

from typing import List

import jiwer
import jiwer.transforms as tr
from packaging import version

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse('3.8'):
		import importlib_metadata
else:
		import importlib.metadata as importlib_metadata


A									=  ''


if version.parse(importlib_metadata.version('jiwer')) < version.parse('2.3.0'):


		class SCREAMING_SNAKE_CASE	(		tr.AbstractTransform				):






					"""simple docstring"""




					def __init__(     self			,					__UpperCamelCase = " "				):

						"""simple docstring"""


						snake_case_   		=			sentence_delimiter


					def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

						"""simple docstring"""


						return list(__UpperCamelCase				)







					def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

						"""simple docstring"""


						snake_case_   		=			[]
						for sent_idx, sentence in enumerate(__UpperCamelCase				):
							chars.extend(self.process_string(__UpperCamelCase				)				)
							if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(__UpperCamelCase				) - 1:
								chars.append(self.sentence_delimiter				)
						return chars







		A									=  tr.Compose(
		    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
		)
else:
		A									=  tr.Compose(
		    [
		        tr.RemoveMultipleSpaces(),
		        tr.Strip(),
		        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
		        tr.ReduceToListOfListOfChars(),
		    ]
		)


A									=  '\\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									=  '\\nCharacter error rate (CER) is a common metric of the performance of an automatic speech recognition system.\n\nCER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.\n\nCharacter error rate can be computed as:\n\nCER = (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 characters,\nN is the number of characters in the reference (N=S+D+C).\n\nCER\'s output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the\nperformance of the ASR system with a CER of 0 being a perfect score.\n'

A									=  '\nComputes CER score of transcribed segments against references.\nArgs:\n    references: list of references for each speech input.\n    predictions: list of transcribtions to score.\n    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.\nReturns:\n    (float): the character 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    >>> cer = datasets.load_metric("cer")\n    >>> cer_score = cer.compute(predictions=predictions, references=references)\n    >>> print(cer_score)\n    0.34146341463414637\n'


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,    _KWARGS_DESCRIPTION				)
class SCREAMING_SNAKE_CASE	(		datasets.Metric				):






			"""simple docstring"""




			def       __lowerCAmelCase			(     self				):

				"""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',
				        'https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates',
				    ]			,					)







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase=False				):

				"""simple docstring"""


				if concatenate_texts:
					return jiwer.compute_measures(
					    __UpperCamelCase			,					__UpperCamelCase			,					truth_transform=__UpperCamelCase			,					hypothesis_transform=__UpperCamelCase			,					)["wer"]

				snake_case_   		=			0
				snake_case_   		=			0
				for prediction, reference in zip(__UpperCamelCase			,					__UpperCamelCase				):
					snake_case_   		=			jiwer.compute_measures(
					    __UpperCamelCase			,					__UpperCamelCase			,					truth_transform=__UpperCamelCase			,					hypothesis_transform=__UpperCamelCase			,					)
					incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
					total += measures["substitutions"] + measures["deletions"] + measures["hits"]

				return incorrect / total



import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation





def a(lowercase__						):





	'''simple docstring'''
	snake_case_   		=			384
	snake_case_   		=			7
	if "tiny" in model_name:
		snake_case_   		=			96
		snake_case_   		=			(2, 2, 6, 2)
		snake_case_   		=			(3, 6, 12, 24)
	elif "small" in model_name:
		snake_case_   		=			96
		snake_case_   		=			(2, 2, 18, 2)
		snake_case_   		=			(3, 6, 12, 24)
	elif "base" in model_name:
		snake_case_   		=			128
		snake_case_   		=			(2, 2, 18, 2)
		snake_case_   		=			(4, 8, 16, 32)
		snake_case_   		=			12
		snake_case_   		=			512
	elif "large" in model_name:
		snake_case_   		=			192
		snake_case_   		=			(2, 2, 18, 2)
		snake_case_   		=			(6, 12, 24, 48)
		snake_case_   		=			12
		snake_case_   		=			768

	# set label information
	snake_case_   		=			150
	snake_case_   		=			'huggingface/label-files'
	snake_case_   		=			'ade20k-id2label.json'
	snake_case_   		=			json.load(open(hf_hub_download(lowercase__      ,			lowercase__      ,			repo_type='dataset'						)      ,			'r'						)						)
	snake_case_   		=			{int(lowercase__						): v for k, v in idalabel.items()}
	snake_case_   		=			{v: k for k, v in idalabel.items()}

	snake_case_   		=			SwinConfig(
	    embed_dim=lowercase__      ,			depths=lowercase__      ,			num_heads=lowercase__      ,			window_size=lowercase__      ,			out_features=['stage1', 'stage2', 'stage3', 'stage4']      ,			)
	snake_case_   		=			UperNetConfig(
	    backbone_config=lowercase__      ,			auxiliary_in_channels=lowercase__      ,			num_labels=lowercase__      ,			idalabel=lowercase__      ,			labelaid=lowercase__      ,			)

	return config





def a(lowercase__						):





