




"""simple docstring"""







import argparse
import json

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

from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation

def 						snake_case__       (					_lowerCamelCase				)			->int:






		"""simple docstring"""


		__lowercase				:		Any												=				3_84
		if "tiny" in model_name:
				__lowercase				:		List[str]												=				[3, 3, 9, 3]
				__lowercase				:		int												=				[96, 1_92, 3_84, 7_68]
		if "small" in model_name:
				__lowercase				:		Optional[int]												=				[3, 3, 27, 3]
				__lowercase				:		Union[str, Any]												=				[96, 1_92, 3_84, 7_68]
		if "base" in model_name:
				__lowercase				:		Optional[Any]												=				[3, 3, 27, 3]
				__lowercase				:		List[Any]												=				[1_28, 2_56, 5_12, 10_24]
				__lowercase				:		Dict												=				5_12
		if "large" in model_name:
				__lowercase				:		str												=				[3, 3, 27, 3]
				__lowercase				:		str												=				[1_92, 3_84, 7_68, 15_36]
				__lowercase				:		str												=				7_68
		if "xlarge" in model_name:
				__lowercase				:		Optional[Any]												=				[3, 3, 27, 3]
				__lowercase				:		str												=				[2_56, 5_12, 10_24, 20_48]
				__lowercase				:		List[Any]												=				10_24

		# set label information
		__lowercase				:		Union[str, Any]												=				1_50
		__lowercase				:		Union[str, Any]												=				"huggingface/label-files"
		__lowercase				:		Dict												=				"ade20k-id2label.json"
		__lowercase				:		str												=				json.load(open(hf_hub_download(_snake_case,  _snake_case,  repo_type="dataset"				),  "r"				)				)
		__lowercase				:		List[Any]												=				{int(_snake_case				): v for k, v in idalabel.items()}
		__lowercase				:		Any												=				{v: k for k, v in idalabel.items()}

		__lowercase				:		Optional[Any]												=				ConvNextConfig(
		    depths=_snake_case,  hidden_sizes=_snake_case,  out_features=["stage1", "stage2", "stage3", "stage4"]				)
		__lowercase				:		Dict												=				UperNetConfig(
		    backbone_config=_snake_case,  auxiliary_in_channels=_snake_case,  num_labels=_snake_case,  idalabel=_snake_case,  labelaid=_snake_case,  )

		return config

def 						snake_case__       (					_lowerCamelCase				)			->Tuple:






		"""simple docstring"""


		__lowercase				:		Optional[Any]												=				[]

		# fmt: off
		# stem
		rename_keys.append(("backbone.downsample_layers.0.0.weight", "backbone.embeddings.patch_embeddings.weight")				)
		rename_keys.append(("backbone.downsample_layers.0.0.bias", "backbone.embeddings.patch_embeddings.bias")				)
		rename_keys.append(("backbone.downsample_layers.0.1.weight", "backbone.embeddings.layernorm.weight")				)
		rename_keys.append(("backbone.downsample_layers.0.1.bias", "backbone.embeddings.layernorm.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}.{j}.gamma', F'backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter')				)
						rename_keys.append((F'backbone.stages.{i}.{j}.depthwise_conv.weight', F'backbone.encoder.stages.{i}.layers.{j}.dwconv.weight')				)
						rename_keys.append((F'backbone.stages.{i}.{j}.depthwise_conv.bias', F'backbone.encoder.stages.{i}.layers.{j}.dwconv.bias')				)
						rename_keys.append((F'backbone.stages.{i}.{j}.norm.weight', F'backbone.encoder.stages.{i}.layers.{j}.layernorm.weight')				)
						rename_keys.append((F'backbone.stages.{i}.{j}.norm.bias', F'backbone.encoder.stages.{i}.layers.{j}.layernorm.bias')				)
						rename_keys.append((F'backbone.stages.{i}.{j}.pointwise_conv1.weight', F'backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight')				)
						rename_keys.append((F'backbone.stages.{i}.{j}.pointwise_conv1.bias', F'backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias')				)
						rename_keys.append((F'backbone.stages.{i}.{j}.pointwise_conv2.weight', F'backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight')				)
						rename_keys.append((F'backbone.stages.{i}.{j}.pointwise_conv2.bias', F'backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias')				)
				if i > 0:
						rename_keys.append((F'backbone.downsample_layers.{i}.0.weight', F'backbone.encoder.stages.{i}.downsampling_layer.0.weight')				)
						rename_keys.append((F'backbone.downsample_layers.{i}.0.bias', F'backbone.encoder.stages.{i}.downsampling_layer.0.bias')				)
						rename_keys.append((F'backbone.downsample_layers.{i}.1.weight', F'backbone.encoder.stages.{i}.downsampling_layer.1.weight')				)
						rename_keys.append((F'backbone.downsample_layers.{i}.1.bias', F'backbone.encoder.stages.{i}.downsampling_layer.1.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 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)			->Dict:






		"""simple docstring"""


		__lowercase				:		List[str]												=				dct.pop(_snake_case				)
		__lowercase				:		int												=				val

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)			->Dict:






