
"""simple docstring"""

import argparse
import logging
import os
import time
import timeit

import datasets
import numpy as np
import pycuda.autoinit  # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions

import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate


SCREAMING_SNAKE_CASE_    					=						trt.Logger(trt.Logger.WARNING)
SCREAMING_SNAKE_CASE_    					=						absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

SCREAMING_SNAKE_CASE_    					=						logging.getLogger(__name__)

SCREAMING_SNAKE_CASE_    					=						argparse.ArgumentParser()

# Required parameters
parser.add_argument(
    '''--onnx_model_path''',
    default=None,
    type=str,
    required=True,
    help='''Path to ONNX model: ''',
)

parser.add_argument(
    '''--output_dir''',
    default=None,
    type=str,
    required=True,
    help='''The output directory where the model checkpoints and predictions will be written.''',
)

# Other parameters

parser.add_argument(
    '''--tokenizer_name''',
    default='''''',
    type=str,
    required=True,
    help='''Pretrained tokenizer name or path if not the same as model_name''',
)

parser.add_argument(
    '''--version_2_with_negative''',
    action='''store_true''',
    help='''If true, the SQuAD examples contain some that do not have an answer.''',
)
parser.add_argument(
    '''--null_score_diff_threshold''',
    type=float,
    default=0.0,
    help='''If null_score - best_non_null is greater than the threshold predict null.''',
)

parser.add_argument(
    '''--max_seq_length''',
    default=384,
    type=int,
    help=(
        '''The maximum total input sequence length after WordPiece tokenization. Sequences '''
        '''longer than this will be truncated, and sequences shorter than this will be padded.'''
    ),
)
parser.add_argument(
    '''--doc_stride''',
    default=128,
    type=int,
    help='''When splitting up a long document into chunks, how much stride to take between chunks.''',
)

parser.add_argument('''--per_device_eval_batch_size''', default=8, type=int, help='''Batch size per GPU/CPU for evaluation.''')

parser.add_argument(
    '''--n_best_size''',
    default=20,
    type=int,
    help='''The total number of n-best predictions to generate in the nbest_predictions.json output file.''',
)
parser.add_argument(
    '''--max_answer_length''',
    default=30,
    type=int,
    help=(
        '''The maximum length of an answer that can be generated. This is needed because the start '''
        '''and end predictions are not conditioned on one another.'''
    ),
)

parser.add_argument('''--seed''', type=int, default=42, help='''random seed for initialization''')

parser.add_argument(
    '''--dataset_name''',
    type=str,
    default=None,
    required=True,
    help='''The name of the dataset to use (via the datasets library).''',
)
parser.add_argument(
    '''--dataset_config_name''',
    type=str,
    default=None,
    help='''The configuration name of the dataset to use (via the datasets library).''',
)
parser.add_argument(
    '''--preprocessing_num_workers''', type=int, default=4, help='''A csv or a json file containing the training data.'''
)
parser.add_argument('''--overwrite_cache''', action='''store_true''', help='''Overwrite the cached training and evaluation sets''')
parser.add_argument(
    '''--fp16''',
    action='''store_true''',
    help='''Whether to use 16-bit (mixed) precision instead of 32-bit''',
)
parser.add_argument(
    '''--int8''',
    action='''store_true''',
    help='''Whether to use INT8''',
)

SCREAMING_SNAKE_CASE_    					=						parser.parse_args()

if args.tokenizer_name:
    SCREAMING_SNAKE_CASE_    					=						AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
    raise ValueError(
        '''You are instantiating a new tokenizer from scratch. This is not supported by this script.'''
        '''You can do it from another script, save it, and load it from here, using --tokenizer_name.'''
    )

logger.info('''Training/evaluation parameters %s''', args)

SCREAMING_SNAKE_CASE_    					=						args.per_device_eval_batch_size

SCREAMING_SNAKE_CASE_    					=						(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
SCREAMING_SNAKE_CASE_    					=						True

