
"""simple docstring"""





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase_       :    str						      =			logging.get_logger(__name__)
def 				A_  (__a					):

					'''simple docstring'''





					A_								=    DPTConfig()

					if "large" in checkpoint_url:
										A_								=    1024
										A_								=    4096
										A_								=    24
										A_								=    16
										A_								=    [5, 11, 17, 23]
										A_								=    [256, 512, 1024, 1024]
										A_								=    (1, 384, 384)

					if "ade" in checkpoint_url:
										A_								=    True

										A_								=    150
										A_								=    "huggingface/label-files"
										A_								=    "ade20k-id2label.json"
										A_								=    json.load(open(cached_download(hf_hub_url(__a					, __a					, repo_type="dataset"					)					)					, "r"					)					)
										A_								=    {int(__a					): v for k, v in idalabel.items()}
										A_								=    idalabel
										A_								=    {v: k for k, v in idalabel.items()}
										A_								=    [1, 150, 480, 480]

					return config, expected_shape
def 				A_  (__a					):

					'''simple docstring'''





					A_								=    ["pretrained.model.head.weight", "pretrained.model.head.bias"]
					for k in ignore_keys:
										state_dict.pop(__a					, __a					)
def 				A_  (__a					):

					'''simple docstring'''





					if (
					    "pretrained.model" in name
					    and "cls_token" not in name
					    and "pos_embed" not in name
					    and "patch_embed" not in name
					):
										A_								=    name.replace("pretrained.model"					, "dpt.encoder"					)
					if "pretrained.model" in name:
										A_								=    name.replace("pretrained.model"					, "dpt.embeddings"					)
					if "patch_embed" in name:
										A_								=    name.replace("patch_embed"					, "patch_embeddings"					)
					if "pos_embed" in name:
										A_								=    name.replace("pos_embed"					, "position_embeddings"					)
					if "attn.proj" in name:
										A_								=    name.replace("attn.proj"					, "attention.output.dense"					)
					if "proj" in name and "project" not in name:
										A_								=    name.replace("proj"					, "projection"					)
					if "blocks" in name:
										A_								=    name.replace("blocks"					, "layer"					)
					if "mlp.fc1" in name:
										A_								=    name.replace("mlp.fc1"					, "intermediate.dense"					)
					if "mlp.fc2" in name:
										A_								=    name.replace("mlp.fc2"					, "output.dense"					)
					if "norm1" in name:
										A_								=    name.replace("norm1"					, "layernorm_before"					)
					if "norm2" in name:
										A_								=    name.replace("norm2"					, "layernorm_after"					)
					if "scratch.output_conv" in name:
										A_								=    name.replace("scratch.output_conv"					, "head"					)
					if "scratch" in name:
										A_								=    name.replace("scratch"					, "neck"					)
					if "layer1_rn" in name:
										A_								=    name.replace("layer1_rn"					, "convs.0"					)
					if "layer2_rn" in name:
										A_								=    name.replace("layer2_rn"					, "convs.1"					)
					if "layer3_rn" in name:
										A_								=    name.replace("layer3_rn"					, "convs.2"					)
					if "layer4_rn" in name:
										A_								=    name.replace("layer4_rn"					, "convs.3"					)
					if "refinenet" in name:
										A_								=    int(name[len("neck.refinenet"					) : len("neck.refinenet"					) + 1]					)
										# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
										A_								=    name.replace(f'refinenet{layer_idx}'					, f'fusion_stage.layers.{abs(layer_idx-4					)}'					)
					if "out_conv" in name:
										A_								=    name.replace("out_conv"					, "projection"					)
					if "resConfUnit1" in name:
										A_								=    name.replace("resConfUnit1"					, "residual_layer1"					)
					if "resConfUnit2" in name:
										A_								=    name.replace("resConfUnit2"					, "residual_layer2"					)
					if "conv1" in name:
										A_								=    name.replace("conv1"					, "convolution1"					)
					if "conv2" in name:
										A_								=    name.replace("conv2"					, "convolution2"					)
					# readout blocks
					if "pretrained.act_postprocess1.0.project.0" in name:
										A_								=    name.replace("pretrained.act_postprocess1.0.project.0"					, "neck.reassemble_stage.readout_projects.0.0"					)
					if "pretrained.act_postprocess2.0.project.0" in name:
										A_								=    name.replace("pretrained.act_postprocess2.0.project.0"					, "neck.reassemble_stage.readout_projects.1.0"					)
					if "pretrained.act_postprocess3.0.project.0" in name:
										A_								=    name.replace("pretrained.act_postprocess3.0.project.0"					, "neck.reassemble_stage.readout_projects.2.0"					)
					if "pretrained.act_postprocess4.0.project.0" in name:
										A_								=    name.replace("pretrained.act_postprocess4.0.project.0"					, "neck.reassemble_stage.readout_projects.3.0"					)
					# resize blocks
					if "pretrained.act_postprocess1.3" in name:
										A_								=    name.replace("pretrained.act_postprocess1.3"					, "neck.reassemble_stage.layers.0.projection"					)
					if "pretrained.act_postprocess1.4" in name:
										A_								=    name.replace("pretrained.act_postprocess1.4"					, "neck.reassemble_stage.layers.0.resize"					)
					if "pretrained.act_postprocess2.3" in name:
										A_								=    name.replace("pretrained.act_postprocess2.3"					, "neck.reassemble_stage.layers.1.projection"					)
					if "pretrained.act_postprocess2.4" in name:
										A_								=    name.replace("pretrained.act_postprocess2.4"					, "neck.reassemble_stage.layers.1.resize"					)
					if "pretrained.act_postprocess3.3" in name:
										A_								=    name.replace("pretrained.act_postprocess3.3"					, "neck.reassemble_stage.layers.2.projection"					)
					if "pretrained.act_postprocess4.3" in name:
										A_								=    name.replace("pretrained.act_postprocess4.3"					, "neck.reassemble_stage.layers.3.projection"					)
					if "pretrained.act_postprocess4.4" in name:
										A_								=    name.replace("pretrained.act_postprocess4.4"					, "neck.reassemble_stage.layers.3.resize"					)
					if "pretrained" in name:
										A_								=    name.replace("pretrained"					, "dpt"					)
					if "bn" in name:
										A_								=    name.replace("bn"					, "batch_norm"					)
					if "head" in name:
										A_								=    name.replace("head"					, "head.head"					)
					if "encoder.norm" in name:
										A_								=    name.replace("encoder.norm"					, "layernorm"					)
					if "auxlayer" in name:
										A_								=    name.replace("auxlayer"					, "auxiliary_head.head"					)

