




import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__A						:							Tuple							=  logging.get_logger()
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE = True   ):
			'''simple docstring'''
			print(f"""Converting {name}..."""   )

			with torch.no_grad():
						if hidden_sizes == 1_28:
									if name[-1] == "S":
												SCREAMING_SNAKE_CASE           =					timm.create_model("""levit_128s"""					,	pretrained=SCREAMING_SNAKE_CASE   )
									else:
												SCREAMING_SNAKE_CASE           =					timm.create_model("""levit_128"""					,	pretrained=SCREAMING_SNAKE_CASE   )
						if hidden_sizes == 1_92:
									SCREAMING_SNAKE_CASE           =					timm.create_model("""levit_192"""					,	pretrained=SCREAMING_SNAKE_CASE   )
						if hidden_sizes == 2_56:
									SCREAMING_SNAKE_CASE           =					timm.create_model("""levit_256"""					,	pretrained=SCREAMING_SNAKE_CASE   )
						if hidden_sizes == 3_84:
									SCREAMING_SNAKE_CASE           =					timm.create_model("""levit_384"""					,	pretrained=SCREAMING_SNAKE_CASE   )

						from_model.eval()
						SCREAMING_SNAKE_CASE           =					LevitForImageClassificationWithTeacher(SCREAMING_SNAKE_CASE   ).eval()
						SCREAMING_SNAKE_CASE           =					OrderedDict()

						SCREAMING_SNAKE_CASE           =					from_model.state_dict()
						SCREAMING_SNAKE_CASE           =					list(from_model.state_dict().keys()   )
						SCREAMING_SNAKE_CASE           =					list(our_model.state_dict().keys()   )
						print(len(SCREAMING_SNAKE_CASE   )					,	len(SCREAMING_SNAKE_CASE   )   )
						for i in range(len(SCREAMING_SNAKE_CASE   )   ):
									SCREAMING_SNAKE_CASE           =					weights[og_keys[i]]
						our_model.load_state_dict(SCREAMING_SNAKE_CASE   )

						SCREAMING_SNAKE_CASE           =					torch.randn((2, 3, 2_24, 2_24)   )
						SCREAMING_SNAKE_CASE           =					from_model(SCREAMING_SNAKE_CASE   )
						SCREAMING_SNAKE_CASE           =					our_model(SCREAMING_SNAKE_CASE   ).logits

			assert torch.allclose(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ), "The model logits don't match the original one."

			SCREAMING_SNAKE_CASE           =					name
			print(SCREAMING_SNAKE_CASE   )

			if push_to_hub:
						our_model.save_pretrained(save_directory / checkpoint_name   )
						SCREAMING_SNAKE_CASE           =					LevitImageProcessor()
						image_processor.save_pretrained(save_directory / checkpoint_name   )

						print(f"""Pushed {checkpoint_name}"""   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE = None					,	SCREAMING_SNAKE_CASE = True   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					"""imagenet-1k-id2label.json"""
			SCREAMING_SNAKE_CASE           =					10_00
			SCREAMING_SNAKE_CASE           =					(1, num_labels)

			SCREAMING_SNAKE_CASE           =					"""huggingface/label-files"""
			SCREAMING_SNAKE_CASE           =					num_labels
			SCREAMING_SNAKE_CASE           =					json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	repo_type="""dataset"""   )					,	"""r"""   )   )
			SCREAMING_SNAKE_CASE           =					{int(SCREAMING_SNAKE_CASE   ): v for k, v in idalabel.items()}

			SCREAMING_SNAKE_CASE           =					idalabel
			SCREAMING_SNAKE_CASE           =					{v: k for k, v in idalabel.items()}

			SCREAMING_SNAKE_CASE           =					partial(SCREAMING_SNAKE_CASE					,	num_labels=SCREAMING_SNAKE_CASE					,	idalabel=SCREAMING_SNAKE_CASE					,	labelaid=SCREAMING_SNAKE_CASE   )

			SCREAMING_SNAKE_CASE           =					{
			    """levit-128S""": 1_28,
			    """levit-128""": 1_28,
			    """levit-192""": 1_92,
			    """levit-256""": 2_56,
			    """levit-384""": 3_84,
			}

			SCREAMING_SNAKE_CASE           =					{
			    """levit-128S""": ImageNetPreTrainedConfig(
			        hidden_sizes=[1_28, 2_56, 3_84]					,	num_attention_heads=[4, 6, 8]					,	depths=[2, 3, 4]					,	key_dim=[16, 16, 16]					,	drop_path_rate=0					,	),
			    """levit-128""": ImageNetPreTrainedConfig(
			        hidden_sizes=[1_28, 2_56, 3_84]					,	num_attention_heads=[4, 8, 12]					,	depths=[4, 4, 4]					,	key_dim=[16, 16, 16]					,	drop_path_rate=0					,	),
			    """levit-192""": ImageNetPreTrainedConfig(
			        hidden_sizes=[1_92, 2_88, 3_84]					,	num_attention_heads=[3, 5, 6]					,	depths=[4, 4, 4]					,	key_dim=[32, 32, 32]					,	drop_path_rate=0					,	),
			    """levit-256""": ImageNetPreTrainedConfig(
			        hidden_sizes=[2_56, 3_84, 5_12]					,	num_attention_heads=[4, 6, 8]					,	depths=[4, 4, 4]					,	key_dim=[32, 32, 32]					,	drop_path_rate=0					,	),
			    """levit-384""": ImageNetPreTrainedConfig(
			        hidden_sizes=[3_84, 5_12, 7_68]					,	num_attention_heads=[6, 9, 12]					,	depths=[4, 4, 4]					,	key_dim=[32, 32, 32]					,	drop_path_rate=0.1					,	),
			}

