






"""simple docstring"""



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						:    Optional[int]		=						False
_UpperCamelCase						:    List[str]		=						Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
_UpperCamelCase						:    Any		=						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, 2_0_4_8), dtype='float32'),
            'sizes': datasets.Sequence(length=2, feature=datasets.Value('float32')),
            'preds_per_image': datasets.Value(dtype='int32'),
        }
    )
)

class 			a	:







    def __init__(     self				,						_lowerCamelCase=sys.argv[1:]			):
      lowercase				       =			None
      lowercase				       =			None
      lowercase				       =			None
      lowercase				       =			1
      lowercase       ,	lowercase				       =			getopt.getopt(_lowerCamelCase				,						'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(_lowerCamelCase			)
        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(_lowerCamelCase			), F'{outputfile}'
      if subset_list is not None:
        with open(os.path.realpath(_lowerCamelCase			)			) as f:
          lowercase				       =			{self._vqa_file_split()[0] for x in tryload(_lowerCamelCase			)}
      else:
        lowercase				       =			None

      lowercase				       =			CONFIG
      if torch.cuda.is_available():
        lowercase				       =			'cuda'
      lowercase				       =			os.path.realpath(_lowerCamelCase			)
      lowercase				       =			os.path.realpath(_lowerCamelCase			)
      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      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      lowercase				       =			int(file.split('.'			)[0].split('_'			)[-1]			)
      lowercase				       =			os.path.join(self.inputdir				,						_lowerCamelCase			)
      return (img_id, filepath)







    @property
    def      UpperCamelCase_ (     self			):
      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(_lowerCamelCase			)			)
        if len(_lowerCamelCase			) == self.batch:
          lowercase				       =			batch
          lowercase				       =			[]
          yield list(map(_lowerCamelCase				,						zip(*_lowerCamelCase			)			)			)

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







    def __call__(     self			):
      # make writer
      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(_lowerCamelCase			)
        lowercase				       =			self.model(
            _lowerCamelCase				,						_lowerCamelCase				,						scales_yx=_lowerCamelCase				,						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(_lowerCamelCase			)
          lowercase				       =			self.schema.encode_batch(_lowerCamelCase			)
          writer.write_batch(_lowerCamelCase			)
        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 >> 2_0} mb'			)


def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    str       ):





  '''simple docstring'''





  try:
    lowercase				       =			json.load(__snake_case       )
    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						:    int		=						Extract(sys.argv[1:])
    extract()
    if not TEST:
        _UpperCamelCase						:    Union[str, Any]		=						datasets.Dataset.from_file(extract.outputfile)
        # wala!
        # print(np.array(dataset[0:2]["roi_features"]).shape)










"""simple docstring"""



import inspect
import unittest

import requests

from transformers import MgpstrConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import MgpstrForSceneTextRecognition


if is_vision_available():
    from PIL import Image

    from transformers import MgpstrProcessor

class 			a	:







    def __init__(     self				,						_lowerCamelCase				,						_lowerCamelCase=False				,						_lowerCamelCase=1_3				,						_lowerCamelCase=(3_2, 1_2_8)				,						_lowerCamelCase=4				,						_lowerCamelCase=3				,						_lowerCamelCase=2_7				,						_lowerCamelCase=3_8				,						_lowerCamelCase=9_9				,						_lowerCamelCase=9_9				,						_lowerCamelCase=3_2				,						_lowerCamelCase=5				,						_lowerCamelCase=4				,						_lowerCamelCase=4.0				,						_lowerCamelCase=2_5_7				,						_lowerCamelCase=None				,						):
      lowercase				       =			parent
      lowercase				       =			is_training
      lowercase				       =			batch_size
      lowercase				       =			image_size
      lowercase				       =			patch_size
      lowercase				       =			num_channels
      lowercase				       =			max_token_length
      lowercase				       =			num_character_labels
      lowercase				       =			num_bpe_labels
      lowercase				       =			num_wordpiece_labels
      lowercase				       =			hidden_size
      lowercase				       =			num_hidden_layers
      lowercase				       =			num_attention_heads
      lowercase				       =			mlp_ratio
      lowercase				       =			patch_embeds_hidden_size
      lowercase				       =			output_hidden_states







