



import argparse
import re

import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SamConfig,
    SamImageProcessor,
    SamModel,
    SamProcessor,
    SamVisionConfig,
)


_lowercase: Optional[int]      			=		{
    "iou_prediction_head.layers.0": "iou_prediction_head.proj_in",
    "iou_prediction_head.layers.1": "iou_prediction_head.layers.0",
    "iou_prediction_head.layers.2": "iou_prediction_head.proj_out",
    "mask_decoder.output_upscaling.0": "mask_decoder.upscale_conv1",
    "mask_decoder.output_upscaling.1": "mask_decoder.upscale_layer_norm",
    "mask_decoder.output_upscaling.3": "mask_decoder.upscale_conv2",
    "mask_downscaling.0": "mask_embed.conv1",
    "mask_downscaling.1": "mask_embed.layer_norm1",
    "mask_downscaling.3": "mask_embed.conv2",
    "mask_downscaling.4": "mask_embed.layer_norm2",
    "mask_downscaling.6": "mask_embed.conv3",
    "point_embeddings": "point_embed",
    "pe_layer.positional_encoding_gaussian_matrix": "shared_embedding.positional_embedding",
    "image_encoder": "vision_encoder",
    "neck.0": "neck.conv1",
    "neck.1": "neck.layer_norm1",
    "neck.2": "neck.conv2",
    "neck.3": "neck.layer_norm2",
    "patch_embed.proj": "patch_embed.projection",
    ".norm": ".layer_norm",
    "blocks": "layers",
}






def     a(      A       :    Union[str, Any]       )		->       List[str]:
					"""simple docstring"""





					a					     =   {}
					state_dict.pop("pixel_mean" ,     A       )
					state_dict.pop("pixel_std" ,     A       )

					a					     =   r".*.output_hypernetworks_mlps.(\d+).layers.(\d+).*"

					for key, value in state_dict.items():
										for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
															if key_to_modify in key:
																				a					     =   key.replace(A ,     A       )

										if re.match(A ,     A       ):
															a					     =   int(re.match(A ,     A       ).group(2       )       )
															if layer_nb == 0:
																				a					     =   key.replace("layers.0" ,     "proj_in"       )
															elif layer_nb == 1:
																				a					     =   key.replace("layers.1" ,     "layers.0"       )
															elif layer_nb == 2:
																				a					     =   key.replace("layers.2" ,     "proj_out"       )

										a					     =   value

					a					     =   model_state_dict[
					    "prompt_encoder.shared_embedding.positional_embedding"
					]

					return model_state_dict






def     a(      A       :    List[Any] ,     A       :    Optional[Any] ,     A       :    Optional[Any] ,     A       :    str="ybelkada/segment-anything"       )		->       Tuple:
					"""simple docstring"""





					a					     =   hf_hub_download(A ,     f'''checkpoints/{model_name}.pth'''       )

					if "sam_vit_b" in model_name:
										a					     =   SamConfig()
					elif "sam_vit_l" in model_name:
										a					     =   SamVisionConfig(
										    hidden_size=1024 ,     num_hidden_layers=24 ,     num_attention_heads=16 ,     global_attn_indexes=[5, 11, 17, 23] ,     )

										a					     =   SamConfig(
										    vision_config=A ,     )
					elif "sam_vit_h" in model_name:
										a					     =   SamVisionConfig(
										    hidden_size=1280 ,     num_hidden_layers=32 ,     num_attention_heads=16 ,     global_attn_indexes=[7, 15, 23, 31] ,     )

										a					     =   SamConfig(
										    vision_config=A ,     )

					a					     =   torch.load(A ,     map_location="cpu"       )
					a					     =   replace_keys(A       )

					a					     =   SamImageProcessor()

					a					     =   SamProcessor(image_processor=A       )
					a					     =   SamModel(A       )

					hf_model.load_state_dict(A       )
					a					     =   hf_model.to("cuda"       )

					a					     =   "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
					a					     =   Image.open(requests.get(A ,     stream=A       ).raw       ).convert("RGB"       )

					a					     =   [[[400, 650]]]
					a					     =   [[1]]

