




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()
UpperCamelCase__ :    int         =      logging.get_logger(__name__)







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

					return new_state_dict







def      _a				(						SCREAMING_SNAKE_CASE_							:    Dict				,				SCREAMING_SNAKE_CASE_							:    Optional[Any]					):
					# 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           =	state_dict.pop(F"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight"""					)
															__lowerCAmelCase           =	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           =	kv_weight[
															    : config.hidden_sizes[i], :
															]
															__lowerCAmelCase           =	kv_bias[: config.hidden_sizes[i]]
															__lowerCAmelCase           =	kv_weight[
															    config.hidden_sizes[i] :, :
															]
															__lowerCAmelCase           =	kv_bias[config.hidden_sizes[i] :]







def      _a				(						):
					__lowerCAmelCase           =	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					__lowerCAmelCase           =	Image.open(requests.get(A_				,				stream=A_					).raw					)

					return image







@torch.no_grad()
def      _a				(						SCREAMING_SNAKE_CASE_							:    List[str]				,				SCREAMING_SNAKE_CASE_							:    Optional[Any]				,				SCREAMING_SNAKE_CASE_							:    Any=False				,				SCREAMING_SNAKE_CASE_							:    List[str]=None					):
					__lowerCAmelCase           =	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)
					__lowerCAmelCase           =	GLPNImageProcessor()

					# prepare image
					__lowerCAmelCase           =	prepare_img()
					__lowerCAmelCase           =	image_processor(images=A_				,				return_tensors="pt"					).pixel_values

					logger.info("Converting model..."					)

					# load original state dict
					__lowerCAmelCase           =	torch.load(A_				,				map_location=torch.device("cpu"					)					)

					# rename keys
					__lowerCAmelCase           =	rename_keys(A_					)

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

					# create HuggingFace model and load state dict
					__lowerCAmelCase           =	GLPNForDepthEstimation(A_					)
					model.load_state_dict(A_					)
					model.eval()

					# forward pass
					__lowerCAmelCase           =	model(A_					)
					__lowerCAmelCase           =	outputs.predicted_depth

					# verify output
					if model_name is not None:
										if "nyu" in model_name:
															__lowerCAmelCase           =	torch.tensor(
															    [[4.41_47, 4.08_73, 4.06_73], [3.78_90, 3.28_81, 3.15_25], [3.76_74, 3.54_23, 3.49_13]]					)
										elif "kitti" in model_name:
															__lowerCAmelCase           =	torch.tensor(
															    [[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]]					)
										else:
															raise ValueError(F"""Unknown model name: {model_name}"""					)

										__lowerCAmelCase           =	torch.Size([1, 4_80, 6_40]					)

										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__":
					UpperCamelCase__ :    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.""",
					)
					UpperCamelCase__ :    List[Any]         =      parser.parse_args()
					convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)










from __future__ import annotations

import copy
import tempfile
import unittest

from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tensorflow_probability,
    require_tf,
    slow,
)

from ..bert.test_modeling_bert import BertModelTester


if is_tf_available():
					from transformers import (
					    TFAutoModel,
					    TFAutoModelForCausalLM,
					    TFAutoModelForMaskedLM,
					    TFAutoModelForPreTraining,
					    TFAutoModelForQuestionAnswering,
					    TFAutoModelForSeqaSeqLM,
					    TFAutoModelForSequenceClassification,
					    TFAutoModelForTableQuestionAnswering,
					    TFAutoModelForTokenClassification,
					    TFAutoModelWithLMHead,
					    TFBertForMaskedLM,
					    TFBertForPreTraining,
					    TFBertForQuestionAnswering,
					    TFBertForSequenceClassification,
					    TFBertModel,
					    TFFunnelBaseModel,
					    TFFunnelModel,
					    TFGPTaLMHeadModel,
					    TFRobertaForMaskedLM,
					    TFTaForConditionalGeneration,
					    TFTapasForQuestionAnswering,
					)
					from transformers.models.auto.modeling_tf_auto import (
					    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
					    TF_MODEL_FOR_MASKED_LM_MAPPING,
					    TF_MODEL_FOR_PRETRAINING_MAPPING,
					    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
					    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
					    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
					    TF_MODEL_MAPPING,
					)
					from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
					from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
					from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
					from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST
class       a__   ( snake_case__				):
						_a							:   Optional[int] =   """new-model"""


if is_tf_available():
					class       a__   ( snake_case__				):
											_a							:   Dict =   NewModelConfig
@require_tf
class       a__   ( unittest.TestCase				):






