






import argparse
import json

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

from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling







def 	_UpperCamelCase   (    UpperCamelCase_       :					int		)  ->   Optional[Any]:

						"""simple docstring"""



						lowerCAmelCase__    						=						[2, 2, 6, 2] if 'tiny' in model_name else [2, 2, 18, 2]
						lowerCAmelCase__    						=						True if 'large' in model_name or 'huge' in model_name else False
						lowerCAmelCase__    						=						True if 'large' in model_name or 'huge' in model_name else False
						lowerCAmelCase__    						=						True if 'large' in model_name or 'huge' in model_name else False

						if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
												if "fl3" in model_name:
																		lowerCAmelCase__    						=						[3, 3, 3, 3]
																		lowerCAmelCase__    						=						[5, 5, 5, 5]
												elif "fl4" in model_name:
																		lowerCAmelCase__    						=						[4, 4, 4, 4]
																		lowerCAmelCase__    						=						[3, 3, 3, 3]

						if "tiny" in model_name or "small" in model_name or "base" in model_name:
												lowerCAmelCase__    						=						[3, 3, 3, 3]
												if "lrf" in model_name:
																		lowerCAmelCase__    						=						[3, 3, 3, 3]
												else:
																		lowerCAmelCase__    						=						[2, 2, 2, 2]

						if "tiny" in model_name:
												lowerCAmelCase__    						=						96
						elif "small" in model_name:
												lowerCAmelCase__    						=						96
						elif "base" in model_name:
												lowerCAmelCase__    						=						128
						elif "large" in model_name:
												lowerCAmelCase__    						=						192
						elif "xlarge" in model_name:
												lowerCAmelCase__    						=						256
						elif "huge" in model_name:
												lowerCAmelCase__    						=						352

						# set label information
						lowerCAmelCase__    						=						'huggingface/label-files'
						if "large" in model_name or "huge" in model_name:
												lowerCAmelCase__    						=						'imagenet-22k-id2label.json'
						else:
												lowerCAmelCase__    						=						'imagenet-1k-id2label.json'

						lowerCAmelCase__    						=						json.load(open(hf_hub_download(__snake_case    ,       __snake_case    ,       repo_type='dataset'		)    ,       'r'		)		)
						lowerCAmelCase__    						=						{int(__snake_case		): v for k, v in idalabel.items()}
						lowerCAmelCase__    						=						{v: k for k, v in idalabel.items()}

						lowerCAmelCase__    						=						FocalNetConfig(
						    embed_dim=__snake_case    ,       depths=__snake_case    ,       focal_levels=__snake_case    ,       focal_windows=__snake_case    ,       use_conv_embed=__snake_case    ,       idalabel=__snake_case    ,       labelaid=__snake_case    ,       use_post_layernorm=__snake_case    ,       use_layerscale=__snake_case    ,       )

						return config







def 	_UpperCamelCase   (    UpperCamelCase_       :					Dict		)  ->   Optional[Any]:

						"""simple docstring"""



						if "patch_embed.proj" in name:
												lowerCAmelCase__    						=						name.replace('patch_embed.proj'    ,       'embeddings.patch_embeddings.projection'		)
						if "patch_embed.norm" in name:
												lowerCAmelCase__    						=						name.replace('patch_embed.norm'    ,       'embeddings.norm'		)
						if "layers" in name:
												lowerCAmelCase__    						=						'encoder.' + name
						if "encoder.layers" in name:
												lowerCAmelCase__    						=						name.replace('encoder.layers'    ,       'encoder.stages'		)
						if "downsample.proj" in name:
												lowerCAmelCase__    						=						name.replace('downsample.proj'    ,       'downsample.projection'		)
						if "blocks" in name:
												lowerCAmelCase__    						=						name.replace('blocks'    ,       'layers'		)
						if "modulation.f.weight" in name or "modulation.f.bias" in name:
												lowerCAmelCase__    						=						name.replace('modulation.f'    ,       'modulation.projection_in'		)
						if "modulation.h.weight" in name or "modulation.h.bias" in name:
												lowerCAmelCase__    						=						name.replace('modulation.h'    ,       'modulation.projection_context'		)
						if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
												lowerCAmelCase__    						=						name.replace('modulation.proj'    ,       'modulation.projection_out'		)

						if name == "norm.weight":
												lowerCAmelCase__    						=						'layernorm.weight'
						if name == "norm.bias":
												lowerCAmelCase__    						=						'layernorm.bias'

						if "head" in name:
												lowerCAmelCase__    						=						name.replace('head'    ,       'classifier'		)
						else:
												lowerCAmelCase__    						=						'focalnet.' + name

						return name







def 	_UpperCamelCase   (    UpperCamelCase_       :					Optional[Any]    ,       UpperCamelCase_       :					Optional[int]    ,       UpperCamelCase_       :					int=False		)  ->   Optional[int]:

