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

def       __A (     a_  :Optional[int])						->						Tuple:
				__a	:  List[Any]         =					DPTConfig(embedding_type='''hybrid''')

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

				if "nyu" or "midas" in checkpoint_url:
								__a	:  Optional[Any]         =					7_68
								__a	:  Any         =					[1, 1, 1, 0.5]
								__a	:  Dict         =					[2_56, 5_12, 7_68, 7_68]
								__a	:  Tuple         =					1_50
								__a	:  str         =					16
								__a	:  Any         =					(1, 3_84, 3_84)
								__a	:  Any         =					False
								__a	:  List[str]         =					"""project"""

				if "ade" in checkpoint_url:
								__a	:  Union[str, Any]         =					True
								__a	:  Union[str, Any]         =					7_68
								__a	:  Union[str, Any]         =					[1, 1, 1, 0.5]
								__a	:  Any         =					1_50
								__a	:  Tuple         =					16
								__a	:  Union[str, Any]         =					"""huggingface/label-files"""
								__a	:  int         =					"""ade20k-id2label.json"""
								__a	:  Dict         =					json.load(open(cached_download(hf_hub_url(_a				,    _a				,    repo_type='''dataset'''))				,    '''r'''))
								__a	:  Optional[Any]         =					{int(_a): v for k, v in idalabel.items()}
								__a	:  str         =					idalabel
								__a	:  List[str]         =					{v: k for k, v in idalabel.items()}
								__a	:  Tuple         =					[1, 1_50, 4_80, 4_80]

				return config, expected_shape

def       __A (     a_  :int)						->						int:
				__a	:  Optional[int]         =					["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
				for k in ignore_keys:
								state_dict.pop(_a				,    _a)

def       __A (     a_  :Dict)						->						Tuple:




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

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

				if "stem.conv" in name:
								__a	:  List[Any]         =					name.replace('''stem.conv'''				,    '''bit.embedder.convolution''')
				if "blocks" in name:
								__a	:  List[Any]         =					name.replace('''blocks'''				,    '''layers''')
				if "convolution" in name and "backbone" in name:
								__a	:  List[str]         =					name.replace('''convolution'''				,    '''conv''')
				if "layer" in name and "backbone" in name:
								__a	:  Dict         =					name.replace('''layer'''				,    '''layers''')
				if "backbone.bit.encoder.bit" in name:
								__a	:  Optional[Any]         =					name.replace('''backbone.bit.encoder.bit'''				,    '''backbone.bit''')
				if "embedder.conv" in name:
								__a	:  Any         =					name.replace('''embedder.conv'''				,    '''embedder.convolution''')
				if "backbone.bit.encoder.stem.norm" in name:
								__a	:  List[Any]         =					name.replace('''backbone.bit.encoder.stem.norm'''				,    '''backbone.bit.embedder.norm''')
				return name

def       __A (     a_  :Dict				,    a_  :int)						->						Tuple:




				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	:  List[str]         =					state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.weight""")
								__a	:  Optional[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
								__a	:  Optional[Any]         =					in_proj_weight[: config.hidden_size, :]
								__a	:  Optional[int]         =					in_proj_bias[: config.hidden_size]
								__a	:  str         =					in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								__a	:  Optional[Any]         =					in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								__a	:  Tuple         =					in_proj_weight[
								    -config.hidden_size :, :
								]
								__a	:  str         =					in_proj_bias[-config.hidden_size :]

def       __A (     )						->						Optional[Any]:
				__a	:  List[str]         =					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
				__a	:  Dict         =					Image.open(requests.get(_a				,    stream=_a).raw)
				return im

@torch.no_grad()
def       __A (     a_  :List[Any]				,    a_  :List[Any]				,    a_  :Union[str, Any]				,    a_  :Dict				,    a_  :Any)						->						Any:
				__a	:  Optional[Any]         =					get_dpt_config(_a)
				# load original state_dict from URL
				# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
				__a	:  Optional[Any]         =					torch.load(_a				,    map_location='''cpu''')
				# remove certain keys
				remove_ignore_keys_(_a)
				# rename keys
				for key in state_dict.copy().keys():
								__a	:  str         =					state_dict.pop(_a)
								__a	:  Union[str, Any]         =					val
				# read in qkv matrices
				read_in_q_k_v(_a				,    _a)

