






"""simple docstring"""

import argparse
import json
import os
import re

import torch

from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging


logging.set_verbosity_info()

lowercase    			=       [
    '''word_embeddings_layernorm.weight''',
    '''word_embeddings_layernorm.bias''',
    '''input_layernorm.weight''',
    '''input_layernorm.bias''',
    '''post_attention_layernorm.weight''',
    '''post_attention_layernorm.bias''',
    '''self_attention.dense.bias''',
    '''mlp.dense_4h_to_h.bias''',
    '''ln_f.weight''',
    '''ln_f.bias''',
]

lowercase    			=       [
    '''mlp.dense_4h_to_h.weight''',
    '''self_attention.dense.weight''',
]






def _lowerCAmelCase	(  __lowerCamelCase:Any ,						__lowerCamelCase:List[str]   ):

						'''simple docstring'''






						__magic_name__            =     {
						    "word_embeddings.weight": "word_embeddings.weight",
						    "word_embeddings.norm.weight": "word_embeddings_layernorm.weight",
						    "word_embeddings.norm.bias": "word_embeddings_layernorm.bias",
						    "weight": "ln_f.weight",
						    "bias": "ln_f.bias",
						}

						if key in layer_rename_map:
												return layer_rename_map[key]

						# Handle transformer blocks
						__magic_name__            =     int(re.match(r".*layer_(\d*).*" ,						__lowerCamelCase   )[1]   )
						layer_number -= 3
						return f'''h.{layer_number}.''' + key






def _lowerCAmelCase	(  __lowerCamelCase:List[str]   ):

						'''simple docstring'''






						if dtype == torch.bool:
												return 1 / 8
						__magic_name__            =     re.search(r"[^\d](\d+)$" ,						str(__lowerCamelCase   )   )
						if bit_search is None:
												raise ValueError(f'''`dtype` is not a valid dtype: {dtype}.'''   )
						__magic_name__            =     int(bit_search.groups()[0]   )
						return bit_size // 8






def _lowerCAmelCase	(  __lowerCamelCase:Union[str, Any] ,						__lowerCamelCase:Optional[int] ,						__lowerCamelCase:Optional[Any] ,						__lowerCamelCase:Optional[Any] ,						__lowerCamelCase:Union[str, Any]   ):

						'''simple docstring'''






						if bloom_config_file == "":
												__magic_name__            =     BloomConfig()
						else:
												__magic_name__            =     BloomConfig.from_json_file(__lowerCamelCase   )

						if shard_model:
												__magic_name__            =     os.listdir(__lowerCamelCase   )
												__magic_name__            =     sorted(filter(lambda __lowerCamelCase   : s.startswith("layer"   ) and "model_00" in s ,						__lowerCamelCase   )   )

												__magic_name__            =     {"weight_map": {}, "metadata": {}}
												__magic_name__            =     0

												__magic_name__            =     None

												__magic_name__            =     BloomConfig()

												for j, file in enumerate(__lowerCamelCase   ):
																		print("Processing file: {}".format(__lowerCamelCase   )   )
																		__magic_name__            =     None

																		for i in range(__lowerCamelCase   ):
																								# load all TP files
																								__magic_name__            =     file.replace("model_00" ,						f'''model_0{i}'''   )
																								__magic_name__            =     torch.load(os.path.join(__lowerCamelCase ,						__lowerCamelCase   ) ,						map_location="cpu"   )

																								# Rename keys in the transformers names
																								__magic_name__            =     list(temp.keys()   )
																								for key in keys:
																														__magic_name__            =     temp.pop(__lowerCamelCase   )

																								if tensors is None:
																														__magic_name__            =     temp
																								else:
																														for key in tensors.keys():
																																				if any(key.endswith(__lowerCamelCase   ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH   ):
																																										# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
																																										tensors[key] += temp[key]
																																				else:
																																										# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
																																										__magic_name__            =     1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN   ) else 0
																																										# We concatenate these weights accross TP ranks
																																										__magic_name__            =     torch.cat([tensors[key], temp[key]] ,						dim=__lowerCamelCase   )

            # Divide by the number of TP the weights we want to average
																		for key in tensors.keys():
																								if any(key.endswith(__lowerCamelCase   ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH   ):
																														__magic_name__            =     tensors[key] / pretraining_tp
																		torch.save(
																		    __lowerCamelCase ,						os.path.join(
																		        __lowerCamelCase ,						"pytorch_model_{}-of-{}.bin".format(str(j + 1   ).zfill(5   ) ,						str(len(__lowerCamelCase   )   ).zfill(5   )   ) ,						) ,						)

																		for key in tensors.keys():
																								__magic_name__            =     tensors[key]
																								total_size += value.numel() * get_dtype_size(value.dtype   )
																								if key not in index_dict["weight_map"]:
																														__magic_name__            =     "pytorch_model_{}-of-{}.bin".format(
																														    str(j + 1   ).zfill(5   ) ,						str(len(__lowerCamelCase   )   ).zfill(5   )   )

