


import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


sys.path.append(str(Path(__file__).parent.parent.parent.parent / 'utils'))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


SCREAMING_SNAKE_CASE							    =							get_tests_dir('fixtures')
SCREAMING_SNAKE_CASE							    =							get_tests_dir('fixtures/dummy_feature_extractor_config.json')
SCREAMING_SNAKE_CASE							    =							get_tests_dir('fixtures/dummy-config.json')


class     __UpperCAmelCase							(       unittest.TestCase							):


     """simple docstring"""


     def 	snake_case_  (	self    ):
        __a					=  0


     def 	snake_case_  (	self    ):
        __a					=  AutoFeatureExtractor.from_pretrained("""facebook/wav2vec2-base-960h"""    )
        self.assertIsInstance(__A  ,       __A    )


     def 	snake_case_  (	self    ):
        __a					=  AutoFeatureExtractor.from_pretrained(__A    )
        self.assertIsInstance(__A  ,       __A    )


     def 	snake_case_  (	self    ):
        with tempfile.TemporaryDirectory() as tmpdirname:
           __a					=  WavaVecaConfig()

           # remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
           __a					=  AutoFeatureExtractor.from_pretrained(__A    ).to_dict()

           config_dict.pop("""feature_extractor_type"""    )
           __a					=  WavaVecaFeatureExtractor(**__A    )

           # save in new folder
           model_config.save_pretrained(__A    )
           config.save_pretrained(__A    )

           __a					=  AutoFeatureExtractor.from_pretrained(__A    )

           # make sure private variable is not incorrectly saved
           __a					=  json.loads(config.to_json_string()    )
           self.assertTrue("""_processor_class""" not in dict_as_saved    )

        self.assertIsInstance(__A  ,       __A    )


     def 	snake_case_  (	self    ):
        __a					=  AutoFeatureExtractor.from_pretrained(__A    )
        self.assertIsInstance(__A  ,       __A    )


     def 	snake_case_  (	self    ):
        with self.assertRaisesRegex(
            __A  ,       """bert-base is not a local folder and is not a valid model identifier"""    ):
           __a					=  AutoFeatureExtractor.from_pretrained("""bert-base"""    )


     def 	snake_case_  (	self    ):
        with self.assertRaisesRegex(
            __A  ,       R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""    ):
           __a					=  AutoFeatureExtractor.from_pretrained(__A  ,       revision="""aaaaaa"""    )


     def 	snake_case_  (	self    ):
        with self.assertRaisesRegex(
            __A  ,       """hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."""  ,       ):
           __a					=  AutoFeatureExtractor.from_pretrained("""hf-internal-testing/config-no-model"""    )


     def 	snake_case_  (	self    ):
        # If remote code is not set, we will time out when asking whether to load the model.
        with self.assertRaises(__A    ):
           __a					=  AutoFeatureExtractor.from_pretrained(
               """hf-internal-testing/test_dynamic_feature_extractor"""    )
        # If remote code is disabled, we can't load this config.
        with self.assertRaises(__A    ):
           __a					=  AutoFeatureExtractor.from_pretrained(
               """hf-internal-testing/test_dynamic_feature_extractor"""  ,       trust_remote_code=__A    )

        __a					=  AutoFeatureExtractor.from_pretrained(
            """hf-internal-testing/test_dynamic_feature_extractor"""  ,       trust_remote_code=__A    )
        self.assertEqual(feature_extractor.__class__.__name__  ,       """NewFeatureExtractor"""    )

        # Test feature extractor can be reloaded.
        with tempfile.TemporaryDirectory() as tmp_dir:
           feature_extractor.save_pretrained(__A    )
           __a					=  AutoFeatureExtractor.from_pretrained(__A  ,       trust_remote_code=__A    )
        self.assertEqual(reloaded_feature_extractor.__class__.__name__  ,       """NewFeatureExtractor"""    )


     def 	snake_case_  (	self    ):
        try:
           AutoConfig.register("""custom"""  ,       __A    )
           AutoFeatureExtractor.register(__A  ,       __A    )
           # Trying to register something existing in the Transformers library will raise an error
           with self.assertRaises(__A    ):
              AutoFeatureExtractor.register(__A  ,       __A    )