						"""simple docstring"""



						lowerCAmelCase__    						=						{
						    'focalnet-tiny': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth',
						    'focalnet-tiny-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth',
						    'focalnet-small': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth',
						    'focalnet-small-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth',
						    'focalnet-base': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth',
						    'focalnet-base-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth',
						    'focalnet-large-lrf-fl3': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth',
						    'focalnet-large-lrf-fl4': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth',
						    'focalnet-xlarge-lrf-fl3': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth',
						    'focalnet-xlarge-lrf-fl4': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth',
						}
						# fmt: on

						lowerCAmelCase__    						=						model_name_to_url[model_name]
						print('Checkpoint URL: '    ,       __snake_case		)
						lowerCAmelCase__    						=						torch.hub.load_state_dict_from_url(__snake_case    ,       map_location='cpu'		)['model']

						# rename keys
						for key in state_dict.copy().keys():
												lowerCAmelCase__    						=						state_dict.pop(__snake_case		)
												lowerCAmelCase__    						=						val

						lowerCAmelCase__    						=						get_focalnet_config(__snake_case		)
						lowerCAmelCase__    						=						FocalNetForImageClassification(__snake_case		)
						model.eval()

						# load state dict
						model.load_state_dict(__snake_case		)

						# verify conversion
						lowerCAmelCase__    						=						'http://images.cocodataset.org/val2017/000000039769.jpg'

						lowerCAmelCase__    						=						BitImageProcessor(
						    do_resize=__snake_case    ,       size={'shortest_edge': 256}    ,       resample=PILImageResampling.BILINEAR    ,       do_center_crop=__snake_case    ,       crop_size=224    ,       do_normalize=__snake_case    ,       image_mean=__snake_case    ,       image_std=__snake_case    ,       )
						lowerCAmelCase__    						=						Image.open(requests.get(__snake_case    ,       stream=__snake_case		).raw		)
						lowerCAmelCase__    						=						processor(images=__snake_case    ,       return_tensors='pt'		)

						lowerCAmelCase__    						=						transforms.Compose(
						    [
						        transforms.Resize(256		),
						        transforms.CenterCrop(224		),
						        transforms.ToTensor(),
						        transforms.Normalize(mean=[0.485, 0.456, 0.406]    ,       std=[0.229, 0.224, 0.225]		),
						    ]		)

						lowerCAmelCase__    						=						image_transforms(__snake_case		).unsqueeze(0		)

						# verify pixel_values
						assert torch.allclose(inputs.pixel_values    ,       __snake_case    ,       atol=1e-4		)

						lowerCAmelCase__    						=						model(**__snake_case		)

						lowerCAmelCase__    						=						outputs.logits.argmax(-1		).item()
						print('Predicted class:'    ,       model.config.idalabel[predicted_class_idx]		)

						print('First values of logits:'    ,       outputs.logits[0, :3]		)

						if model_name == "focalnet-tiny":
												lowerCAmelCase__    						=						torch.tensor([0.2166, -0.4368, 0.2191]		)
						elif model_name == "focalnet-tiny-lrf":
												lowerCAmelCase__    						=						torch.tensor([1.1669, 0.0125, -0.1695]		)
						elif model_name == "focalnet-small":
												lowerCAmelCase__    						=						torch.tensor([0.4917, -0.0430, 0.1341]		)
						elif model_name == "focalnet-small-lrf":
												lowerCAmelCase__    						=						torch.tensor([-0.2588, -0.5342, -0.2331]		)
						elif model_name == "focalnet-base":
												lowerCAmelCase__    						=						torch.tensor([-0.1655, -0.4090, -0.1730]		)
						elif model_name == "focalnet-base-lrf":
												lowerCAmelCase__    						=						torch.tensor([0.5306, -0.0483, -0.3928]		)
						assert torch.allclose(outputs.logits[0, :3]    ,       __snake_case    ,       atol=1e-4		)
						print('Looks ok!'		)

						if pytorch_dump_folder_path is not None:
												print(F"Saving model and processor of {model_name} to {pytorch_dump_folder_path}"		)
												model.save_pretrained(__snake_case		)
												processor.save_pretrained(__snake_case		)

						if push_to_hub:
												print(F"Pushing model and processor of {model_name} to the hub..."		)
												model.push_to_hub(F"{model_name}"		)
												processor.push_to_hub(F"{model_name}"		)


if __name__ == "__main__":
							__snake_case    :    Tuple         =     argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--model_name""",
							    default="""focalnet-tiny""",
							    type=str,
							    help="""Name of the FocalNet 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 to push the model and processor to the hub.""",
							)

							__snake_case    :    List[Any]         =     parser.parse_args()
							convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)













# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

__snake_case    :    Optional[int]         =     """facebook/wmt19-en-de"""
__snake_case    :    Optional[Any]         =     FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
__snake_case    :    List[str]         =     FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

__snake_case    :    Optional[Any]         =     FSMTForConditionalGeneration(config)
print(f'num of params {tiny_model.num_parameters()}')

# Test
__snake_case    :    List[Any]         =     tokenizer(["""Making tiny model"""], return_tensors="""pt""")
__snake_case    :    Any         =     tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
__snake_case    :    Any         =     """tiny-wmt19-en-de"""
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f'Generated {mname_tiny}')

# Upload
# transformers-cli upload tiny-wmt19-en-de