		"""simple docstring"""


		__lowercase				:		List[Any]												=				{
		    "upernet-convnext-tiny": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth",
		    "upernet-convnext-small": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth",
		    "upernet-convnext-base": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth",
		    "upernet-convnext-large": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth",
		    "upernet-convnext-xlarge": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth",
		}
		__lowercase				:		List[str]												=				model_name_to_url[model_name]
		__lowercase				:		int												=				torch.hub.load_state_dict_from_url(_snake_case,  map_location="cpu"				)["state_dict"]

		__lowercase				:		Tuple												=				get_upernet_config(_snake_case				)
		__lowercase				:		Optional[Any]												=				UperNetForSemanticSegmentation(_snake_case				)
		model.eval()

		# replace "bn" => "batch_norm"
		for key in state_dict.copy().keys():
				__lowercase				:		Union[str, Any]												=				state_dict.pop(_snake_case				)
				if "bn" in key:
						__lowercase				:		Union[str, Any]												=				key.replace("bn",  "batch_norm"				)
				__lowercase				:		Union[str, Any]												=				val

		# rename keys
		__lowercase				:		str												=				create_rename_keys(_snake_case				)
		for src, dest in rename_keys:
				rename_key(_snake_case,  _snake_case,  _snake_case				)

		model.load_state_dict(_snake_case				)

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

		__lowercase				:		Dict												=				SegformerImageProcessor()
		__lowercase				:		Any												=				processor(_snake_case,  return_tensors="pt"				).pixel_values

		with torch.no_grad():
				__lowercase				:		Any												=				model(_snake_case				)

		if model_name == "upernet-convnext-tiny":
				__lowercase				:		Dict												=				torch.tensor(
				    [[-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.7_7_4_6, -8.7_7_4_6, -8.6_1_3_0]]				)
		elif model_name == "upernet-convnext-small":
				__lowercase				:		Union[str, Any]												=				torch.tensor(
				    [[-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.7_6_3_8, -8.7_6_3_8, -8.6_2_4_0]]				)
		elif model_name == "upernet-convnext-base":
				__lowercase				:		str												=				torch.tensor(
				    [[-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.7_6_6_9, -8.7_6_6_9, -8.6_0_2_1]]				)
		elif model_name == "upernet-convnext-large":
				__lowercase				:		Optional[int]												=				torch.tensor(
				    [[-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_3_1_0, -8.6_3_1_0, -8.5_9_6_4]]				)
		elif model_name == "upernet-convnext-xlarge":
				__lowercase				:		List[str]												=				torch.tensor(
				    [[-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_3_7_9, -8.4_3_7_9, -8.3_4_1_2]]				)
		print("Logits:",  outputs.logits[0, 0, :3, :3]				)
		assert torch.allclose(outputs.logits[0, 0, :3, :3],  _snake_case,  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(_snake_case				)
				print(F'Saving processor to {pytorch_dump_folder_path}'				)
				processor.save_pretrained(_snake_case				)

		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   :  List[str]					=      argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--model_name',
					    default='upernet-convnext-tiny',
					    type=str,
					    choices=[F"""upernet-convnext-{size}""" for size in ['tiny', 'small', 'base', 'large', 'xlarge']],
					    help='Name of the ConvNext 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   :  Tuple					=      parser.parse_args()
					convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






"""simple docstring"""







import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer

import datasets
from datasets import logging


__A   :  str					=      '\\n\n'

__A   :  int					=      '\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n'

__A   :  Optional[Any]					=      '\nArgs:\n    model_id (str): model used for calculating Perplexity\n            NOTE: Perplexity can only be calculated for causal language models.\n                    This includes models such as gpt2, causal variations of bert,\n                    causal versions of t5, and more (the full list can be found\n                    in the AutoModelForCausalLM documentation here:\n                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n    input_texts (list of str): input text, each separate text snippet\n        is one list entry.\n    batch_size (int): the batch size to run texts through the model. Defaults to 16.\n    add_start_token (bool): whether to add the start token to the texts,\n        so the perplexity can include the probability of the first word. Defaults to True.\n    device (str): device to run on, defaults to \'cuda\' when available\nReturns:\n    perplexity: dictionary containing the perplexity scores for the texts\n        in the input list, as well as the mean perplexity. If one of the input texts is\n        longer than the max input length of the model, then it is truncated to the\n        max length for the perplexity computation.\nExamples:\n    Example 1:\n        >>> perplexity = datasets.load_metric("perplexity")\n        >>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]\n        >>> results = perplexity.compute(model_id=\'gpt2\',\n        ...                              add_start_token=False,\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        [\'perplexities\', \'mean_perplexity\']\n        >>> print(round(results["mean_perplexity"], 2))\n        78.22\n        >>> print(round(results["perplexities"][0], 2))\n        11.11\n\n    Example 2:\n        >>> perplexity = datasets.load_metric("perplexity")\n        >>> input_texts = datasets.load_dataset("wikitext",\n        ...                                     "wikitext-2-raw-v1",\n        ...                                     split="test")["text"][:50] # doctest:+ELLIPSIS\n        [...]\n        >>> input_texts = [s for s in input_texts if s!=\'\']\n        >>> results = perplexity.compute(model_id=\'gpt2\',\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        [\'perplexities\', \'mean_perplexity\']\n        >>> print(round(results["mean_perplexity"], 2))\n        60.35\n        >>> print(round(results["perplexities"][0], 2))\n        81.12\n'