					return name
def 				A_  (__a					, __a					):

					'''simple docstring'''





					for i in range(config.num_hidden_layers					):
										# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
										A_								=    state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.weight'					)
										A_								=    state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.bias'					)
										# next, add query, keys and values (in that order) to the state dict
										A_								=    in_proj_weight[: config.hidden_size, :]
										A_								=    in_proj_bias[: config.hidden_size]
										A_								=    in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										A_								=    in_proj_bias[
										    config.hidden_size : config.hidden_size * 2
										]
										A_								=    in_proj_weight[
										    -config.hidden_size :, :
										]
										A_								=    in_proj_bias[-config.hidden_size :]
def 				A_  ():

					'''simple docstring'''





					A_								=    "http://images.cocodataset.org/val2017/000000039769.jpg"
					A_								=    Image.open(requests.get(__a					, stream=__a					).raw					)
					return im
@torch.no_grad()
def 				A_  (__a					, __a					, __a					, __a					):

					'''simple docstring'''





					A_			,     A_								=    get_dpt_config(__a					)
					# load original state_dict from URL
					A_								=    torch.hub.load_state_dict_from_url(__a					, map_location="cpu"					)
					# remove certain keys
					remove_ignore_keys_(__a					)
					# rename keys
					for key in state_dict.copy().keys():
										A_								=    state_dict.pop(__a					)
										A_								=    val
					# read in qkv matrices
					read_in_q_k_v(__a					, __a					)

					# load HuggingFace model
					A_								=    DPTForSemanticSegmentation(__a					) if "ade" in checkpoint_url else DPTForDepthEstimation(__a					)
					model.load_state_dict(__a					)
					model.eval()

					# Check outputs on an image
					A_								=    480 if "ade" in checkpoint_url else 384
					A_								=    DPTImageProcessor(size=__a					)

					A_								=    prepare_img()
					A_								=    image_processor(__a					, return_tensors="pt"					)

