






"""simple docstring"""

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowercase    			=       logging.get_logger(__name__)






def _lowerCAmelCase	(  __lowerCamelCase:Dict ,						__lowerCamelCase:List[str]=False ,						__lowerCamelCase:Tuple=False   ):

						'''simple docstring'''






						__magic_name__            =     "backbone." if is_semantic else ""

						__magic_name__            =     []
						for i in range(config.num_hidden_layers   ):
												# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
												rename_keys.append((f'''{prefix}blocks.{i}.norm1.weight''', f'''beit.encoder.layer.{i}.layernorm_before.weight''')   )
												rename_keys.append((f'''{prefix}blocks.{i}.norm1.bias''', f'''beit.encoder.layer.{i}.layernorm_before.bias''')   )
												rename_keys.append(
												    (f'''{prefix}blocks.{i}.attn.proj.weight''', f'''beit.encoder.layer.{i}.attention.output.dense.weight''')   )
												rename_keys.append(
												    (f'''{prefix}blocks.{i}.attn.proj.bias''', f'''beit.encoder.layer.{i}.attention.output.dense.bias''')   )
												rename_keys.append((f'''{prefix}blocks.{i}.norm2.weight''', f'''beit.encoder.layer.{i}.layernorm_after.weight''')   )
												rename_keys.append((f'''{prefix}blocks.{i}.norm2.bias''', f'''beit.encoder.layer.{i}.layernorm_after.bias''')   )
												rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc1.weight''', f'''beit.encoder.layer.{i}.intermediate.dense.weight''')   )
												rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc1.bias''', f'''beit.encoder.layer.{i}.intermediate.dense.bias''')   )
												rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.weight''', f'''beit.encoder.layer.{i}.output.dense.weight''')   )
												rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.bias''', f'''beit.encoder.layer.{i}.output.dense.bias''')   )

						# projection layer + position embeddings
						rename_keys.extend(
						    [
						        (f'''{prefix}cls_token''', "beit.embeddings.cls_token"),
						        (f'''{prefix}patch_embed.proj.weight''', "beit.embeddings.patch_embeddings.projection.weight"),
						        (f'''{prefix}patch_embed.proj.bias''', "beit.embeddings.patch_embeddings.projection.bias"),
						        (f'''{prefix}pos_embed''', "beit.embeddings.position_embeddings"),
						    ]   )

						if has_lm_head:
												# mask token + layernorm
												rename_keys.extend(
												    [
												        ("mask_token", "beit.embeddings.mask_token"),
												        ("norm.weight", "layernorm.weight"),
												        ("norm.bias", "layernorm.bias"),
												    ]   )
						else:
												# layernorm + classification head
												rename_keys.extend(
												    [
												        ("fc_norm.weight", "beit.pooler.layernorm.weight"),
												        ("fc_norm.bias", "beit.pooler.layernorm.bias"),
												        ("head.weight", "classifier.weight"),
												        ("head.bias", "classifier.bias"),
												    ]   )

						return rename_keys






def _lowerCAmelCase	(  __lowerCamelCase:int ,						__lowerCamelCase:Tuple ,						__lowerCamelCase:Dict=False ,						__lowerCamelCase:Tuple=False   ):

						'''simple docstring'''






						for i in range(config.num_hidden_layers   ):
												__magic_name__            =     "backbone." if is_semantic else ""
												# queries, keys and values
												__magic_name__            =     state_dict.pop(f'''{prefix}blocks.{i}.attn.qkv.weight'''   )
												__magic_name__            =     state_dict.pop(f'''{prefix}blocks.{i}.attn.q_bias'''   )
												__magic_name__            =     state_dict.pop(f'''{prefix}blocks.{i}.attn.v_bias'''   )

												__magic_name__            =     in_proj_weight[
												    : config.hidden_size, :
												]
												__magic_name__            =     q_bias
												__magic_name__            =     in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												__magic_name__            =     in_proj_weight[
												    -config.hidden_size :, :
												]
												__magic_name__            =     v_bias

												# gamma_1 and gamma_2
												# we call them lambda because otherwise they are renamed when using .from_pretrained
												__magic_name__            =     state_dict.pop(f'''{prefix}blocks.{i}.gamma_1'''   )
												__magic_name__            =     state_dict.pop(f'''{prefix}blocks.{i}.gamma_2'''   )

												__magic_name__            =     gamma_a
												__magic_name__            =     gamma_a






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

						'''simple docstring'''






						__magic_name__            =     dct.pop(_UpperCamelCase   )
						__magic_name__            =     val






def _lowerCAmelCase	(  ):

						'''simple docstring'''






