



'''simple docstring'''
import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
					import numpy as np
					import torch

					from . import (
					    AlbertForPreTraining,
					    BartForConditionalGeneration,
					    BertForPreTraining,
					    BertForQuestionAnswering,
					    BertForSequenceClassification,
					    CamembertForMaskedLM,
					    CTRLLMHeadModel,
					    DistilBertForMaskedLM,
					    DistilBertForQuestionAnswering,
					    DPRContextEncoder,
					    DPRQuestionEncoder,
					    DPRReader,
					    ElectraForPreTraining,
					    FlaubertWithLMHeadModel,
					    GPTaLMHeadModel,
					    LayoutLMForMaskedLM,
					    LxmertForPreTraining,
					    LxmertVisualFeatureEncoder,
					    OpenAIGPTLMHeadModel,
					    RobertaForMaskedLM,
					    RobertaForSequenceClassification,
					    TaForConditionalGeneration,
					    TransfoXLLMHeadModel,
					    XLMRobertaForMaskedLM,
					    XLMWithLMHeadModel,
					    XLNetLMHeadModel,
					)


logging.set_verbosity_info()

snake_case_			:     Dict  	=  {
    'bart': (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'bert': (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-uncased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-cased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-base-cased-finetuned-mrpc': (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'dpr': (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'gpt2': (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlnet': (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm': (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm-roberta': (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'transfo-xl': (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'openai-gpt': (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'roberta': (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'layoutlm': (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'roberta-large-mnli': (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'camembert': (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'flaubert': (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert': (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert-base-distilled-squad': (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert': (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert-visual-feature-encoder': (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'ctrl': (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'albert': (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    't5': (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'electra': (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'wav2vec2': (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_=False  ,  UpperCAmelCase_=True							):
						if model_type not in MODEL_CLASSES:
												raise ValueError(f'Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()							)}.'							)

						_UpperCamelCase    ,      _UpperCamelCase    ,      _UpperCamelCase    ,      _UpperCamelCase :		List[str]			    =       MODEL_CLASSES[model_type]

						# Initialise TF model
						if config_file in aws_config_map:
												_UpperCamelCase :		Optional[int]			    =       cached_file(UpperCAmelCase_  ,  UpperCAmelCase_  ,  force_download=not use_cached_models							)
						_UpperCamelCase :		Optional[Any]			    =       config_class.from_json_file(UpperCAmelCase_							)
						_UpperCamelCase :		Optional[Any]			    =       True
						_UpperCamelCase :		List[str]			    =       True
						print(f'Building TensorFlow model from configuration: {config}'							)
						_UpperCamelCase :		Any			    =       model_class(UpperCAmelCase_							)

						# Load weights from tf checkpoint
						if pytorch_checkpoint_path in aws_config_map.keys():
												_UpperCamelCase :		Union[str, Any]			    =       cached_file(
												    UpperCAmelCase_  ,  UpperCAmelCase_  ,  force_download=not use_cached_models							)
						# Load PyTorch checkpoint in tf2 model:
						_UpperCamelCase :		List[Any]			    =       load_pytorch_checkpoint_in_tfa_model(UpperCAmelCase_  ,  UpperCAmelCase_							)

						if compare_with_pt_model:
												_UpperCamelCase :		Optional[int]			    =       tf_model(tf_model.dummy_inputs  ,  training=UpperCAmelCase_							)  # build the network

												_UpperCamelCase :		Optional[Any]			    =       torch.load(UpperCAmelCase_  ,  map_location='cpu'							)
												_UpperCamelCase :		List[str]			    =       pt_model_class.from_pretrained(
												    pretrained_model_name_or_path=UpperCAmelCase_  ,  config=UpperCAmelCase_  ,  state_dict=UpperCAmelCase_							)

												with torch.no_grad():
																		_UpperCamelCase :		Optional[Any]			    =       pt_model(**pt_model.dummy_inputs							)

												_UpperCamelCase :		int			    =       pto[0].numpy()
												_UpperCamelCase :		Any			    =       tfo[0].numpy()
												_UpperCamelCase :		Dict			    =       np.amax(np.abs(np_pt - np_tf							)							)
												print(f'Max absolute difference between models outputs {diff}'							)
												assert diff <= 2E-2, f'Error, model absolute difference is >2e-2: {diff}'