					# forward pass
					A_								=    model(**__a					).logits if "ade" in checkpoint_url else model(**__a					).predicted_depth

					# Assert logits
					A_								=    torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]					)
					if "ade" in checkpoint_url:
										A_								=    torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]					)
					assert outputs.shape == torch.Size(__a					)
					assert (
					    torch.allclose(outputs[0, 0, :3, :3]					, __a					, atol=1e-4					)
					    if "ade" in checkpoint_url
					    else torch.allclose(outputs[0, :3, :3]					, __a					)
					)

					Path(__a					).mkdir(exist_ok=__a					)
					print(f'Saving model to {pytorch_dump_folder_path}'					)
					model.save_pretrained(__a					)
					print(f'Saving image processor to {pytorch_dump_folder_path}'					)
					image_processor.save_pretrained(__a					)

					if push_to_hub:
										print("Pushing model to 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_       :    List[Any]						      =			argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--checkpoint_url''',
		    default='''https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt''',
		    type=str,
		    help='''URL of the original DPT checkpoint you\'d like to convert.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''',
		    default=None,
		    type=str,
		    required=True,
		    help='''Path to the output PyTorch model directory.''',
		)
		parser.add_argument(
		    '''--push_to_hub''',
		    action='''store_true''',
		)
		parser.add_argument(
		    '''--model_name''',
		    default='''dpt-large''',
		    type=str,
		    help='''Name of the model, in case you\'re pushing to the hub.''',
		)

		UpperCamelCase_       :    Union[str, Any]						      =			parser.parse_args()
		convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)






"""simple docstring"""





import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
		import os

		import jax.numpy as jnp
		from jax import jit

		from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
		from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

		UpperCamelCase_       :    List[Any]						      =			'''0.12'''  # assumed parallelism: 8


if is_torch_available():
		import torch
def 				A_  (__a					, __a					, __a=None					):

					'''simple docstring'''





					if rng is None:
										A_								=    random.Random()

					A_								=    1
					for dim in shape:
										total_dims *= dim

					A_								=    []
					for _ in range(__a					):
										values.append(rng.randint(0					, vocab_size - 1					)					)

					A_								=    np.array(__a					, dtype=jnp.intaa					).reshape(__a					)

					return output
def 				A_  (__a					, __a=None					):

					'''simple docstring'''





					A_								=    ids_tensor(__a					, vocab_size=2					, rng=__a					)
					# make sure that at least one token is attended to for each batch
					A_								=    1
					return attn_mask



@require_flax
class __lowerCAmelCase					:




			"""simple docstring"""







			snake_case						   =					None
			snake_case						   =					()




			def   lowerCamelCase__  (					self    :       Any						)   ->			Tuple:







								"""simple docstring"""
								A_			,     A_								=    self.model_tester.prepare_config_and_inputs_for_common()

								# cut to half length & take max batch_size 3
								A_								=    2
								A_								=    inputs["input_ids"].shape[-1] // 2
								A_								=    inputs["input_ids"][:max_batch_size, :sequence_length]

								A_								=    jnp.ones_like(_snake_case						)
								A_								=    attention_mask[:max_batch_size, :sequence_length]

								# generate max 5 tokens
								A_								=    input_ids.shape[-1] + 5
								if config.eos_token_id is not None and config.pad_token_id is None:
													# hack to allow generate for models such as GPT2 as is done in `generate()`
													A_								=    config.eos_token_id
								return config, input_ids, attention_mask, max_length




			@is_pt_flax_cross_test
			def   lowerCamelCase__  (					self    :       List[Any]						)   ->			List[Any]:







								"""simple docstring"""
								A_			,     A_			,     A_			,     A_								=    self._get_input_ids_and_config()
								A_								=    False
								A_								=    max_length
								A_								=    0

								for model_class in self.all_generative_model_classes:
													A_								=    model_class(_snake_case						)

													A_								=    model_class.__name__[4:]  # Skip the "Flax" at the beginning
													A_								=    getattr(_snake_case							, _snake_case						)
													A_								=    pt_model_class(_snake_case						).eval()
													A_								=    load_flax_weights_in_pytorch_model(_snake_case							, flax_model.params						)