						@slow
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	"bert-base-cased"
											__lowerCAmelCase           =	AutoConfig.from_pretrained(_A      )
											self.assertIsNotNone(_A      )
											self.assertIsInstance(_A		,      _A      )

											__lowerCAmelCase           =	TFAutoModel.from_pretrained(_A      )
											self.assertIsNotNone(_A      )
											self.assertIsInstance(_A		,      _A      )






						@slow
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	"bert-base-cased"
											__lowerCAmelCase           =	AutoConfig.from_pretrained(_A      )
											self.assertIsNotNone(_A      )
											self.assertIsInstance(_A		,      _A      )

											__lowerCAmelCase           =	TFAutoModelForPreTraining.from_pretrained(_A      )
											self.assertIsNotNone(_A      )
											self.assertIsInstance(_A		,      _A      )






						@slow
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__lowerCAmelCase           =	AutoConfig.from_pretrained(_A      )
																self.assertIsNotNone(_A      )
																self.assertIsInstance(_A		,      _A      )

																__lowerCAmelCase           =	TFAutoModelForCausalLM.from_pretrained(_A      )
																__lowerCAmelCase       ,							__lowerCAmelCase           =	TFAutoModelForCausalLM.from_pretrained(_A		,      output_loading_info=_A      )
																self.assertIsNotNone(_A      )
																self.assertIsInstance(_A		,      _A      )






						@slow
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__lowerCAmelCase           =	AutoConfig.from_pretrained(_A      )
																self.assertIsNotNone(_A      )
																self.assertIsInstance(_A		,      _A      )

																__lowerCAmelCase           =	TFAutoModelWithLMHead.from_pretrained(_A      )
																self.assertIsNotNone(_A      )
																self.assertIsInstance(_A		,      _A      )






						@slow
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__lowerCAmelCase           =	AutoConfig.from_pretrained(_A      )
																self.assertIsNotNone(_A      )
																self.assertIsInstance(_A		,      _A      )

																__lowerCAmelCase           =	TFAutoModelForMaskedLM.from_pretrained(_A      )
																__lowerCAmelCase       ,							__lowerCAmelCase           =	TFAutoModelForMaskedLM.from_pretrained(_A		,      output_loading_info=_A      )
																self.assertIsNotNone(_A      )
																self.assertIsInstance(_A		,      _A      )






						@slow
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__lowerCAmelCase           =	AutoConfig.from_pretrained(_A      )
																self.assertIsNotNone(_A      )
																self.assertIsInstance(_A		,      _A      )

																__lowerCAmelCase           =	TFAutoModelForSeqaSeqLM.from_pretrained(_A      )
																__lowerCAmelCase       ,							__lowerCAmelCase           =	TFAutoModelForSeqaSeqLM.from_pretrained(_A		,      output_loading_info=_A      )
																self.assertIsNotNone(_A      )
																self.assertIsInstance(_A		,      _A      )






						@slow
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											for model_name in ["bert-base-uncased"]:
																__lowerCAmelCase           =	AutoConfig.from_pretrained(_A      )
																self.assertIsNotNone(_A      )
																self.assertIsInstance(_A		,      _A      )

																__lowerCAmelCase           =	TFAutoModelForSequenceClassification.from_pretrained(_A      )
																self.assertIsNotNone(_A      )
																self.assertIsInstance(_A		,      _A      )






						@slow
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											for model_name in ["bert-base-uncased"]:
																__lowerCAmelCase           =	AutoConfig.from_pretrained(_A      )
																self.assertIsNotNone(_A      )
																self.assertIsInstance(_A		,      _A      )

																__lowerCAmelCase           =	TFAutoModelForQuestionAnswering.from_pretrained(_A      )
																self.assertIsNotNone(_A      )
																self.assertIsInstance(_A		,      _A      )






						@slow
						@require_tensorflow_probability
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
																__lowerCAmelCase           =	AutoConfig.from_pretrained(_A      )
																self.assertIsNotNone(_A      )
																self.assertIsInstance(_A		,      _A      )

																__lowerCAmelCase           =	TFAutoModelForTableQuestionAnswering.from_pretrained(_A      )
																__lowerCAmelCase       ,							__lowerCAmelCase           =	TFAutoModelForTableQuestionAnswering.from_pretrained(
																    _A		,      output_loading_info=_A      )
																self.assertIsNotNone(_A      )
																self.assertIsInstance(_A		,      _A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	TFAutoModelWithLMHead.from_pretrained(_A      )
											self.assertIsInstance(_A		,      _A      )
											self.assertEqual(model.num_parameters()		,      1_4_4_1_0      )
											self.assertEqual(model.num_parameters(only_trainable=_A      )		,      1_4_4_1_0      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	TFAutoModelWithLMHead.from_pretrained(_A      )
											self.assertIsInstance(_A		,      _A      )
											self.assertEqual(model.num_parameters()		,      1_4_4_1_0      )
											self.assertEqual(model.num_parameters(only_trainable=_A      )		,      1_4_4_1_0      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	TFAutoModel.from_pretrained("sgugger/funnel-random-tiny"      )
											self.assertIsInstance(_A		,      _A      )

