import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel

class 	UpperCamelCase    :



				def __init__(self       :   Union[str, Any]			,						_A       :   Dict			,						_A       :   Optional[int]=13			,						_A       :   Optional[Any]=7			,						_A       :   int=True			,						_A       :   Tuple=True			,						_A       :   Tuple=False			,						_A       :   Optional[Any]=True			,						_A       :   Optional[int]=99			,						_A       :   int=32			,						_A       :   int=5			,						_A       :   List[str]=4			,						_A       :   Dict=37			,						_A       :   Union[str, Any]="gelu"			,						_A       :   Union[str, Any]=0.1			,						_A       :   str=0.1			,						_A       :   Optional[Any]=5_12			,						_A       :   Dict=16			,						_A       :   Dict=2			,						_A       :   Optional[int]=0.02			,						_A       :   Any=3			,						_A       :   Union[str, Any]=4			,						_A       :   List[Any]=None			,						)					->			int:
					__snake_case    :							Any   	=				parent
					__snake_case    :							Dict   	=				batch_size
					__snake_case    :							Any   	=				seq_length
					__snake_case    :							Optional[int]   	=				is_training
					__snake_case    :							Optional[Any]   	=				use_input_mask
					__snake_case    :							Union[str, Any]   	=				use_token_type_ids
					__snake_case    :							Optional[Any]   	=				use_labels
					__snake_case    :							List[Any]   	=				vocab_size
					__snake_case    :							List[Any]   	=				hidden_size
					__snake_case    :							Optional[Any]   	=				num_hidden_layers
					__snake_case    :							Optional[Any]   	=				num_attention_heads
					__snake_case    :							Optional[Any]   	=				intermediate_size
					__snake_case    :							Union[str, Any]   	=				hidden_act
					__snake_case    :							List[Any]   	=				hidden_dropout_prob
					__snake_case    :							List[Any]   	=				attention_probs_dropout_prob
					__snake_case    :							Dict   	=				max_position_embeddings
					__snake_case    :							List[str]   	=				type_vocab_size
					__snake_case    :							Optional[int]   	=				type_sequence_label_size
					__snake_case    :							str   	=				initializer_range
					__snake_case    :							Union[str, Any]   	=				num_labels
					__snake_case    :							Union[str, Any]   	=				num_choices
					__snake_case    :							Dict   	=				scope



				def       _lowercase  (self       :   Any)					->			int:
					__snake_case    :							Dict   	=				ids_tensor([self.batch_size, self.seq_length]			,						self.vocab_size)

					__snake_case    :							List[Any]   	=				None
					if self.use_input_mask:
						__snake_case    :							Union[str, Any]   	=				random_attention_mask([self.batch_size, self.seq_length])

					__snake_case    :							int   	=				None
					if self.use_token_type_ids:
						__snake_case    :							Optional[Any]   	=				ids_tensor([self.batch_size, self.seq_length]			,						self.type_vocab_size)

					__snake_case    :							Optional[Any]   	=				None
					__snake_case    :							Optional[int]   	=				None
					__snake_case    :							Optional[Any]   	=				None
					if self.use_labels:
						__snake_case    :							int   	=				ids_tensor([self.batch_size]			,						self.type_sequence_label_size)
						__snake_case    :							Optional[Any]   	=				ids_tensor([self.batch_size, self.seq_length]			,						self.num_labels)
						__snake_case    :							Any   	=				ids_tensor([self.batch_size]			,						self.num_choices)

					__snake_case    :							Dict   	=				self.get_config()

					return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



				def       _lowercase  (self       :   Optional[Any])					->			Optional[Any]:
					return OpenLlamaConfig(
					    vocab_size=self.vocab_size			,						hidden_size=self.hidden_size			,						num_hidden_layers=self.num_hidden_layers			,						num_attention_heads=self.num_attention_heads			,						intermediate_size=self.intermediate_size			,						hidden_act=self.hidden_act			,						hidden_dropout_prob=self.hidden_dropout_prob			,						attention_probs_dropout_prob=self.attention_probs_dropout_prob			,						max_position_embeddings=self.max_position_embeddings			,						type_vocab_size=self.type_vocab_size			,						is_decoder=UpperCamelCase_			,						initializer_range=self.initializer_range			,						use_stable_embedding=UpperCamelCase_			,						)