SCREAMING_SNAKE_CASE_    					=						'''temp_engine/bert-fp32.engine'''
if args.fpaa:
    SCREAMING_SNAKE_CASE_    					=						'''temp_engine/bert-fp16.engine'''
if args.inta:
    SCREAMING_SNAKE_CASE_    					=						'''temp_engine/bert-int8.engine'''

# import ONNX file
if not os.path.exists('''temp_engine'''):
    os.makedirs('''temp_engine''')

SCREAMING_SNAKE_CASE_    					=						1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
    network, TRT_LOGGER
) as parser:
    with open(args.onnx_model_path, '''rb''') as model:
        if not parser.parse(model.read()):
            for error in range(parser.num_errors):
                print(parser.get_error(error))

    # Query input names and shapes from parsed TensorRT network
    SCREAMING_SNAKE_CASE_    					=						[network.get_input(i) for i in range(network.num_inputs)]
    SCREAMING_SNAKE_CASE_    					=						[_input.name for _input in network_inputs]  # ex: ["actual_input1"]

    with builder.create_builder_config() as config:
        SCREAMING_SNAKE_CASE_    					=						1 << 50
        if STRICT_TYPES:
            config.set_flag(trt.BuilderFlag.STRICT_TYPES)
        if args.fpaa:
            config.set_flag(trt.BuilderFlag.FPaa)
        if args.inta:
            config.set_flag(trt.BuilderFlag.INTa)
        SCREAMING_SNAKE_CASE_    					=						builder.create_optimization_profile()
        config.add_optimization_profile(profile)
        for i in range(len(input_names)):
            profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
        SCREAMING_SNAKE_CASE_    					=						builder.build_engine(network, config)

        # serialize_engine and store in file (can be directly loaded and deserialized):
        with open(engine_name, '''wb''') as f:
            f.write(engine.serialize())




def     lowercase__						(					lowerCAmelCase				:					Union[str, Any] ,				lowerCAmelCase				:					Optional[int] ,				lowerCAmelCase				:					Any ,				lowerCAmelCase				:					Tuple ,				lowerCAmelCase				:					str ,				lowerCAmelCase				:					List[Any] ,				lowerCAmelCase				:					Any ,				lowerCAmelCase				:					int ) ->      Union[str, Any]:






    """simple docstring"""




    UpperCAmelCase									=					np.asarray(inputs['input_ids'] ,				dtype=np.intaa )
    UpperCAmelCase									=					np.asarray(inputs['attention_mask'] ,				dtype=np.intaa )
    UpperCAmelCase									=					np.asarray(inputs['token_type_ids'] ,				dtype=np.intaa )

    # Copy inputs
    cuda.memcpy_htod_async(d_inputs[0] ,				input_ids.ravel() ,				lowerCAmelCase )
    cuda.memcpy_htod_async(d_inputs[1] ,				attention_mask.ravel() ,				lowerCAmelCase )
    cuda.memcpy_htod_async(d_inputs[2] ,				token_type_ids.ravel() ,				lowerCAmelCase )
    # start time
    UpperCAmelCase									=					time.time()
    # Run inference
    context.execute_async(
        bindings=[int(lowerCAmelCase ) for d_inp in d_inputs] + [int(lowerCAmelCase ), int(lowerCAmelCase )] ,				stream_handle=stream.handle )
    # Transfer predictions back from GPU
    cuda.memcpy_dtoh_async(lowerCAmelCase ,				lowerCAmelCase ,				lowerCAmelCase )
    cuda.memcpy_dtoh_async(lowerCAmelCase ,				lowerCAmelCase ,				lowerCAmelCase )
    # Synchronize the stream and take time
    stream.synchronize()
    # end time
    UpperCAmelCase									=					time.time()
    UpperCAmelCase									=					end_time - start_time
    UpperCAmelCase									=					(h_outputa, h_outputa)
    # print(outputs)
    return outputs, infer_time


# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
SCREAMING_SNAKE_CASE_    					=						Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',
    datefmt='''%m/%d/%Y %H:%M:%S''',
    level=logging.INFO,
)

# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
    datasets.utils.logging.set_verbosity_warning()
    transformers.utils.logging.set_verbosity_info()
else:
    datasets.utils.logging.set_verbosity_error()
    transformers.utils.logging.set_verbosity_error()

# If passed along, set the training seed now.
if args.seed is not None:
    set_seed(args.seed)

# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
    # Downloading and loading a dataset from the hub.
    SCREAMING_SNAKE_CASE_    					=						load_dataset(args.dataset_name, args.dataset_config_name)
else:
    raise ValueError('''Evaluation requires a dataset name''')
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.

# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.

SCREAMING_SNAKE_CASE_    					=						raw_datasets['''validation'''].column_names

SCREAMING_SNAKE_CASE_    					=						'''question''' if '''question''' in column_names else column_names[0]
SCREAMING_SNAKE_CASE_    					=						'''context''' if '''context''' in column_names else column_names[1]
SCREAMING_SNAKE_CASE_    					=						'''answers''' if '''answers''' in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
SCREAMING_SNAKE_CASE_    					=						tokenizer.padding_side == '''right'''

if args.max_seq_length > tokenizer.model_max_length:
    logger.warning(
        F'The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the'
        F'model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.'
    )

SCREAMING_SNAKE_CASE_    					=						min(args.max_seq_length, tokenizer.model_max_length)




def     lowercase__						(					lowerCAmelCase				:					Optional[Any] ) ->      int:






    """simple docstring"""




    UpperCAmelCase									=					[q.lstrip() for q in examples[question_column_name]]

    # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
    # in one example possible giving several features when a context is long, each of those features having a
    # context that overlaps a bit the context of the previous feature.
    UpperCAmelCase									=					tokenizer(
        examples[question_column_name if pad_on_right else context_column_name] ,				examples[context_column_name if pad_on_right else question_column_name] ,				truncation='only_second' if pad_on_right else 'only_first' ,				max_length=lowerCAmelCase ,				stride=args.doc_stride ,				return_overflowing_tokens=lowerCAmelCase ,				return_offsets_mapping=lowerCAmelCase ,				padding='max_length' ,				)

    # Since one example might give us several features if it has a long context, we need a map from a feature to
    # its corresponding example. This key gives us just that.
    UpperCAmelCase									=					tokenized_examples.pop('overflow_to_sample_mapping' )

    # For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
    # corresponding example_id and we will store the offset mappings.
    UpperCAmelCase									=					[]

    for i in range(len(tokenized_examples['input_ids'] ) ):
        # Grab the sequence corresponding to that example (to know what is the context and what is the question).
        UpperCAmelCase									=					tokenized_examples.sequence_ids(lowerCAmelCase )
        UpperCAmelCase									=					1 if pad_on_right else 0

        # One example can give several spans, this is the index of the example containing this span of text.
        UpperCAmelCase									=					sample_mapping[i]
        tokenized_examples["example_id"].append(examples['id'][sample_index] )

        # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
        # position is part of the context or not.
        UpperCAmelCase									=					[
            (o if sequence_ids[k] == context_index else None)
            for k, o in enumerate(tokenized_examples['offset_mapping'][i] )
        ]

    return tokenized_examples


SCREAMING_SNAKE_CASE_    					=						raw_datasets['''validation''']
# Validation Feature Creation
SCREAMING_SNAKE_CASE_    					=						eval_examples.map(
    prepare_validation_features,
    batched=True,
    num_proc=args.preprocessing_num_workers,
    remove_columns=column_names,
    load_from_cache_file=not args.overwrite_cache,
    desc='''Running tokenizer on validation dataset''',
)

SCREAMING_SNAKE_CASE_    					=						default_data_collator

SCREAMING_SNAKE_CASE_    					=						eval_dataset.remove_columns(['''example_id''', '''offset_mapping'''])
SCREAMING_SNAKE_CASE_    					=						DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)




def     lowercase__						(					lowerCAmelCase				:					Union[str, Any] ,				lowerCAmelCase				:					Tuple ,				lowerCAmelCase				:					Dict ,				lowerCAmelCase				:					int="eval" ) ->      str:






    """simple docstring"""




