from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, 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 (
	    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
	    TFTransfoXLForSequenceClassification,
	    TFTransfoXLLMHeadModel,
	    TFTransfoXLModel,
	)

class 	UpperCamelCase    :



				def __init__(self       :   Dict			,						_A       :   Optional[Any]			,						)					->			Optional[int]:
					__snake_case    :							Tuple   	=				parent
					__snake_case    :							List[str]   	=				13
					__snake_case    :							Tuple   	=				7
					__snake_case    :							List[Any]   	=				30
					__snake_case    :							Optional[int]   	=				self.seq_length + self.mem_len
					__snake_case    :							List[str]   	=				15
					__snake_case    :							Union[str, Any]   	=				True
					__snake_case    :							Dict   	=				True
					__snake_case    :							List[Any]   	=				99
					__snake_case    :							List[Any]   	=				[10, 50, 80]
					__snake_case    :							int   	=				32
					__snake_case    :							Tuple   	=				32
					__snake_case    :							List[Any]   	=				4
					__snake_case    :							Any   	=				8
					__snake_case    :							str   	=				1_28
					__snake_case    :							Any   	=				2
					__snake_case    :							List[str]   	=				2
					__snake_case    :							Dict   	=				None
					__snake_case    :							List[Any]   	=				1
					__snake_case    :							List[str]   	=				0
					__snake_case    :							str   	=				3
					__snake_case    :							Union[str, Any]   	=				self.vocab_size - 1
					__snake_case    :							str   	=				0.01



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

					__snake_case    :							Dict   	=				None
					if self.use_labels:
						__snake_case    :							int   	=				ids_tensor([self.batch_size, self.seq_length]			,						self.vocab_size)

					__snake_case    :							str   	=				TransfoXLConfig(
					    vocab_size=self.vocab_size			,						mem_len=self.mem_len			,						clamp_len=self.clamp_len			,						cutoffs=self.cutoffs			,						d_model=self.hidden_size			,						d_embed=self.d_embed			,						n_head=self.num_attention_heads			,						d_head=self.d_head			,						d_inner=self.d_inner			,						div_val=self.div_val			,						n_layer=self.num_hidden_layers			,						eos_token_id=self.eos_token_id			,						pad_token_id=self.vocab_size - 1			,						init_range=self.init_range			,						num_labels=self.num_labels			,						)

					return (config, input_ids_a, input_ids_a, lm_labels)



				def       _lowercase  (self       :   Dict)					->			int:
					random.seed(self.seed)
					tf.random.set_seed(self.seed)



				def       _lowercase  (self       :   Optional[int]			,						_A       :   Tuple			,						_A       :   int			,						_A       :   Tuple			,						_A       :   Tuple)					->			Optional[Any]:
					__snake_case    :							List[Any]   	=				TFTransfoXLModel(lowercase_)

					__snake_case    :							Union[str, Any]   	=				model(lowercase_).to_tuple()

					__snake_case    :							Optional[int]   	=				{"input_ids": input_ids_a, "mems": mems_a}

					__snake_case    :							Dict   	=				model(lowercase_).to_tuple()

					self.parent.assertEqual(hidden_states_a.shape			,						(self.batch_size, self.seq_length, self.hidden_size))
					self.parent.assertEqual(hidden_states_a.shape			,						(self.batch_size, self.seq_length, self.hidden_size))
					self.parent.assertListEqual(
					    [mem.shape for mem in mems_a]			,						[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers			,						)
					self.parent.assertListEqual(
					    [mem.shape for mem in mems_a]			,						[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers			,						)



				def       _lowercase  (self       :   Optional[int]			,						_A       :   Dict			,						_A       :   Optional[int]			,						_A       :   str			,						_A       :   List[Any])					->			Dict:
					__snake_case    :							str   	=				TFTransfoXLLMHeadModel(lowercase_)

					__snake_case    :							int   	=				model(lowercase_).to_tuple()

					__snake_case    :							List[str]   	=				{"input_ids": input_ids_a, "labels": lm_labels}
					__snake_case    :							List[Any]   	=				model(lowercase_).to_tuple()

					__snake_case    :							str   	=				model([input_ids_a, mems_a]).to_tuple()

					__snake_case    :							List[Any]   	=				{"input_ids": input_ids_a, "mems": mems_a, "labels": lm_labels}

					__snake_case    :							Dict   	=				model(lowercase_).to_tuple()

					self.parent.assertEqual(lm_logits_a.shape			,						(self.batch_size, self.seq_length, self.vocab_size))
					self.parent.assertListEqual(
					    [mem.shape for mem in mems_a]			,						[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers			,						)

					self.parent.assertEqual(lm_logits_a.shape			,						(self.batch_size, self.seq_length, self.vocab_size))
					self.parent.assertListEqual(
					    [mem.shape for mem in mems_a]			,						[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers			,						)



				def       _lowercase  (self       :   Optional[Any]			,						_A       :   int			,						_A       :   Any			,						_A       :   List[Any]			,						_A       :   str)					->			List[str]:
					__snake_case    :							Tuple   	=				TFTransfoXLForSequenceClassification(lowercase_)
					__snake_case    :							Any   	=				model(lowercase_)
					self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.num_labels))






				def       _lowercase  (self       :   Tuple)					->			str:
					__snake_case    :							Any   	=				self.prepare_config_and_inputs()
					(__snake_case)    :							Union[str, Any]   	=				config_and_inputs
					__snake_case    :							int   	=				{"input_ids": input_ids_a}
					return config, inputs_dict







