





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

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

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


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


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     List[str]:
		lowerCamelCase   : Union[str, Any]								=original_name.split('''.'''		)[0]
		lowerCamelCase   : Optional[Any]								=key.split('''.'''		)
		lowerCamelCase   : List[Any]								=int(key_list[key_list.index(SCREAMING_SNAKE_CASE_		) - 2]		)
		lowerCamelCase   : Union[str, Any]								=int(key_list[key_list.index(SCREAMING_SNAKE_CASE_		) - 1]		)
		lowerCamelCase   : Union[str, Any]								=orig_block_num - offset

		lowerCamelCase   : Any								=key.replace(F"{orig_block_num}.{layer_num}.{original_name}"							,       F"block.{new_block_num}.{layer_num}.{new_name}"		)
		return key


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     Any:
		lowerCamelCase   : str								=OrderedDict()
		lowerCamelCase		,   lowerCamelCase   : Dict								=0, 0
		for key, value in state_dict.items():
				if key.startswith('''network'''		):
						lowerCamelCase   : Optional[int]								=key.replace('''network'''							,       '''poolformer.encoder'''		)
				if "proj" in key:
						# Works for the first embedding as well as the internal embedding layers
						if key.endswith('''bias'''		) and "patch_embed" not in key:
								patch_emb_offset += 1
						lowerCamelCase   : Union[str, Any]								=key[: key.find('''proj'''		)]
						lowerCamelCase   : Dict								=key.replace(SCREAMING_SNAKE_CASE_							,       F"patch_embeddings.{total_embed_found}."		)
						lowerCamelCase   : Union[str, Any]								=key.replace('''proj'''							,       '''projection'''		)
						if key.endswith('''bias'''		):
								total_embed_found += 1
				if "patch_embeddings" in key:
						lowerCamelCase   : List[Any]								='''poolformer.encoder.''' + key
				if "mlp.fc1" in key:
						lowerCamelCase   : int								=replace_key_with_offset(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       '''mlp.fc1'''							,       '''output.conv1'''		)
				if "mlp.fc2" in key:
						lowerCamelCase   : Dict								=replace_key_with_offset(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       '''mlp.fc2'''							,       '''output.conv2'''		)
				if "norm1" in key:
						lowerCamelCase   : List[str]								=replace_key_with_offset(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       '''norm1'''							,       '''before_norm'''		)
				if "norm2" in key:
						lowerCamelCase   : List[Any]								=replace_key_with_offset(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       '''norm2'''							,       '''after_norm'''		)
				if "layer_scale_1" in key:
						lowerCamelCase   : str								=replace_key_with_offset(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       '''layer_scale_1'''							,       '''layer_scale_1'''		)
				if "layer_scale_2" in key:
						lowerCamelCase   : Any								=replace_key_with_offset(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       '''layer_scale_2'''							,       '''layer_scale_2'''		)
				if "head" in key:
						lowerCamelCase   : int								=key.replace('''head'''							,       '''classifier'''		)
				lowerCamelCase   : int								=value
		return new_state_dict


def 					A__	(       )   ->     Tuple:
		lowerCamelCase   : Union[str, Any]								='''http://images.cocodataset.org/val2017/000000039769.jpg'''
		lowerCamelCase   : Optional[Any]								=Image.open(requests.get(SCREAMING_SNAKE_CASE_							,       stream=SCREAMING_SNAKE_CASE_		).raw		)

		return image


@torch.no_grad()
def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     List[Any]:
		lowerCamelCase   : Optional[int]								=PoolFormerConfig()

		# set attributes based on model_name
		lowerCamelCase   : Optional[Any]								='''huggingface/label-files'''
		lowerCamelCase   : List[str]								=model_name[-3:]
		lowerCamelCase   : int								=1_0_0_0
		lowerCamelCase   : List[Any]								='''imagenet-1k-id2label.json'''
		lowerCamelCase   : Tuple								=(1, 1_0_0_0)

