




"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
a_				:    List[Any]										=     logging.get_logger(__name__)





def 						UpperCAmelCase				(			A__:    Optional[Any]     )  ->     List[str]:
			__lowerCamelCase					:	Tuple	       =				OrderedDict()
			for key, value in state_dict.items():
						if key.startswith('module.encoder'     ):
									__lowerCamelCase					:	Optional[Any]	       =				key.replace('module.encoder'							,				'glpn.encoder'     )
						if key.startswith('module.decoder'     ):
									__lowerCamelCase					:	Union[str, Any]	       =				key.replace('module.decoder'							,				'decoder.stages'     )
						if "patch_embed" in key:
									# replace for example patch_embed1 by patch_embeddings.0
									__lowerCamelCase					:	List[Any]	       =				key[key.find('patch_embed'     ) + len('patch_embed'     )]
									__lowerCamelCase					:	Optional[int]	       =				key.replace(f'''patch_embed{idx}'''							,				f'''patch_embeddings.{int(A__     )-1}'''     )
						if "norm" in key:
									__lowerCamelCase					:	Tuple	       =				key.replace('norm'							,				'layer_norm'     )
						if "glpn.encoder.layer_norm" in key:
									# replace for example layer_norm1 by layer_norm.0
									__lowerCamelCase					:	Optional[int]	       =				key[key.find('glpn.encoder.layer_norm'     ) + len('glpn.encoder.layer_norm'     )]
									__lowerCamelCase					:	Tuple	       =				key.replace(f'''layer_norm{idx}'''							,				f'''layer_norm.{int(A__     )-1}'''     )
						if "layer_norm1" in key:
									__lowerCamelCase					:	Any	       =				key.replace('layer_norm1'							,				'layer_norm_1'     )
						if "layer_norm2" in key:
									__lowerCamelCase					:	Any	       =				key.replace('layer_norm2'							,				'layer_norm_2'     )
						if "block" in key:
									# replace for example block1 by block.0
									__lowerCamelCase					:	List[Any]	       =				key[key.find('block'     ) + len('block'     )]
									__lowerCamelCase					:	Optional[Any]	       =				key.replace(f'''block{idx}'''							,				f'''block.{int(A__     )-1}'''     )
						if "attn.q" in key:
									__lowerCamelCase					:	Optional[int]	       =				key.replace('attn.q'							,				'attention.self.query'     )
						if "attn.proj" in key:
									__lowerCamelCase					:	Optional[int]	       =				key.replace('attn.proj'							,				'attention.output.dense'     )
						if "attn" in key:
									__lowerCamelCase					:	str	       =				key.replace('attn'							,				'attention.self'     )
						if "fc1" in key:
									__lowerCamelCase					:	Optional[int]	       =				key.replace('fc1'							,				'dense1'     )
						if "fc2" in key:
									__lowerCamelCase					:	int	       =				key.replace('fc2'							,				'dense2'     )
						if "linear_pred" in key:
									__lowerCamelCase					:	Any	       =				key.replace('linear_pred'							,				'classifier'     )
						if "linear_fuse" in key:
									__lowerCamelCase					:	str	       =				key.replace('linear_fuse.conv'							,				'linear_fuse'     )
									__lowerCamelCase					:	Dict	       =				key.replace('linear_fuse.bn'							,				'batch_norm'     )
						if "linear_c" in key:
									# replace for example linear_c4 by linear_c.3
									__lowerCamelCase					:	Optional[Any]	       =				key[key.find('linear_c'     ) + len('linear_c'     )]
									__lowerCamelCase					:	str	       =				key.replace(f'''linear_c{idx}'''							,				f'''linear_c.{int(A__     )-1}'''     )
						if "bot_conv" in key:
									__lowerCamelCase					:	int	       =				key.replace('bot_conv'							,				'0.convolution'     )
						if "skip_conv1" in key:
									__lowerCamelCase					:	Dict	       =				key.replace('skip_conv1'							,				'1.convolution'     )
						if "skip_conv2" in key:
									__lowerCamelCase					:	Any	       =				key.replace('skip_conv2'							,				'2.convolution'     )
						if "fusion1" in key:
									__lowerCamelCase					:	List[Any]	       =				key.replace('fusion1'							,				'1.fusion'     )
						if "fusion2" in key:
									__lowerCamelCase					:	Any	       =				key.replace('fusion2'							,				'2.fusion'     )
						if "fusion3" in key:
									__lowerCamelCase					:	List[str]	       =				key.replace('fusion3'							,				'3.fusion'     )
						if "fusion" in key and "conv" in key:
									__lowerCamelCase					:	Dict	       =				key.replace('conv'							,				'convolutional_layer'     )
						if key.startswith('module.last_layer_depth'     ):
									__lowerCamelCase					:	Optional[int]	       =				key.replace('module.last_layer_depth'							,				'head.head'     )
						__lowerCamelCase					:	Dict	       =				value