													A_								=    flax_model.generate(_snake_case						).sequences
													A_								=    pt_model.generate(torch.tensor(_snake_case							, dtype=torch.long						)						)

													if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
																		A_								=    flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

													self.assertListEqual(pt_generation_outputs.numpy().tolist()							, flax_generation_outputs.tolist()						)




			def   lowerCamelCase__  (					self    :       Any						)   ->			Optional[Any]:







								"""simple docstring"""
								A_			,     A_			,     A_			,     A_								=    self._get_input_ids_and_config()
								A_								=    False
								A_								=    max_length

								for model_class in self.all_generative_model_classes:
													A_								=    model_class(_snake_case						)

													A_								=    model.generate(_snake_case						).sequences
													self.assertEqual(generation_outputs.shape[-1]							, _snake_case						)

													A_								=    jit(model.generate						)
													A_								=    jit_generate(_snake_case						).sequences

													self.assertListEqual(generation_outputs.tolist()							, jit_generation_outputs.tolist()						)




			def   lowerCamelCase__  (					self    :       Tuple						)   ->			List[str]:







								"""simple docstring"""
								A_			,     A_			,     A_			,     A_								=    self._get_input_ids_and_config()
								A_								=    True
								A_								=    max_length

								for model_class in self.all_generative_model_classes:
													A_								=    model_class(_snake_case						)

													A_								=    model.generate(_snake_case						).sequences
													self.assertEqual(generation_outputs.shape[-1]							, _snake_case						)

													A_								=    jit(model.generate						)
													A_								=    jit_generate(_snake_case						).sequences

													self.assertListEqual(generation_outputs.tolist()							, jit_generation_outputs.tolist()						)




			def   lowerCamelCase__  (					self    :       Tuple						)   ->			Dict:







								"""simple docstring"""
								A_			,     A_			,     A_			,     A_								=    self._get_input_ids_and_config()
								A_								=    False
								A_								=    max_length
								A_								=    2

								for model_class in self.all_generative_model_classes:
													A_								=    model_class(_snake_case						)

													A_								=    model.generate(_snake_case						).sequences
													self.assertEqual(generation_outputs.shape[-1]							, _snake_case						)

													A_								=    jit(model.generate						)
													A_								=    jit_generate(_snake_case						).sequences

													self.assertListEqual(generation_outputs.tolist()							, jit_generation_outputs.tolist()						)




			def   lowerCamelCase__  (					self    :       str						)   ->			Union[str, Any]:







								"""simple docstring"""
								A_			,     A_			,     A_			,     A_								=    self._get_input_ids_and_config()
								A_								=    False
								A_								=    max_length
								A_								=    2
								A_								=    2

								for model_class in self.all_generative_model_classes:
													A_								=    model_class(_snake_case						)

													A_								=    model.generate(_snake_case						).sequences
													self.assertEqual(generation_outputs.shape[0]							, input_ids.shape[0] * config.num_return_sequences						)




			def   lowerCamelCase__  (					self    :       Union[str, Any]						)   ->			Dict:







								"""simple docstring"""
								A_			,     A_			,     A_			,     A_								=    self._get_input_ids_and_config()
								A_								=    True
								A_								=    max_length
								A_								=    0.8
								A_								=    10
								A_								=    0.3
								A_								=    1
								A_								=    8
								A_								=    9

								for model_class in self.all_generative_model_classes:
													A_								=    model_class(_snake_case						)

													A_								=    model.generate(_snake_case						).sequences
													self.assertEqual(generation_outputs.shape[-1]							, _snake_case						)

													A_								=    jit(model.generate						)
													A_								=    jit_generate(_snake_case						).sequences

													self.assertListEqual(generation_outputs.tolist()							, jit_generation_outputs.tolist()						)




			def   lowerCamelCase__  (					self    :       List[str]						)   ->			Any:







								"""simple docstring"""
								A_			,     A_			,     A_			,     A_								=    self._get_input_ids_and_config()
								A_								=    max_length
								A_								=    1
								A_								=    8
								A_								=    9

								for model_class in self.all_generative_model_classes:
													A_								=    model_class(_snake_case						)

													A_								=    model.generate(_snake_case						).sequences
													self.assertEqual(generation_outputs.shape[-1]							, _snake_case						)