    UpperCAmelCase									=					postprocess_qa_predictions(
        examples=lowerCAmelCase ,				features=lowerCAmelCase ,				predictions=lowerCAmelCase ,				version_2_with_negative=args.version_2_with_negative ,				n_best_size=args.n_best_size ,				max_answer_length=args.max_answer_length ,				null_score_diff_threshold=args.null_score_diff_threshold ,				output_dir=args.output_dir ,				prefix=lowerCAmelCase ,				)
    # Format the result to the format the metric expects.
    if args.version_2_with_negative:
        UpperCAmelCase									=					[
            {'id': k, 'prediction_text': v, 'no_answer_probability': 0.0} for k, v in predictions.items()
        ]
    else:
        UpperCAmelCase									=					[{'id': k, 'prediction_text': v} for k, v in predictions.items()]

    UpperCAmelCase									=					[{'id': ex['id'], 'answers': ex[answer_column_name]} for ex in examples]
    return EvalPrediction(predictions=lowerCAmelCase ,				label_ids=lowerCAmelCase )


SCREAMING_SNAKE_CASE_    					=						load_metric('''squad_v2''' if args.version_2_with_negative else '''squad''')

# Evaluation!
logger.info('''Loading ONNX model %s for evaluation''', args.onnx_model_path)
with open(engine_name, '''rb''') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
    f.read()
) as engine, engine.create_execution_context() as context:
    # setup for TRT inferrence
    for i in range(len(input_names)):
        context.set_binding_shape(i, INPUT_SHAPE)
    assert context.all_binding_shapes_specified




    def     lowercase__						(					lowerCAmelCase				:					List[Any] ) ->      Any:






        """simple docstring"""




        return trt.volume(engine.get_binding_shape(lowerCAmelCase ) ) * engine.get_binding_dtype(lowerCAmelCase ).itemsize

    # Allocate device memory for inputs and outputs.
    SCREAMING_SNAKE_CASE_    					=						[cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]

    # Allocate output buffer
    SCREAMING_SNAKE_CASE_    					=						cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
    SCREAMING_SNAKE_CASE_    					=						cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
    SCREAMING_SNAKE_CASE_    					=						cuda.mem_alloc(h_outputa.nbytes)
    SCREAMING_SNAKE_CASE_    					=						cuda.mem_alloc(h_outputa.nbytes)

    # Create a stream in which to copy inputs/outputs and run inference.
    SCREAMING_SNAKE_CASE_    					=						cuda.Stream()

    # Evaluation
    logger.info('''***** Running Evaluation *****''')
    logger.info(F'  Num examples = {len(eval_dataset)}')
    logger.info(F'  Batch size = {args.per_device_eval_batch_size}')

    SCREAMING_SNAKE_CASE_    					=						0.0
    SCREAMING_SNAKE_CASE_    					=						0
    SCREAMING_SNAKE_CASE_    					=						timeit.default_timer()

    SCREAMING_SNAKE_CASE_    					=						None
    for step, batch in enumerate(eval_dataloader):
        SCREAMING_SNAKE_CASE_					,						SCREAMING_SNAKE_CASE_    					=						model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
        total_time += infer_time
        niter += 1

        SCREAMING_SNAKE_CASE_					,						SCREAMING_SNAKE_CASE_    					=						outputs
        SCREAMING_SNAKE_CASE_    					=						torch.tensor(start_logits)
        SCREAMING_SNAKE_CASE_    					=						torch.tensor(end_logits)

        # necessary to pad predictions and labels for being gathered
        SCREAMING_SNAKE_CASE_    					=						accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
        SCREAMING_SNAKE_CASE_    					=						accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)

        SCREAMING_SNAKE_CASE_    					=						(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
        SCREAMING_SNAKE_CASE_    					=						logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)

    if all_preds is not None:
        SCREAMING_SNAKE_CASE_    					=						nested_truncate(all_preds, len(eval_dataset))