			return new_state_dict





def 						UpperCAmelCase				(			A__:    Union[str, Any]							,				A__:    str     )  ->     int:
			# for each of the encoder blocks:
			for i in range(config.num_encoder_blocks     ):
						for j in range(config.depths[i]     ):
									# read in weights + bias of keys and values (which is a single matrix in the original implementation)
									__lowerCamelCase					:	Optional[int]	       =				state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight'''     )
									__lowerCamelCase					:	str	       =				state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.bias'''     )
									# next, add keys and values (in that order) to the state dict
									__lowerCamelCase					:	Optional[int]	       =				kv_weight[
									    : config.hidden_sizes[i], :
									]
									__lowerCamelCase					:	List[Any]	       =				kv_bias[: config.hidden_sizes[i]]
									__lowerCamelCase					:	Dict	       =				kv_weight[
									    config.hidden_sizes[i] :, :
									]
									__lowerCamelCase					:	str	       =				kv_bias[config.hidden_sizes[i] :]





def 						UpperCAmelCase				(			)  ->     List[str]:
			__lowerCamelCase					:	Union[str, Any]	       =				'http://images.cocodataset.org/val2017/000000039769.jpg'
			__lowerCamelCase					:	List[Any]	       =				Image.open(requests.get(A__							,				stream=A__     ).raw     )

			return image





@torch.no_grad()
def 						UpperCAmelCase				(			A__:    Union[str, Any]							,				A__:    Tuple							,				A__:    Any=False							,				A__:    Union[str, Any]=None     )  ->     str:
			__lowerCamelCase					:	Union[str, Any]	       =				GLPNConfig(hidden_sizes=[64, 128, 320, 512]							,				decoder_hidden_size=64							,				depths=[3, 8, 27, 3]     )

			# load image processor (only resize + rescale)
			__lowerCamelCase					:	List[str]	       =				GLPNImageProcessor()

			# prepare image
			__lowerCamelCase					:	List[str]	       =				prepare_img()
			__lowerCamelCase					:	Any	       =				image_processor(images=A__							,				return_tensors='pt'     ).pixel_values

			logger.info('Converting model...'     )

			# load original state dict
			__lowerCamelCase					:	int	       =				torch.load(A__							,				map_location=torch.device('cpu'     )     )

			# rename keys
			__lowerCamelCase					:	Dict	       =				rename_keys(A__     )

			# key and value matrices need special treatment
			read_in_k_v(A__							,				A__     )

			# create HuggingFace model and load state dict
			__lowerCamelCase					:	Dict	       =				GLPNForDepthEstimation(A__     )
			model.load_state_dict(A__     )
			model.eval()

			# forward pass
			__lowerCamelCase					:	List[Any]	       =				model(A__     )
			__lowerCamelCase					:	Any	       =				outputs.predicted_depth

			# verify output
			if model_name is not None:
						if "nyu" in model_name:
									__lowerCamelCase					:	List[str]	       =				torch.tensor(
									    [[4.4_147, 4.0_873, 4.0_673], [3.7_890, 3.2_881, 3.1_525], [3.7_674, 3.5_423, 3.4_913]]     )
						elif "kitti" in model_name:
									__lowerCamelCase					:	Optional[int]	       =				torch.tensor(
									    [[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]]     )
						else:
									raise ValueError(f'''Unknown model name: {model_name}'''     )

						__lowerCamelCase					:	str	       =				torch.Size([1, 480, 640]     )

						assert predicted_depth.shape == expected_shape
						assert torch.allclose(predicted_depth[0, :3, :3]							,				A__							,				atol=1E-4     )
						print('Looks ok!'     )

