'''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()
__lowercase        =    logging.get_logger(__name__)






def 							snake_case__				(     _A:   List[str]					)       ->      Dict:

		'''simple docstring'''


		lowerCAmelCase     =    DPTConfig()

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

		if "ade" in checkpoint_url:
				lowerCAmelCase     =    True

				lowerCAmelCase     =    150
				lowerCAmelCase     =    """huggingface/label-files"""
				lowerCAmelCase     =    """ade20k-id2label.json"""
				lowerCAmelCase     =    json.load(open(cached_download(hf_hub_url(lowerCamelCase_ ,  lowerCamelCase_ ,  repo_type="""dataset"""					)					) ,  """r"""					)					)
				lowerCAmelCase     =    {int(lowerCamelCase_					): v for k, v in idalabel.items()}
				lowerCAmelCase     =    idalabel
				lowerCAmelCase     =    {v: k for k, v in idalabel.items()}
				lowerCAmelCase     =    [1, 150, 480, 480]

		return config, expected_shape






def 							snake_case__				(     _A:   Any					)       ->      int:

		'''simple docstring'''


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






def 							snake_case__				(     _A:   Union[str, Any]					)       ->      str:

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

		return name






def 							snake_case__				(     _A:   Dict ,  _A:   Dict					)       ->      str:

		'''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)
				lowerCAmelCase     =    state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.weight"					)
				lowerCAmelCase     =    state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.bias"					)
				# next, add query, keys and values (in that order) to the state dict
				lowerCAmelCase     =    in_proj_weight[: config.hidden_size, :]
				lowerCAmelCase     =    in_proj_bias[: config.hidden_size]
				lowerCAmelCase     =    in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				lowerCAmelCase     =    in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				lowerCAmelCase     =    in_proj_weight[
				    -config.hidden_size :, :
				]
				lowerCAmelCase     =    in_proj_bias[-config.hidden_size :]






def 							snake_case__				(     )       ->      Optional[Any]:

		'''simple docstring'''


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






@torch.no_grad()
def 							snake_case__				(     _A:   Optional[int] ,  _A:   List[str] ,  _A:   Union[str, Any] ,  _A:   Tuple					)       ->      List[Any]:

		'''simple docstring'''


		lowerCAmelCase   ,	lowerCAmelCase     =    get_dpt_config(lowerCamelCase_					)
		# load original state_dict from URL
		lowerCAmelCase     =    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():
				lowerCAmelCase     =    state_dict.pop(lowerCamelCase_					)
				lowerCAmelCase     =    val
		# read in qkv matrices
		read_in_q_k_v(lowerCamelCase_ ,  lowerCamelCase_					)

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

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

		lowerCAmelCase     =    prepare_img()
		lowerCAmelCase     =    image_processor(lowerCamelCase_ ,  return_tensors="""pt"""					)

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

		# Assert logits
		lowerCAmelCase     =    torch.tensor([[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]]					)
		if "ade" in checkpoint_url:
				lowerCAmelCase     =    torch.tensor([[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]]					)
		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__":
						__lowercase        =    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.''',
						)

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












from jiwer import compute_measures

import datasets


A__      :     Tuple       =					'\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n'

A__      :     Optional[int]       =					'\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n'

A__      :     Dict       =					'\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word error rate\n\nExamples:\n\n    >>> predictions = ["this is the prediction", "there is an other sample"]\n    >>> references = ["this is the reference", "there is another one"]\n    >>> wer = datasets.load_metric("wer")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,_KWARGS_DESCRIPTION      )
class  _UpperCAmelCase   (			datasets.Metric      ):

				"""simple docstring"""




				def 							lowercase__  (	self		:       Union[str, Any]    ):

								'''simple docstring'''
								return datasets.MetricInfo(
								    description=_DESCRIPTION,    citation=_CITATION,    inputs_description=_KWARGS_DESCRIPTION,    features=datasets.Features(
								        {
								            '''predictions''': datasets.Value('''string''',    id='''sequence'''    ),
								            '''references''': datasets.Value('''string''',    id='''sequence'''    ),
								        }    ),    codebase_urls=['''https://github.com/jitsi/jiwer/'''],    reference_urls=[
								        '''https://en.wikipedia.org/wiki/Word_error_rate''',
								    ],    )




				def 							lowercase__  (	self		:       Union[str, Any],    lowerCamelCase		:       str=None,    lowerCamelCase		:       Union[str, Any]=None,    lowerCamelCase		:       List[str]=False    ):

								'''simple docstring'''
								if concatenate_texts:
												return compute_measures(lowerCamelCase,    lowerCamelCase    )["wer"]
								else:
												lowercase__  					=     0
												lowercase__  					=     0
												for prediction, reference in zip(lowerCamelCase,    lowerCamelCase    ):
																lowercase__  					=     compute_measures(lowerCamelCase,    lowerCamelCase    )
																incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
																total += measures["substitutions"] + measures["deletions"] + measures["hits"]
												return incorrect / total