						__magic_name__            =     "http://images.cocodataset.org/val2017/000000039769.jpg"
						__magic_name__            =     Image.open(requests.get(_UpperCamelCase ,						stream=_UpperCamelCase   ).raw   )
						return im






@torch.no_grad()
def _lowerCAmelCase	(  __lowerCamelCase:List[Any] ,						__lowerCamelCase:Dict ,						__lowerCamelCase:int=False   ):

						'''simple docstring'''






						__magic_name__            =     False if "rvlcdip" in checkpoint_url else True
						__magic_name__            =     BeitConfig(use_absolute_position_embeddings=_UpperCamelCase ,						use_mask_token=_UpperCamelCase   )

						# size of the architecture
						if "large" in checkpoint_url or "dit-l" in checkpoint_url:
												__magic_name__            =     1_0_2_4
												__magic_name__            =     4_0_9_6
												__magic_name__            =     2_4
												__magic_name__            =     1_6

						# labels
						if "rvlcdip" in checkpoint_url:
												__magic_name__            =     1_6
												__magic_name__            =     "huggingface/label-files"
												__magic_name__            =     "rvlcdip-id2label.json"
												__magic_name__            =     json.load(open(hf_hub_download(_UpperCamelCase ,						_UpperCamelCase ,						repo_type="dataset"   ) ,						"r"   )   )
												__magic_name__            =     {int(_UpperCamelCase   ): v for k, v in idalabel.items()}
												__magic_name__            =     idalabel
												__magic_name__            =     {v: k for k, v in idalabel.items()}

						# load state_dict of original model, remove and rename some keys
						__magic_name__            =     torch.hub.load_state_dict_from_url(_UpperCamelCase ,						map_location="cpu"   )["model"]

						__magic_name__            =     create_rename_keys(_UpperCamelCase ,						has_lm_head=_UpperCamelCase   )
						for src, dest in rename_keys:
												rename_key(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase   )
						read_in_q_k_v(_UpperCamelCase ,						_UpperCamelCase ,						has_lm_head=_UpperCamelCase   )

						# load HuggingFace model
						__magic_name__            =     BeitForMaskedImageModeling(_UpperCamelCase   ) if has_lm_head else BeitForImageClassification(_UpperCamelCase   )
						model.eval()
						model.load_state_dict(_UpperCamelCase   )

						# Check outputs on an image
						__magic_name__            =     BeitImageProcessor(
						    size=config.image_size ,						resample=PILImageResampling.BILINEAR ,						do_center_crop=_UpperCamelCase   )
						__magic_name__            =     prepare_img()

						__magic_name__            =     image_processor(images=_UpperCamelCase ,						return_tensors="pt"   )
						__magic_name__            =     encoding["pixel_values"]

						__magic_name__            =     model(_UpperCamelCase   )
						__magic_name__            =     outputs.logits

						# verify logits
						__magic_name__            =     [1, 1_6] if "rvlcdip" in checkpoint_url else [1, 1_9_6, 8_1_9_2]
						assert logits.shape == torch.Size(_UpperCamelCase   ), "Shape of logits not as expected"

						Path(_UpperCamelCase   ).mkdir(exist_ok=_UpperCamelCase   )
						print(f'''Saving model to {pytorch_dump_folder_path}'''   )
						model.save_pretrained(_UpperCamelCase   )
						print(f'''Saving image processor to {pytorch_dump_folder_path}'''   )
						image_processor.save_pretrained(_UpperCamelCase   )

						if push_to_hub:
												if has_lm_head:
																		__magic_name__            =     "dit-base" if "base" in checkpoint_url else "dit-large"
												else:
																		__magic_name__            =     "dit-base-finetuned-rvlcdip" if "dit-b" in checkpoint_url else "dit-large-finetuned-rvlcdip"
												image_processor.push_to_hub(
												    repo_path_or_name=Path(_UpperCamelCase ,						_UpperCamelCase   ) ,						organization="nielsr" ,						commit_message="Add image processor" ,						use_temp_dir=_UpperCamelCase ,						)
												model.push_to_hub(
												    repo_path_or_name=Path(_UpperCamelCase ,						_UpperCamelCase   ) ,						organization="nielsr" ,						commit_message="Add model" ,						use_temp_dir=_UpperCamelCase ,						)


if __name__ == "__main__":
			lowercase    			=       argparse.ArgumentParser()

			parser.add_argument(
			    '''--checkpoint_url''',
			    default='''https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth''',
			    type=str,
			    help='''URL to the original PyTorch checkpoint (.pth file).''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
			)
			parser.add_argument(
			    '''--push_to_hub''',
			    action='''store_true''',
			)
			lowercase    			=       parser.parse_args()
			convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)











"""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	)



