





'''simple docstring'''



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

_SCREAMING_SNAKE_CASE					=    logging.get_logger(__name__)






@add_end_docstrings(lowerCAmelCase__						)
class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	def __init__(self     ,**_lowerCamelCase )							->							Tuple:







				'''simple docstring'''





				super().__init__(**UpperCamelCase__ )

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

				requires_backends(self     ,'''vision''' )
				self.check_model_type(UpperCamelCase__ )


	def __call__(self     ,_lowerCamelCase     ,_lowerCamelCase = None     ,**_lowerCamelCase     ,)							->							Optional[int]:







				'''simple docstring'''





				if "text_queries" in kwargs:
							__lowercase										=							kwargs.pop('''text_queries''' )

				if isinstance(UpperCamelCase__     ,(str, Image.Image) ):
							__lowercase										=							{'''image''': image, '''candidate_labels''': candidate_labels}
				else:
							__lowercase										=							image
				__lowercase										=							super().__call__(UpperCamelCase__     ,**UpperCamelCase__ )
				return results


	def 						_UpperCAmelCase     (self     ,**_lowerCamelCase )							->							Optional[Any]:







				'''simple docstring'''





				__lowercase										=							{}
				if "threshold" in kwargs:
							__lowercase										=							kwargs['''threshold''']
				if "top_k" in kwargs:
							__lowercase										=							kwargs['''top_k''']
				return {}, {}, postprocess_params


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							Optional[int]:







				'''simple docstring'''





				__lowercase										=							load_image(inputs['''image'''] )
				__lowercase										=							inputs['''candidate_labels''']
				if isinstance(UpperCamelCase__     ,UpperCamelCase__ ):
							__lowercase										=							candidate_labels.split(''',''' )

				__lowercase										=							torch.tensor([[image.height, image.width]]     ,dtype=torch.intaa )
				for i, candidate_label in enumerate(UpperCamelCase__ ):
							__lowercase										=							self.tokenizer(UpperCamelCase__     ,return_tensors=self.framework )
							__lowercase										=							self.image_processor(UpperCamelCase__     ,return_tensors=self.framework )
							yield {
							    "is_last": i == len(UpperCamelCase__ ) - 1,
							    "target_size": target_size,
							    "candidate_label": candidate_label,
							    **text_inputs,
							    **image_features,
							}


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							List[Any]:







				'''simple docstring'''





				__lowercase										=							model_inputs.pop('''target_size''' )
				__lowercase										=							model_inputs.pop('''candidate_label''' )
				__lowercase										=							model_inputs.pop('''is_last''' )

				__lowercase										=							self.model(**UpperCamelCase__ )

				__lowercase										=							{'''target_size''': target_size, '''candidate_label''': candidate_label, '''is_last''': is_last, **outputs}
				return model_outputs


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase=0.1     ,_lowerCamelCase=None )							->							Dict:







				'''simple docstring'''





				__lowercase										=							[]
				for model_output in model_outputs:
							__lowercase										=							model_output['''candidate_label''']
							__lowercase										=							BaseModelOutput(UpperCamelCase__ )
							__lowercase										=							self.image_processor.post_process_object_detection(
							    outputs=UpperCamelCase__     ,threshold=UpperCamelCase__     ,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(UpperCamelCase__ )

				__lowercase										=							sorted(UpperCamelCase__     ,key=lambda _lowerCamelCase : x["score"]     ,reverse=UpperCamelCase__ )
				if top_k:
							__lowercase										=							results[:top_k]

				return results


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							Dict[str, int]:







				'''simple docstring'''





				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












'''simple docstring'''



import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


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




def    _lowerCAmelCase						(				lowerCamelCase_			:  int				):
			__lowercase										=							torch.load(lowerCamelCase_		,       map_location='''cpu'''				)
			if "model" in sd.keys():
						__lowercase										=							torch.load(lowerCamelCase_		,       map_location='''cpu'''				)['''model''']

			# pop unnecessary weights
			__lowercase										=							[
			    '''decoder.version''',
			    '''decoder.output_projection.weight''',
			]
			for key in keys_to_delete:
						if key in sd:
									sd.pop(lowerCamelCase_				)

			__lowercase										=							{
			    '''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
			    '''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
			    '''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
			    '''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
			}
			for old_key, new_key in keys_to_rename.items():
						if old_key in sd:
									__lowercase										=							sd.pop(lowerCamelCase_				)

			__lowercase										=							list(sd.keys()				)
			for key in keys:
						if ".qkv_proj." in key:
									__lowercase										=							sd[key]
									# We split QKV in separate Q,K,V

									__lowercase										=							key.replace('''.qkv_proj.'''		,       '''.q_proj.'''				)
									__lowercase										=							key.replace('''.qkv_proj.'''		,       '''.k_proj.'''				)
									__lowercase										=							key.replace('''.qkv_proj.'''		,       '''.v_proj.'''				)

									__lowercase										=							value.shape[0]
									assert depth % 3 == 0
									# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
									# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
									__lowercase						,		__lowercase						,		__lowercase										=							torch.split(lowerCamelCase_		,       depth // 3		,       dim=0				)

									__lowercase										=							q
									__lowercase										=							k
									__lowercase										=							v
									del sd[key]

			return sd




@torch.no_grad()
def    _lowerCAmelCase						(				lowerCamelCase_			:  Union[str, Any]		,       lowerCamelCase_			:  Tuple		,       lowerCamelCase_			:  Union[str, Any]=None				):
			__lowercase										=							load_checkpoint(lowerCamelCase_				)

			if config is not None:
						__lowercase										=							OPTConfig.from_pretrained(lowerCamelCase_				)
			else:
						__lowercase										=							OPTConfig()

			__lowercase										=							OPTModel(lowerCamelCase_				).half().eval()
			model.load_state_dict(lowerCamelCase_				)

			# Check results
			Path(lowerCamelCase_				).mkdir(exist_ok=lowerCamelCase_				)
			model.save_pretrained(lowerCamelCase_				)


if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE					=    argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--fairseq_path''',
	    type=str,
	    help=(
	        '''path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'''
	        ''' https://huggingface.co/models?other=opt_metasq'''
	    ),
	)
	parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
	parser.add_argument('''--hf_config''', default=None, type=str, help='''Define HF config.''')
	_SCREAMING_SNAKE_CASE					=    parser.parse_args()
	convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)