						# Save pytorch-model
						print(f'Save TensorFlow model to {tf_dump_path}'							)
						tf_model.save_weights(UpperCAmelCase_  ,  save_format='h5'							)






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_=None  ,  UpperCAmelCase_=None  ,  UpperCAmelCase_=False  ,  UpperCAmelCase_=False  ,  UpperCAmelCase_=False  ,  UpperCAmelCase_=False  ,  ):
						if args_model_type is None:
												_UpperCamelCase :		List[Any]			    =       list(MODEL_CLASSES.keys()							)
						else:
												_UpperCamelCase :		Tuple			    =       [args_model_type]

						for j, model_type in enumerate(UpperCAmelCase_  ,  start=1							):
												print('=' * 1_0_0							)
												print(f' Converting model type {j}/{len(UpperCAmelCase_							)}: {model_type}'							)
												print('=' * 1_0_0							)
												if model_type not in MODEL_CLASSES:
																		raise ValueError(f'Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()							)}.'							)

												_UpperCamelCase    ,      _UpperCamelCase    ,      _UpperCamelCase    ,      _UpperCamelCase    ,      _UpperCamelCase :		Any			    =       MODEL_CLASSES[model_type]

												if model_shortcut_names_or_path is None:
																		_UpperCamelCase :		List[Any]			    =       list(aws_model_maps.keys()							)
												if config_shortcut_names_or_path is None:
																		_UpperCamelCase :		int			    =       model_shortcut_names_or_path

												for i, (model_shortcut_name, config_shortcut_name) in enumerate(
												    zip(UpperCAmelCase_  ,  UpperCAmelCase_							)  ,  start=1							):
																		print('-' * 1_0_0							)
																		if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
																								if not only_convert_finetuned_models:
																														print(f'    Skipping finetuned checkpoint {model_shortcut_name}'							)
																														continue
																								_UpperCamelCase :		Dict			    =       model_shortcut_name
																		elif only_convert_finetuned_models:
																								print(f'    Skipping not finetuned checkpoint {model_shortcut_name}'							)
																								continue
																		print(
																		    f'    Converting checkpoint {i}/{len(UpperCAmelCase_							)}: {model_shortcut_name} - model_type {model_type}'							)
																		print('-' * 1_0_0							)

																		if config_shortcut_name in aws_config_map:
																								_UpperCamelCase :		Any			    =       cached_file(UpperCAmelCase_  ,  UpperCAmelCase_  ,  force_download=not use_cached_models							)
																		else:
																								_UpperCamelCase :		str			    =       config_shortcut_name

																		if model_shortcut_name in aws_model_maps:
																								_UpperCamelCase :		int			    =       cached_file(UpperCAmelCase_  ,  UpperCAmelCase_  ,  force_download=not use_cached_models							)
																		else:
																								_UpperCamelCase :		List[str]			    =       model_shortcut_name

																		if os.path.isfile(UpperCAmelCase_							):
																								_UpperCamelCase :		Union[str, Any]			    =       'converted_model'

																		convert_pt_checkpoint_to_tf(
																		    model_type=UpperCAmelCase_  ,  pytorch_checkpoint_path=UpperCAmelCase_  ,  config_file=UpperCAmelCase_  ,  tf_dump_path=os.path.join(UpperCAmelCase_  ,  model_shortcut_name + '-tf_model.h5'							)  ,  compare_with_pt_model=UpperCAmelCase_  ,  )
																		if remove_cached_files:
																								os.remove(UpperCAmelCase_							)
																								os.remove(UpperCAmelCase_							)