    def      UpperCamelCase_ (     self			):
      lowercase				       =			floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]			)
      lowercase				       =			self.get_config()
      return config, pixel_values







    def      UpperCamelCase_ (     self			):
      return MgpstrConfig(
          image_size=self.image_size				,						patch_size=self.patch_size				,						num_channels=self.num_channels				,						max_token_length=self.max_token_length				,						num_character_labels=self.num_character_labels				,						num_bpe_labels=self.num_bpe_labels				,						num_wordpiece_labels=self.num_wordpiece_labels				,						hidden_size=self.hidden_size				,						num_hidden_layers=self.num_hidden_layers				,						num_attention_heads=self.num_attention_heads				,						mlp_ratio=self.mlp_ratio				,						output_hidden_states=self.output_hidden_states				,						)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase			):
      lowercase				       =			MgpstrForSceneTextRecognition(_lowerCamelCase			)
      model.to(_lowerCamelCase			)
      model.eval()
      with torch.no_grad():
        lowercase				       =			model(_lowerCamelCase			)
      self.parent.assertEqual(
          generated_ids[0][0].shape				,						(self.batch_size, self.max_token_length, self.num_character_labels)			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.prepare_config_and_inputs()
      lowercase       ,	lowercase				       =			config_and_inputs
      lowercase				       =			{'pixel_values': pixel_values}
      return config, inputs_dict


@require_torch
class 			a	(  a_,							a_,							unittest.TestCase      ):
    UpperCAmelCase_					:	Optional[int]      	=(MgpstrForSceneTextRecognition,) if is_torch_available() else ()
    UpperCAmelCase_					:	Optional[Any]      	={"feature-extraction": MgpstrForSceneTextRecognition} if is_torch_available() else {}
    UpperCAmelCase_					:	Union[str, Any]      	=False

    UpperCAmelCase_					:	Optional[Any]      	=False
    UpperCAmelCase_					:	Dict      	=False
    UpperCAmelCase_					:	str      	=False
    UpperCAmelCase_					:	str      	=False







    def      UpperCamelCase_ (     self			):
      lowercase				       =			MgpstrModelTester(self			)
      lowercase				       =			ConfigTester(self				,						config_class=_lowerCamelCase				,						has_text_modality=_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      self.config_tester.run_common_tests()







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_lowerCamelCase			)







    @unittest.skip(reason='MgpstrModel does not use inputs_embeds'			)
    def      UpperCamelCase_ (     self			):
      pass







    def      UpperCamelCase_ (     self			):
      lowercase       ,	lowercase				       =			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowercase				       =			model_class(_lowerCamelCase			)
        self.assertIsInstance(model.get_input_embeddings()				,						(nn.Module)			)
        lowercase				       =			model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(_lowerCamelCase				,						nn.Linear			)			)







    def      UpperCamelCase_ (     self			):
      lowercase       ,	lowercase				       =			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowercase				       =			model_class(_lowerCamelCase			)
        lowercase				       =			inspect.signature(model.forward			)
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        lowercase				       =			[*signature.parameters.keys()]

        lowercase				       =			['pixel_values']
        self.assertListEqual(arg_names[:1]				,						_lowerCamelCase			)







