'''simple docstring'''




import argparse
import json
from pathlib import Path

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

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_lowercase		:				Any	       =		logging.get_logger(__name__)

_lowercase		:				Union[str, Any]	       =		torch.device("cpu")







def 							lowerCamelCase			(   )    ->   Union[str, Any]:
							lowercase_      :    List[str]       		= """http://images.cocodataset.org/val2017/000000039769.jpg"""
							lowercase_      :    Optional[Any]       		= Image.open(requests.get(snake_case_   ,   stream=snake_case_				).raw				)
							return im







def 							lowerCamelCase			(   UpperCAmelCase__ :      Union[str, Any]				)    ->   List[str]:
							if swiftformer_name == "swiftformer_xs":
														return torch.tensor([-2.1703e00, 2.1107e00, -2.0811e00, 8.8685e-01, 2.4360e-01]				)

							elif swiftformer_name == "swiftformer_s":
														return torch.tensor([3.9636e-01, 2.3478e-01, -1.6963e00, -1.7381e00, -8.6337e-01]				)

							elif swiftformer_name == "swiftformer_l1":
														return torch.tensor([-4.2768e-01, -4.7429e-01, -1.0897e00, -1.0248e00, 3.5523e-02]				)

							elif swiftformer_name == "swiftformer_l3":
														return torch.tensor([-2.5330e-01, 2.4211e-01, -6.0185e-01, -8.2789e-01, -6.0446e-02]				)







def 							lowerCamelCase			(   UpperCAmelCase__ :      Union[str, Any]   ,   UpperCAmelCase__ :      Any   ,   UpperCAmelCase__ :      Tuple				)    ->   Optional[Any]:
							lowercase_      :    Tuple       		= dct.pop(snake_case_				)
							lowercase_      :    Optional[int]       		= val







def 							lowerCamelCase			(   UpperCAmelCase__ :      Any				)    ->   Tuple:
							lowercase_      :    int       		= []
							for k in state_dict.keys():
														lowercase_      :    Dict       		= k
														if ".pwconv" in k:
																					lowercase_      :    Dict       		= k_new.replace(""".pwconv"""   ,   """.point_wise_conv"""				)
														if ".dwconv" in k:
																					lowercase_      :    int       		= k_new.replace(""".dwconv"""   ,   """.depth_wise_conv"""				)
														if ".Proj." in k:
																					lowercase_      :    Tuple       		= k_new.replace(""".Proj."""   ,   """.proj."""				)
														if "patch_embed" in k_new:
																					lowercase_      :    str       		= k_new.replace("""patch_embed"""   ,   """swiftformer.patch_embed.patch_embedding"""				)
														if "network" in k_new:
																					lowercase_      :    Optional[int]       		= k_new.split("""."""				)
																					if ls[2].isdigit():
																												lowercase_      :    Any       		= """swiftformer.encoder.network.""" + ls[1] + """.blocks.""" + ls[2] + """.""" + """.""".join(ls[3:]				)
																					else:
																												lowercase_      :    int       		= k_new.replace("""network"""   ,   """swiftformer.encoder.network"""				)
														rename_keys.append((k, k_new)				)
							return rename_keys







@torch.no_grad()
def 							lowerCamelCase			(   UpperCAmelCase__ :      Tuple   ,   UpperCAmelCase__ :      Any   ,   UpperCAmelCase__ :      Optional[int]				)    ->   Optional[Any]:
							lowercase_      :    List[Any]       		= SwiftFormerConfig()

							# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
							lowercase_      :    Dict       		= 1000
							lowercase_      :    Optional[int]       		= """huggingface/label-files"""
							lowercase_      :    Optional[Any]       		= """imagenet-1k-id2label.json"""
							lowercase_      :    Optional[int]       		= json.load(open(hf_hub_download(snake_case_   ,   snake_case_   ,   repo_type="""dataset"""				)   ,   """r"""				)				)
							lowercase_      :    Any       		= {int(snake_case_				): v for k, v in idalabel.items()}
							lowercase_      :    Optional[Any]       		= idalabel
							lowercase_      :    Optional[Any]       		= {v: k for k, v in idalabel.items()}

