



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 A						(       __UpperCamelCase					)						->      int:
			A__       =    384
			if "tiny" in model_name:
						A__       =    [3, 3, 9, 3]
						A__       =    [96, 192, 384, 768]
			if "small" in model_name:
						A__       =    [3, 3, 27, 3]
						A__       =    [96, 192, 384, 768]
			if "base" in model_name:
						A__       =    [3, 3, 27, 3]
						A__       =    [128, 256, 512, 1_024]
						A__       =    512
			if "large" in model_name:
						A__       =    [3, 3, 27, 3]
						A__       =    [192, 384, 768, 1_536]
						A__       =    768
			if "xlarge" in model_name:
						A__       =    [3, 3, 27, 3]
						A__       =    [256, 512, 1_024, 2_048]
						A__       =    1_024

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

			A__       =    ConvNextConfig(
			    depths=__UpperCamelCase ,	hidden_sizes=__UpperCamelCase ,	out_features=['stage1', 'stage2', 'stage3', 'stage4']					)
			A__       =    UperNetConfig(
			    backbone_config=__UpperCamelCase ,	auxiliary_in_channels=__UpperCamelCase ,	num_labels=__UpperCamelCase ,	idalabel=__UpperCamelCase ,	labelaid=__UpperCamelCase ,	)

			return config


def A						(       __UpperCamelCase					)						->      Union[str, Any]:
			A__       =    []

			# 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 A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)						->      Optional[Any]:
			A__       =    dct.pop(__UpperCamelCase					)
			A__       =    val


def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)						->      List[str]:
			A__       =    {
			    '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',
			}
			A__       =    model_name_to_url[model_name]
			A__       =    torch.hub.load_state_dict_from_url(__UpperCamelCase ,	map_location='cpu'					)['state_dict']

			A__       =    get_upernet_config(__UpperCamelCase					)
			A__       =    UperNetForSemanticSegmentation(__UpperCamelCase					)
			model.eval()

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

			# rename keys
			A__       =    create_rename_keys(__UpperCamelCase					)
			for src, dest in rename_keys:
						rename_key(__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)

			model.load_state_dict(__UpperCamelCase					)

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

			A__       =    SegformerImageProcessor()
			A__       =    processor(__UpperCamelCase ,	return_tensors='pt'					).pixel_values

			with torch.no_grad():
						A__       =    model(__UpperCamelCase					)

			if model_name == "upernet-convnext-tiny":
						A__       =    torch.tensor(
						    [[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]]					)
			elif model_name == "upernet-convnext-small":
						A__       =    torch.tensor(
						    [[-8.8236, -8.8236, -8.6771], [-8.8236, -8.8236, -8.6771], [-8.7638, -8.7638, -8.6240]]					)
			elif model_name == "upernet-convnext-base":
						A__       =    torch.tensor(
						    [[-8.8558, -8.8558, -8.6905], [-8.8558, -8.8558, -8.6905], [-8.7669, -8.7669, -8.6021]]					)
			elif model_name == "upernet-convnext-large":
						A__       =    torch.tensor(
						    [[-8.6660, -8.6660, -8.6210], [-8.6660, -8.6660, -8.6210], [-8.6310, -8.6310, -8.5964]]					)
			elif model_name == "upernet-convnext-xlarge":
						A__       =    torch.tensor(
						    [[-8.4980, -8.4980, -8.3977], [-8.4980, -8.4980, -8.3977], [-8.4379, -8.4379, -8.3412]]					)
			print('Logits:' ,	outputs.logits[0, 0, :3, :3]					)
			assert torch.allclose(outputs.logits[0, 0, :3, :3] ,	__UpperCamelCase ,	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(__UpperCamelCase					)
						print(f'''Saving processor to {pytorch_dump_folder_path}'''					)
						processor.save_pretrained(__UpperCamelCase					)

			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__":
							SCREAMING_SNAKE_CASE__       =      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.'''
							)

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









import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
							import optax
							from flax.training.common_utils import onehot

							from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
							from transformers.models.ta.modeling_flax_ta import shift_tokens_right


@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class      __lowerCAmelCase     (		unittest.TestCase      ):
					"""simple docstring"""






					@slow
					def     _a							(     self					:       int					):






								"""simple docstring"""


								A__       =    FlaxMTaForConditionalGeneration.from_pretrained('google/mt5-small'					)
								A__       =    AutoTokenizer.from_pretrained('google/mt5-small'					)

								A__       =    tokenizer('Hello there'				,					return_tensors='np'					).input_ids
								A__       =    tokenizer('Hi I am'				,					return_tensors='np'					).input_ids

								A__       =    shift_tokens_right(_snake_case				,					model.config.pad_token_id				,					model.config.decoder_start_token_id					)

								A__       =    model(_snake_case				,					decoder_input_ids=_snake_case					).logits
								A__       =    optax.softmax_cross_entropy(_snake_case				,					onehot(_snake_case				,					logits.shape[-1]					)					).mean()

								A__       =    -(labels.shape[-1] * loss.item())

								A__       =    -84.9127
								self.assertTrue(abs(mtf_score - EXPECTED_SCORE					) < 1E-4					)




