



import getopt
import json
import os

# import numpy as np
import sys
from collections import OrderedDict

import datasets
import numpy as np
import torch
from modeling_frcnn import GeneralizedRCNN
from processing_image import Preprocess

from utils import Config
UpperCamelCase__			=    False
UpperCamelCase__			=    Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
UpperCamelCase__			=    datasets.Features(
    OrderedDict(
        {
            "attr_ids": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
            "attr_probs": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
            "boxes": datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 4), dtype="float32"),
            "img_id": datasets.Value("int32"),
            "obj_ids": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
            "obj_probs": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
            "roi_features": datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 20_48), dtype="float32"),
            "sizes": datasets.Sequence(length=2, feature=datasets.Value("float32")),
            "preds_per_image": datasets.Value(dtype="int32"),
        }
    )
)
class       _lowerCAmelCase :






			"""simple docstring"""





			def __init__(					self						:							Union[str, Any]      ,  lowercase						:							List[Any]=sys.argv[1:] )    ->       List[str]:
									"""simple docstring"""







									__lowercase											=					None
									__lowercase											=					None
									__lowercase											=					None
									__lowercase											=					1
									__lowercase						,      __lowercase											=					getopt.getopt(lowercase      ,  """i:o:b:s"""      ,  ["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""] )
									for opt, arg in opts:
															if opt in ("-i", "--inputdir"):
																					__lowercase											=					arg
															elif opt in ("-o", "--outfile"):
																					__lowercase											=					arg
															elif opt in ("-b", "--batch_size"):
																					__lowercase											=					int(lowercase )
															elif opt in ("-s", "--subset_list"):
																					__lowercase											=					arg

									assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
									assert outputfile is not None and not os.path.isfile(lowercase ), F"{outputfile}"
									if subset_list is not None:
															with open(os.path.realpath(lowercase ) ) as f:
																					__lowercase											=					{self._vqa_file_split()[0] for x in tryload(lowercase )}
									else:
															__lowercase											=					None

									__lowercase											=					CONFIG
									if torch.cuda.is_available():
															__lowercase											=					"""cuda"""
									__lowercase											=					os.path.realpath(lowercase )
									__lowercase											=					os.path.realpath(lowercase )
									__lowercase											=					Preprocess(self.config )
									__lowercase											=					GeneralizedRCNN.from_pretrained("""unc-nlp/frcnn-vg-finetuned"""      ,  config=self.config )
									__lowercase											=					batch_size if batch_size != 0 else 1
									__lowercase											=					DEFAULT_SCHEMA





			def 		snake_case__      (					self						:							Any      ,  lowercase						:							List[Any] )    ->       Dict:
									"""simple docstring"""







									__lowercase											=					int(file.split(""".""" )[0].split("""_""" )[-1] )
									__lowercase											=					os.path.join(self.inputdir      ,  lowercase )
									return (img_id, filepath)





			@property
			def 		snake_case__      (					self						:							Optional[int] )    ->       Tuple:
									"""simple docstring"""







									__lowercase											=					[]
									for i, file in enumerate(os.listdir(self.inputdir ) ):
															if self.subset_list is not None and i not in self.subset_list:
																					continue
															batch.append(self._vqa_file_split(lowercase ) )
															if len(lowercase ) == self.batch:
																					__lowercase											=					batch
																					__lowercase											=					[]
																					yield list(map(lowercase      ,  zip(*lowercase ) ) )

									for i in range(1 ):
															yield list(map(lowercase      ,  zip(*lowercase ) ) )







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







									if not TEST:
															__lowercase											=					datasets.ArrowWriter(features=self.schema      ,  path=self.outputfile )
									# do file generator
									for i, (img_ids, filepaths) in enumerate(self.file_generator ):
															__lowercase						,      __lowercase						,      __lowercase											=					self.preprocess(lowercase )
															__lowercase											=					self.model(
															    lowercase      ,  lowercase      ,  scales_yx=lowercase      ,  padding="""max_detections"""      ,  max_detections=self.config.MAX_DETECTIONS      ,  pad_value=0      ,  return_tensors="""np"""      ,  location="""cpu"""      ,  )
															__lowercase											=					output_dict.pop("""normalized_boxes""" )
															if not TEST:
																					__lowercase											=					np.array(lowercase )
																					__lowercase											=					self.schema.encode_batch(lowercase )
																					writer.write_batch(lowercase )
															if TEST:
																					break
															# finalizer the writer
									if not TEST:
															__lowercase						,      __lowercase											=					writer.finalize()
															print(F"Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb" )






def 			UpperCAmelCase__     (     lowercase__ )			->   Optional[int]:
						try:
												__lowercase											=					json.load(lowercase__ )
												try:
																		__lowercase											=					list(data.keys() )
												except Exception:
																		__lowercase											=					[d["""img_id"""] for d in data]
						except Exception:
												try:
																		__lowercase											=					eval(stream.read() )
												except Exception:
																		__lowercase											=					stream.read().split("""\n""" )
						return data


if __name__ == "__main__":
			UpperCamelCase__			=    Extract(sys.argv[1:])
			extract()
			if not TEST:
						UpperCamelCase__			=    datasets.Dataset.from_file(extract.outputfile)
						# wala!
						# print(np.array(dataset[0:2]["roi_features"]).shape)










# 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.
from typing import TYPE_CHECKING

from ..utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCamelCase__			=    {
    "agents": ["Agent", "AzureOpenAiAgent", "HfAgent", "LocalAgent", "OpenAiAgent"],
    "base": ["PipelineTool", "RemoteTool", "Tool", "launch_gradio_demo", "load_tool"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCamelCase__			=    ["DocumentQuestionAnsweringTool"]
			UpperCamelCase__			=    ["ImageCaptioningTool"]
			UpperCamelCase__			=    ["ImageQuestionAnsweringTool"]
			UpperCamelCase__			=    ["ImageSegmentationTool"]
			UpperCamelCase__			=    ["SpeechToTextTool"]
			UpperCamelCase__			=    ["TextClassificationTool"]
			UpperCamelCase__			=    ["TextQuestionAnsweringTool"]
			UpperCamelCase__			=    ["TextSummarizationTool"]
			UpperCamelCase__			=    ["TextToSpeechTool"]
			UpperCamelCase__			=    ["TranslationTool"]

if TYPE_CHECKING:
			from .agents import Agent, AzureOpenAiAgent, HfAgent, LocalAgent, OpenAiAgent
			from .base import PipelineTool, RemoteTool, Tool, launch_gradio_demo, load_tool

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .document_question_answering import DocumentQuestionAnsweringTool
						from .image_captioning import ImageCaptioningTool
						from .image_question_answering import ImageQuestionAnsweringTool
						from .image_segmentation import ImageSegmentationTool
						from .speech_to_text import SpeechToTextTool
						from .text_classification import TextClassificationTool
						from .text_question_answering import TextQuestionAnsweringTool
						from .text_summarization import TextSummarizationTool
						from .text_to_speech import TextToSpeechTool
						from .translation import TranslationTool
else:
			import sys

			UpperCamelCase__			=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