				def       _lowercase  (self       :   Optional[int]			,						_A       :   Union[str, Any]			,						_A       :   Union[str, Any]			,						_A       :   Union[str, Any]			,						_A       :   Union[str, Any]			,						_A       :   Union[str, Any]			,						_A       :   List[Any]			,						_A       :   Union[str, Any])					->			List[Any]:
					__snake_case    :							List[str]   	=				OpenLlamaModel(config=UpperCamelCase_)
					model.to(UpperCamelCase_)
					model.eval()
					__snake_case    :							Optional[Any]   	=				model(UpperCamelCase_			,						attention_mask=UpperCamelCase_)
					__snake_case    :							List[Any]   	=				model(UpperCamelCase_)
					self.parent.assertEqual(result.last_hidden_state.shape			,						(self.batch_size, self.seq_length, self.hidden_size))



				def       _lowercase  (self       :   Tuple			,						_A       :   List[Any]			,						_A       :   List[str]			,						_A       :   str			,						_A       :   Union[str, Any]			,						_A       :   Optional[int]			,						_A       :   Union[str, Any]			,						_A       :   Dict			,						_A       :   str			,						_A       :   Tuple			,						)					->			Optional[int]:
					__snake_case    :							Optional[int]   	=				True
					__snake_case    :							int   	=				OpenLlamaModel(UpperCamelCase_)
					model.to(UpperCamelCase_)
					model.eval()
					__snake_case    :							Union[str, Any]   	=				model(
					    UpperCamelCase_			,						attention_mask=UpperCamelCase_			,						encoder_hidden_states=UpperCamelCase_			,						encoder_attention_mask=UpperCamelCase_			,						)
					__snake_case    :							Union[str, Any]   	=				model(
					    UpperCamelCase_			,						attention_mask=UpperCamelCase_			,						encoder_hidden_states=UpperCamelCase_			,						)
					__snake_case    :							Union[str, Any]   	=				model(UpperCamelCase_			,						attention_mask=UpperCamelCase_)
					self.parent.assertEqual(result.last_hidden_state.shape			,						(self.batch_size, self.seq_length, self.hidden_size))



				def       _lowercase  (self       :   Optional[Any]			,						_A       :   int			,						_A       :   List[Any]			,						_A       :   Optional[int]			,						_A       :   List[str]			,						_A       :   str			,						_A       :   Any			,						_A       :   List[Any]			,						_A       :   Any			,						_A       :   List[str]			,						)					->			Any:
					__snake_case    :							Tuple   	=				OpenLlamaForCausalLM(config=UpperCamelCase_)
					model.to(UpperCamelCase_)
					model.eval()
					__snake_case    :							int   	=				model(UpperCamelCase_			,						attention_mask=UpperCamelCase_			,						labels=UpperCamelCase_)
					self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.seq_length, self.vocab_size))



				def       _lowercase  (self       :   str			,						_A       :   List[Any]			,						_A       :   Union[str, Any]			,						_A       :   List[str]			,						_A       :   str			,						_A       :   List[Any]			,						_A       :   Optional[Any]			,						_A       :   Optional[Any]			,						_A       :   Dict			,						_A       :   Optional[Any]			,						)					->			Optional[Any]:
					__snake_case    :							List[Any]   	=				True
					__snake_case    :							Optional[Any]   	=				True
					__snake_case    :							Optional[Any]   	=				OpenLlamaForCausalLM(config=UpperCamelCase_)
					model.to(UpperCamelCase_)
					model.eval()

					# first forward pass
					__snake_case    :							Any   	=				model(
					    UpperCamelCase_			,						attention_mask=UpperCamelCase_			,						encoder_hidden_states=UpperCamelCase_			,						encoder_attention_mask=UpperCamelCase_			,						use_cache=UpperCamelCase_			,						)
					__snake_case    :							Any   	=				outputs.past_key_values

					# create hypothetical multiple next token and extent to next_input_ids
					__snake_case    :							Any   	=				ids_tensor((self.batch_size, 3)			,						config.vocab_size)
					__snake_case    :							Tuple   	=				ids_tensor((self.batch_size, 3)			,						vocab_size=2)

