



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

def 			_lowerCAmelCase				(       lowercase_						):
						UpperCAmelCase					=			DPTConfig(embedding_type='hybrid'						)

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

						if "nyu" or "midas" in checkpoint_url:
												UpperCAmelCase					=			768
												UpperCAmelCase					=			[1, 1, 1, 0.5]
												UpperCAmelCase					=			[256, 512, 768, 768]
												UpperCAmelCase					=			150
												UpperCAmelCase					=			16
												UpperCAmelCase					=			(1, 384, 384)
												UpperCAmelCase					=			False
												UpperCAmelCase					=			'project'

						if "ade" in checkpoint_url:
												UpperCAmelCase					=			True
												UpperCAmelCase					=			768
												UpperCAmelCase					=			[1, 1, 1, 0.5]
												UpperCAmelCase					=			150
												UpperCAmelCase					=			16
												UpperCAmelCase					=			'huggingface/label-files'
												UpperCAmelCase					=			'ade20k-id2label.json'
												UpperCAmelCase					=			json.load(open(cached_download(hf_hub_url(lowercase_		,     lowercase_		,     repo_type='dataset'						)						)		,     'r'						)						)
												UpperCAmelCase					=			{int(lowercase_						): v for k, v in idalabel.items()}
												UpperCAmelCase					=			idalabel
												UpperCAmelCase					=			{v: k for k, v in idalabel.items()}
												UpperCAmelCase					=			[1, 150, 480, 480]

						return config, expected_shape

def 			_lowerCAmelCase				(       lowercase_						):
						UpperCAmelCase					=			['pretrained.model.head.weight', 'pretrained.model.head.bias']
						for k in ignore_keys:
												state_dict.pop(lowercase_		,     lowercase_						)