    @unittest.skip(reason='MgpstrModel does not support feedforward chunking'			)
    def      UpperCamelCase_ (     self			):
      pass







    def      UpperCamelCase_ (     self			):
      lowercase       ,	lowercase				       =			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        if not model_class.supports_gradient_checkpointing:
          continue

        lowercase				       =			True
        lowercase				       =			model_class(_lowerCamelCase			)
        self.assertTrue(model.is_gradient_checkpointing			)







    def      UpperCamelCase_ (     self			):
      def check_hidden_states_output(_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase			):
        lowercase				       =			model_class(_lowerCamelCase			)
        model.to(_lowerCamelCase			)
        model.eval()

        with torch.no_grad():
          lowercase				       =			model(**self._prepare_for_class(_lowerCamelCase				,						_lowerCamelCase			)			)

        lowercase				       =			outputs.hidden_states

        lowercase				       =			getattr(
            self.model_tester				,						'expected_num_hidden_layers'				,						self.model_tester.num_hidden_layers + 1			)
        self.assertEqual(len(_lowerCamelCase			)				,						_lowerCamelCase			)

        self.assertListEqual(
            list(hidden_states[0].shape[-2:]			)				,						[self.model_tester.patch_embeds_hidden_size, self.model_tester.hidden_size]				,						)

      lowercase       ,	lowercase				       =			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowercase				       =			True
        check_hidden_states_output(_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase			)

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        lowercase				       =			True

        check_hidden_states_output(_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase       ,	lowercase				       =			self.model_tester.prepare_config_and_inputs_for_common()

      lowercase				       =			_config_zero_init(_lowerCamelCase			)
      for model_class in self.all_model_classes:
        lowercase				       =			model_class(config=_lowerCamelCase			)
        for name, param in model.named_parameters():
          if isinstance(_lowerCamelCase				,						(nn.Linear, nn.Convad, nn.LayerNorm)			):
            if param.requires_grad:
              self.assertIn(
                  ((param.data.mean() * 1e9).round() / 1e9).item()				,						[0.0, 1.0]				,						msg=F'Parameter {name} of model {model_class} seems not properly initialized'				,						)







    @unittest.skip(reason='Retain_grad is tested in individual model tests'			)
    def      UpperCamelCase_ (     self			):
      pass


def 		_SCREAMING_SNAKE_CASE					(       ):





  '''simple docstring'''





  lowercase				       =			'https://i.postimg.cc/ZKwLg2Gw/367-14.png'
  lowercase				       =			Image.open(requests.get(__snake_case						,   stream=__snake_case       ).raw       ).convert('RGB'       )
  return im



@require_vision
@require_torch
class 			a	(  unittest.TestCase      ):







    @slow
    def      UpperCamelCase_ (     self			):
      lowercase				       =			'alibaba-damo/mgp-str-base'
      lowercase				       =			MgpstrForSceneTextRecognition.from_pretrained(_lowerCamelCase			).to(_lowerCamelCase			)
      lowercase				       =			MgpstrProcessor.from_pretrained(_lowerCamelCase			)

      lowercase				       =			prepare_img()
      lowercase				       =			processor(images=_lowerCamelCase				,						return_tensors='pt'			).pixel_values.to(_lowerCamelCase			)

      # forward pass
      with torch.no_grad():
        lowercase				       =			model(_lowerCamelCase			)

      # verify the logits
      self.assertEqual(outputs.logits[0].shape				,						torch.Size((1, 2_7, 3_8)			)			)

      lowercase				       =			processor.batch_decode(outputs.logits			)
      lowercase				       =			'ticket'

      self.assertEqual(out_strs['generated_text'][0]				,						_lowerCamelCase			)

      lowercase				       =			torch.tensor(
          [[[-3_9.5_3_9_7, -4_4.4_0_2_4, -3_6.1_8_4_4], [-6_1.4_7_0_9, -6_3.8_6_3_9, -5_8.3_4_5_4], [-7_4.0_2_2_5, -6_8.5_4_9_4, -7_1.2_1_6_4]]]				,						device=_lowerCamelCase				,						)

      self.assertTrue(torch.allclose(outputs.logits[0][:, 1:4, 1:4]				,						_lowerCamelCase				,						atol=1e-4			)			)