		# set config attributes
		lowerCamelCase   : Dict								=json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       repo_type='''dataset'''		)							,       '''r'''		)		)
		lowerCamelCase   : List[Any]								={int(SCREAMING_SNAKE_CASE_		): v for k, v in idalabel.items()}
		lowerCamelCase   : str								=idalabel
		lowerCamelCase   : Optional[int]								={v: k for k, v in idalabel.items()}
		if size == "s12":
				lowerCamelCase   : Dict								=[2, 2, 6, 2]
				lowerCamelCase   : List[Any]								=[6_4, 1_2_8, 3_2_0, 5_1_2]
				lowerCamelCase   : Optional[int]								=4.0
				lowerCamelCase   : Tuple								=0.9
		elif size == "s24":
				lowerCamelCase   : int								=[4, 4, 1_2, 4]
				lowerCamelCase   : List[Any]								=[6_4, 1_2_8, 3_2_0, 5_1_2]
				lowerCamelCase   : str								=4.0
				lowerCamelCase   : Union[str, Any]								=0.9
		elif size == "s36":
				lowerCamelCase   : List[Any]								=[6, 6, 1_8, 6]
				lowerCamelCase   : Tuple								=[6_4, 1_2_8, 3_2_0, 5_1_2]
				lowerCamelCase   : Dict								=4.0
				lowerCamelCase   : Union[str, Any]								=1E-6
				lowerCamelCase   : Union[str, Any]								=0.9
		elif size == "m36":
				lowerCamelCase   : List[str]								=[6, 6, 1_8, 6]
				lowerCamelCase   : Dict								=[9_6, 1_9_2, 3_8_4, 7_6_8]
				lowerCamelCase   : Optional[Any]								=4.0
				lowerCamelCase   : Union[str, Any]								=1E-6
				lowerCamelCase   : Tuple								=0.9_5
		elif size == "m48":
				lowerCamelCase   : str								=[8, 8, 2_4, 8]
				lowerCamelCase   : Optional[Any]								=[9_6, 1_9_2, 3_8_4, 7_6_8]
				lowerCamelCase   : Tuple								=4.0
				lowerCamelCase   : Optional[int]								=1E-6
				lowerCamelCase   : List[Any]								=0.9_5
		else:
				raise ValueError(F"Size {size} not supported"		)

		# load image processor
		lowerCamelCase   : Union[str, Any]								=PoolFormerImageProcessor(crop_pct=SCREAMING_SNAKE_CASE_		)

		# Prepare image
		lowerCamelCase   : Dict								=prepare_img()
		lowerCamelCase   : Union[str, Any]								=image_processor(images=SCREAMING_SNAKE_CASE_							,       return_tensors='''pt'''		).pixel_values

		logger.info(F"Converting model {model_name}..."		)

		# load original state dict
		lowerCamelCase   : Optional[Any]								=torch.load(SCREAMING_SNAKE_CASE_							,       map_location=torch.device('''cpu'''		)		)

		# rename keys
		lowerCamelCase   : Optional[int]								=rename_keys(SCREAMING_SNAKE_CASE_		)

		# create HuggingFace model and load state dict
		lowerCamelCase   : List[str]								=PoolFormerForImageClassification(SCREAMING_SNAKE_CASE_		)
		model.load_state_dict(SCREAMING_SNAKE_CASE_		)
		model.eval()

		# Define image processor
		lowerCamelCase   : Dict								=PoolFormerImageProcessor(crop_pct=SCREAMING_SNAKE_CASE_		)
		lowerCamelCase   : Union[str, Any]								=image_processor(images=prepare_img()							,       return_tensors='''pt'''		).pixel_values

		# forward pass
		lowerCamelCase   : List[str]								=model(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase   : Union[str, Any]								=outputs.logits

		# define expected logit slices for different models
		if size == "s12":
				lowerCamelCase   : int								=torch.tensor([-0.3_0_4_5, -0.6_7_5_8, -0.4_8_6_9]		)
		elif size == "s24":
				lowerCamelCase   : List[str]								=torch.tensor([0.4_4_0_2, -0.1_3_7_4, -0.8_0_4_5]		)
		elif size == "s36":
				lowerCamelCase   : str								=torch.tensor([-0.6_0_8_0, -0.5_1_3_3, -0.5_8_9_8]		)
		elif size == "m36":
				lowerCamelCase   : List[str]								=torch.tensor([0.3_9_5_2, 0.2_2_6_3, -1.2_6_6_8]		)
		elif size == "m48":
				lowerCamelCase   : int								=torch.tensor([0.1_1_6_7, -0.0_6_5_6, -0.3_4_2_3]		)
		else:
				raise ValueError(F"Size {size} not supported"		)

		# verify logits
		assert logits.shape == expected_shape
		assert torch.allclose(logits[0, :3]							,       SCREAMING_SNAKE_CASE_							,       atol=1E-2		)