					a					     =   processor(images=np.array(A       ) ,     return_tensors="pt"       ).to("cuda"       )

					with torch.no_grad():
										a					     =   hf_model(**A       )
					a					     =   output.iou_scores.squeeze()

					if model_name == "sam_vit_h_4b8939":
										assert scores[-1].item() == 0.579_890_251_159_668

										a					     =   processor(
										    images=np.array(A       ) ,     input_points=A ,     input_labels=A ,     return_tensors="pt"       ).to("cuda"       )

										with torch.no_grad():
															a					     =   hf_model(**A       )
										a					     =   output.iou_scores.squeeze()

										assert scores[-1].item() == 0.9_712_603_092_193_604

										a					     =   ((75, 275, 1725, 850),)

										a					     =   processor(images=np.array(A       ) ,     input_boxes=A ,     return_tensors="pt"       ).to("cuda"       )

										with torch.no_grad():
															a					     =   hf_model(**A       )
										a					     =   output.iou_scores.squeeze()

										assert scores[-1].item() == 0.8_686_015_605_926_514

										# Test with 2 points and 1 image.
										a					     =   [[[400, 650], [800, 650]]]
										a					     =   [[1, 1]]

										a					     =   processor(
										    images=np.array(A       ) ,     input_points=A ,     input_labels=A ,     return_tensors="pt"       ).to("cuda"       )

										with torch.no_grad():
															a					     =   hf_model(**A       )
										a					     =   output.iou_scores.squeeze()

										assert scores[-1].item() == 0.9_936_047_792_434_692


if __name__ == "__main__":
				_lowercase: List[Any]      			=		argparse.ArgumentParser()
				_lowercase: List[str]      			=		["sam_vit_b_01ec64", "sam_vit_h_4b8939", "sam_vit_l_0b3195"]
				parser.add_argument(
				    "--model_name",
				    default="sam_vit_h_4b8939",
				    choices=choices,
				    type=str,
				    help="Path to hf config.json of model to convert",
				)
				parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
				parser.add_argument(
				    "--push_to_hub",
				    action="store_true",
				    help="Whether to push the model and processor to the hub after converting",
				)
				parser.add_argument(
				    "--model_hub_id",
				    default="ybelkada/segment-anything",
				    choices=choices,
				    type=str,
				    help="Path to hf config.json of model to convert",
				)

				_lowercase: Dict      			=		parser.parse_args()

				convert_sam_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.model_hub_id)





import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


_lowercase: Union[str, Any]      			=		get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
				_lowercase: List[str]      			=		json.load(f)



@require_torch
class 	_lowercase							(   unittest.TestCase	):





	"""simple docstring"""




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						return FSMTTokenizer.from_pretrained(lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   FSMTForConditionalGeneration.from_pretrained(lowerCamelCase_		).to(lowerCamelCase_		)
						if torch_device == "cuda":
											model.half()
						return model




	@parameterized.expand(
	    [
	        ["en-ru", 26.0],
	        ["ru-en", 22.0],
	        ["en-de", 22.0],
	        ["de-en", 29.0],
	    ]		)
	@slow
	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   F'''facebook/wmt19-{pair}'''
						a					     =   self.get_tokenizer(lowerCamelCase_		)
						a					     =   self.get_model(lowerCamelCase_		)

						a					     =   bleu_data[pair]["src"]
						a					     =   bleu_data[pair]["tgt"]

						a					     =   tokenizer(lowerCamelCase_		,       return_tensors="pt"		,       truncation=lowerCamelCase_		,       padding="longest"		).to(lowerCamelCase_		)
						a					     =   model.generate(
						    input_ids=batch.input_ids		,       num_beams=8		,       )
						a					     =   tokenizer.batch_decode(
						    lowerCamelCase_		,       skip_special_tokens=lowerCamelCase_		,       clean_up_tokenization_spaces=lowerCamelCase_		)
						a					     =   calculate_bleu(lowerCamelCase_		,       lowerCamelCase_		)
						print(lowerCamelCase_		)
						self.assertGreaterEqual(scores["bleu"]		,       lowerCamelCase_		)