if __name__ == "__main__":
					snake_case_			:     Optional[Any]  	=  argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--tf_dump_path', default=None, type=str, required=True, help='Path to the output Tensorflow dump file.'
					)
					parser.add_argument(
					    '--model_type',
					    default=None,
					    type=str,
					    help=(
					        F"""Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and """
					        'convert all the models from AWS.'
					    ),
					)
					parser.add_argument(
					    '--pytorch_checkpoint_path',
					    default=None,
					    type=str,
					    help=(
					        'Path to the PyTorch checkpoint path or shortcut name to download from AWS. '
					        'If not given, will download and convert all the checkpoints from AWS.'
					    ),
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    help=(
					        'The config json file corresponding to the pre-trained model. \n'
					        'This specifies the model architecture. If not given and '
					        '--pytorch_checkpoint_path is not given or is a shortcut name '
					        'use the configuration associated to the shortcut name on the AWS'
					    ),
					)
					parser.add_argument(
					    '--compare_with_pt_model', action='store_true', help='Compare Tensorflow and PyTorch model predictions.'
					)
					parser.add_argument(
					    '--use_cached_models',
					    action='store_true',
					    help='Use cached models if possible instead of updating to latest checkpoint versions.',
					)
					parser.add_argument(
					    '--remove_cached_files',
					    action='store_true',
					    help='Remove pytorch models after conversion (save memory when converting in batches).',
					)
					parser.add_argument('--only_convert_finetuned_models', action='store_true', help='Only convert finetuned models.')
					snake_case_			:     Optional[int]  	=  parser.parse_args()

					# if args.pytorch_checkpoint_path is not None:
					#     convert_pt_checkpoint_to_tf(args.model_type.lower(),
					#                                 args.pytorch_checkpoint_path,
					#                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
					#                                 args.tf_dump_path,
					#                                 compare_with_pt_model=args.compare_with_pt_model,
					#                                 use_cached_models=args.use_cached_models)
					# else:
					convert_all_pt_checkpoints_to_tf(
					    args.model_type.lower() if args.model_type is not None else None,
					    args.tf_dump_path,
					    model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
					    if args.pytorch_checkpoint_path is not None
					    else None,
					    config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
					    compare_with_pt_model=args.compare_with_pt_model,
					    use_cached_models=args.use_cached_models,
					    remove_cached_files=args.remove_cached_files,
					    only_convert_finetuned_models=args.only_convert_finetuned_models,
					)







'''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_			:     int  	=  logging.get_logger(__name__)






def A__   (					UpperCAmelCase_							):
						_UpperCamelCase :		str			    =       DPTConfig(embedding_type='hybrid'							)

						if "large" in checkpoint_url:
												_UpperCamelCase :		Any			    =       1_0_2_4
												_UpperCamelCase :		List[Any]			    =       4_0_9_6
												_UpperCamelCase :		List[str]			    =       2_4
												_UpperCamelCase :		Tuple			    =       1_6
												_UpperCamelCase :		Union[str, Any]			    =       [5, 1_1, 1_7, 2_3]
												_UpperCamelCase :		Any			    =       [2_5_6, 5_1_2, 1_0_2_4, 1_0_2_4]
												_UpperCamelCase :		Tuple			    =       (1, 3_8_4, 3_8_4)

						if "nyu" or "midas" in checkpoint_url:
												_UpperCamelCase :		Optional[int]			    =       7_6_8
												_UpperCamelCase :		Optional[Any]			    =       [1, 1, 1, 0.5]
												_UpperCamelCase :		List[Any]			    =       [2_5_6, 5_1_2, 7_6_8, 7_6_8]
												_UpperCamelCase :		Optional[int]			    =       1_5_0
												_UpperCamelCase :		Tuple			    =       1_6
												_UpperCamelCase :		Dict			    =       (1, 3_8_4, 3_8_4)
												_UpperCamelCase :		Optional[int]			    =       False
												_UpperCamelCase :		Optional[int]			    =       'project'