			# finally, push to hub if required
			if push_to_hub:
						logger.info('Pushing model and image processor to the hub...'     )
						model.push_to_hub(
						    repo_path_or_name=Path(A__							,				A__     )							,				organization='nielsr'							,				commit_message='Add model'							,				use_temp_dir=A__							,				)
						image_processor.push_to_hub(
						    repo_path_or_name=Path(A__							,				A__     )							,				organization='nielsr'							,				commit_message='Add image processor'							,				use_temp_dir=A__							,				)


if __name__ == "__main__":
					a_				:    int										=     argparse.ArgumentParser()

					parser.add_argument(
					    '''--checkpoint_path''',
					    default=None,
					    type=str,
					    help='''Path 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''', help='''Whether to upload the model to the HuggingFace hub.'''
					)
					parser.add_argument(
					    '''--model_name''',
					    default='''glpn-kitti''',
					    type=str,
					    help='''Name of the model in case you\'re pushing to the hub.''',
					)
					a_				:    int										=     parser.parse_args()
					convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)









"""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 __lowercase:




					'''simple docstring'''


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

								__lowerCamelCase					:	str	       =				hidden_dropout_prob
								__lowerCamelCase					:	Union[str, Any]	       =				attention_probs_dropout_prob
								__lowerCamelCase					:	Any	       =				max_position_embeddings
								__lowerCamelCase					:	str	       =				eos_token_id
								__lowerCamelCase					:	str	       =				pad_token_id
								__lowerCamelCase					:	str	       =				bos_token_id
								__lowerCamelCase					:	int	       =				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					:	Dict	       =				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					:	int	       =				(
								    self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
								)
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Tuple	       =				ids_tensor([self.batch_size, self.seq_length - 1]						,	self.vocab_size     )
								__lowerCamelCase					:	Optional[int]	       =				tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size     )						,	1     )
								__lowerCamelCase					:	Union[str, Any]	       =				tf.concat([input_ids, eos_tensor]						,	axis=1     )

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

								__lowerCamelCase					:	Dict	       =				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(__a						,	__a						,	__a     )
								__lowerCamelCase					:	Union[str, Any]	       =				tf.concat(
								    [tf.zeros_like(__a     )[:, :-1], tf.ones_like(__a     )[:, -1:]]						,	axis=-1						,	)
								__lowerCamelCase					:	Union[str, Any]	       =				global_attention_mask
								return config, inputs_dict





					def     snake_case_				(    self						,	__a						,	__a     ):
								__lowerCamelCase					:	Optional[int]	       =				TFLEDModel(config=__a     ).get_decoder()
								__lowerCamelCase					:	List[str]	       =				inputs_dict['input_ids']

								__lowerCamelCase					:	Dict	       =				input_ids[:1, :]
								__lowerCamelCase					:	Any	       =				inputs_dict['attention_mask'][:1, :]
								__lowerCamelCase					:	Tuple	       =				1

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

								__lowerCamelCase       ,      __lowerCamelCase					:	int	       =				outputs.to_tuple()

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

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

								__lowerCamelCase					:	Dict	       =				model(__a						,	attention_mask=__a     )[0]
								__lowerCamelCase					:	Optional[int]	       =				model(__a						,	attention_mask=__a						,	past_key_values=__a     )[0]

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

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

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





def 						UpperCAmelCase				(			A__:    Any							,				A__:    Union[str, Any]							,				A__:    Dict							,				A__:    int=None							,				A__:    str=None							,				A__:    Tuple=None							,				A__:    List[Any]=None							,				)  ->     Optional[Any]:
			if attention_mask is None:
						__lowerCamelCase					:	Optional[int]	       =				tf.cast(tf.math.not_equal(A__							,				config.pad_token_id     )							,				tf.inta     )
			if decoder_attention_mask is None:
						__lowerCamelCase					:	Dict	       =				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					:	Tuple	       =				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(      lowercase__	,	lowercase__	,	unittest.TestCase				):




					'''simple docstring'''


