



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      __lowercase       (			a__       )    ->			Optional[Any]:
							__SCREAMING_SNAKE_CASE									= [2, 2, 6, 2] if 'tiny' in model_name else [2, 2, 18, 2]
							__SCREAMING_SNAKE_CASE									= True if 'large' in model_name or 'huge' in model_name else False
							__SCREAMING_SNAKE_CASE									= True if 'large' in model_name or 'huge' in model_name else False
							__SCREAMING_SNAKE_CASE									= 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:
																					__SCREAMING_SNAKE_CASE									= [3, 3, 3, 3]
																					__SCREAMING_SNAKE_CASE									= [5, 5, 5, 5]
														elif "fl4" in model_name:
																					__SCREAMING_SNAKE_CASE									= [4, 4, 4, 4]
																					__SCREAMING_SNAKE_CASE									= [3, 3, 3, 3]

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

							if "tiny" in model_name:
														__SCREAMING_SNAKE_CASE									= 96
							elif "small" in model_name:
														__SCREAMING_SNAKE_CASE									= 96
							elif "base" in model_name:
														__SCREAMING_SNAKE_CASE									= 1_28
							elif "large" in model_name:
														__SCREAMING_SNAKE_CASE									= 1_92
							elif "xlarge" in model_name:
														__SCREAMING_SNAKE_CASE									= 2_56
							elif "huge" in model_name:
														__SCREAMING_SNAKE_CASE									= 3_52

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

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

							__SCREAMING_SNAKE_CASE									= FocalNetConfig(
							    embed_dim=a__							,       depths=a__							,       focal_levels=a__							,       focal_windows=a__							,       use_conv_embed=a__							,       idalabel=a__							,       labelaid=a__							,       use_post_layernorm=a__							,       use_layerscale=a__							,       )

							return config






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

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

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

							return name






def      __lowercase       (			a__							,       a__							,       a__=False       )    ->			Dict:
							# fmt: off
							__SCREAMING_SNAKE_CASE									= {
							    '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

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

							# rename keys
							for key in state_dict.copy().keys():
														__SCREAMING_SNAKE_CASE									= state_dict.pop(a__       )
														__SCREAMING_SNAKE_CASE									= val

							__SCREAMING_SNAKE_CASE									= get_focalnet_config(a__       )
							__SCREAMING_SNAKE_CASE									= FocalNetForImageClassification(a__       )
							model.eval()

							# load state dict
							model.load_state_dict(a__       )

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

							__SCREAMING_SNAKE_CASE									= BitImageProcessor(
							    do_resize=a__							,       size={'shortest_edge': 2_56}							,       resample=PILImageResampling.BILINEAR							,       do_center_crop=a__							,       crop_size=2_24							,       do_normalize=a__							,       image_mean=a__							,       image_std=a__							,       )
							__SCREAMING_SNAKE_CASE									= Image.open(requests.get(a__							,       stream=a__       ).raw       )
							__SCREAMING_SNAKE_CASE									= processor(images=a__							,       return_tensors='pt'       )

							__SCREAMING_SNAKE_CASE									= transforms.Compose(
							    [
							        transforms.Resize(2_56       ),
							        transforms.CenterCrop(2_24       ),
							        transforms.ToTensor(),
							        transforms.Normalize(mean=[0.485, 0.456, 0.406]							,       std=[0.229, 0.224, 0.225]       ),
							    ]       )

