

'''simple docstring'''

import argparse
import json
from pathlib import Path

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

from transformers import ConvNextConfig, ConvNextForImageClassification, ConvNextImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_     =      logging.get_logger(__name__)

def      __magic_name__							(			A				)      ->		List[str]:
	snake_case							=					ConvNextConfig()

	if "tiny" in checkpoint_url:
		snake_case							=					[3, 3, 9, 3]
		snake_case							=					[9_6, 1_9_2, 3_8_4, 7_6_8]
	if "small" in checkpoint_url:
		snake_case							=					[3, 3, 2_7, 3]
		snake_case							=					[9_6, 1_9_2, 3_8_4, 7_6_8]
	if "base" in checkpoint_url:
		snake_case							=					[3, 3, 2_7, 3]
		snake_case							=					[1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
	if "large" in checkpoint_url:
		snake_case							=					[3, 3, 2_7, 3]
		snake_case							=					[1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
	if "xlarge" in checkpoint_url:
		snake_case							=					[3, 3, 2_7, 3]
		snake_case							=					[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]

	if "1k" in checkpoint_url:
		snake_case							=					1_0_0_0
		snake_case							=					'imagenet-1k-id2label.json'
		snake_case							=					(1, 1_0_0_0)
	else:
		snake_case							=					2_1_8_4_1
		snake_case							=					'imagenet-22k-id2label.json'
		snake_case							=					(1, 2_1_8_4_1)

	snake_case							=					'huggingface/label-files'
	snake_case							=					num_labels
	snake_case							=					json.load(open(hf_hub_download(A       , A       , repo_type='dataset'				)       , 'r'				)				)
	snake_case							=					{int(A				): v for k, v in idalabel.items()}
	if "1k" not in checkpoint_url:
		# this dataset contains 21843 labels but the model only has 21841
		# we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
		del idalabel[9_2_0_5]
		del idalabel[1_5_0_2_7]
	snake_case							=					idalabel
	snake_case							=					{v: k for k, v in idalabel.items()}
	snake_case							=					hidden_sizes
	snake_case							=					depths

	return config, expected_shape

def      __magic_name__							(			A				)      ->		str:
	if "downsample_layers.0.0" in name:
		snake_case							=					name.replace('downsample_layers.0.0'       , 'embeddings.patch_embeddings'				)
	if "downsample_layers.0.1" in name:
		snake_case							=					name.replace('downsample_layers.0.1'       , 'embeddings.norm'				)  # we rename to layernorm later on
	if "downsample_layers.1.0" in name:
		snake_case							=					name.replace('downsample_layers.1.0'       , 'stages.1.downsampling_layer.0'				)
	if "downsample_layers.1.1" in name:
		snake_case							=					name.replace('downsample_layers.1.1'       , 'stages.1.downsampling_layer.1'				)
	if "downsample_layers.2.0" in name:
		snake_case							=					name.replace('downsample_layers.2.0'       , 'stages.2.downsampling_layer.0'				)
	if "downsample_layers.2.1" in name:
		snake_case							=					name.replace('downsample_layers.2.1'       , 'stages.2.downsampling_layer.1'				)
	if "downsample_layers.3.0" in name:
		snake_case							=					name.replace('downsample_layers.3.0'       , 'stages.3.downsampling_layer.0'				)
	if "downsample_layers.3.1" in name:
		snake_case							=					name.replace('downsample_layers.3.1'       , 'stages.3.downsampling_layer.1'				)
	if "stages" in name and "downsampling_layer" not in name:
		# stages.0.0. for instance should be renamed to stages.0.layers.0.
		snake_case							=					name[: len('stages.0'				)] + '.layers' + name[len('stages.0'				) :]
	if "stages" in name:
		snake_case							=					name.replace('stages'       , 'encoder.stages'				)
	if "norm" in name:
		snake_case							=					name.replace('norm'       , 'layernorm'				)
	if "gamma" in name:
		snake_case							=					name.replace('gamma'       , 'layer_scale_parameter'				)
	if "head" in name:
		snake_case							=					name.replace('head'       , 'classifier'				)

	return name

def      __magic_name__							(			)      ->		int:
	snake_case							=					'http://images.cocodataset.org/val2017/000000039769.jpg'
	snake_case							=					Image.open(requests.get(A       , stream=A				).raw				)
	return im