					__a						:						Union[str, Any]     		= (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
					__a						:						Any     		= (TFLEDForConditionalGeneration,) if is_tf_available() else ()
					__a						:						str     		= (
					    {
					        'conversational': TFLEDForConditionalGeneration,
					        'feature-extraction': TFLEDModel,
					        'summarization': TFLEDForConditionalGeneration,
					        'text2text-generation': TFLEDForConditionalGeneration,
					        'translation': TFLEDForConditionalGeneration,
					    }
					    if is_tf_available()
					    else {}
					)
					__a						:						Union[str, Any]     		= True
					__a						:						str     		= False
					__a						:						Dict     		= False
					__a						:						List[Any]     		= False
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	int	       =				TFLEDModelTester(self     )
								__lowerCamelCase					:	List[str]	       =				ConfigTester(self						,	config_class=__a     )
					def     snake_case_				(    self     ):
								self.config_tester.run_common_tests()
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Optional[int]	       =				self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*__a     )
					def     snake_case_				(    self     ):
								__lowerCamelCase       ,      __lowerCamelCase					:	Optional[Any]	       =				self.model_tester.prepare_config_and_inputs_for_common()
								__lowerCamelCase					:	Optional[Any]	       =				tf.zeros_like(inputs_dict['attention_mask']     )
								__lowerCamelCase					:	Any	       =				2
								__lowerCamelCase					:	Dict	       =				tf.where(
								    tf.range(self.model_tester.seq_length     )[None, :] < num_global_attn_indices						,	1						,	inputs_dict['global_attention_mask']						,	)

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

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

											if self.is_encoder_decoder:
														__lowerCamelCase					:	Any	       =				model_class(__a     )
														__lowerCamelCase					:	List[str]	       =				model(self._prepare_for_class(__a						,	__a     )     )
														self.assertEqual(config.output_hidden_states						,	__a     )
														check_decoder_attentions_output(__a     )

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

											# Check attention is always last and order is fine
											__lowerCamelCase					:	List[Any]	       =				True
											__lowerCamelCase					:	int	       =				True
											__lowerCamelCase					:	List[str]	       =				model_class(__a     )
											__lowerCamelCase					:	Dict	       =				model(self._prepare_for_class(__a						,	__a     )     )

											self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)						,	len(__a     )     )
											self.assertEqual(model.config.output_hidden_states						,	__a     )
											check_encoder_attentions_output(__a     )
					@unittest.skip('LED keeps using potentially symbolic tensors in conditionals and breaks tracing.'     )
					def     snake_case_				(    self     ):
								pass





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





def 						UpperCAmelCase				(			A__:    Union[str, Any]     )  ->     List[Any]:
			return tf.constant(A__							,				dtype=tf.intaa     )


a_				:    Tuple										=     1e-4






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




					'''simple docstring'''


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

								# change to intended input here
								__lowerCamelCase					:	Union[str, Any]	       =				_long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]     )
								__lowerCamelCase					:	Dict	       =				_long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]     )
								__lowerCamelCase					:	Optional[int]	       =				prepare_led_inputs_dict(model.config						,	__a						,	__a     )
								__lowerCamelCase					:	str	       =				model(**__a     )[0]
								__lowerCamelCase					:	Optional[int]	       =				(1, 1024, 768)
								self.assertEqual(output.shape						,	__a     )
								# change to expected output here
								__lowerCamelCase					:	int	       =				tf.convert_to_tensor(
								    [[2.3_050, 2.8_279, 0.6_531], [-1.8_457, -0.1_455, -3.5_661], [-1.0_186, 0.4_586, -2.2_043]]						,	)
								tf.debugging.assert_near(output[:, :3, :3]						,	__a						,	atol=1E-3     )





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

								# change to intended input here
								__lowerCamelCase					:	Union[str, Any]	       =				_long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]     )
								__lowerCamelCase					:	Dict	       =				_long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]     )
								__lowerCamelCase					:	Optional[Any]	       =				prepare_led_inputs_dict(model.config						,	__a						,	__a     )
								__lowerCamelCase					:	Dict	       =				model(**__a     )[0]
								__lowerCamelCase					:	List[Any]	       =				(1, 1024, model.config.vocab_size)
								self.assertEqual(output.shape						,	__a     )
								# change to expected output here
								__lowerCamelCase					:	Optional[Any]	       =				tf.convert_to_tensor(
								    [[33.6_507, 6.4_572, 16.8_089], [5.8_739, -2.4_238, 11.2_902], [-3.2_139, -4.3_149, 4.2_783]]						,	)
								tf.debugging.assert_near(output[:, :3, :3]						,	__a						,	atol=1E-3						,	rtol=1E-3     )