												__magic_name__            =     BloomConfig()
												__magic_name__            =     pytorch_dump_folder_path + "/" + CONFIG_NAME
												__magic_name__            =     total_size
												with open(__lowerCamelCase ,						"w" ,						encoding="utf-8"   ) as f:
																		f.write(config.to_json_string()   )
												with open(os.path.join(__lowerCamelCase ,						WEIGHTS_NAME + ".index.json"   ) ,						"w" ,						encoding="utf-8"   ) as f:
																		__magic_name__            =     json.dumps(__lowerCamelCase ,						indent=2 ,						sort_keys=__lowerCamelCase   ) + "\n"
																		f.write(__lowerCamelCase   )
						else:
												__magic_name__            =     BloomModel(__lowerCamelCase   )

												__magic_name__            =     os.listdir(__lowerCamelCase   )
												__magic_name__            =     sorted(filter(lambda __lowerCamelCase   : s.startswith("layer"   ) and "model_00" in s ,						__lowerCamelCase   )   )

												__magic_name__            =     None
												for i, file in enumerate(__lowerCamelCase   ):
																		__magic_name__            =     None
																		for i in range(__lowerCamelCase   ):
																								# load all TP files
																								__magic_name__            =     file.replace("model_00" ,						f'''model_0{i}'''   )
																								__magic_name__            =     torch.load(os.path.join(__lowerCamelCase ,						__lowerCamelCase   ) ,						map_location="cpu"   )

																								# Rename keys in the transformers names
																								__magic_name__            =     list(temp.keys()   )
																								for key in keys:
																														__magic_name__            =     temp.pop(__lowerCamelCase   )

																								if tensors is None:
																														__magic_name__            =     temp
																								else:
																														for key in tensors.keys():
																																				# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
																																				if any(key.endswith(__lowerCamelCase   ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH   ):
																																										tensors[key] += temp[key]
																																				else:
																																										# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
																																										__magic_name__            =     1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN   ) else 0
																																										# We concatenate these weights accross TP ranks
																																										__magic_name__            =     torch.cat([tensors[key], temp[key]] ,						dim=__lowerCamelCase   )

            # Divide by the number of TP the weights we want to average
																		for key in tensors.keys():
																								if any(key.endswith(__lowerCamelCase   ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH   ):
																														__magic_name__            =     tensors[key] / pretraining_tp

																		__magic_name__            =     model.load_state_dict(__lowerCamelCase ,						strict=__lowerCamelCase   )
																		assert not other_keys.unexpected_keys, f'''The keys {other_keys.unexpected_keys} are unexpected'''
																		if missing_keys is None:
																								__magic_name__            =     set(other_keys.missing_keys   )
																		else:
																								__magic_name__            =     missing_keys.intersection(set(other_keys.missing_keys   )   )

												assert not missing_keys, f'''The keys {missing_keys} are missing'''

												# Save pytorch-model
												os.makedirs(__lowerCamelCase ,						exist_ok=__lowerCamelCase   )
												__magic_name__            =     pytorch_dump_folder_path + "/" + WEIGHTS_NAME
												__magic_name__            =     pytorch_dump_folder_path + "/" + CONFIG_NAME
												print(f'''Save PyTorch model to {pytorch_weights_dump_path} with dtype {config.torch_dtype}'''   )
												if config.torch_dtype is not None:
																		__magic_name__            =     model.to(config.torch_dtype   )
												torch.save(model.state_dict() ,						__lowerCamelCase   )
												print(f'''Save configuration file to {pytorch_config_dump_path}'''   )
												with open(__lowerCamelCase ,						"w" ,						encoding="utf-8"   ) as f:
																		f.write(config.to_json_string()   )


if __name__ == "__main__":
			lowercase    			=       argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--bloom_checkpoint_path''',
			    default=None,
			    type=str,
			    required=True,
			    help='''Path to the Megatron-LM checkpoint path.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			parser.add_argument(
			    '''--bloom_config_file''',
			    default='''''',
			    type=str,
			    help=(
			        '''An optional config json file corresponding to the pre-trained model. \n'''
			        '''This specifies the model architecture.'''
			    ),
			)
			parser.add_argument(
			    '''--shard_model''',
			    action='''store_true''',
			    help='''An optional setting to shard the output model \nThis enables sharding the converted checkpoint''',
			)
			parser.add_argument(
			    '''--pretraining_tp''',
			    default=4,
			    type=int,
			    help='''Pretraining TP rank that has been used when training the model in Megatron-LM \n''',
			)
			lowercase    			=       parser.parse_args()
			convert_bloom_checkpoint_to_pytorch(
			    args.bloom_checkpoint_path,
			    args.bloom_config_file,
			    args.pytorch_dump_folder_path,
			    args.shard_model,
			    args.pretraining_tp,
			)











