


import unittest

import requests

from transformers import MODEL_FOR_VISION_2_SEQ_MAPPING, TF_MODEL_FOR_VISION_2_SEQ_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
		from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
		lowerCamelCase		=	False


if is_vision_available():
		from PIL import Image
else:





		class       _a							:




			@staticmethod
			def 							UpperCAmelCase__(      *_SCREAMING_SNAKE_CASE		:	Any	,      **_SCREAMING_SNAKE_CASE		:	int	)-> Optional[int]:
					pass





@is_pipeline_test
@require_vision
class       _a							(       unittest.TestCase):
	_a						:						List[Any]   					=							MODEL_FOR_VISION_2_SEQ_MAPPING
	_a						:						str   					=							TF_MODEL_FOR_VISION_2_SEQ_MAPPING




	def 							UpperCAmelCase__(      self		:	Union[str, Any]	,      _SCREAMING_SNAKE_CASE		:	List[Any]	,      _SCREAMING_SNAKE_CASE		:	Union[str, Any]	,      _SCREAMING_SNAKE_CASE		:	str	)-> List[Any]:
			lowerCAmelCase__     :    Optional[Any]					  =      pipeline('''image-to-text'''	,      model=_SCREAMING_SNAKE_CASE	,      tokenizer=_SCREAMING_SNAKE_CASE	,      image_processor=_SCREAMING_SNAKE_CASE	)
			lowerCAmelCase__     :    str					  =      [
			    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	),
			    '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
			]
			return pipe, examples




	def 							UpperCAmelCase__(      self		:	Union[str, Any]	,      _SCREAMING_SNAKE_CASE		:	Tuple	,      _SCREAMING_SNAKE_CASE		:	Union[str, Any]	)-> Tuple:
			lowerCAmelCase__     :    int					  =      pipe(_SCREAMING_SNAKE_CASE	)
			self.assertEqual(
			    _SCREAMING_SNAKE_CASE	,      [
			        [{'''generated_text''': ANY(_SCREAMING_SNAKE_CASE	)}],
			        [{'''generated_text''': ANY(_SCREAMING_SNAKE_CASE	)}],
			    ]	,      )




	@require_tf
	def 							UpperCAmelCase__(      self		:	Optional[int]	)-> int:
			lowerCAmelCase__     :    Any					  =      pipeline('''image-to-text'''	,      model='''hf-internal-testing/tiny-random-vit-gpt2'''	,      framework='''tf'''	)
			lowerCAmelCase__     :    int					  =      '''./tests/fixtures/tests_samples/COCO/000000039769.png'''

			lowerCAmelCase__     :    Dict					  =      pipe(_SCREAMING_SNAKE_CASE	)
			self.assertEqual(
			    _SCREAMING_SNAKE_CASE	,      [
			        {
			            '''generated_text''': '''growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'''
			        },
			    ]	,      )

			lowerCAmelCase__     :    Union[str, Any]					  =      pipe([image, image]	)
			self.assertEqual(
			    _SCREAMING_SNAKE_CASE	,      [
			        [
			            {
			                '''generated_text''': '''growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'''
			            }
			        ],
			        [
			            {
			                '''generated_text''': '''growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'''
			            }
			        ],
			    ]	,      )

			lowerCAmelCase__     :    int					  =      pipe(_SCREAMING_SNAKE_CASE	,      max_new_tokens=1	)
			self.assertEqual(
			    _SCREAMING_SNAKE_CASE	,      [{'''generated_text''': '''growth'''}]	,      )




	@require_torch
	def 							UpperCAmelCase__(      self		:	Tuple	)-> Tuple:
			lowerCAmelCase__     :    str					  =      pipeline('''image-to-text'''	,      model='''hf-internal-testing/tiny-random-vit-gpt2'''	)
			lowerCAmelCase__     :    List[Any]					  =      '''./tests/fixtures/tests_samples/COCO/000000039769.png'''

			lowerCAmelCase__     :    List[str]					  =      pipe(_SCREAMING_SNAKE_CASE	)
			self.assertEqual(
			    _SCREAMING_SNAKE_CASE	,      [
			        {
			            '''generated_text''': '''growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'''
			        },
			    ]	,      )

			lowerCAmelCase__     :    List[Any]					  =      pipe([image, image]	)
			self.assertEqual(
			    _SCREAMING_SNAKE_CASE	,      [
			        [
			            {
			                '''generated_text''': '''growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'''
			            }
			        ],
			        [
			            {
			                '''generated_text''': '''growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'''
			            }
			        ],
			    ]	,      )