@require_tf
class 	UpperCamelCase    (					snake_case__   ,			snake_case__   ,			unittest.TestCase		):
				UpperCAmelCase       :   int					=	(
				    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
				)
				UpperCAmelCase       :   List[str]					=	() if is_tf_available() else ()
				UpperCAmelCase       :   int					=	(
				    {
				        """feature-extraction""": TFTransfoXLModel,
				        """text-classification""": TFTransfoXLForSequenceClassification,
				        """text-generation""": TFTransfoXLLMHeadModel,
				        """zero-shot""": TFTransfoXLForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
				UpperCAmelCase       :   str					=	False
				UpperCAmelCase       :   Dict					=	False
				UpperCAmelCase       :   Tuple					=	False
				UpperCAmelCase       :   Optional[int]					=	False



				def       _lowercase  (self       :   Tuple			,						_A       :   int			,						_A       :   List[str]			,						_A       :   Union[str, Any]			,						_A       :   Optional[int]			,						_A       :   List[str])					->			Optional[Any]:
					if pipeline_test_casse_name == "TextGenerationPipelineTests":
						# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
						# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
						# tokenizer.
						return True

					return False



				def       _lowercase  (self       :   List[str])					->			str:
					__snake_case    :							int   	=				TFTransfoXLModelTester(self)
					__snake_case    :							Any   	=				ConfigTester(self			,						config_class=lowercase_			,						d_embed=37)



				def       _lowercase  (self       :   Tuple)					->			str:
					self.config_tester.run_common_tests()



				def       _lowercase  (self       :   Tuple)					->			str:
					self.model_tester.set_seed()
					__snake_case    :							Optional[int]   	=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_transfo_xl_model(*lowercase_)



				def       _lowercase  (self       :   Tuple)					->			str:
					self.model_tester.set_seed()
					__snake_case    :							str   	=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_transfo_xl_lm_head(*lowercase_)



				def       _lowercase  (self       :   Optional[Any])					->			Union[str, Any]:
					__snake_case    :							Tuple   	=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*lowercase_)



				def       _lowercase  (self       :   Union[str, Any])					->			Any:
					__snake_case    :							Optional[Any]   	=				self.model_tester.prepare_config_and_inputs_for_common()
					__snake_case    :							str   	=				[TFTransfoXLForSequenceClassification]

					for model_class in self.all_model_classes:
						__snake_case    :							Dict   	=				model_class(lowercase_)
						assert isinstance(model.get_input_embeddings()			,						tf.keras.layers.Layer)
						if model_class in list_other_models_with_output_ebd:
							__snake_case    :							Union[str, Any]   	=				model.get_output_embeddings()
							assert isinstance(lowercase_			,						tf.keras.layers.Layer)
							__snake_case    :							Union[str, Any]   	=				model.get_bias()
							assert name is None
						else:
							__snake_case    :							Dict   	=				model.get_output_embeddings()
							assert x is None
							__snake_case    :							str   	=				model.get_bias()
							assert name is None



				def       _lowercase  (self       :   Union[str, Any])					->			Dict:
					# TODO JP: Make TransfoXL XLA compliant
					pass



				@slow
				def       _lowercase  (self       :   str)					->			List[str]:
					for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						__snake_case    :							int   	=				TFTransfoXLModel.from_pretrained(lowercase_)
						self.assertIsNotNone(lowercase_)






				@unittest.skip(reason='This model doesn\'t play well with fit() due to not returning a single loss.')
				def       _lowercase  (self       :   Optional[int])					->			Dict:
					pass







@require_tf
class 	UpperCamelCase    (					unittest.TestCase		):



				@unittest.skip('Skip test until #12651 is resolved.')
				@slow
				def       _lowercase  (self       :   Optional[Any])					->			str:
					__snake_case    :							List[str]   	=				TFTransfoXLLMHeadModel.from_pretrained('transfo-xl-wt103')
					# fmt: off
					__snake_case    :							Union[str, Any]   	=				tf.convert_to_tensor([[33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0]]			,						dtype=tf.intaa)  # noqa: E231
					# fmt: on
					#  In 1991 , the remains of Russian Tsar Nicholas II and his family
					#  ( except for Alexei and Maria ) are discovered .
					#  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
					#  remainder of the story . 1883 Western Siberia ,
					#  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
					#  Rasputin has a vision and denounces one of the men as a horse thief . Although his
					#  father initially slaps him for making such an accusation , Rasputin watches as the
					#  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
					#  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
					#  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

					# fmt: off
					__snake_case    :							Union[str, Any]   	=				[33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0,33,1,18_57,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,28,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,0]  # noqa: E231
					# fmt: on
					#  In 1991, the remains of Russian Tsar Nicholas II and his family (
					#  except for Alexei and Maria ) are discovered. The voice of young son,
					#  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
					#  1883 Western Siberia, a young Grigori Rasputin is asked by his father
					#  and a group of men to perform magic. Rasputin has a vision and
					#  denounces one of the men as a horse thief. Although his father initially
					#  slaps him for making such an accusation, Rasputin watches as the man
					#  is chased outside and beaten. Twenty years later, Rasputin sees a vision
					#  of the Virgin Mary, prompting him to become a priest.
					#  Rasputin quickly becomes famous, with people, even a bishop, begging for
					#  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
					# Nicholas II and his family were discovered. The voice of <unk> young son,
					# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

					__snake_case    :							Dict   	=				model.generate(lowercase_			,						max_length=2_00			,						do_sample=lowercase_)
					self.assertListEqual(output_ids[0].numpy().tolist()			,						lowercase_)






"""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)





