
from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
						from PIL import Image

						from ..image_utils import load_image

if is_torch_available():
						import torch

						from transformers.modeling_outputs import BaseModelOutput

						from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

__a  			=					logging.get_logger(__name__)
@add_end_docstrings(UpperCAmelCase							)
class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	def __init__(       self  :			List[Any]       ,							**SCREAMING_SNAKE_CASE_  :			List[Any]       )       ->     Tuple:
					super().__init__(**SCREAMING_SNAKE_CASE_       )

					if self.framework == "tf":
									raise ValueError(f'''The {self.__class__} is only available in PyTorch.'''       )

					requires_backends(self       ,							'''vision'''       )
					self.check_model_type(SCREAMING_SNAKE_CASE_       )
	def __call__(       self  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			Union[str, "Image.Image", List[Dict[str, Any]]]       ,							SCREAMING_SNAKE_CASE_  :			Union[str, List[str]] = None       ,							**SCREAMING_SNAKE_CASE_  :			Any       ,							)       ->     Dict:
					if "text_queries" in kwargs:
									lowercase_       =					kwargs.pop('''text_queries'''       )

					if isinstance(SCREAMING_SNAKE_CASE_       ,							(str, Image.Image)       ):
									lowercase_       =					{'''image''': image, '''candidate_labels''': candidate_labels}
					else:
									lowercase_       =					image
					lowercase_       =					super().__call__(SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )
					return results
	def 						_lowercase  (       self  :			Optional[int]       ,							**SCREAMING_SNAKE_CASE_  :			Tuple       )       ->     Optional[Any]:
					lowercase_       =					{}
					if "threshold" in kwargs:
									lowercase_       =					kwargs['''threshold''']
					if "top_k" in kwargs:
									lowercase_       =					kwargs['''top_k''']
					return {}, {}, postprocess_params
	def 						_lowercase  (       self  :			int       ,							SCREAMING_SNAKE_CASE_  :			int       )       ->     List[str]:
					lowercase_       =					load_image(inputs['''image''']       )
					lowercase_       =					inputs['''candidate_labels''']
					if isinstance(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ):
									lowercase_       =					candidate_labels.split(''','''       )

					lowercase_       =					torch.tensor([[image.height, image.width]]       ,							dtype=torch.intaa       )
					for i, candidate_label in enumerate(SCREAMING_SNAKE_CASE_       ):
									lowercase_       =					self.tokenizer(SCREAMING_SNAKE_CASE_       ,							return_tensors=self.framework       )
									lowercase_       =					self.image_processor(SCREAMING_SNAKE_CASE_       ,							return_tensors=self.framework       )
									yield {
									    "is_last": i == len(SCREAMING_SNAKE_CASE_       ) - 1,
									    "target_size": target_size,
									    "candidate_label": candidate_label,
									    **text_inputs,
									    **image_features,
									}
	def 						_lowercase  (       self  :			str       ,							SCREAMING_SNAKE_CASE_  :			str       )       ->     Optional[int]:
					lowercase_       =					model_inputs.pop('''target_size'''       )
					lowercase_       =					model_inputs.pop('''candidate_label'''       )
					lowercase_       =					model_inputs.pop('''is_last'''       )

					lowercase_       =					self.model(**SCREAMING_SNAKE_CASE_       )

					lowercase_       =					{'''target_size''': target_size, '''candidate_label''': candidate_label, '''is_last''': is_last, **outputs}
					return model_outputs
	def 						_lowercase  (       self  :			Tuple       ,							SCREAMING_SNAKE_CASE_  :			Any       ,							SCREAMING_SNAKE_CASE_  :			Any=0.1       ,							SCREAMING_SNAKE_CASE_  :			Tuple=None       )       ->     Dict:
					lowercase_       =					[]
					for model_output in model_outputs:
									lowercase_       =					model_output['''candidate_label''']
									lowercase_       =					BaseModelOutput(SCREAMING_SNAKE_CASE_       )
									lowercase_       =					self.image_processor.post_process_object_detection(
									    outputs=SCREAMING_SNAKE_CASE_       ,							threshold=SCREAMING_SNAKE_CASE_       ,							target_sizes=model_output['''target_size''']       )[0]

									for index in outputs["scores"].nonzero():
													lowercase_       =					outputs['''scores'''][index].item()
													lowercase_       =					self._get_bounding_box(outputs['''boxes'''][index][0]       )

													lowercase_       =					{'''score''': score, '''label''': label, '''box''': box}
													results.append(SCREAMING_SNAKE_CASE_       )

					lowercase_       =					sorted(SCREAMING_SNAKE_CASE_       ,							key=lambda SCREAMING_SNAKE_CASE_       : x["score"]       ,							reverse=SCREAMING_SNAKE_CASE_       )
					if top_k:
									lowercase_       =					results[:top_k]