			if model_name:
						convert_weight_and_push(
						    names_to_hidden_sizes[model_name]					,	SCREAMING_SNAKE_CASE					,	names_to_config[model_name]					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
			else:
						for model_name, config in names_to_config.items():
									convert_weight_and_push(names_to_hidden_sizes[model_name]					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
			return config, expected_shape


if __name__ == "__main__":
		__A						:							str							=  argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--model_name""",
		    default=None,
		    type=str,
		    help="""The name of the model you wish to convert, it must be one of the supported Levit* architecture,""",
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""",
		    default="""levit-dump-folder/""",
		    type=Path,
		    required=False,
		    help="""Path to the output PyTorch model directory.""",
		)
		parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
		parser.add_argument(
		    """--no-push_to_hub""",
		    dest="""push_to_hub""",
		    action="""store_false""",
		    help="""Do not push model and image processor to the hub""",
		)

		__A						:							int							=  parser.parse_args()
		__A						:							Path							=  args.pytorch_dump_folder_path
		pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
		convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)












import os
import sys
import unittest


__A						:							Any							=  os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__A						:							List[Any]							=  os.path.join(git_repo_path, """src""", """diffusers""")




class 			UpperCAmelCase_      (   unittest.TestCase       ):




							'''simple docstring'''



							def    _UpperCAmelCase     (					self       :	Any			)			->					Optional[Any]:
										SCREAMING_SNAKE_CASE           =					find_backend("""    if not is_torch_available():"""			)
										self.assertEqual(a       ,     """torch"""			)

										# backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
										# self.assertEqual(backend_with_underscore, "tensorflow_text")

										SCREAMING_SNAKE_CASE           =					find_backend("""    if not (is_torch_available() and is_transformers_available()):"""			)
										self.assertEqual(a       ,     """torch_and_transformers"""			)

										# double_backend_with_underscore = find_backend(
										#    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
										# )
										# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

										SCREAMING_SNAKE_CASE           =					find_backend(
										    """    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):"""			)
										self.assertEqual(a       ,     """torch_and_transformers_and_onnx"""			)





							def    _UpperCAmelCase     (					self       :	List[str]			)			->					int:
										SCREAMING_SNAKE_CASE           =					read_init()
										# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
										self.assertIn("""torch"""       ,     a			)
										self.assertIn("""torch_and_transformers"""       ,     a			)
										self.assertIn("""flax_and_transformers"""       ,     a			)
										self.assertIn("""torch_and_transformers_and_onnx"""       ,     a			)

										# Likewise, we can't assert on the exact content of a key
										self.assertIn("""UNet2DModel"""       ,     objects["""torch"""]			)
										self.assertIn("""FlaxUNet2DConditionModel"""       ,     objects["""flax"""]			)
										self.assertIn("""StableDiffusionPipeline"""       ,     objects["""torch_and_transformers"""]			)
										self.assertIn("""FlaxStableDiffusionPipeline"""       ,     objects["""flax_and_transformers"""]			)
										self.assertIn("""LMSDiscreteScheduler"""       ,     objects["""torch_and_scipy"""]			)
										self.assertIn("""OnnxStableDiffusionPipeline"""       ,     objects["""torch_and_transformers_and_onnx"""]			)





							def    _UpperCAmelCase     (					self       :	Any			)			->					int:
										SCREAMING_SNAKE_CASE           =					create_dummy_object("""CONSTANT"""       ,     """'torch'"""			)
										self.assertEqual(a       ,     """\nCONSTANT = None\n"""			)

										SCREAMING_SNAKE_CASE           =					create_dummy_object("""function"""       ,     """'torch'"""			)
										self.assertEqual(
										    a       ,     """\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"""			)

										SCREAMING_SNAKE_CASE           =					"""
class FakeClass(metaclass=DummyObject):
    _backends = 'torch'

    def __init__(self, *args, **kwargs):
        requires_backends(self, 'torch')

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, 'torch')

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, 'torch')
"""
										SCREAMING_SNAKE_CASE           =					create_dummy_object("""FakeClass"""       ,     """'torch'"""			)
										self.assertEqual(a       ,     a			)







							def    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					Union[str, Any]:
										SCREAMING_SNAKE_CASE           =					"""# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, [\"torch\"])


class FakeClass(metaclass=DummyObject):
    _backends = [\"torch\"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, [\"torch\"])

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, [\"torch\"])

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, [\"torch\"])
"""
										SCREAMING_SNAKE_CASE           =					create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]}			)
										self.assertEqual(dummy_files["""torch"""]       ,     a			)