def 			_lowerCAmelCase				(       lowercase_						):
						if (
						    "pretrained.model" in name
						    and "cls_token" not in name
						    and "pos_embed" not in name
						    and "patch_embed" not in name
						):
												UpperCAmelCase					=			name.replace('pretrained.model'		,     'dpt.encoder'						)
						if "pretrained.model" in name:
												UpperCAmelCase					=			name.replace('pretrained.model'		,     'dpt.embeddings'						)
						if "patch_embed" in name:
												UpperCAmelCase					=			name.replace('patch_embed'		,     ''						)
						if "pos_embed" in name:
												UpperCAmelCase					=			name.replace('pos_embed'		,     'position_embeddings'						)
						if "attn.proj" in name:
												UpperCAmelCase					=			name.replace('attn.proj'		,     'attention.output.dense'						)
						if "proj" in name and "project" not in name:
												UpperCAmelCase					=			name.replace('proj'		,     'projection'						)
						if "blocks" in name:
												UpperCAmelCase					=			name.replace('blocks'		,     'layer'						)
						if "mlp.fc1" in name:
												UpperCAmelCase					=			name.replace('mlp.fc1'		,     'intermediate.dense'						)
						if "mlp.fc2" in name:
												UpperCAmelCase					=			name.replace('mlp.fc2'		,     'output.dense'						)
						if "norm1" in name and "backbone" not in name:
												UpperCAmelCase					=			name.replace('norm1'		,     'layernorm_before'						)
						if "norm2" in name and "backbone" not in name:
												UpperCAmelCase					=			name.replace('norm2'		,     'layernorm_after'						)
						if "scratch.output_conv" in name:
												UpperCAmelCase					=			name.replace('scratch.output_conv'		,     'head'						)
						if "scratch" in name:
												UpperCAmelCase					=			name.replace('scratch'		,     'neck'						)
						if "layer1_rn" in name:
												UpperCAmelCase					=			name.replace('layer1_rn'		,     'convs.0'						)
						if "layer2_rn" in name:
												UpperCAmelCase					=			name.replace('layer2_rn'		,     'convs.1'						)
						if "layer3_rn" in name:
												UpperCAmelCase					=			name.replace('layer3_rn'		,     'convs.2'						)
						if "layer4_rn" in name:
												UpperCAmelCase					=			name.replace('layer4_rn'		,     'convs.3'						)
						if "refinenet" in name:
												UpperCAmelCase					=			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					=			name.replace(F"""refinenet{layer_idx}"""		,     F"""fusion_stage.layers.{abs(layer_idx-4						)}"""						)
						if "out_conv" in name:
												UpperCAmelCase					=			name.replace('out_conv'		,     'projection'						)
						if "resConfUnit1" in name:
												UpperCAmelCase					=			name.replace('resConfUnit1'		,     'residual_layer1'						)
						if "resConfUnit2" in name:
												UpperCAmelCase					=			name.replace('resConfUnit2'		,     'residual_layer2'						)
						if "conv1" in name:
												UpperCAmelCase					=			name.replace('conv1'		,     'convolution1'						)
						if "conv2" in name:
												UpperCAmelCase					=			name.replace('conv2'		,     'convolution2'						)
						# readout blocks
						if "pretrained.act_postprocess1.0.project.0" in name:
												UpperCAmelCase					=			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					=			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					=			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					=			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					=			name.replace('pretrained.act_postprocess1.3'		,     'neck.reassemble_stage.layers.0.projection'						)
						if "pretrained.act_postprocess1.4" in name:
												UpperCAmelCase					=			name.replace('pretrained.act_postprocess1.4'		,     'neck.reassemble_stage.layers.0.resize'						)
						if "pretrained.act_postprocess2.3" in name:
												UpperCAmelCase					=			name.replace('pretrained.act_postprocess2.3'		,     'neck.reassemble_stage.layers.1.projection'						)
						if "pretrained.act_postprocess2.4" in name:
												UpperCAmelCase					=			name.replace('pretrained.act_postprocess2.4'		,     'neck.reassemble_stage.layers.1.resize'						)
						if "pretrained.act_postprocess3.3" in name:
												UpperCAmelCase					=			name.replace('pretrained.act_postprocess3.3'		,     'neck.reassemble_stage.layers.2.projection'						)
						if "pretrained.act_postprocess4.3" in name:
												UpperCAmelCase					=			name.replace('pretrained.act_postprocess4.3'		,     'neck.reassemble_stage.layers.3.projection'						)
						if "pretrained.act_postprocess4.4" in name:
												UpperCAmelCase					=			name.replace('pretrained.act_postprocess4.4'		,     'neck.reassemble_stage.layers.3.resize'						)
						if "pretrained" in name:
												UpperCAmelCase					=			name.replace('pretrained'		,     'dpt'						)
						if "bn" in name:
												UpperCAmelCase					=			name.replace('bn'		,     'batch_norm'						)
						if "head" in name:
												UpperCAmelCase					=			name.replace('head'		,     'head.head'						)
						if "encoder.norm" in name:
												UpperCAmelCase					=			name.replace('encoder.norm'		,     'layernorm'						)
						if "auxlayer" in name:
												UpperCAmelCase					=			name.replace('auxlayer'		,     'auxiliary_head.head'						)
						if "backbone" in name:
												UpperCAmelCase					=			name.replace('backbone'		,     'backbone.bit.encoder'						)

						if ".." in name:
												UpperCAmelCase					=			name.replace('..'		,     '.'						)

						if "stem.conv" in name:
												UpperCAmelCase					=			name.replace('stem.conv'		,     'bit.embedder.convolution'						)
						if "blocks" in name:
												UpperCAmelCase					=			name.replace('blocks'		,     'layers'						)
						if "convolution" in name and "backbone" in name:
												UpperCAmelCase					=			name.replace('convolution'		,     'conv'						)
						if "layer" in name and "backbone" in name:
												UpperCAmelCase					=			name.replace('layer'		,     'layers'						)
						if "backbone.bit.encoder.bit" in name:
												UpperCAmelCase					=			name.replace('backbone.bit.encoder.bit'		,     'backbone.bit'						)
						if "embedder.conv" in name:
												UpperCAmelCase					=			name.replace('embedder.conv'		,     'embedder.convolution'						)
						if "backbone.bit.encoder.stem.norm" in name:
												UpperCAmelCase					=			name.replace('backbone.bit.encoder.stem.norm'		,     'backbone.bit.embedder.norm'						)
						return name