	'''simple docstring'''
	snake_case_   		=			[]

	# fmt: off
	# stem
	rename_keys.append(('backbone.patch_embed.projection.weight', 'backbone.embeddings.patch_embeddings.projection.weight')						)
	rename_keys.append(('backbone.patch_embed.projection.bias', 'backbone.embeddings.patch_embeddings.projection.bias')						)
	rename_keys.append(('backbone.patch_embed.norm.weight', 'backbone.embeddings.norm.weight')						)
	rename_keys.append(('backbone.patch_embed.norm.bias', 'backbone.embeddings.norm.bias')						)
	# stages
	for i in range(len(config.backbone_config.depths						)						):
		for j in range(config.backbone_config.depths[i]						):
			rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm1.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")						)
			rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm1.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")						)
			rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")						)
			rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")						)
			rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")						)
			rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")						)
			rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm2.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")						)
			rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm2.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")						)
			rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")						)
			rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")						)
			rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight""")						)
			rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias""")						)

		if i < 3:
			rename_keys.append((f"""backbone.stages.{i}.downsample.reduction.weight""", f"""backbone.encoder.layers.{i}.downsample.reduction.weight""")						)
			rename_keys.append((f"""backbone.stages.{i}.downsample.norm.weight""", f"""backbone.encoder.layers.{i}.downsample.norm.weight""")						)
			rename_keys.append((f"""backbone.stages.{i}.downsample.norm.bias""", f"""backbone.encoder.layers.{i}.downsample.norm.bias""")						)
		rename_keys.append((f"""backbone.norm{i}.weight""", f"""backbone.hidden_states_norms.stage{i+1}.weight""")						)
		rename_keys.append((f"""backbone.norm{i}.bias""", f"""backbone.hidden_states_norms.stage{i+1}.bias""")						)

	# decode head
	rename_keys.extend(
	    [
	        ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
	        ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
	        ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
	        ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
	    ]						)
	# fmt: on

	return rename_keys





def a(lowercase__      ,			lowercase__      ,			lowercase__						):





	'''simple docstring'''
	snake_case_   		=			dct.pop(lowercase__						)
	snake_case_   		=			val





def a(lowercase__      ,			lowercase__						):





	'''simple docstring'''
	snake_case_   		=			[int(backbone_config.embed_dim * 2**i						) for i in range(len(backbone_config.depths						)						)]
	for i in range(len(backbone_config.depths						)						):
		snake_case_   		=			num_features[i]
		for j in range(backbone_config.depths[i]						):
			# fmt: off
			# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
			snake_case_   		=			state_dict.pop(f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight"""						)
			snake_case_   		=			state_dict.pop(f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias"""						)
			# next, add query, keys and values (in that order) to the state dict
			snake_case_   		=			in_proj_weight[:dim, :]
			snake_case_   		=			in_proj_bias[: dim]
			snake_case_   		=			in_proj_weight[
			    dim : dim * 2, :
			]
			snake_case_   		=			in_proj_bias[
			    dim : dim * 2
			]
			snake_case_   		=			in_proj_weight[
			    -dim :, :
			]
			snake_case_   		=			in_proj_bias[-dim :]
			# fmt: on





def a(lowercase__						):





	'''simple docstring'''
	snake_case_		,							snake_case_   		=			x.shape
	snake_case_   		=			x.reshape(lowercase__      ,			4      ,			in_channel // 4						)
	snake_case_   		=			x[:, [0, 2, 1, 3], :].transpose(1      ,			2						).reshape(lowercase__      ,			lowercase__						)
	return x





def a(lowercase__						):





	'''simple docstring'''
	snake_case_		,							snake_case_   		=			x.shape
	snake_case_   		=			x.reshape(lowercase__      ,			in_channel // 4      ,			4						)
	snake_case_   		=			x[:, :, [0, 2, 1, 3]].transpose(1      ,			2						).reshape(lowercase__      ,			lowercase__						)

	return x





def a(lowercase__						):





	'''simple docstring'''
	snake_case_   		=			x.shape[0]
	snake_case_   		=			x.reshape(4      ,			in_channel // 4						)
	snake_case_   		=			x[[0, 2, 1, 3], :].transpose(0      ,			1						).reshape(lowercase__						)
	return x





def a(lowercase__						):