							__SCREAMING_SNAKE_CASE									= image_transforms(a__       ).unsqueeze(0       )

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

							__SCREAMING_SNAKE_CASE									= model(**a__       )

							__SCREAMING_SNAKE_CASE									= 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":
														__SCREAMING_SNAKE_CASE									= torch.tensor([0.2166, -0.4368, 0.2191]       )
							elif model_name == "focalnet-tiny-lrf":
														__SCREAMING_SNAKE_CASE									= torch.tensor([1.1669, 0.0125, -0.1695]       )
							elif model_name == "focalnet-small":
														__SCREAMING_SNAKE_CASE									= torch.tensor([0.4917, -0.0430, 0.1341]       )
							elif model_name == "focalnet-small-lrf":
														__SCREAMING_SNAKE_CASE									= torch.tensor([-0.2588, -0.5342, -0.2331]       )
							elif model_name == "focalnet-base":
														__SCREAMING_SNAKE_CASE									= torch.tensor([-0.1655, -0.4090, -0.1730]       )
							elif model_name == "focalnet-base-lrf":
														__SCREAMING_SNAKE_CASE									= torch.tensor([0.5306, -0.0483, -0.3928]       )
							assert torch.allclose(outputs.logits[0, :3]							,       a__							,       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(a__       )
														processor.save_pretrained(a__       )

							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__":
			lowerCAmelCase__		:					int							=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.''',
			)

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











import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


lowerCAmelCase__		:					Dict							=random.Random()

if is_torch_available():
			import torch






def      __lowercase       (			a__							,       a__=1.0							,       a__=None							,       a__=None       )    ->			Any:
							if rng is None:
														__SCREAMING_SNAKE_CASE									= global_rng

							__SCREAMING_SNAKE_CASE									= []
							for batch_idx in range(shape[0]       ):
														values.append([]       )
														for _ in range(shape[1]       ):
																					values[-1].append(rng.random() * scale       )

							return values

class        UpperCAmelCase_  (       unittest.TestCase	):

						'''simple docstring'''




						def __init__(   self				,							_A				,							_A=7				,							_A=400				,							_A=2_000				,							_A=1				,							_A=0.0				,							_A=16_000				,							_A=True				,							_A=True				,							):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= parent
													__SCREAMING_SNAKE_CASE									= batch_size
													__SCREAMING_SNAKE_CASE									= min_seq_length
													__SCREAMING_SNAKE_CASE									= max_seq_length
													__SCREAMING_SNAKE_CASE									= (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
													__SCREAMING_SNAKE_CASE									= feature_size
													__SCREAMING_SNAKE_CASE									= padding_value
													__SCREAMING_SNAKE_CASE									= sampling_rate
													__SCREAMING_SNAKE_CASE									= return_attention_mask
													__SCREAMING_SNAKE_CASE									= do_normalize






						def 	_A     (   self		):







													'''simple docstring'''

													return {
													    "feature_size": self.feature_size,
													    "padding_value": self.padding_value,
													    "sampling_rate": self.sampling_rate,
													    "return_attention_mask": self.return_attention_mask,
													    "do_normalize": self.do_normalize,
													}






						def 	_A     (   self				,							_A=False				,							_A=False		):







													'''simple docstring'''

													def _flatten(_A		):
																				return list(itertools.chain(*_A		)		)

													if equal_length:
																				__SCREAMING_SNAKE_CASE									= floats_list((self.batch_size, self.max_seq_length)		)
													else:
																				# make sure that inputs increase in size
																				__SCREAMING_SNAKE_CASE									= [
																				    _flatten(floats_list((x, self.feature_size)		)		)
																				    for x in range(self.min_seq_length				,							self.max_seq_length				,							self.seq_length_diff		)
																				]

													if numpify:
																				__SCREAMING_SNAKE_CASE									= [np.asarray(_A		) for x in speech_inputs]

													return speech_inputs







@require_torch
@require_torchaudio
class        UpperCAmelCase_  (       UpperCamelCase_   ,   unittest.TestCase	):

						'''simple docstring'''




						UpperCamelCase__      :			Optional[int]      =  ASTFeatureExtractor






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= ASTFeatureExtractionTester(self		)






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
													# create three inputs of length 800, 1000, and 1200
													__SCREAMING_SNAKE_CASE									= [floats_list((1, x)		)[0] for x in range(800				,							1_400				,							200		)]
													__SCREAMING_SNAKE_CASE									= [np.asarray(_A		) for speech_input in speech_inputs]

