							atol=1e-3		,							rtol=1e-3 )