					# append to next input_ids and
					__snake_case    :							Union[str, Any]   	=				torch.cat([input_ids, next_tokens]			,						dim=-1)
					__snake_case    :							List[Any]   	=				torch.cat([input_mask, next_mask]			,						dim=-1)

					__snake_case    :							int   	=				model(
					    UpperCamelCase_			,						attention_mask=UpperCamelCase_			,						encoder_hidden_states=UpperCamelCase_			,						encoder_attention_mask=UpperCamelCase_			,						output_hidden_states=UpperCamelCase_			,						)['hidden_states'][0]
					__snake_case    :							List[Any]   	=				model(
					    UpperCamelCase_			,						attention_mask=UpperCamelCase_			,						encoder_hidden_states=UpperCamelCase_			,						encoder_attention_mask=UpperCamelCase_			,						past_key_values=UpperCamelCase_			,						output_hidden_states=UpperCamelCase_			,						)['hidden_states'][0]

					# select random slice
					__snake_case    :							Any   	=				ids_tensor((1,)			,						output_from_past.shape[-1]).item()
					__snake_case    :							Union[str, Any]   	=				output_from_no_past[:, -3:, random_slice_idx].detach()
					__snake_case    :							Dict   	=				output_from_past[:, :, random_slice_idx].detach()

					self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])

					# test that outputs are equal for slice
					self.parent.assertTrue(torch.allclose(UpperCamelCase_			,						UpperCamelCase_			,						atol=1E-3))






				def       _lowercase  (self       :   Optional[int])					->			int:
					__snake_case    :							List[Any]   	=				self.prepare_config_and_inputs()
					(
					    __snake_case    
					)    :							Optional[Any]   	=				config_and_inputs
					__snake_case    :							str   	=				{'input_ids': input_ids, 'attention_mask': input_mask}
					return config, inputs_dict







@require_torch
class 	UpperCamelCase    (					lowercase   ,			lowercase   ,			lowercase   ,			unittest.TestCase		):
				UpperCAmelCase       :   Optional[Any]					=	(
				    (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
				)
				UpperCAmelCase       :   List[str]					=	(OpenLlamaForCausalLM,) if is_torch_available() else ()
				UpperCAmelCase       :   Tuple					=	(
				    {
				        """feature-extraction""": OpenLlamaModel,
				        """text-classification""": OpenLlamaForSequenceClassification,
				        """text-generation""": OpenLlamaForCausalLM,
				        """zero-shot""": OpenLlamaForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				UpperCAmelCase       :   Union[str, Any]					=	False
				UpperCAmelCase       :   Union[str, Any]					=	False



				def       _lowercase  (self       :   Any)					->			Union[str, Any]:
					__snake_case    :							Dict   	=				OpenLlamaModelTester(self)
					__snake_case    :							Dict   	=				ConfigTester(self			,						config_class=UpperCamelCase_			,						hidden_size=37)



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



				def       _lowercase  (self       :   Dict)					->			Optional[Any]:
					__snake_case    :							Union[str, Any]   	=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*UpperCamelCase_)



				def       _lowercase  (self       :   Any)					->			Tuple:
					__snake_case    :							Union[str, Any]   	=				self.model_tester.prepare_config_and_inputs()
					for type in ["absolute", "relative_key", "relative_key_query"]:
						__snake_case    :							int   	=				type
						self.model_tester.create_and_check_model(*UpperCamelCase_)



				def       _lowercase  (self       :   Any)					->			Optional[int]:
					__snake_case    :							int   	=				self.model_tester.prepare_config_and_inputs_for_common()
					__snake_case    :							Optional[int]   	=				3
					__snake_case    :							Optional[Any]   	=				input_dict['input_ids']
					__snake_case    :							Dict   	=				input_ids.ne(1).to(UpperCamelCase_)
					__snake_case    :							Dict   	=				ids_tensor([self.model_tester.batch_size]			,						self.model_tester.type_sequence_label_size)
					__snake_case    :							str   	=				OpenLlamaForSequenceClassification(UpperCamelCase_)
					model.to(UpperCamelCase_)
					model.eval()
					__snake_case    :							int   	=				model(UpperCamelCase_			,						attention_mask=UpperCamelCase_			,						labels=UpperCamelCase_)
					self.assertEqual(result.logits.shape			,						(self.model_tester.batch_size, self.model_tester.num_labels))