													# Test not batched input
													__SCREAMING_SNAKE_CASE									= feat_extract(speech_inputs[0]				,							return_tensors='np'		).input_values
													__SCREAMING_SNAKE_CASE									= feat_extract(np_speech_inputs[0]				,							return_tensors='np'		).input_values
													self.assertTrue(np.allclose(_A				,							_A				,							atol=1e-3		)		)

													# Test batched
													__SCREAMING_SNAKE_CASE									= feat_extract(_A				,							padding=_A				,							return_tensors='np'		).input_values
													__SCREAMING_SNAKE_CASE									= feat_extract(_A				,							padding=_A				,							return_tensors='np'		).input_values
													for enc_seq_a, enc_seq_a in zip(_A				,							_A		):
																				self.assertTrue(np.allclose(_A				,							_A				,							atol=1e-3		)		)

													# Test 2-D numpy arrays are batched.
													__SCREAMING_SNAKE_CASE									= [floats_list((1, x)		)[0] for x in (800, 800, 800)]
													__SCREAMING_SNAKE_CASE									= np.asarray(_A		)
													__SCREAMING_SNAKE_CASE									= feat_extract(_A				,							return_tensors='np'		).input_values
													__SCREAMING_SNAKE_CASE									= feat_extract(_A				,							return_tensors='np'		).input_values
													for enc_seq_a, enc_seq_a in zip(_A				,							_A		):
																				self.assertTrue(np.allclose(_A				,							_A				,							atol=1e-3		)		)






						@require_torch
						def 	_A     (   self		):







													'''simple docstring'''

													import torch

													__SCREAMING_SNAKE_CASE									= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
													__SCREAMING_SNAKE_CASE									= np.random.rand(100		).astype(np.floataa		)
													__SCREAMING_SNAKE_CASE									= np_speech_inputs.tolist()

													for inputs in [py_speech_inputs, np_speech_inputs]:
																				__SCREAMING_SNAKE_CASE									= feature_extractor.pad([{'input_values': inputs}]				,							return_tensors='np'		)
																				self.assertTrue(np_processed.input_values.dtype == np.floataa		)
																				__SCREAMING_SNAKE_CASE									= feature_extractor.pad([{'input_values': inputs}]				,							return_tensors='pt'		)
																				self.assertTrue(pt_processed.input_values.dtype == torch.floataa		)






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													from datasets import load_dataset

													__SCREAMING_SNAKE_CASE									= load_dataset('hf-internal-testing/librispeech_asr_dummy'				,							'clean'				,							split='validation'		)
													# automatic decoding with librispeech
													__SCREAMING_SNAKE_CASE									= ds.sort('id'		).select(range(_A		)		)[:num_samples]['audio']

													return [x["array"] for x in speech_samples]






						@require_torch
						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= torch.tensor(
													    [-0.9_8_9_4, -1.2_7_7_6, -0.9_0_6_6, -1.2_7_7_6, -0.9_3_4_9, -1.2_6_0_9, -1.0_3_8_6, -1.2_7_7_6,
													     -1.1_5_6_1, -1.2_7_7_6, -1.2_0_5_2, -1.2_7_2_3, -1.2_1_9_0, -1.2_1_3_2, -1.2_7_7_6, -1.1_1_3_3,
													     -1.1_9_5_3, -1.1_3_4_3, -1.1_5_8_4, -1.2_2_0_3, -1.1_7_7_0, -1.2_4_7_4, -1.2_3_8_1, -1.1_9_3_6,
													     -0.9_2_7_0, -0.8_3_1_7, -0.8_0_4_9, -0.7_7_0_6, -0.7_5_6_5, -0.7_8_6_9]		)
													# fmt: on

													__SCREAMING_SNAKE_CASE									= self._load_datasamples(1		)
													__SCREAMING_SNAKE_CASE									= ASTFeatureExtractor()
													__SCREAMING_SNAKE_CASE									= feature_extractor(_A				,							return_tensors='pt'		).input_values
													self.assertEquals(input_values.shape				,							(1, 1_024, 128)		)
													self.assertTrue(torch.allclose(input_values[0, 0, :30]				,							_A				,							atol=1e-4		)		)