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

				# Check outputs on an image
				__a	:  int         =					4_80 if """ade""" in checkpoint_url else 3_84
				__a	:  Tuple         =					DPTImageProcessor(size=_a)

				__a	:  Optional[int]         =					prepare_img()
				__a	:  Union[str, Any]         =					image_processor(_a				,    return_tensors='''pt''')

				# forward pass
				__a	:  List[Any]         =					model(**_a).logits if """ade""" in checkpoint_url else model(**_a).predicted_depth

				if show_prediction:
								__a	:  Union[str, Any]         =					(
								    torch.nn.functional.interpolate(
								        outputs.unsqueeze(1)				,    size=(image.size[1], image.size[0])				,    mode='''bicubic'''				,    align_corners=_a				,    )
								    .squeeze()
								    .cpu()
								    .numpy()
								)

								Image.fromarray((prediction / prediction.max()) * 2_55).show()

				if pytorch_dump_folder_path is not None:
								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:
								model.push_to_hub('''ybelkada/dpt-hybrid-midas''')
								image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas''')


if __name__ == "__main__":
		A	     =					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''',
		)

		A	     =					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"""






import argparse
import os

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
A	     =					'''src/transformers'''
A	     =					'''docs/source/en/tasks'''

def       __A (     a_  :List[Any]				,    a_  :List[Any]				,    a_  :List[str])						->						List[str]:
				with open(a_				,    '''r'''				,    encoding='''utf-8'''				,    newline='''\n''') as f:
								__a	:  List[str]         =					f.readlines()
				# Find the start prompt.
				__a	:  Optional[Any]         =					0
				while not lines[start_index].startswith(a_):
								start_index += 1
				start_index += 1

				__a	:  int         =					start_index
				while not lines[end_index].startswith(a_):
								end_index += 1
				end_index -= 1

				while len(lines[start_index]) <= 1:
								start_index += 1
				while len(lines[end_index]) <= 1:
								end_index -= 1
				end_index += 1
				return "".join(lines[start_index:end_index]), start_index, end_index, lines


# This is to make sure the transformers module imported is the one in the repo.
A	     =					direct_transformers_import(TRANSFORMERS_PATH)

A	     =					{
    '''asr.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    '''audio_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    '''language_modeling.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    '''image_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    '''masked_language_modeling.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    '''multiple_choice.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    '''object_detection.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    '''question_answering.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    '''semantic_segmentation.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    '''sequence_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    '''summarization.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    '''token_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    '''translation.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    '''video_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    '''document_question_answering.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    '''monocular_depth_estimation.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
A	     =					{
    '''summarization.md''': ('''nllb''',),
    '''translation.md''': ('''nllb''',),
}

def       __A (     a_  :Optional[Any])						->						Any:
				__a	:  List[Any]         =					TASK_GUIDE_TO_MODELS[task_guide]
				__a	:  List[Any]         =					SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(a_				,    set())
				__a	:  Any         =					{
				    code: name
				    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
				    if (code in model_maping_names or code in special_model_types)
				}
				return ", ".join([F"""[{name}](../model_doc/{code})""" for code, name in model_names.items()]) + "\n"

def       __A (     a_  :Optional[int]				,    a_  :Dict=False)						->						Any:
				__a    ,  __a    ,  __a    ,  __a	:  Any         =					_find_text_in_file(
				    filename=os.path.join(a_				,    a_)				,    start_prompt='''<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->'''				,    end_prompt='''<!--End of the generated tip-->'''				,    )

				__a	:  Optional[Any]         =					get_model_list_for_task(a_)

				if current_list != new_list:
								if overwrite:
												with open(os.path.join(a_				,    a_)				,    '''w'''				,    encoding='''utf-8'''				,    newline='''\n''') as f:
																f.writelines(lines[:start_index] + [new_list] + lines[end_index:])
								else:
												raise ValueError(
												    F"""The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`"""
												    ''' to fix this.''')


if __name__ == "__main__":
		A	     =					argparse.ArgumentParser()
		parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
		A	     =					parser.parse_args()

		for task_guide in TASK_GUIDE_TO_MODELS.keys():
				check_model_list_for_task(task_guide, args.fix_and_overwrite)