				def       _lowercase  (self       :   int)					->			str:
					__snake_case    :							List[Any]   	=				self.model_tester.prepare_config_and_inputs_for_common()
					__snake_case    :							Optional[int]   	=				3
					__snake_case    :							int   	=				'single_label_classification'
					__snake_case    :							Dict   	=				input_dict['input_ids']
					__snake_case    :							int   	=				input_ids.ne(1).to(UpperCamelCase_)
					__snake_case    :							List[Any]   	=				ids_tensor([self.model_tester.batch_size]			,						self.model_tester.type_sequence_label_size)
					__snake_case    :							Any   	=				OpenLlamaForSequenceClassification(UpperCamelCase_)
					model.to(UpperCamelCase_)
					model.eval()
					__snake_case    :							Tuple   	=				model(UpperCamelCase_			,						attention_mask=UpperCamelCase_			,						labels=UpperCamelCase_)
					self.assertEqual(result.logits.shape			,						(self.model_tester.batch_size, self.model_tester.num_labels))



				def       _lowercase  (self       :   str)					->			List[str]:
					__snake_case    :							Dict   	=				self.model_tester.prepare_config_and_inputs_for_common()
					__snake_case    :							Any   	=				3
					__snake_case    :							List[str]   	=				'multi_label_classification'
					__snake_case    :							List[Any]   	=				input_dict['input_ids']
					__snake_case    :							List[str]   	=				input_ids.ne(1).to(UpperCamelCase_)
					__snake_case    :							Optional[int]   	=				ids_tensor(
					    [self.model_tester.batch_size, config.num_labels]			,						self.model_tester.type_sequence_label_size).to(torch.float)
					__snake_case    :							Union[str, Any]   	=				OpenLlamaForSequenceClassification(UpperCamelCase_)
					model.to(UpperCamelCase_)
					model.eval()
					__snake_case    :							str   	=				model(UpperCamelCase_			,						attention_mask=UpperCamelCase_			,						labels=UpperCamelCase_)
					self.assertEqual(result.logits.shape			,						(self.model_tester.batch_size, self.model_tester.num_labels))



				@unittest.skip('Open-Llama buffers include complex numbers, which breaks this test')
				def       _lowercase  (self       :   List[Any])					->			List[Any]:
					pass






				@parameterized.expand([('linear',), ('dynamic',)])
				def       _lowercase  (self       :   str			,						_A       :   List[str])					->			Tuple:
					__snake_case    :							List[Any]   	=				self.model_tester.prepare_config_and_inputs_for_common()
					__snake_case    :							Optional[Any]   	=				ids_tensor([1, 10]			,						config.vocab_size)
					__snake_case    :							Union[str, Any]   	=				ids_tensor([1, int(config.max_position_embeddings * 1.5)]			,						config.vocab_size)

					set_seed(42)  # Fixed seed at init time so the two models get the same random weights
					__snake_case    :							List[str]   	=				OpenLlamaModel(UpperCamelCase_)
					original_model.to(UpperCamelCase_)
					original_model.eval()
					__snake_case    :							Optional[int]   	=				original_model(UpperCamelCase_).last_hidden_state
					__snake_case    :							Dict   	=				original_model(UpperCamelCase_).last_hidden_state

					set_seed(42)  # Fixed seed at init time so the two models get the same random weights
					__snake_case    :							List[str]   	=				{'type': scaling_type, 'factor': 10.0}
					__snake_case    :							Any   	=				OpenLlamaModel(UpperCamelCase_)
					scaled_model.to(UpperCamelCase_)
					scaled_model.eval()
					__snake_case    :							Tuple   	=				scaled_model(UpperCamelCase_).last_hidden_state
					__snake_case    :							int   	=				scaled_model(UpperCamelCase_).last_hidden_state

					# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
					# maximum sequence length, so the outputs for the short input should match.
					if scaling_type == "dynamic":
						self.assertTrue(torch.allclose(UpperCamelCase_			,						UpperCamelCase_			,						atol=1E-5))
					else:
						self.assertFalse(torch.allclose(UpperCamelCase_			,						UpperCamelCase_			,						atol=1E-5))

					# The output should be different for long inputs
					self.assertFalse(torch.allclose(UpperCamelCase_			,						UpperCamelCase_			,						atol=1E-5))






"""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  :  Union[str, Any]=				logging.get_logger(__name__)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Union[str, Any]						)							->    List[Any]:

	'''simple docstring'''





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

	return new_state_dict
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Union[str, Any]    ,							UpperCAmelCase_					:       List[Any]						)							->    str:

	'''simple docstring'''





	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)
			__snake_case    :							Union[str, Any]   	=				state_dict.pop(F"glpn.encoder.block.{i}.{j}.attention.self.kv.weight"						)
			__snake_case    :							Any   	=				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
			__snake_case    :							int   	=				kv_weight[
			    : config.hidden_sizes[i], :
			]
			__snake_case    :							List[str]   	=				kv_bias[: config.hidden_sizes[i]]
			__snake_case    :							Dict   	=				kv_weight[
			    config.hidden_sizes[i] :, :
			]
			__snake_case    :							Any   	=				kv_bias[config.hidden_sizes[i] :]
def 						__UpperCAmelCase       (					)							->    Any:

	'''simple docstring'''





	__snake_case    :							Tuple   	=				'http://images.cocodataset.org/val2017/000000039769.jpg'
	__snake_case    :							Tuple   	=				Image.open(requests.get(UpperCAmelCase_    ,							stream=UpperCAmelCase_						).raw						)

	return image
@torch.no_grad()
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Optional[int]    ,							UpperCAmelCase_					:       List[str]    ,							UpperCAmelCase_					:       Dict=False    ,							UpperCAmelCase_					:       Optional[int]=None						)							->    Dict:

	'''simple docstring'''





	__snake_case    :							Dict   	=				GLPNConfig(hidden_sizes=[64, 1_28, 3_20, 5_12]    ,							decoder_hidden_size=64    ,							depths=[3, 8, 27, 3]						)

	# load image processor (only resize + rescale)
	__snake_case    :							Tuple   	=				GLPNImageProcessor()

	# prepare image
	__snake_case    :							Optional[Any]   	=				prepare_img()
	__snake_case    :							Any   	=				image_processor(images=UpperCAmelCase_    ,							return_tensors='pt'						).pixel_values

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

	# load original state dict
	__snake_case    :							Dict   	=				torch.load(UpperCAmelCase_    ,							map_location=torch.device('cpu'						)						)

	# rename keys
	__snake_case    :							Union[str, Any]   	=				rename_keys(UpperCAmelCase_						)

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

	# create HuggingFace model and load state dict
	__snake_case    :							Optional[int]   	=				GLPNForDepthEstimation(UpperCAmelCase_						)
	model.load_state_dict(UpperCAmelCase_						)
	model.eval()

	# forward pass
	__snake_case    :							List[Any]   	=				model(UpperCAmelCase_						)
	__snake_case    :							Optional[int]   	=				outputs.predicted_depth

	# verify output
	if model_name is not None:
		if "nyu" in model_name:
			__snake_case    :							int   	=				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:
			__snake_case    :							Union[str, Any]   	=				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}"						)

		__snake_case    :							Tuple   	=				torch.Size([1, 4_80, 6_40]						)

		assert predicted_depth.shape == expected_shape
		assert torch.allclose(predicted_depth[0, :3, :3]    ,							UpperCAmelCase_    ,							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(UpperCAmelCase_    ,							UpperCAmelCase_						)    ,							organization='nielsr'    ,							commit_message='Add model'    ,							use_temp_dir=UpperCAmelCase_    ,							)
		image_processor.push_to_hub(
		    repo_path_or_name=Path(UpperCAmelCase_    ,							UpperCAmelCase_						)    ,							organization='nielsr'    ,							commit_message='Add image processor'    ,							use_temp_dir=UpperCAmelCase_    ,							)


if __name__ == "__main__":
	_a  :  List[Any]=				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)