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_						):
						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					=			state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.weight"""						)
												UpperCAmelCase					=			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					=			in_proj_weight[: config.hidden_size, :]
												UpperCAmelCase					=			in_proj_bias[: config.hidden_size]
												UpperCAmelCase					=			in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												UpperCAmelCase					=			in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												UpperCAmelCase					=			in_proj_weight[
												    -config.hidden_size :, :
												]
												UpperCAmelCase					=			in_proj_bias[-config.hidden_size :]

def 			_lowerCAmelCase				(       ):
						UpperCAmelCase					=			'http://images.cocodataset.org/val2017/000000039769.jpg'
						UpperCAmelCase					=			Image.open(requests.get(lowercase_		,     stream=lowercase_						).raw						)
						return im

@torch.no_grad()
def 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_		,     lowercase_		,     lowercase_						):
						UpperCAmelCase				,   UpperCAmelCase					=			get_dpt_config(lowercase_						)
						# load original state_dict from URL
						# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
						UpperCAmelCase					=			torch.load(lowercase_		,     map_location='cpu'						)
						# remove certain keys
						remove_ignore_keys_(lowercase_						)
						# rename keys
						for key in state_dict.copy().keys():
												UpperCAmelCase					=			state_dict.pop(lowercase_						)
												UpperCAmelCase					=			val
						# read in qkv matrices
						read_in_q_k_v(lowercase_		,     lowercase_						)

						# load HuggingFace model
						UpperCAmelCase					=			DPTForSemanticSegmentation(lowercase_						) if 'ade' in checkpoint_url else DPTForDepthEstimation(lowercase_						)
						model.load_state_dict(lowercase_						)
						model.eval()

						# Check outputs on an image
						UpperCAmelCase					=			480 if 'ade' in checkpoint_url else 384
						UpperCAmelCase					=			DPTImageProcessor(size=lowercase_						)

						UpperCAmelCase					=			prepare_img()
						UpperCAmelCase					=			image_processor(lowercase_		,     return_tensors='pt'						)

						# forward pass
						UpperCAmelCase					=			model(**lowercase_						).logits if 'ade' in checkpoint_url else model(**lowercase_						).predicted_depth

						if show_prediction:
												UpperCAmelCase					=			(
												    torch.nn.functional.interpolate(
												        outputs.unsqueeze(1						)		,     size=(image.size[1], image.size[0])		,     mode='bicubic'		,     align_corners=lowercase_		,     )
												    .squeeze()
												    .cpu()
												    .numpy()
												)

												Image.fromarray((prediction / prediction.max()) * 255						).show()

						if pytorch_dump_folder_path is not None:
												Path(lowercase_						).mkdir(exist_ok=lowercase_						)
												print(F"""Saving model to {pytorch_dump_folder_path}"""						)
												model.save_pretrained(lowercase_						)
												print(F"""Saving image processor to {pytorch_dump_folder_path}"""						)
												image_processor.save_pretrained(lowercase_						)

						if push_to_hub:
												model.push_to_hub('ybelkada/dpt-hybrid-midas'						)
												image_processor.push_to_hub('ybelkada/dpt-hybrid-midas'						)


if __name__ == "__main__":
				snake_case_    					=	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=False,
				    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.""",
				)
				parser.add_argument(
				    """--show_prediction""",
				    action="""store_true""",
				)

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










"""simple docstring"""


# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool







class 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""
						__UpperCamelCase		    =							"""philschmid/bart-large-cnn-samsum"""
						__UpperCamelCase		    =							(
						    """This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, """
						    """and returns a summary of the text."""
						)
						__UpperCamelCase		    =							"""summarizer"""
						__UpperCamelCase		    =							AutoTokenizer
						__UpperCamelCase		    =							AutoModelForSeqaSeqLM

						__UpperCamelCase		    =							["""text"""]
						__UpperCamelCase		    =							["""text"""]






						def      UpperCAmelCase__					( self			:int       ,  lowercase_			:Tuple )    ->      int:
												return self.pre_processor(lowercase_       ,  return_tensors='pt'       ,  truncation=lowercase_ )






						def      UpperCAmelCase__					( self			:Dict       ,  lowercase_			:Dict )    ->      Optional[int]:
												return self.model.generate(**lowercase_ )[0]






						def      UpperCAmelCase__					( self			:Dict       ,  lowercase_			:Tuple )    ->      List[str]:
												return self.pre_processor.decode(lowercase_       ,  skip_special_tokens=lowercase_       ,  clean_up_tokenization_spaces=lowercase_ )