													A_								=    jit(model.generate						)
													A_								=    jit_generate(_snake_case						).sequences

													self.assertListEqual(generation_outputs.tolist()							, jit_generation_outputs.tolist()						)




			def   lowerCamelCase__  (					self    :       Optional[Any]						)   ->			Dict:







								"""simple docstring"""
								A_			,     A_			,     A_			,     A_								=    self._get_input_ids_and_config()
								A_								=    max_length
								A_								=    2
								A_								=    1
								A_								=    8
								A_								=    9

								for model_class in self.all_generative_model_classes:
													A_								=    model_class(_snake_case						)

													A_								=    model.generate(_snake_case						).sequences
													self.assertEqual(generation_outputs.shape[-1]							, _snake_case						)

													A_								=    jit(model.generate						)
													A_								=    jit_generate(_snake_case						).sequences

													self.assertListEqual(generation_outputs.tolist()							, jit_generation_outputs.tolist()						)




			def   lowerCamelCase__  (					self    :       Tuple						)   ->			Any:







								"""simple docstring"""
								A_			,     A_			,     A_			,     A_								=    self._get_input_ids_and_config()

								# pad attention mask on the left
								A_								=    attention_mask.at[(0, 0)].set(0						)

								A_								=    False
								A_								=    max_length

								for model_class in self.all_generative_model_classes:
													A_								=    model_class(_snake_case						)

													A_								=    model.generate(_snake_case							, attention_mask=_snake_case						).sequences
													self.assertEqual(generation_outputs.shape[-1]							, _snake_case						)

													A_								=    jit(model.generate						)
													A_								=    jit_generate(_snake_case							, attention_mask=_snake_case						).sequences

													self.assertListEqual(generation_outputs.tolist()							, jit_generation_outputs.tolist()						)




			def   lowerCamelCase__  (					self    :       List[str]						)   ->			int:







								"""simple docstring"""
								A_			,     A_			,     A_			,     A_								=    self._get_input_ids_and_config()

								# pad attention mask on the left
								A_								=    attention_mask.at[(0, 0)].set(0						)

								A_								=    True
								A_								=    max_length

								for model_class in self.all_generative_model_classes:
													A_								=    model_class(_snake_case						)

													A_								=    model.generate(_snake_case							, attention_mask=_snake_case						).sequences
													self.assertEqual(generation_outputs.shape[-1]							, _snake_case						)

													A_								=    jit(model.generate						)
													A_								=    jit_generate(_snake_case							, attention_mask=_snake_case						).sequences

													self.assertListEqual(generation_outputs.tolist()							, jit_generation_outputs.tolist()						)






			def   lowerCamelCase__  (					self    :       Dict						)   ->			int:







								"""simple docstring"""
								A_			,     A_			,     A_			,     A_								=    self._get_input_ids_and_config()

								# pad attention mask on the left
								A_								=    attention_mask.at[(0, 0)].set(0						)

								A_								=    2
								A_								=    max_length

								for model_class in self.all_generative_model_classes:
													A_								=    model_class(_snake_case						)

													A_								=    model.generate(_snake_case							, attention_mask=_snake_case						).sequences
													self.assertEqual(generation_outputs.shape[-1]							, _snake_case						)

													A_								=    jit(model.generate						)
													A_								=    jit_generate(_snake_case							, attention_mask=_snake_case						).sequences

													self.assertListEqual(generation_outputs.tolist()							, jit_generation_outputs.tolist()						)





@require_flax
class __lowerCAmelCase					(       unittest.TestCase   ):




			"""simple docstring"""







			def   lowerCamelCase__  (					self    :       Optional[Any]						)   ->			Any:







								"""simple docstring"""
								A_								=    AutoTokenizer.from_pretrained("hf-internal-testing/tiny-bert"						)
								A_								=    FlaxAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-bert-flax-only"						)

								A_								=    "Hello world"
								A_								=    tokenizer(_snake_case							, return_tensors="np"						).input_ids

								# typos are quickly detected (the correct argument is `do_sample`)
								with self.assertRaisesRegex(_snake_case							, "do_samples"						):
													model.generate(_snake_case							, do_samples=_snake_case						)

								# arbitrary arguments that will not be used anywhere are also not accepted
								with self.assertRaisesRegex(_snake_case							, "foo"						):
													A_								=    {"foo": "bar"}
													model.generate(_snake_case							, **_snake_case						)