	@require_torch
	def 							UpperCAmelCase__(      self		:	Union[str, Any]	)-> Tuple:
			lowerCAmelCase__     :    str					  =      pipeline('''image-to-text'''	,      model='''hf-internal-testing/tiny-random-BlipForConditionalGeneration'''	)
			lowerCAmelCase__     :    List[str]					  =      '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
			lowerCAmelCase__     :    Tuple					  =      '''a photo of'''

			lowerCAmelCase__     :    List[str]					  =      pipe(_SCREAMING_SNAKE_CASE	,      prompt=_SCREAMING_SNAKE_CASE	)
			self.assertTrue(outputs[0]['''generated_text'''].startswith(_SCREAMING_SNAKE_CASE	)	)




	@slow
	@require_torch
	def 							UpperCAmelCase__(      self		:	Tuple	)-> Any:
			lowerCAmelCase__     :    int					  =      pipeline('''image-to-text'''	,      model='''ydshieh/vit-gpt2-coco-en'''	)
			lowerCAmelCase__     :    Optional[Any]					  =      '''./tests/fixtures/tests_samples/COCO/000000039769.png'''

			lowerCAmelCase__     :    List[Any]					  =      pipe(_SCREAMING_SNAKE_CASE	)
			self.assertEqual(_SCREAMING_SNAKE_CASE	,      [{'''generated_text''': '''a cat laying on a blanket next to a cat laying on a bed '''}]	)

			lowerCAmelCase__     :    Optional[Any]					  =      pipe([image, image]	)
			self.assertEqual(
			    _SCREAMING_SNAKE_CASE	,      [
			        [{'''generated_text''': '''a cat laying on a blanket next to a cat laying on a bed '''}],
			        [{'''generated_text''': '''a cat laying on a blanket next to a cat laying on a bed '''}],
			    ]	,      )




	@slow
	@require_torch
	def 							UpperCAmelCase__(      self		:	Union[str, Any]	)-> int:
			lowerCAmelCase__     :    Dict					  =      pipeline('''image-to-text'''	,      model='''Salesforce/blip-image-captioning-base'''	)
			lowerCAmelCase__     :    Tuple					  =      '''https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'''
			lowerCAmelCase__     :    Union[str, Any]					  =      Image.open(requests.get(_SCREAMING_SNAKE_CASE	,      stream=_SCREAMING_SNAKE_CASE	).raw	)

			lowerCAmelCase__     :    Optional[int]					  =      pipe(_SCREAMING_SNAKE_CASE	)
			self.assertEqual(_SCREAMING_SNAKE_CASE	,      [{'''generated_text''': '''a pink pokemon pokemon with a blue shirt and a blue shirt'''}]	)




	@slow
	@require_torch
	def 							UpperCAmelCase__(      self		:	Optional[int]	)-> int:
			lowerCAmelCase__     :    Dict					  =      pipeline('''image-to-text'''	,      model='''microsoft/git-base-coco'''	)
			lowerCAmelCase__     :    Optional[Any]					  =      '''https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'''
			lowerCAmelCase__     :    Union[str, Any]					  =      Image.open(requests.get(_SCREAMING_SNAKE_CASE	,      stream=_SCREAMING_SNAKE_CASE	).raw	)

			lowerCAmelCase__     :    Optional[int]					  =      pipe(_SCREAMING_SNAKE_CASE	)
			self.assertEqual(_SCREAMING_SNAKE_CASE	,      [{'''generated_text''': '''a cartoon of a purple character.'''}]	)




	@slow
	@require_torch
	def 							UpperCAmelCase__(      self		:	int	)-> Union[str, Any]:
			lowerCAmelCase__     :    Any					  =      pipeline('''image-to-text'''	,      model='''Salesforce/blip-image-captioning-base'''	)
			lowerCAmelCase__     :    int					  =      '''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'''
			lowerCAmelCase__     :    Optional[int]					  =      Image.open(requests.get(_SCREAMING_SNAKE_CASE	,      stream=_SCREAMING_SNAKE_CASE	).raw	)

			lowerCAmelCase__     :    List[Any]					  =      '''a photography of'''

			lowerCAmelCase__     :    List[str]					  =      pipe(_SCREAMING_SNAKE_CASE	,      prompt=_SCREAMING_SNAKE_CASE	)
			self.assertEqual(_SCREAMING_SNAKE_CASE	,      [{'''generated_text''': '''a photography of a volcano'''}]	)

			with self.assertRaises(_SCREAMING_SNAKE_CASE	):
					lowerCAmelCase__     :    Optional[int]					  =      pipe([image, image]	,      prompt=[prompt, prompt]	)




