"""simple docstring"""







import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors

@require_tokenizers
class 	UpperCamelCase    (					lowercase   ,			unittest.TestCase		):
				UpperCAmelCase       :   List[Any]					=	MvpTokenizer
				UpperCAmelCase       :   int					=	MvpTokenizerFast
				UpperCAmelCase       :   Optional[Any]					=	True
				UpperCAmelCase       :   Tuple					=	filter_roberta_detectors



				def       _lowercase  (self       :   Any)					->			str:
					super().setUp()
					__snake_case    :							Optional[Any]   	=				[
					    'l',
					    'o',
					    'w',
					    'e',
					    'r',
					    's',
					    't',
					    'i',
					    'd',
					    'n',
					    '\u0120',
					    '\u0120l',
					    '\u0120n',
					    '\u0120lo',
					    '\u0120low',
					    'er',
					    '\u0120lowest',
					    '\u0120newer',
					    '\u0120wider',
					    '<unk>',
					]
					__snake_case    :							List[Any]   	=				dict(zip(_A			,						range(len(_A))))
					__snake_case    :							Optional[Any]   	=				['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
					__snake_case    :							Optional[Any]   	=				{'unk_token': '<unk>'}

					__snake_case    :							int   	=				os.path.join(self.tmpdirname			,						VOCAB_FILES_NAMES['vocab_file'])
					__snake_case    :							str   	=				os.path.join(self.tmpdirname			,						VOCAB_FILES_NAMES['merges_file'])
					with open(self.vocab_file			,						'w'			,						encoding='utf-8') as fp:
						fp.write(json.dumps(_A) + '\n')
					with open(self.merges_file			,						'w'			,						encoding='utf-8') as fp:
						fp.write('\n'.join(_A))



				def       _lowercase  (self       :   List[str]			,						**_A       :   Any)					->			str:
					kwargs.update(self.special_tokens_map)
					return self.tokenizer_class.from_pretrained(self.tmpdirname			,						**_A)



				def       _lowercase  (self       :   Optional[Any]			,						**_A       :   Tuple)					->			Any:
					kwargs.update(self.special_tokens_map)
					return self.rust_tokenizer_class.from_pretrained(self.tmpdirname			,						**_A)



				def       _lowercase  (self       :   Union[str, Any]			,						_A       :   str)					->			Optional[Any]:
					return "lower newer", "lower newer"



				@cached_property
				def       _lowercase  (self       :   Tuple)					->			int:
					return MvpTokenizer.from_pretrained('RUCAIBox/mvp')



				@cached_property
				def       _lowercase  (self       :   int)					->			Tuple:
					return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp')



				@require_torch
				def       _lowercase  (self       :   Optional[int])					->			List[Any]:
					__snake_case    :							Optional[int]   	=				['A long paragraph for summarization.', 'Another paragraph for summarization.']
					__snake_case    :							Any   	=				[0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						__snake_case    :							str   	=				tokenizer(_A			,						max_length=len(_A)			,						padding=_A			,						return_tensors='pt')
						self.assertIsInstance(_A			,						_A)

						self.assertEqual((2, 9)			,						batch.input_ids.shape)
						self.assertEqual((2, 9)			,						batch.attention_mask.shape)
						__snake_case    :							List[str]   	=				batch.input_ids.tolist()[0]
						self.assertListEqual(_A			,						_A)
						# Test that special tokens are reset



				@require_torch
				def       _lowercase  (self       :   List[str])					->			Any:
					__snake_case    :							List[Any]   	=				['A long paragraph for summarization.', 'Another paragraph for summarization.']
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						__snake_case    :							Tuple   	=				tokenizer(_A			,						padding=_A			,						return_tensors='pt')
						# check if input_ids are returned and no labels
						self.assertIn('input_ids'			,						_A)
						self.assertIn('attention_mask'			,						_A)
						self.assertNotIn('labels'			,						_A)
						self.assertNotIn('decoder_attention_mask'			,						_A)



				@require_torch
				def       _lowercase  (self       :   List[Any])					->			Tuple:
					__snake_case    :							List[Any]   	=				[
					    'Summary of the text.',
					    'Another summary.',
					]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						__snake_case    :							Any   	=				tokenizer(text_target=_A			,						max_length=32			,						padding='max_length'			,						return_tensors='pt')
						self.assertEqual(32			,						targets['input_ids'].shape[1])



				@require_torch
				def       _lowercase  (self       :   Tuple)					->			Dict:
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						__snake_case    :							Dict   	=				tokenizer(
						    ['I am a small frog' * 10_24, 'I am a small frog']			,						padding=_A			,						truncation=_A			,						return_tensors='pt')
						self.assertIsInstance(_A			,						_A)
						self.assertEqual(batch.input_ids.shape			,						(2, 10_24))



				@require_torch
				def       _lowercase  (self       :   Optional[int])					->			Dict:
					__snake_case    :							List[Any]   	=				['A long paragraph for summarization.']
					__snake_case    :							str   	=				[
					    'Summary of the text.',
					]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						__snake_case    :							Any   	=				tokenizer(_A			,						text_target=_A			,						return_tensors='pt')
						__snake_case    :							str   	=				inputs['input_ids']
						__snake_case    :							Optional[Any]   	=				inputs['labels']
						self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item())
						self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item())
						self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item())
						self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item())



				def       _lowercase  (self       :   str)					->			Tuple:
					pass






				def       _lowercase  (self       :   str)					->			Any:
					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
						with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
							__snake_case    :							Any   	=				self.rust_tokenizer_class.from_pretrained(_A			,						**_A)
							__snake_case    :							List[Any]   	=				self.tokenizer_class.from_pretrained(_A			,						**_A)
							__snake_case    :							List[Any]   	=				'A, <mask> AllenNLP sentence.'
							__snake_case    :							List[Any]   	=				tokenizer_r.encode_plus(_A			,						add_special_tokens=_A			,						return_token_type_ids=_A)
							__snake_case    :							int   	=				tokenizer_p.encode_plus(_A			,						add_special_tokens=_A			,						return_token_type_ids=_A)

							# token_type_ids should put 0 everywhere
							self.assertEqual(sum(tokens_r['token_type_ids'])			,						sum(tokens_p['token_type_ids']))

							# attention_mask should put 1 everywhere, so sum over length should be 1
							self.assertEqual(
							    sum(tokens_r['attention_mask']) / len(tokens_r['attention_mask'])			,						sum(tokens_p['attention_mask']) / len(tokens_p['attention_mask'])			,						)

							__snake_case    :							Tuple   	=				tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'])
							__snake_case    :							Union[str, Any]   	=				tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'])

							# Rust correctly handles the space before the mask while python doesnt
							self.assertSequenceEqual(tokens_p['input_ids']			,						[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2])
							self.assertSequenceEqual(tokens_r['input_ids']			,						[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2])

							self.assertSequenceEqual(
							    _A			,						['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'])
							self.assertSequenceEqual(
							    _A			,						['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'])






"""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)