@torch.no_grad()
def      __magic_name__							(			A       , A				)      ->		Dict:
	snake_case			,			snake_case							=					get_convnext_config(A				)
	# load original state_dict from URL
	snake_case							=					torch.hub.load_state_dict_from_url(A				)['model']
	# rename keys
	for key in state_dict.copy().keys():
		snake_case							=					state_dict.pop(A				)
		snake_case							=					val
	# add prefix to all keys expect classifier head
	for key in state_dict.copy().keys():
		snake_case							=					state_dict.pop(A				)
		if not key.startswith('classifier'				):
			snake_case							=					'convnext.' + key
		snake_case							=					val

	# load HuggingFace model
	snake_case							=					ConvNextForImageClassification(A				)
	model.load_state_dict(A				)
	model.eval()

	# Check outputs on an image, prepared by ConvNextImageProcessor
	snake_case							=					2_2_4 if '224' in checkpoint_url else 3_8_4
	snake_case							=					ConvNextImageProcessor(size=A				)
	snake_case							=					image_processor(images=prepare_img()       , return_tensors='pt'				).pixel_values

	snake_case							=					model(A				).logits

	# note: the logits below were obtained without center cropping
	if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
		snake_case							=					torch.tensor([-0.1_210, -0.6_605, 0.1_918]				)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
		snake_case							=					torch.tensor([-0.4_473, -0.1_847, -0.6_365]				)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
		snake_case							=					torch.tensor([0.4_525, 0.7_539, 0.0_308]				)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
		snake_case							=					torch.tensor([0.3_561, 0.6_350, -0.0_384]				)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
		snake_case							=					torch.tensor([0.4_174, -0.0_989, 0.1_489]				)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
		snake_case							=					torch.tensor([0.2_513, -0.1_349, -0.1_613]				)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
		snake_case							=					torch.tensor([1.2_980, 0.3_631, -0.1_198]				)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
		snake_case							=					torch.tensor([1.2_963, 0.1_227, 0.1_723]				)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
		snake_case							=					torch.tensor([1.7_956, 0.8_390, 0.2_820]				)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
		snake_case							=					torch.tensor([-0.2_822, -0.0_502, -0.0_878]				)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
		snake_case							=					torch.tensor([-0.5_672, -0.0_730, -0.4_348]				)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
		snake_case							=					torch.tensor([0.2_681, 0.2_365, 0.6_246]				)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
		snake_case							=					torch.tensor([-0.2_642, 0.3_931, 0.5_116]				)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
		snake_case							=					torch.tensor([-0.6_677, -0.1_873, -0.8_379]				)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
		snake_case							=					torch.tensor([-0.7_749, -0.2_967, -0.6_444]				)
	else:
		raise ValueError(F'''Unknown URL: {checkpoint_url}'''				)

	assert torch.allclose(logits[0, :3]       , A       , atol=1E-3				)
	assert logits.shape == expected_shape

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

	print('Pushing model to the hub...'				)
	snake_case							=					'convnext'
	if "tiny" in checkpoint_url:
		model_name += "-tiny"
	elif "small" in checkpoint_url:
		model_name += "-small"
	elif "base" in checkpoint_url:
		model_name += "-base"
	elif "xlarge" in checkpoint_url:
		model_name += "-xlarge"
	elif "large" in checkpoint_url:
		model_name += "-large"
	if "224" in checkpoint_url:
		model_name += "-224"
	elif "384" in checkpoint_url:
		model_name += "-384"
	if "22k" in checkpoint_url and "1k" not in checkpoint_url:
		model_name += "-22k"
	if "22k" in checkpoint_url and "1k" in checkpoint_url:
		model_name += "-22k-1k"

	model.push_to_hub(
	    repo_path_or_name=Path(A       , A				)       , organization='nielsr'       , commit_message='Add model'       , )


if __name__ == "__main__":
						lowerCAmelCase_     =      argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--checkpoint_url",
						    default="https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth",
						    type=str,
						    help="URL of the original ConvNeXT 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.",
						)

						lowerCAmelCase_     =      parser.parse_args()
						convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)








