






"""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()
lowerCamelCase_       			=   logging.get_logger(__name__)

def        snake_case   (     A__		):
			UpperCAmelCase_  :				List[str]      =      DPTConfig()

			if "large" in checkpoint_url:
						UpperCAmelCase_  :				Any      =      10_24
						UpperCAmelCase_  :				Optional[Any]      =      40_96
						UpperCAmelCase_  :				str      =      24
						UpperCAmelCase_  :				Optional[int]      =      16
						UpperCAmelCase_  :				Tuple      =      [5, 11, 17, 23]
						UpperCAmelCase_  :				Dict      =      [2_56, 5_12, 10_24, 10_24]
						UpperCAmelCase_  :				str      =      (1, 3_84, 3_84)

			if "ade" in checkpoint_url:
						UpperCAmelCase_  :				Any      =      True

						UpperCAmelCase_  :				Optional[int]      =      1_50
						UpperCAmelCase_  :				List[Any]      =      "huggingface/label-files"
						UpperCAmelCase_  :				Union[str, Any]      =      "ade20k-id2label.json"
						UpperCAmelCase_  :				int      =      json.load(open(cached_download(hf_hub_url(A__			,A__			,repo_type="dataset"		)		)			,"r"		)		)
						UpperCAmelCase_  :				Any      =      {int(A__		): v for k, v in idalabel.items()}
						UpperCAmelCase_  :				List[Any]      =      idalabel
						UpperCAmelCase_  :				Any      =      {v: k for k, v in idalabel.items()}
						UpperCAmelCase_  :				str      =      [1, 1_50, 4_80, 4_80]

			return config, expected_shape

def        snake_case   (     A__		):
			UpperCAmelCase_  :				Any      =      ["pretrained.model.head.weight", "pretrained.model.head.bias"]
			for k in ignore_keys:
						state_dict.pop(A__			,A__		)

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

			return name

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

def        snake_case   (     ):
			UpperCAmelCase_  :				str      =      "http://images.cocodataset.org/val2017/000000039769.jpg"
			UpperCAmelCase_  :				List[str]      =      Image.open(requests.get(A__			,stream=A__		).raw		)
			return im

@torch.no_grad()
def        snake_case   (     A__			,A__			,A__			,A__		):
			UpperCAmelCase_  ,			UpperCAmelCase_  :				Union[str, Any]      =      get_dpt_config(A__		)
			# load original state_dict from URL
			UpperCAmelCase_  :				Tuple      =      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():
						UpperCAmelCase_  :				str      =      state_dict.pop(A__		)
						UpperCAmelCase_  :				int      =      val
			# read in qkv matrices
			read_in_q_k_v(A__			,A__		)

			# load HuggingFace model
			UpperCAmelCase_  :				List[Any]      =      DPTForSemanticSegmentation(A__		) if "ade" in checkpoint_url else DPTForDepthEstimation(A__		)
			model.load_state_dict(A__		)
			model.eval()

			# Check outputs on an image
			UpperCAmelCase_  :				Tuple      =      4_80 if "ade" in checkpoint_url else 3_84
			UpperCAmelCase_  :				List[str]      =      DPTImageProcessor(size=A__		)

			UpperCAmelCase_  :				int      =      prepare_img()
			UpperCAmelCase_  :				List[str]      =      image_processor(A__			,return_tensors="pt"		)

			# forward pass
			UpperCAmelCase_  :				List[str]      =      model(**A__		).logits if "ade" in checkpoint_url else model(**A__		).predicted_depth

			# Assert logits
			UpperCAmelCase_  :				Dict      =      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:
						UpperCAmelCase_  :				Optional[int]      =      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__":
			lowerCamelCase_       			=   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.''',
			)

			lowerCamelCase_       			=   parser.parse_args()
			convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)











"""simple docstring"""
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

lowerCamelCase_       			=   '''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
lowerCamelCase_       			=   [
    '''l''',
    '''o''',
    '''w''',
    '''e''',
    '''r''',
    '''s''',
    '''t''',
    '''i''',
    '''d''',
    '''n''',
    '''w</w>''',
    '''r</w>''',
    '''t</w>''',
    '''lo''',
    '''low''',
    '''er</w>''',
    '''low</w>''',
    '''lowest</w>''',
    '''newer</w>''',
    '''wider</w>''',
    '''<unk>''',
]
lowerCamelCase_       			=   dict(zip(vocab, range(len(vocab))))
lowerCamelCase_       			=   ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
			lowerCamelCase_       			=   Path(tmpdirname)
			lowerCamelCase_       			=   build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
			lowerCamelCase_       			=   build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
			lowerCamelCase_       			=   build_dir / VOCAB_FILES_NAMES['''merges_file''']
			with open(src_vocab_file, '''w''') as fp:
						fp.write(json.dumps(vocab_tokens))
			with open(tgt_vocab_file, '''w''') as fp:
						fp.write(json.dumps(vocab_tokens))
			with open(merges_file, '''w''') as fp:
						fp.write('''\n'''.join(merges))

			lowerCamelCase_       			=   FSMTTokenizer(
			    langs=['''en''', '''ru'''],
			    src_vocab_size=len(vocab),
			    tgt_vocab_size=len(vocab),
			    src_vocab_file=src_vocab_file,
			    tgt_vocab_file=tgt_vocab_file,
			    merges_file=merges_file,
			)

lowerCamelCase_       			=   FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1000,
    tgt_vocab_size=1000,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

lowerCamelCase_       			=   FSMTForConditionalGeneration(config)
print(f'num of params {tiny_model.num_parameters()}')

# Test
lowerCamelCase_       			=   tokenizer(['''Making tiny model'''], return_tensors='''pt''')
lowerCamelCase_       			=   tiny_model(**batch)

print('''test output:''', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f'Generated {mname_tiny}')

# Upload
# transformers-cli upload tiny-wmt19-en-ru