											__lowerCAmelCase           =	copy.deepcopy(model.config      )
											__lowerCAmelCase           =	["FunnelBaseModel"]
											__lowerCAmelCase           =	TFAutoModel.from_config(_A      )
											self.assertIsInstance(_A		,      _A      )

											with tempfile.TemporaryDirectory() as tmp_dir:
																model.save_pretrained(_A      )
																__lowerCAmelCase           =	TFAutoModel.from_pretrained(_A      )
																self.assertIsInstance(_A		,      _A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											try:
																AutoConfig.register("new-model"		,      _A      )

																__lowerCAmelCase           =	[
																    TFAutoModel,
																    TFAutoModelForCausalLM,
																    TFAutoModelForMaskedLM,
																    TFAutoModelForPreTraining,
																    TFAutoModelForQuestionAnswering,
																    TFAutoModelForSequenceClassification,
																    TFAutoModelForTokenClassification,
																]

																for auto_class in auto_classes:
																					with self.subTest(auto_class.__name__      ):
																										# Wrong config class will raise an error
																										with self.assertRaises(_A      ):
																															auto_class.register(_A		,      _A      )
																										auto_class.register(_A		,      _A      )
																										# Trying to register something existing in the Transformers library will raise an error
																										with self.assertRaises(_A      ):
																															auto_class.register(_A		,      _A      )

																										# Now that the config is registered, it can be used as any other config with the auto-API
																										__lowerCAmelCase           =	BertModelTester(self      ).get_config()
																										__lowerCAmelCase           =	NewModelConfig(**tiny_config.to_dict()      )
																										__lowerCAmelCase           =	auto_class.from_config(_A      )
																										self.assertIsInstance(_A		,      _A      )

																										with tempfile.TemporaryDirectory() as tmp_dir:
																															model.save_pretrained(_A      )
																															__lowerCAmelCase           =	auto_class.from_pretrained(_A      )
																															self.assertIsInstance(_A		,      _A      )

											finally:
																if "new-model" in CONFIG_MAPPING._extra_content:
																					del CONFIG_MAPPING._extra_content["new-model"]
																for mapping in (
																    TF_MODEL_MAPPING,
																    TF_MODEL_FOR_PRETRAINING_MAPPING,
																    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
																    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
																    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
																    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
																    TF_MODEL_FOR_MASKED_LM_MAPPING,
																):
																					if NewModelConfig in mapping._extra_content:
																										del mapping._extra_content[NewModelConfig]






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											with self.assertRaisesRegex(
											    _A		,      "bert-base is not a local folder and is not a valid model identifier"      ):
																__lowerCAmelCase           =	TFAutoModel.from_pretrained("bert-base"      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											with self.assertRaisesRegex(
											    _A		,      R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"      ):
																__lowerCAmelCase           =	TFAutoModel.from_pretrained(_A		,      revision="aaaaaa"      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											with self.assertRaisesRegex(
											    _A		,      "hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin"		,      ):
																__lowerCAmelCase           =	TFAutoModel.from_pretrained("hf-internal-testing/config-no-model"      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											with self.assertRaisesRegex(_A		,      "Use `from_pt=True` to load this model"      ):
																__lowerCAmelCase           =	TFAutoModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only"      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	TFAutoModel.from_pretrained("hf-internal-testing/tiny-random-bert"      )
											with RequestCounter() as counter:
																__lowerCAmelCase           =	TFAutoModel.from_pretrained("hf-internal-testing/tiny-random-bert"      )
																self.assertEqual(counter.get_request_count		,      0      )
																self.assertEqual(counter.head_request_count		,      1      )
																self.assertEqual(counter.other_request_count		,      0      )

											# With a sharded checkpoint
											__lowerCAmelCase           =	TFAutoModel.from_pretrained("ArthurZ/tiny-random-bert-sharded"      )
											with RequestCounter() as counter:
																__lowerCAmelCase           =	TFAutoModel.from_pretrained("ArthurZ/tiny-random-bert-sharded"      )
																self.assertEqual(counter.get_request_count		,      0      )
																self.assertEqual(counter.head_request_count		,      1      )
																self.assertEqual(counter.other_request_count		,      0      )