           # Now that the config is registered, it can be used as any other config with the auto-API
           __a					=  CustomFeatureExtractor.from_pretrained(__A    )
           with tempfile.TemporaryDirectory() as tmp_dir:
              feature_extractor.save_pretrained(__A    )
              __a					=  AutoFeatureExtractor.from_pretrained(__A    )
              self.assertIsInstance(__A  ,       __A    )

        finally:
           if "custom" in CONFIG_MAPPING._extra_content:
              del CONFIG_MAPPING._extra_content["custom"]
           if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
              del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]


     def 	snake_case_  (	self    ):


        class     __UpperCAmelCase							(       __A							):


             """simple docstring"""
             _lowerCamelCase   =      True

        try:
           AutoConfig.register("""custom"""  ,       __A    )
           AutoFeatureExtractor.register(__A  ,       __A    )
           # If remote code is not set, the default is to use local
           __a					=  AutoFeatureExtractor.from_pretrained(
               """hf-internal-testing/test_dynamic_feature_extractor"""    )
           self.assertEqual(feature_extractor.__class__.__name__  ,       """NewFeatureExtractor"""    )
           self.assertTrue(feature_extractor.is_local    )

           # If remote code is disabled, we load the local one.
           __a					=  AutoFeatureExtractor.from_pretrained(
               """hf-internal-testing/test_dynamic_feature_extractor"""  ,       trust_remote_code=__A    )
           self.assertEqual(feature_extractor.__class__.__name__  ,       """NewFeatureExtractor"""    )
           self.assertTrue(feature_extractor.is_local    )

           # If remote is enabled, we load from the Hub
           __a					=  AutoFeatureExtractor.from_pretrained(
               """hf-internal-testing/test_dynamic_feature_extractor"""  ,       trust_remote_code=__A    )
           self.assertEqual(feature_extractor.__class__.__name__  ,       """NewFeatureExtractor"""    )
           self.assertTrue(not hasattr(__A  ,       """is_local"""    )    )

        finally:
           if "custom" in CONFIG_MAPPING._extra_content:
              del CONFIG_MAPPING._extra_content["custom"]
           if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
              del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]





"""simple docstring"""


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						(	lowerCAmelCase__						):
			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(lowerCAmelCase__	,lowerCAmelCase__	,repo_type='''dataset'''						)	,'''r'''						)						)
			lowerCamelCase_					=  {int(lowerCAmelCase__						): v for k, v in idalabel.items()}
			lowerCamelCase_					=  {v: k for k, v in idalabel.items()}

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

			return config






def  lowercase						(	lowerCAmelCase__						):
			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  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__=False						):
			# fmt: off
			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: '''	,lowerCAmelCase__						)
			lowerCamelCase_					=  torch.hub.load_state_dict_from_url(lowerCAmelCase__	,map_location='''cpu'''						)['''model''']

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

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

			# load state dict
			model.load_state_dict(lowerCAmelCase__						)

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

			lowerCamelCase_					=  BitImageProcessor(
			    do_resize=lowerCAmelCase__	,size={'''shortest_edge''': 256}	,resample=PILImageResampling.BILINEAR	,do_center_crop=lowerCAmelCase__	,crop_size=224	,do_normalize=lowerCAmelCase__	,image_mean=lowerCAmelCase__	,image_std=lowerCAmelCase__	,)
			lowerCamelCase_					=  Image.open(requests.get(lowerCAmelCase__	,stream=lowerCAmelCase__						).raw						)
			lowerCamelCase_					=  processor(images=lowerCAmelCase__	,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(lowerCAmelCase__						).unsqueeze(0						)

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

			lowerCamelCase_					=  model(**lowerCAmelCase__						)

			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([0.2_166, -0.4_368, 0.2_191]						)
			elif model_name == "focalnet-tiny-lrf":
						lowerCamelCase_					=  torch.tensor([1.1_669, 0.0_125, -0.1_695]						)
			elif model_name == "focalnet-small":
						lowerCamelCase_					=  torch.tensor([0.4_917, -0.0_430, 0.1_341]						)
			elif model_name == "focalnet-small-lrf":
						lowerCamelCase_					=  torch.tensor([-0.2_588, -0.5_342, -0.2_331]						)
			elif model_name == "focalnet-base":
						lowerCamelCase_					=  torch.tensor([-0.1_655, -0.4_090, -0.1_730]						)
			elif model_name == "focalnet-base-lrf":
						lowerCamelCase_					=  torch.tensor([0.5_306, -0.0_483, -0.3_928]						)
			assert torch.allclose(outputs.logits[0, :3]	,lowerCAmelCase__	,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(lowerCAmelCase__						)
						processor.save_pretrained(lowerCAmelCase__						)

			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__":
						A_    							=					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.""",
						)

						A_    							=					parser.parse_args()
						convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