    SCREAMING_SNAKE_CASE_    					=						timeit.default_timer() - start_time
    logger.info('''  Evaluation done in total %f secs (%f sec per example)''', evalTime, evalTime / len(eval_dataset))
    # Inference time from TRT
    logger.info('''Average Inference Time = {:.3f} ms'''.format(total_time * 1000 / niter))
    logger.info('''Total Inference Time =  {:.3f} ms'''.format(total_time * 1000))







    logger.info('''Total Number of Inference =  %d''', niter)

SCREAMING_SNAKE_CASE_    					=						post_processing_function(eval_examples, eval_dataset, all_preds)
SCREAMING_SNAKE_CASE_    					=						metric.compute(predictions=prediction.predictions, references=prediction.label_ids)







logger.info(F'Evaluation metrics: {eval_metric}')





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




def     lowercase__						(					lowerCAmelCase				:					int ) ->      Any:






    """simple docstring"""




    UpperCAmelCase									=					DPTConfig()

    if "large" in checkpoint_url:
        UpperCAmelCase									=					1_024
        UpperCAmelCase									=					4_096
        UpperCAmelCase									=					24
        UpperCAmelCase									=					16
        UpperCAmelCase									=					[5, 11, 17, 23]
        UpperCAmelCase									=					[256, 512, 1_024, 1_024]
        UpperCAmelCase									=					(1, 384, 384)

    if "ade" in checkpoint_url:
        UpperCAmelCase									=					True

        UpperCAmelCase									=					150
        UpperCAmelCase									=					'huggingface/label-files'
        UpperCAmelCase									=					'ade20k-id2label.json'
        UpperCAmelCase									=					json.load(open(cached_download(hf_hub_url(lowerCAmelCase ,				lowerCAmelCase ,				repo_type='dataset' ) ) ,				'r' ) )
        UpperCAmelCase									=					{int(lowerCAmelCase ): 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     lowercase__						(					lowerCAmelCase				:					str ) ->      Any:






    """simple docstring"""




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




def     lowercase__						(					lowerCAmelCase				:					Union[str, Any] ) ->      List[Any]:






    """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
    ):
        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' ,				'patch_embeddings' )
    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:
        UpperCAmelCase									=					name.replace('norm1' ,				'layernorm_before' )
    if "norm2" 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' )

    return name




def     lowercase__						(					lowerCAmelCase				:					List[Any] ,				lowerCAmelCase				:					int ) ->      int:






    """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)
        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     lowercase__						(					) ->      Optional[Any]:






    """simple docstring"""




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




@torch.no_grad()
def     lowercase__						(					lowerCAmelCase				:					Dict ,				lowerCAmelCase				:					Optional[int] ,				lowerCAmelCase				:					List[str] ,				lowerCAmelCase				:					Union[str, Any] ) ->      Union[str, Any]:






    """simple docstring"""




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

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

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

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

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

    # Assert logits
    UpperCAmelCase									=					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									=					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(lowerCAmelCase )
    assert (
        torch.allclose(outputs[0, 0, :3, :3] ,				lowerCAmelCase ,				atol=1E-4 )
        if "ade" in checkpoint_url
        else torch.allclose(outputs[0, :3, :3] ,				lowerCAmelCase )
    )

    Path(lowerCAmelCase ).mkdir(exist_ok=lowerCAmelCase )
    print(F"Saving model to {pytorch_dump_folder_path}" )
    model.save_pretrained(lowerCAmelCase )
    print(F"Saving image processor to {pytorch_dump_folder_path}" )
    image_processor.save_pretrained(lowerCAmelCase )

    if push_to_hub:
        print('Pushing model to hub...' )
        model.push_to_hub(
            repo_path_or_name=Path(lowerCAmelCase ,				lowerCAmelCase ) ,				organization='nielsr' ,				commit_message='Add model' ,				use_temp_dir=lowerCAmelCase ,				)
        image_processor.push_to_hub(
            repo_path_or_name=Path(lowerCAmelCase ,				lowerCAmelCase ) ,				organization='nielsr' ,				commit_message='Add image processor' ,				use_temp_dir=lowerCAmelCase ,				)


if __name__ == "__main__":
   SCREAMING_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=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.''',
   )

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