	@slow
	@require_torch
	def 							UpperCAmelCase__(      self		:	Optional[Any]	)-> List[str]:
			lowerCAmelCase__     :    Optional[Any]					  =      pipeline('''image-to-text'''	,      model='''microsoft/git-base-coco'''	)
			lowerCAmelCase__     :    Union[str, Any]					  =      '''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'''
			lowerCAmelCase__     :    List[Any]					  =      Image.open(requests.get(_SCREAMING_SNAKE_CASE	,      stream=_SCREAMING_SNAKE_CASE	).raw	)

			lowerCAmelCase__     :    int					  =      '''a photo of a'''

			lowerCAmelCase__     :    Optional[int]					  =      pipe(_SCREAMING_SNAKE_CASE	,      prompt=_SCREAMING_SNAKE_CASE	)
			self.assertEqual(_SCREAMING_SNAKE_CASE	,      [{'''generated_text''': '''a photo of a tent with a tent and a tent in the background.'''}]	)

			with self.assertRaises(_SCREAMING_SNAKE_CASE	):
					lowerCAmelCase__     :    List[Any]					  =      pipe([image, image]	,      prompt=[prompt, prompt]	)




	@unittest.skipIf(
	    not is_torch_greater_or_equal_than_1_11	,      reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''	)
	@slow
	@require_torch
	def 							UpperCAmelCase__(      self		:	Optional[int]	)-> Union[str, Any]:
			lowerCAmelCase__     :    Optional[Any]					  =      pipeline('''image-to-text'''	,      model='''google/pix2struct-ai2d-base'''	)
			lowerCAmelCase__     :    Any					  =      '''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'''
			lowerCAmelCase__     :    Optional[Any]					  =      Image.open(requests.get(_SCREAMING_SNAKE_CASE	,      stream=_SCREAMING_SNAKE_CASE	).raw	)

			lowerCAmelCase__     :    Dict					  =      '''What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud'''

			lowerCAmelCase__     :    Tuple					  =      pipe(_SCREAMING_SNAKE_CASE	,      prompt=_SCREAMING_SNAKE_CASE	)
			self.assertEqual(_SCREAMING_SNAKE_CASE	,      [{'''generated_text''': '''ash cloud'''}]	)

			with self.assertRaises(_SCREAMING_SNAKE_CASE	):
					lowerCAmelCase__     :    str					  =      pipe([image, image]	,      prompt=[prompt, prompt]	)




	@slow
	@require_tf
	def 							UpperCAmelCase__(      self		:	Dict	)-> List[str]:
			lowerCAmelCase__     :    Dict					  =      pipeline('''image-to-text'''	,      model='''ydshieh/vit-gpt2-coco-en'''	,      framework='''tf'''	)
			lowerCAmelCase__     :    Dict					  =      '''./tests/fixtures/tests_samples/COCO/000000039769.png'''

			lowerCAmelCase__     :    List[str]					  =      pipe(_SCREAMING_SNAKE_CASE	)
			self.assertEqual(_SCREAMING_SNAKE_CASE	,      [{'''generated_text''': '''a cat laying on a blanket next to a cat laying on a bed '''}]	)

			lowerCAmelCase__     :    Optional[int]					  =      pipe([image, image]	)
			self.assertEqual(
			    _SCREAMING_SNAKE_CASE	,      [
			        [{'''generated_text''': '''a cat laying on a blanket next to a cat laying on a bed '''}],
			        [{'''generated_text''': '''a cat laying on a blanket next to a cat laying on a bed '''}],
			    ]	,      )








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
lowerCamelCase		=	False
lowerCamelCase		=	Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
lowerCamelCase		=	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, 2048), dtype='''float32'''),
            '''sizes''': datasets.Sequence(length=2, feature=datasets.Value('''float32''')),
            '''preds_per_image''': datasets.Value(dtype='''int32'''),
        }
    )
)





class       _a							:




	def __init__(      self		:	str	,      _SCREAMING_SNAKE_CASE		:	Dict=sys.argv[1:]	)-> str:
			lowerCAmelCase__     :    int					  =      None
			lowerCAmelCase__     :    Optional[int]					  =      None
			lowerCAmelCase__     :    Union[str, Any]					  =      None
			lowerCAmelCase__     :    Union[str, Any]					  =      1
			lowerCAmelCase__  ,			lowerCAmelCase__     :    List[str]					  =      getopt.getopt(_SCREAMING_SNAKE_CASE	,      '''i:o:b:s'''	,      ['''inputdir=''', '''outfile=''', '''batch_size=''', '''subset_list=''']	)
			for opt, arg in opts:
					if opt in ("-i", "--inputdir"):
							lowerCAmelCase__     :    List[Any]					  =      arg
					elif opt in ("-o", "--outfile"):
							lowerCAmelCase__     :    Optional[Any]					  =      arg
					elif opt in ("-b", "--batch_size"):
							lowerCAmelCase__     :    Tuple					  =      int(_SCREAMING_SNAKE_CASE	)
					elif opt in ("-s", "--subset_list"):
							lowerCAmelCase__     :    str					  =      arg