	'''simple docstring'''
	snake_case_   		=			x.shape[0]
	snake_case_   		=			x.reshape(in_channel // 4      ,			4						)
	snake_case_   		=			x[:, [0, 2, 1, 3]].transpose(0      ,			1						).reshape(lowercase__						)
	return x





def a(lowercase__      ,			lowercase__      ,			lowercase__						):





	'''simple docstring'''
	snake_case_   		=			{
	    'upernet-swin-tiny': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth',
	    'upernet-swin-small': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth',
	    'upernet-swin-base': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth',
	    'upernet-swin-large': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth',
	}
	snake_case_   		=			model_name_to_url[model_name]
	snake_case_   		=			torch.hub.load_state_dict_from_url(lowercase__      ,			map_location='cpu'      ,			file_name=lowercase__						)[
	    'state_dict'
	]

	for name, param in state_dict.items():
		print(lowercase__      ,			param.shape						)

	snake_case_   		=			get_upernet_config(lowercase__						)
	snake_case_   		=			UperNetForSemanticSegmentation(lowercase__						)
	model.eval()

	# replace "bn" => "batch_norm"
	for key in state_dict.copy().keys():
		snake_case_   		=			state_dict.pop(lowercase__						)
		if "bn" in key:
			snake_case_   		=			key.replace('bn'      ,			'batch_norm'						)
		snake_case_   		=			val

	# rename keys
	snake_case_   		=			create_rename_keys(lowercase__						)
	for src, dest in rename_keys:
		rename_key(lowercase__      ,			lowercase__      ,			lowercase__						)
	read_in_q_k_v(lowercase__      ,			config.backbone_config						)

	# fix downsample parameters
	for key, value in state_dict.items():
		if "downsample" in key:
			if "reduction" in key:
				snake_case_   		=			reverse_correct_unfold_reduction_order(lowercase__						)
			if "norm" in key:
				snake_case_   		=			reverse_correct_unfold_norm_order(lowercase__						)

	model.load_state_dict(lowercase__						)

	# verify on image
	snake_case_   		=			'https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'
	snake_case_   		=			Image.open(requests.get(lowercase__      ,			stream=lowercase__						).raw						).convert('RGB'						)

	snake_case_   		=			SegformerImageProcessor()
	snake_case_   		=			processor(lowercase__      ,			return_tensors='pt'						).pixel_values

	with torch.no_grad():
		snake_case_   		=			model(lowercase__						)
		snake_case_   		=			outputs.logits

	print(logits.shape						)
	print('First values of logits:'      ,			logits[0, 0, :3, :3]						)
	# assert values
	if model_name == "upernet-swin-tiny":
		snake_case_   		=			torch.tensor(
		    [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]						)
	elif model_name == "upernet-swin-small":
		snake_case_   		=			torch.tensor(
		    [[-7.1921, -7.1921, -6.9532], [-7.1921, -7.1921, -6.9532], [-7.0908, -7.0908, -6.8534]]						)
	elif model_name == "upernet-swin-base":
		snake_case_   		=			torch.tensor(
		    [[-6.5851, -6.5851, -6.4330], [-6.5851, -6.5851, -6.4330], [-6.4763, -6.4763, -6.3254]]						)
	elif model_name == "upernet-swin-large":
		snake_case_   		=			torch.tensor(
		    [[-7.5297, -7.5297, -7.3802], [-7.5297, -7.5297, -7.3802], [-7.4044, -7.4044, -7.2586]]						)
	print('Logits:'      ,			outputs.logits[0, 0, :3, :3]						)
	assert torch.allclose(outputs.logits[0, 0, :3, :3]      ,			lowercase__      ,			atol=1e-4						)
	print('Looks ok!'						)

	if pytorch_dump_folder_path is not None:
		print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""						)
		model.save_pretrained(lowercase__						)
		print(f"""Saving processor to {pytorch_dump_folder_path}"""						)
		processor.save_pretrained(lowercase__						)

	if push_to_hub:
		print(f"""Pushing model and processor for {model_name} to hub"""						)
		model.push_to_hub(f"""openmmlab/{model_name}"""						)
		processor.push_to_hub(f"""openmmlab/{model_name}"""						)


if __name__ == "__main__":
		A									=  argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--model_name',
		    default='upernet-swin-tiny',
		    type=str,
		    choices=[f"""upernet-swin-{size}""" for size in ['tiny', 'small', 'base', 'large']],
		    help='Name of the Swin + UperNet model you\'d like to convert.',
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
		)
		parser.add_argument(
		    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
		)

		A									=  parser.parse_args()
		convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
