

import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Union[str, Any]	)						->				Union[str, Any]:




	'''simple docstring'''




	_UpperCamelCase   :		Union[str, Any]			=						SwinConfig()
	_UpperCamelCase   :		Optional[int]			=						swin_name.split('_'	)

	_UpperCamelCase   :		str			=						name_split[1]
	_UpperCamelCase   :		str			=						int(name_split[4]	)
	_UpperCamelCase   :		Optional[Any]			=						int(name_split[3][-1]	)

	if model_size == "tiny":
		_UpperCamelCase   :		Union[str, Any]			=						9_6
		_UpperCamelCase   :		Optional[Any]			=						(2, 2, 6, 2)
		_UpperCamelCase   :		str			=						(3, 6, 1_2, 2_4)
	elif model_size == "small":
		_UpperCamelCase   :		List[str]			=						9_6
		_UpperCamelCase   :		Optional[Any]			=						(2, 2, 1_8, 2)
		_UpperCamelCase   :		List[Any]			=						(3, 6, 1_2, 2_4)
	elif model_size == "base":
		_UpperCamelCase   :		List[str]			=						1_2_8
		_UpperCamelCase   :		List[str]			=						(2, 2, 1_8, 2)
		_UpperCamelCase   :		List[str]			=						(4, 8, 1_6, 3_2)
	else:
		_UpperCamelCase   :		Dict			=						1_9_2
		_UpperCamelCase   :		List[str]			=						(2, 2, 1_8, 2)
		_UpperCamelCase   :		str			=						(6, 1_2, 2_4, 4_8)

	if "in22k" in swin_name:
		_UpperCamelCase   :		Any			=						2_1_8_4_1
	else:
		_UpperCamelCase   :		int			=						1_0_0_0
		_UpperCamelCase   :		List[Any]			=						'huggingface/label-files'
		_UpperCamelCase   :		Any			=						'imagenet-1k-id2label.json'
		_UpperCamelCase   :		Optional[Any]			=						json.load(open(hf_hub_download(UpperCAmelCase_							,       UpperCAmelCase_							,       repo_type='dataset'	)							,       'r'	)	)
		_UpperCamelCase   :		Optional[int]			=						{int(UpperCAmelCase_	): v for k, v in idalabel.items()}
		_UpperCamelCase   :		List[str]			=						idalabel
		_UpperCamelCase   :		int			=						{v: k for k, v in idalabel.items()}

	_UpperCamelCase   :		Dict			=						img_size
	_UpperCamelCase   :		int			=						num_classes
	_UpperCamelCase   :		Union[str, Any]			=						embed_dim
	_UpperCamelCase   :		List[Any]			=						depths
	_UpperCamelCase   :		Optional[Any]			=						num_heads
	_UpperCamelCase   :		Tuple			=						window_size

	return config






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Optional[int]	)						->				int:




	'''simple docstring'''




	if "patch_embed.proj" in name:
		_UpperCamelCase   :		Any			=						name.replace('patch_embed.proj'							,       'embeddings.patch_embeddings.projection'	)
	if "patch_embed.norm" in name:
		_UpperCamelCase   :		List[Any]			=						name.replace('patch_embed.norm'							,       'embeddings.norm'	)
	if "layers" in name:
		_UpperCamelCase   :		Dict			=						'encoder.' + name
	if "attn.proj" in name:
		_UpperCamelCase   :		int			=						name.replace('attn.proj'							,       'attention.output.dense'	)
	if "attn" in name:
		_UpperCamelCase   :		Any			=						name.replace('attn'							,       'attention.self'	)
	if "norm1" in name:
		_UpperCamelCase   :		Any			=						name.replace('norm1'							,       'layernorm_before'	)
	if "norm2" in name:
		_UpperCamelCase   :		Any			=						name.replace('norm2'							,       'layernorm_after'	)
	if "mlp.fc1" in name:
		_UpperCamelCase   :		Optional[Any]			=						name.replace('mlp.fc1'							,       'intermediate.dense'	)
	if "mlp.fc2" in name:
		_UpperCamelCase   :		Union[str, Any]			=						name.replace('mlp.fc2'							,       'output.dense'	)

	if name == "norm.weight":
		_UpperCamelCase   :		Dict			=						'layernorm.weight'
	if name == "norm.bias":
		_UpperCamelCase   :		int			=						'layernorm.bias'

	if "head" in name:
		_UpperCamelCase   :		List[Any]			=						name.replace('head'							,       'classifier'	)
	else:
		_UpperCamelCase   :		Union[str, Any]			=						'swin.' + name

	return name






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Union[str, Any]							,       UpperCAmelCase_							:     str	)						->				int:




	'''simple docstring'''




	for key in orig_state_dict.copy().keys():
		_UpperCamelCase   :		Any			=						orig_state_dict.pop(UpperCAmelCase_	)

		if "mask" in key:
			continue
		elif "qkv" in key:
			_UpperCamelCase   :		Dict			=						key.split('.'	)
			_UpperCamelCase   :		Any			=						int(key_split[1]	)
			_UpperCamelCase   :		Optional[Any]			=						int(key_split[3]	)
			_UpperCamelCase   :		List[str]			=						model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

			if "weight" in key:
				_UpperCamelCase   :		Tuple			=						val[:dim, :]
				_UpperCamelCase   :		str			=						val[
				    dim : dim * 2, :
				]
				_UpperCamelCase   :		Any			=						val[-dim:, :]
			else:
				_UpperCamelCase   :		Optional[int]			=						val[
				    :dim
				]
				_UpperCamelCase   :		Optional[int]			=						val[
				    dim : dim * 2
				]
				_UpperCamelCase   :		List[Any]			=						val[
				    -dim:
				]
		else:
			_UpperCamelCase   :		str			=						val

	return orig_state_dict






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Optional[Any]							,       UpperCAmelCase_							:     int	)						->				Union[str, Any]:




	'''simple docstring'''




	_UpperCamelCase   :		Optional[Any]			=						timm.create_model(UpperCAmelCase_							,       pretrained=UpperCAmelCase_	)
	timm_model.eval()

	_UpperCamelCase   :		Optional[Any]			=						get_swin_config(UpperCAmelCase_	)
	_UpperCamelCase   :		Any			=						SwinForImageClassification(UpperCAmelCase_	)
	model.eval()

	_UpperCamelCase   :		Any			=						convert_state_dict(timm_model.state_dict()							,       UpperCAmelCase_	)
	model.load_state_dict(UpperCAmelCase_	)

	_UpperCamelCase   :		str			=						'http://images.cocodataset.org/val2017/000000039769.jpg'

	_UpperCamelCase   :		Optional[Any]			=						AutoImageProcessor.from_pretrained('microsoft/{}'.format(swin_name.replace('_'							,       '-'	)	)	)
	_UpperCamelCase   :		Any			=						Image.open(requests.get(UpperCAmelCase_							,       stream=UpperCAmelCase_	).raw	)
	_UpperCamelCase   :		Optional[int]			=						image_processor(images=UpperCAmelCase_							,       return_tensors='pt'	)

	_UpperCamelCase   :		Union[str, Any]			=						timm_model(inputs['pixel_values']	)
	_UpperCamelCase   :		int			=						model(**UpperCAmelCase_	).logits

	assert torch.allclose(UpperCAmelCase_							,       UpperCAmelCase_							,       atol=1e-3	)

	print(F'''Saving model {swin_name} to {pytorch_dump_folder_path}'''	)
	model.save_pretrained(UpperCAmelCase_	)

	print(F'''Saving image processor to {pytorch_dump_folder_path}'''	)
	image_processor.save_pretrained(UpperCAmelCase_	)


if __name__ == "__main__":
							lowerCAmelCase__								=		argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--swin_name""",
							    default="""swin_tiny_patch4_window7_224""",
							    type=str,
							    help="""Name of the Swin timm 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."""
							)

							lowerCAmelCase__								=		parser.parse_args()
							convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  "facebook/bart-large-mnli"
		a__					       =  (
		    "This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which "
		    "should be the text to classify, and `labels`, which should be the list of labels to use for classification. "
		    "It returns the most likely label in the list of provided `labels` for the input text."
		)
		a__					       =  "text_classifier"
		a__					       =  AutoTokenizer
		a__					       =  AutoModelForSequenceClassification

		a__					       =  ["text", ["text"]]
		a__					       =  ["text"]


		def 		A__					(				self):
			super().setup()
			_UpperCamelCase   :		List[Any]			=						self.model.config
			_UpperCamelCase   :		Optional[int]			=						-1
			for idx, label in config.idalabel.items():
				if label.lower().startswith('entail'):
					_UpperCamelCase   :		Tuple			=						int(__snake_case)
			if self.entailment_id == -1:
				raise ValueError('Could not determine the entailment ID from the model config, please pass it at init.')


		def 		A__					(				self	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		List[Any]			=						labels
			return self.pre_processor(
			    [text] * len(__snake_case)	,       [f'''This example is {label}''' for label in labels]	,       return_tensors='pt'	,       padding='max_length'	,       )






		def 		A__					(				self	,       __snake_case):
			_UpperCamelCase   :		str			=						outputs.logits
			_UpperCamelCase   :		Optional[Any]			=						torch.argmax(logits[:, 2]).item()
			return self._labels[label_id]