		# finally, save model and image processor
		logger.info(F"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."		)
		Path(SCREAMING_SNAKE_CASE_		).mkdir(exist_ok=SCREAMING_SNAKE_CASE_		)
		model.save_pretrained(SCREAMING_SNAKE_CASE_		)
		print(F"Saving image processor to {pytorch_dump_folder_path}"		)
		image_processor.save_pretrained(SCREAMING_SNAKE_CASE_		)


if __name__ == "__main__":
	snake_case_							=			argparse.ArgumentParser()

	parser.add_argument(
	    '''--model_name''',
	    default='''poolformer_s12''',
	    type=str,
	    help='''Name of the model you\'d like to convert.''',
	)
	parser.add_argument(
	    '''--checkpoint_path''', default=None, type=str, help='''Path to the original PyTorch checkpoint (.pth file).'''
	)
	parser.add_argument(
	    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
	)
	snake_case_							=			parser.parse_args()
	convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)








from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image


snake_case_							=			['''text''', '''image''', '''audio''']


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     List[str]:
		lowerCamelCase   : Union[str, Any]								=[]

		for input_type in input_types:
				if input_type == "text":
						inputs.append('''Text input'''		)
				elif input_type == "image":
						inputs.append(
						    Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''		)		) / '''000000039769.png'''		).resize((5_1_2, 5_1_2)		)		)
				elif input_type == "audio":
						inputs.append(torch.ones(3_0_0_0		)		)
				elif isinstance(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		):
						inputs.append(create_inputs(SCREAMING_SNAKE_CASE_		)		)
				else:
						raise ValueError(F"Invalid type requested: {input_type}"		)

		return inputs


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     Optional[int]:
		lowerCamelCase   : str								=[]

		for output in outputs:
				if isinstance(SCREAMING_SNAKE_CASE_							,       (str, AgentText)		):
						output_types.append('''text'''		)
				elif isinstance(SCREAMING_SNAKE_CASE_							,       (Image.Image, AgentImage)		):
						output_types.append('''image'''		)
				elif isinstance(SCREAMING_SNAKE_CASE_							,       (torch.Tensor, AgentAudio)		):
						output_types.append('''audio'''		)
				else:
						raise ValueError(F"Invalid output: {output}"		)

		return output_types



@is_tool_test
class        snake_case_		:







		def 			__lowercase   (		self   )				->   Tuple:
				self.assertTrue(hasattr(self.tool			,     '''inputs'''   )   )
				self.assertTrue(hasattr(self.tool			,     '''outputs'''   )   )

				lowerCamelCase   : List[Any]								=self.tool.inputs
				for _input in inputs:
						if isinstance(_input			,     __lowercase   ):
								for __input in _input:
										self.assertTrue(__input in authorized_types   )
						else:
								self.assertTrue(_input in authorized_types   )

				lowerCamelCase   : List[Any]								=self.tool.outputs
				for _output in outputs:
						self.assertTrue(_output in authorized_types   )







		def 			__lowercase   (		self   )				->   Any:
				lowerCamelCase   : Dict								=create_inputs(self.tool.inputs   )
				lowerCamelCase   : Any								=self.tool(*__lowercase   )

				# There is a single output
				if len(self.tool.outputs   ) == 1:
						lowerCamelCase   : Union[str, Any]								=[outputs]

				self.assertListEqual(output_types(__lowercase   )			,     self.tool.outputs   )







		def 			__lowercase   (		self   )				->   Any:
				self.assertTrue(hasattr(self.tool			,     '''description'''   )   )
				self.assertTrue(hasattr(self.tool			,     '''default_checkpoint'''   )   )
				self.assertTrue(self.tool.description.startswith('''This is a tool that'''   )   )







		def 			__lowercase   (		self   )				->   Any:
				lowerCamelCase   : Dict								=create_inputs(self.tool.inputs   )
				lowerCamelCase   : Dict								=self.tool(*__lowercase   )

				if not isinstance(__lowercase			,     __lowercase   ):
						lowerCamelCase   : int								=[outputs]

				self.assertEqual(len(__lowercase   )			,     len(self.tool.outputs   )   )

				for output, output_type in zip(__lowercase			,     self.tool.outputs   ):
						lowerCamelCase   : Any								=AGENT_TYPE_MAPPING[output_type]
						self.assertTrue(isinstance(__lowercase			,     __lowercase   )   )







		def 			__lowercase   (		self   )				->   List[str]:
				lowerCamelCase   : Optional[Any]								=create_inputs(self.tool.inputs   )

				lowerCamelCase   : str								=[]

				for _input, input_type in zip(__lowercase			,     self.tool.inputs   ):
						if isinstance(__lowercase			,     __lowercase   ):
								_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input   ) for _input_type in input_type]   )
						else:
								_inputs.append(AGENT_TYPE_MAPPING[input_type](_input   )   )

        # Should not raise an error
				lowerCamelCase   : Union[str, Any]								=self.tool(*__lowercase   )

				if not isinstance(__lowercase			,     __lowercase   ):
						lowerCamelCase   : int								=[outputs]

				self.assertEqual(len(__lowercase   )			,     len(self.tool.outputs   )   )