						if "ade" in checkpoint_url:
												_UpperCamelCase :		Dict			    =       True
												_UpperCamelCase :		Dict			    =       7_6_8
												_UpperCamelCase :		Union[str, Any]			    =       [1, 1, 1, 0.5]
												_UpperCamelCase :		Union[str, Any]			    =       1_5_0
												_UpperCamelCase :		str			    =       1_6
												_UpperCamelCase :		Tuple			    =       'huggingface/label-files'
												_UpperCamelCase :		Tuple			    =       'ade20k-id2label.json'
												_UpperCamelCase :		Tuple			    =       json.load(open(cached_download(hf_hub_url(UpperCAmelCase_  ,  UpperCAmelCase_  ,  repo_type='dataset'							)							)  ,  'r'							)							)
												_UpperCamelCase :		str			    =       {int(UpperCAmelCase_							): v for k, v in idalabel.items()}
												_UpperCamelCase :		List[str]			    =       idalabel
												_UpperCamelCase :		Union[str, Any]			    =       {v: k for k, v in idalabel.items()}
												_UpperCamelCase :		int			    =       [1, 1_5_0, 4_8_0, 4_8_0]

						return config, expected_shape






def A__   (					UpperCAmelCase_							):
						_UpperCamelCase :		str			    =       ['pretrained.model.head.weight', 'pretrained.model.head.bias']
						for k in ignore_keys:
												state_dict.pop(UpperCAmelCase_  ,  UpperCAmelCase_							)






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

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

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






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_							):
						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 :		List[str]			    =       state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.weight'							)
												_UpperCamelCase :		List[str]			    =       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 :		List[str]			    =       in_proj_weight[: config.hidden_size, :]
												_UpperCamelCase :		int			    =       in_proj_bias[: config.hidden_size]
												_UpperCamelCase :		str			    =       in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												_UpperCamelCase :		Union[str, Any]			    =       in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												_UpperCamelCase :		List[Any]			    =       in_proj_weight[
												    -config.hidden_size :, :
												]
												_UpperCamelCase :		Union[str, Any]			    =       in_proj_bias[-config.hidden_size :]






def A__   (					):
						_UpperCamelCase :		Optional[int]			    =       'http://images.cocodataset.org/val2017/000000039769.jpg'
						_UpperCamelCase :		List[Any]			    =       Image.open(requests.get(UpperCAmelCase_  ,  stream=UpperCAmelCase_							).raw							)
						return im






@torch.no_grad()
def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase    ,      _UpperCamelCase :		int			    =       get_dpt_config(UpperCAmelCase_							)
						# load original state_dict from URL
						# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
						_UpperCamelCase :		List[str]			    =       torch.load(UpperCAmelCase_  ,  map_location='cpu'							)
						# remove certain keys
						remove_ignore_keys_(UpperCAmelCase_							)
						# rename keys
						for key in state_dict.copy().keys():
												_UpperCamelCase :		Any			    =       state_dict.pop(UpperCAmelCase_							)
												_UpperCamelCase :		int			    =       val
						# read in qkv matrices
						read_in_q_k_v(UpperCAmelCase_  ,  UpperCAmelCase_							)

						# load HuggingFace model
						_UpperCamelCase :		Union[str, Any]			    =       DPTForSemanticSegmentation(UpperCAmelCase_							) if 'ade' in checkpoint_url else DPTForDepthEstimation(UpperCAmelCase_							)
						model.load_state_dict(UpperCAmelCase_							)
						model.eval()

						# Check outputs on an image
						_UpperCamelCase :		Tuple			    =       4_8_0 if 'ade' in checkpoint_url else 3_8_4
						_UpperCamelCase :		Any			    =       DPTImageProcessor(size=UpperCAmelCase_							)

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

						# forward pass
						_UpperCamelCase :		Optional[Any]			    =       model(**UpperCAmelCase_							).logits if 'ade' in checkpoint_url else model(**UpperCAmelCase_							).predicted_depth

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

												Image.fromarray((prediction / prediction.max()) * 2_5_5							).show()

						if pytorch_dump_folder_path is not None:
												Path(UpperCAmelCase_							).mkdir(exist_ok=UpperCAmelCase_							)
												print(f'Saving model to {pytorch_dump_folder_path}'							)
												model.save_pretrained(UpperCAmelCase_							)
												print(f'Saving image processor to {pytorch_dump_folder_path}'							)
												image_processor.save_pretrained(UpperCAmelCase_							)

						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_			:     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=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_			:     Tuple  	=  parser.parse_args()
					convert_dpt_checkpoint(
					    args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
					)