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


				"""simple docstring"""





				def       snake_case    (     self					:	Dict						):
						return datasets.MetricInfo(
						    description=_DESCRIPTION    ,   citation=_CITATION    ,   inputs_description=_KWARGS_DESCRIPTION    ,   features=datasets.Features(
						        {
						            "input_texts": datasets.Value("string"						),
						        }						)    ,   reference_urls=["https://huggingface.co/docs/transformers/perplexity"]    ,   )



				def       snake_case    (     self					:	Optional[Any]    ,   lowercase__					:	int    ,   lowercase__					:	str    ,   lowercase__					:	int = 1_6    ,   lowercase__					:	bool = True    ,   lowercase__					:	List[Any]=None						):
						if device is not None:
								assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
								if device == "gpu":
										__lowercase				:		Optional[Any]												=				"cuda"
						else:
								__lowercase				:		Optional[Any]												=				"cuda" if torch.cuda.is_available() else "cpu"

						__lowercase				:		List[str]												=				AutoModelForCausalLM.from_pretrained(lowercase__						)
						__lowercase				:		str												=				model.to(lowercase__						)

						__lowercase				:		Any												=				AutoTokenizer.from_pretrained(lowercase__						)

						# if batch_size > 1 (which generally leads to padding being required), and
						# if there is not an already assigned pad_token, assign an existing
						# special token to also be the padding token
						if tokenizer.pad_token is None and batch_size > 1:
								__lowercase				:		Any												=				list(tokenizer.special_tokens_map_extended.values()						)
								# check that the model already has at least one special token defined
								assert (
								    len(lowercase__						) > 0
								), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
								# assign one of the special tokens to also be the pad token
								tokenizer.add_special_tokens({"pad_token": existing_special_tokens[0]}						)

						if add_start_token:
								# leave room for <BOS> token to be added:
								assert (
								    tokenizer.bos_token is not None
								), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
								__lowercase				:		Any												=				model.config.max_length - 1
						else:
								__lowercase				:		Union[str, Any]												=				model.config.max_length

						__lowercase				:		List[Any]												=				tokenizer(
						    lowercase__    ,   add_special_tokens=lowercase__    ,   padding=lowercase__    ,   truncation=lowercase__    ,   max_length=lowercase__    ,   return_tensors="pt"    ,   return_attention_mask=lowercase__    ,   ).to(lowercase__						)

						__lowercase				:		Tuple												=				encodings["input_ids"]
						__lowercase				:		Tuple												=				encodings["attention_mask"]

						# check that each input is long enough:
						if add_start_token:
								assert torch.all(torch.ge(attn_masks.sum(1						)    ,   1						)						), "Each input text must be at least one token long."
						else:
								assert torch.all(
								    torch.ge(attn_masks.sum(1						)    ,   2						)						), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."

						__lowercase				:		Optional[int]												=				[]
						__lowercase				:		List[Any]												=				CrossEntropyLoss(reduction="none"						)

						for start_index in logging.tqdm(range(0    ,   len(lowercase__						)    ,   lowercase__						)						):
								__lowercase				:		List[str]												=				min(start_index + batch_size    ,   len(lowercase__						)						)
								__lowercase				:		Any												=				encoded_texts[start_index:end_index]
								__lowercase				:		Dict												=				attn_masks[start_index:end_index]

								if add_start_token:
										__lowercase				:		str												=				torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0						)						).to(lowercase__						)
										__lowercase				:		Optional[Any]												=				torch.cat([bos_tokens_tensor, encoded_batch]    ,   dim=1						)
										__lowercase				:		Union[str, Any]												=				torch.cat(
										    [torch.ones(bos_tokens_tensor.size()    ,   dtype=torch.intaa						).to(lowercase__						), attn_mask]    ,   dim=1						)

								__lowercase				:		Optional[int]												=				encoded_batch

								with torch.no_grad():
										__lowercase				:		Any												=				model(lowercase__    ,   attention_mask=lowercase__						).logits

								__lowercase				:		Any												=				out_logits[..., :-1, :].contiguous()
								__lowercase				:		Tuple												=				labels[..., 1:].contiguous()
								__lowercase				:		str												=				attn_mask[..., 1:].contiguous()

								__lowercase				:		Optional[int]												=				torch.expa(
								    (loss_fct(shift_logits.transpose(1    ,   2						)    ,   lowercase__						) * shift_attention_mask_batch).sum(1						)
								    / shift_attention_mask_batch.sum(1						)						)

								ppls += perplexity_batch.tolist()

						return {"perplexities": ppls, "mean_perplexity": np.mean(lowercase__						)}