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

			lowerCAmelCase__     :    List[Any]					  =      CONFIG
			if torch.cuda.is_available():
					lowerCAmelCase__     :    List[Any]					  =      '''cuda'''
			lowerCAmelCase__     :    Optional[int]					  =      os.path.realpath(_SCREAMING_SNAKE_CASE	)
			lowerCAmelCase__     :    str					  =      os.path.realpath(_SCREAMING_SNAKE_CASE	)
			lowerCAmelCase__     :    str					  =      Preprocess(self.config	)
			lowerCAmelCase__     :    str					  =      GeneralizedRCNN.from_pretrained('''unc-nlp/frcnn-vg-finetuned'''	,      config=self.config	)
			lowerCAmelCase__     :    Tuple					  =      batch_size if batch_size != 0 else 1
			lowerCAmelCase__     :    Dict					  =      DEFAULT_SCHEMA




	def 							UpperCAmelCase__(      self		:	List[str]	,      _SCREAMING_SNAKE_CASE		:	str	)-> Optional[Any]:
			lowerCAmelCase__     :    Optional[Any]					  =      int(file.split('''.'''	)[0].split('''_'''	)[-1]	)
			lowerCAmelCase__     :    List[Any]					  =      os.path.join(self.inputdir	,      _SCREAMING_SNAKE_CASE	)
			return (img_id, filepath)




	@property
	def 							UpperCAmelCase__(      self		:	Dict	)-> Tuple:
			lowerCAmelCase__     :    Any					  =      []
			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(_SCREAMING_SNAKE_CASE	)	)
					if len(_SCREAMING_SNAKE_CASE	) == self.batch:
							lowerCAmelCase__     :    Optional[int]					  =      batch
							lowerCAmelCase__     :    Union[str, Any]					  =      []
							yield list(map(_SCREAMING_SNAKE_CASE	,      zip(*_SCREAMING_SNAKE_CASE	)	)	)

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




	def __call__(      self		:	int	)-> Dict:
			# make writer
			if not TEST:
					lowerCAmelCase__     :    Union[str, Any]					  =      datasets.ArrowWriter(features=self.schema	,      path=self.outputfile	)
			# do file generator
			for i, (img_ids, filepaths) in enumerate(self.file_generator	):
					lowerCAmelCase__  ,			lowerCAmelCase__  ,			lowerCAmelCase__     :    Optional[Any]					  =      self.preprocess(_SCREAMING_SNAKE_CASE	)
					lowerCAmelCase__     :    List[Any]					  =      self.model(
					    _SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	,      scales_yx=_SCREAMING_SNAKE_CASE	,      padding='''max_detections'''	,      max_detections=self.config.MAX_DETECTIONS	,      pad_value=0	,      return_tensors='''np'''	,      location='''cpu'''	,      )
					lowerCAmelCase__     :    Any					  =      output_dict.pop('''normalized_boxes'''	)
					if not TEST:
							lowerCAmelCase__     :    Optional[int]					  =      np.array(_SCREAMING_SNAKE_CASE	)
							lowerCAmelCase__     :    str					  =      self.schema.encode_batch(_SCREAMING_SNAKE_CASE	)
							writer.write_batch(_SCREAMING_SNAKE_CASE	)
					if TEST:
							break
					# finalizer the writer
			if not TEST:
					lowerCAmelCase__  ,			lowerCAmelCase__     :    Optional[int]					  =      writer.finalize()
					print(F'Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'	)






def   lowerCamelCase_					( _a ):




		"""simple docstring"""






		try:
				lowerCAmelCase__     :    Any					  =      json.load(_a )
				try:
						lowerCAmelCase__     :    Tuple					  =      list(data.keys() )
				except Exception:
						lowerCAmelCase__     :    List[str]					  =      [d['''img_id'''] for d in data]
		except Exception:
				try:
						lowerCAmelCase__     :    int					  =      eval(stream.read() )
				except Exception:
						lowerCAmelCase__     :    Tuple					  =      stream.read().split('''\n''' )
		return data


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