							# size of the architecture
							if swiftformer_name == "swiftformer_xs":
														lowercase_      :    Dict       		= [3, 3, 6, 4]
														lowercase_      :    Optional[Any]       		= [48, 56, 112, 220]

							elif swiftformer_name == "swiftformer_s":
														lowercase_      :    List[str]       		= [3, 3, 9, 6]
														lowercase_      :    List[str]       		= [48, 64, 168, 224]

							elif swiftformer_name == "swiftformer_l1":
														lowercase_      :    Optional[Any]       		= [4, 3, 10, 5]
														lowercase_      :    Dict       		= [48, 96, 192, 384]

							elif swiftformer_name == "swiftformer_l3":
														lowercase_      :    List[str]       		= [4, 4, 12, 6]
														lowercase_      :    Any       		= [64, 128, 320, 512]

							# load state_dict of original model, remove and rename some keys
							if original_ckpt:
														if original_ckpt.startswith("""https"""				):
																					lowercase_      :    Any       		= torch.hub.load_state_dict_from_url(snake_case_   ,   map_location="""cpu"""   ,   check_hash=snake_case_				)
														else:
																					lowercase_      :    Union[str, Any]       		= torch.load(snake_case_   ,   map_location="""cpu"""				)
							lowercase_      :    List[str]       		= checkpoint

							lowercase_      :    List[Any]       		= create_rename_keys(snake_case_				)
							for rename_key_src, rename_key_dest in rename_keys:
														rename_key(snake_case_   ,   snake_case_   ,   snake_case_				)

							# load HuggingFace model
							lowercase_      :    Union[str, Any]       		= SwiftFormerForImageClassification(snake_case_				).eval()
							hf_model.load_state_dict(snake_case_				)

							# prepare test inputs
							lowercase_      :    Tuple       		= prepare_img()
							lowercase_      :    Dict       		= ViTImageProcessor.from_pretrained("""preprocessor_config"""				)
							lowercase_      :    Optional[Any]       		= processor(images=snake_case_   ,   return_tensors="""pt"""				)

							# compare outputs from both models
							lowercase_      :    Optional[int]       		= get_expected_output(snake_case_				)
							lowercase_      :    Union[str, Any]       		= hf_model(inputs["""pixel_values"""]				).logits

							assert hf_logits.shape == torch.Size([1, 1000]				)
							assert torch.allclose(hf_logits[0, 0:5]   ,   snake_case_   ,   atol=1e-3				)

							Path(snake_case_				).mkdir(exist_ok=snake_case_				)
							print(F'''Saving model {swiftformer_name} to {pytorch_dump_folder_path}'''				)
							hf_model.save_pretrained(snake_case_				)


if __name__ == "__main__":
						_lowercase		:				Union[str, Any]	       =		argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--swiftformer_name",
						    default="swiftformer_xs",
						    choices=["swiftformer_xs", "swiftformer_s", "swiftformer_l1", "swiftformer_l3"],
						    type=str,
						    help="Name of the SwiftFormer model you'd like to convert.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path",
						    default="./converted_outputs/",
						    type=str,
						    help="Path to the output PyTorch model directory.",
						)
						parser.add_argument("--original_ckpt", default=None, type=str, help="Path to the original model checkpoint.")

						_lowercase		:				str	       =		parser.parse_args()
						convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)






'''simple docstring'''




from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
						import tensorflow as tf

						from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM





@require_tf
@require_sentencepiece
@require_tokenizers
class 					__magic_name__		(					unittest.TestCase):







			@slow
			def 			SCREAMING_SNAKE_CASE_  (			self       :						str					):
										lowercase_      :    Any       		= TFAutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""					)
										lowercase_      :    Dict       		= AutoTokenizer.from_pretrained("""google/mt5-small"""					)

										lowercase_      :    Union[str, Any]       		= tokenizer("""Hello there"""       ,    return_tensors="""tf"""					).input_ids
										lowercase_      :    List[str]       		= tokenizer("""Hi I am"""       ,    return_tensors="""tf"""					).input_ids

										lowercase_      :    Optional[Any]       		= model(lowercase_       ,    labels=lowercase_					).loss
										lowercase_      :    Optional[int]       		= -tf.math.reduce_mean(lowercase_					).numpy()

										lowercase_      :    Optional[int]       		= -21.22_81_68
										self.assertTrue(abs(mtf_score - EXPECTED_SCORE					) < 2E-4					)