'''simple docstring'''

import tempfile
import unittest

import numpy as np
import torch

from diffusers import DiffusionPipeline
from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor







class lowerCamelCase   (    unittest.TestCase				):

				def 					_lowerCamelCase    (       self,						lowercase_ = False )    ->							Union[str, Any]:
					snake_case							=					10

					if only_cross_attention:
						snake_case							=					12
					else:
						# when only cross attention is not set, the cross attention dim must be the same as the query dim
						snake_case							=					query_dim

					return {
					    "query_dim": query_dim,
					    "cross_attention_dim": cross_attention_dim,
					    "heads": 2,
					    "dim_head": 4,
					    "added_kv_proj_dim": 6,
					    "norm_num_groups": 1,
					    "only_cross_attention": only_cross_attention,
					    "processor": AttnAddedKVProcessor(),
					}

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_ )    ->							Optional[int]:
					snake_case							=					2

					snake_case							=					torch.rand(lowercase_,						lowercase_,						3,						2 )
					snake_case							=					torch.rand(lowercase_,						4,						lowercase_ )
					snake_case							=					None

					return {
					    "hidden_states": hidden_states,
					    "encoder_hidden_states": encoder_hidden_states,
					    "attention_mask": attention_mask,
					}






				def 					_lowerCamelCase    (       self )    ->							str:
					# self and cross attention

					torch.manual_seed(0 )

					snake_case							=					self.get_constructor_arguments(only_cross_attention=lowercase_ )
					snake_case							=					Attention(**lowercase_ )

					self.assertTrue(attn.to_k is not None )
					self.assertTrue(attn.to_v is not None )

					snake_case							=					self.get_forward_arguments(
					    query_dim=constructor_args['query_dim'],						added_kv_proj_dim=constructor_args['added_kv_proj_dim'] )

					snake_case							=					attn(**lowercase_ )

					# only self attention

					torch.manual_seed(0 )

					snake_case							=					self.get_constructor_arguments(only_cross_attention=lowercase_ )
					snake_case							=					Attention(**lowercase_ )

					self.assertTrue(attn.to_k is None )
					self.assertTrue(attn.to_v is None )

					snake_case							=					self.get_forward_arguments(
					    query_dim=constructor_args['query_dim'],						added_kv_proj_dim=constructor_args['added_kv_proj_dim'] )

					snake_case							=					attn(**lowercase_ )

					self.assertTrue((only_cross_attn_out != self_and_cross_attn_out).all() )







class lowerCamelCase   (    unittest.TestCase				):

				def 					_lowerCamelCase    (       self )    ->							List[Any]:
					snake_case							=					DiffusionPipeline.from_pretrained('hf-internal-testing/tiny-stable-diffusion-pipe',						safety_checker=lowercase_ )

					snake_case							=					pipe(
					    'foo',						num_inference_steps=2,						generator=torch.Generator('cpu' ).manual_seed(0 ),						output_type='np',						).images

					# the initial conversion succeeds
					snake_case							=					DiffusionPipeline.from_pretrained(
					    'hf-internal-testing/tiny-stable-diffusion-pipe',						device_map='sequential',						safety_checker=lowercase_ )

					snake_case							=					pipe(
					    'foo',						num_inference_steps=2,						generator=torch.Generator('cpu' ).manual_seed(0 ),						output_type='np',						).images

					with tempfile.TemporaryDirectory() as tmpdir:
						# save the converted model
						pipe.save_pretrained(lowercase_ )

						# can also load the converted weights
						snake_case							=					DiffusionPipeline.from_pretrained(lowercase_,						device_map='sequential',						safety_checker=lowercase_ )

					snake_case							=					pipe(
					    'foo',						num_inference_steps=2,						generator=torch.Generator('cpu' ).manual_seed(0 ),						output_type='np',						).images

					self.assertTrue(np.allclose(lowercase_,						lowercase_,						atol=1E-5 ) )
					self.assertTrue(np.allclose(lowercase_,						lowercase_,						atol=1E-5 ) )