					return results




	def 						_lowercase  (       self  :			Any       ,							SCREAMING_SNAKE_CASE_  :			"torch.Tensor"       )       ->     Dict[str, int]:
					if self.framework != "pt":
									raise ValueError('''The ZeroShotObjectDetectionPipeline is only available in PyTorch.'''       )
					lowercase_  ,	lowercase_  ,	lowercase_  ,	lowercase_       =					box.int().tolist()
					lowercase_       =					{
					    '''xmin''': xmin,
					    '''ymin''': ymin,
					    '''xmax''': xmax,
					    '''ymax''': ymax,
					}
					return bbox










import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[Any] ,     UpperCamelCase__				:      Optional[int] ,     UpperCamelCase__				:      str ,     UpperCamelCase__				:      Any=1024      )     ->       Dict:





				"""simple docstring"""


				__lowerCamelCase       ,						__lowerCamelCase		       =    [], []

				__lowerCamelCase		       =    list(zip(UpperCamelCase__ ,     UpperCamelCase__      )      )
				__lowerCamelCase       ,						__lowerCamelCase		       =    sorted_examples[0]

				def is_too_big(UpperCamelCase__				:      List[str]      ):
								return tok(UpperCamelCase__ ,     return_tensors='pt'      ).input_ids.shape[1] > max_tokens

				for src, tgt in tqdm(sorted_examples[1:]      ):
								__lowerCamelCase		       =    new_src + ' ' + src
								__lowerCamelCase		       =    new_tgt + ' ' + tgt
								if is_too_big(UpperCamelCase__      ) or is_too_big(UpperCamelCase__      ):  # cant fit, finalize example
												finished_src.append(UpperCamelCase__      )
												finished_tgt.append(UpperCamelCase__      )
												__lowerCamelCase       ,						__lowerCamelCase		       =    src, tgt
								else:  # can fit, keep adding
												__lowerCamelCase       ,						__lowerCamelCase		       =    cand_src, cand_tgt

    # cleanup
				if new_src:
								assert new_tgt
								finished_src.append(UpperCamelCase__      )
								finished_tgt.append(UpperCamelCase__      )
				return finished_src, finished_tgt







def 			lowerCamelCase_		(       UpperCamelCase__				:      str ,     UpperCamelCase__				:      Path ,     UpperCamelCase__				:      Optional[int] ,     UpperCamelCase__				:      str      )     ->       Optional[int]:





				"""simple docstring"""


				__lowerCamelCase		       =    Path(UpperCamelCase__      )
				save_path.mkdir(exist_ok=UpperCamelCase__      )
				for split in ["train"]:
								__lowerCamelCase       ,						__lowerCamelCase		       =    data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
								__lowerCamelCase		       =    [x.rstrip() for x in Path(UpperCamelCase__      ).open().readlines()]
								__lowerCamelCase		       =    [x.rstrip() for x in Path(UpperCamelCase__      ).open().readlines()]
								__lowerCamelCase       ,						__lowerCamelCase		       =    pack_examples(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )
								print(F"""packed {split} split from {len(UpperCamelCase__      )} examples -> {len(UpperCamelCase__      )}."""      )
								Path(save_path / F"""{split}.source"""      ).open('w'      ).write('\n'.join(UpperCamelCase__      )      )
								Path(save_path / F"""{split}.target"""      ).open('w'      ).write('\n'.join(UpperCamelCase__      )      )
				for split in ["val", "test"]:
								__lowerCamelCase       ,						__lowerCamelCase		       =    data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
								shutil.copyfile(UpperCamelCase__ ,     save_path / F"""{split}.source"""      )
								shutil.copyfile(UpperCamelCase__ ,     save_path / F"""{split}.target"""      )







def 			lowerCamelCase_		(       )     ->       List[str]:





				"""simple docstring"""


				__lowerCamelCase		       =    argparse.ArgumentParser()
				parser.add_argument('--tok_name' ,     type=UpperCamelCase__ ,     help='like facebook/bart-large-cnn,t5-base, etc.'      )
				parser.add_argument('--max_seq_len' ,     type=UpperCamelCase__ ,     default=128      )
				parser.add_argument('--data_dir' ,     type=UpperCamelCase__      )
				parser.add_argument('--save_path' ,     type=UpperCamelCase__      )
				__lowerCamelCase		       =    parser.parse_args()
				__lowerCamelCase		       =    AutoTokenizer.from_pretrained(args.tok_name      )
				return pack_data_dir(UpperCamelCase__ ,     Path(args.data_dir      ) ,     args.max_seq_len ,     args.save_path      )


if __name__ == "__main__":
					packer_cli()