"""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 						A_							:
				UpperCAmelCase__        =				LEDConfig
				UpperCAmelCase__        =				{}
				UpperCAmelCase__        =				'''gelu'''







				def __init__(							self							:						Optional[Any]						,    __lowerCamelCase							:						Dict						,    __lowerCamelCase							:						List[Any]=1_3						,    __lowerCamelCase							:						str=7						,    __lowerCamelCase							:						Any=True						,    __lowerCamelCase							:						int=False						,    __lowerCamelCase							:						List[Any]=9_9						,    __lowerCamelCase							:						Any=3_2						,    __lowerCamelCase							:						Optional[Any]=2						,    __lowerCamelCase							:						Dict=4						,    __lowerCamelCase							:						int=3_7						,    __lowerCamelCase							:						Tuple=0.1						,    __lowerCamelCase							:						Union[str, Any]=0.1						,    __lowerCamelCase							:						Optional[Any]=2_0						,    __lowerCamelCase							:						List[str]=2						,    __lowerCamelCase							:						str=1						,    __lowerCamelCase							:						List[Any]=0						,    __lowerCamelCase							:						Dict=4						,    )				->					Optional[Any]:
										__magic_name__            =     parent
										__magic_name__            =     batch_size
										__magic_name__            =     seq_length
										__magic_name__            =     is_training
										__magic_name__            =     use_labels
										__magic_name__            =     vocab_size
										__magic_name__            =     hidden_size
										__magic_name__            =     num_hidden_layers
										__magic_name__            =     num_attention_heads
										__magic_name__            =     intermediate_size

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







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

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

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







				def     _snake_case					(							self							:						List[str]						,    __lowerCamelCase							:						List[str]						,    __lowerCamelCase							:						Any	)				->					Union[str, Any]:
										__magic_name__            =     TFLEDModel(config=__lowerCamelCase	).get_decoder()
										__magic_name__            =     inputs_dict["input_ids"]

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

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

										__magic_name__      ,    __magic_name__            =     outputs.to_tuple()

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

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

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

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






def _lowerCAmelCase	(  __lowerCamelCase:str ,						__lowerCamelCase:str ,						__lowerCamelCase:List[Any] ,						__lowerCamelCase:Any=None ,						__lowerCamelCase:Dict=None ,						__lowerCamelCase:List[Any]=None ,						__lowerCamelCase:Union[str, Any]=None ,						):

						'''simple docstring'''






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







				def     _snake_case					(							self							:						int	)				->					Optional[int]:
										__magic_name__            =     TFLEDModelTester(self	)
										__magic_name__            =     ConfigTester(self						,    config_class=__lowerCamelCase	)







				def     _snake_case					(							self							:						Optional[Any]	)				->					Dict:
										self.config_tester.run_common_tests()







				def     _snake_case					(							self							:						Optional[int]	)				->					List[Any]:
										__magic_name__            =     self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.check_decoder_model_past_large_inputs(*__lowerCamelCase	)







				def     _snake_case					(							self							:						List[str]	)				->					str:
										__magic_name__      ,    __magic_name__            =     self.model_tester.prepare_config_and_inputs_for_common()
										__magic_name__            =     tf.zeros_like(inputs_dict["attention_mask"]	)
										__magic_name__            =     2
										__magic_name__            =     tf.where(
										    tf.range(self.model_tester.seq_length	)[None, :] < num_global_attn_indices						,    1						,    inputs_dict["global_attention_mask"]						,    )

										__magic_name__            =     True
										__magic_name__            =     self.model_tester.seq_length
										__magic_name__            =     self.model_tester.encoder_seq_length

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

																if self.is_encoder_decoder:
																						__magic_name__            =     model_class(__lowerCamelCase	)
																						__magic_name__            =     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"]
																__magic_name__            =     True
																__magic_name__            =     model_class(__lowerCamelCase	)
																__magic_name__            =     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
																__magic_name__            =     True
																__magic_name__            =     True
																__magic_name__            =     model_class(__lowerCamelCase	)
																__magic_name__            =     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     _snake_case					(							self							:						Union[str, Any]	)				->					List[str]:
										pass







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






def _lowerCAmelCase	(  __lowerCamelCase:Optional[int]   ):

						'''simple docstring'''






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


lowercase    			=       1e-4


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







				def     _snake_case					(							self							:						Optional[Any]	)				->					List[str]:
										__magic_name__            =     TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"	).led

										# change to intended input here
										__magic_name__            =     _long_tensor([5_1_2 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]	)
										__magic_name__            =     _long_tensor([1_2_8 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]	)
										__magic_name__            =     prepare_led_inputs_dict(model.config						,    __lowerCamelCase						,    __lowerCamelCase	)
										__magic_name__            =     model(**__lowerCamelCase	)[0]
										__magic_name__            =     (1, 1_0_2_4, 7_6_8)
										self.assertEqual(output.shape						,    __lowerCamelCase	)
										# change to expected output here
										__magic_name__            =     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     _snake_case					(							self							:						Any	)				->					Dict:
										__magic_name__            =     TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"	)

										# change to intended input here
										__magic_name__            =     _long_tensor([5_1_2 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]	)
										__magic_name__            =     _long_tensor([1_2_8 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]	)
										__magic_name__            =     prepare_led_inputs_dict(model.config						,    __lowerCamelCase						,    __lowerCamelCase	)
										__magic_name__            =     model(**__lowerCamelCase	)[0]
										__magic_name__            =     (1, 1_0_2_4, model.config.vocab_size)
										self.assertEqual(output.shape						,    __lowerCamelCase	)
										# change to expected output here
										__magic_name__            =     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	)



