



import collections
import inspect
import unittest

from transformers import NatConfig
from transformers.testing_utils import require_natten, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
     import torch
     from torch import nn

     from transformers import NatBackbone, NatForImageClassification, NatModel
     from transformers.models.nat.modeling_nat import NAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor



class 		_lowercase		:

   def __init__(   self      :							int				, __lowerCAmelCase      :							Optional[int]				, __lowerCAmelCase      :							List[str]=13				, __lowerCAmelCase      :							Dict=64				, __lowerCAmelCase      :							List[str]=4				, __lowerCAmelCase      :							int=3				, __lowerCAmelCase      :							Dict=16				, __lowerCAmelCase      :							Any=[1, 2, 1]				, __lowerCAmelCase      :							Optional[Any]=[2, 4, 8]				, __lowerCAmelCase      :							str=3				, __lowerCAmelCase      :							Any=2.0				, __lowerCAmelCase      :							str=True				, __lowerCAmelCase      :							Optional[Any]=0.0				, __lowerCAmelCase      :							List[Any]=0.0				, __lowerCAmelCase      :							Optional[Any]=0.1				, __lowerCAmelCase      :							Any="gelu"				, __lowerCAmelCase      :							Any=True				, __lowerCAmelCase      :							Union[str, Any]=0.0_2				, __lowerCAmelCase      :							Any=1E-5				, __lowerCAmelCase      :							Any=True				, __lowerCAmelCase      :							Optional[int]=None				, __lowerCAmelCase      :							Optional[int]=True				, __lowerCAmelCase      :							Optional[int]=10				, __lowerCAmelCase      :							List[str]=["stage1", "stage2"]				, __lowerCAmelCase      :							List[str]=[1, 2]				, )							->					Any:


    """simple docstring"""






    a							=		parent
    a							=		batch_size
    a							=		image_size
    a							=		patch_size
    a							=		num_channels
    a							=		embed_dim
    a							=		depths
    a							=		num_heads
    a							=		kernel_size
    a							=		mlp_ratio
    a							=		qkv_bias
    a							=		hidden_dropout_prob
    a							=		attention_probs_dropout_prob
    a							=		drop_path_rate
    a							=		hidden_act
    a							=		patch_norm
    a							=		layer_norm_eps
    a							=		initializer_range
    a							=		is_training
    a							=		scope
    a							=		use_labels
    a							=		num_labels
    a							=		out_features
    a							=		out_indices

   def 		A				(   self      :							int				)							->					Dict:


    """simple docstring"""






    a							=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

    a							=		None
    if self.use_labels:
     a							=		ids_tensor([self.batch_size]				, self.num_labels				)

    a							=		self.get_config()

    return config, pixel_values, labels

   def 		A				(   self      :							Dict				)							->					str:


    """simple docstring"""






    return NatConfig(
        num_labels=self.num_labels				, image_size=self.image_size				, patch_size=self.patch_size				, num_channels=self.num_channels				, embed_dim=self.embed_dim				, depths=self.depths				, num_heads=self.num_heads				, kernel_size=self.kernel_size				, mlp_ratio=self.mlp_ratio				, qkv_bias=self.qkv_bias				, hidden_dropout_prob=self.hidden_dropout_prob				, attention_probs_dropout_prob=self.attention_probs_dropout_prob				, drop_path_rate=self.drop_path_rate				, hidden_act=self.hidden_act				, patch_norm=self.patch_norm				, layer_norm_eps=self.layer_norm_eps				, initializer_range=self.initializer_range				, out_features=self.out_features				, out_indices=self.out_indices				, )

   def 		A				(   self      :							Any				, __lowerCAmelCase      :							str				, __lowerCAmelCase      :							List[Any]				, __lowerCAmelCase      :							Optional[int]				)							->					Dict:


    """simple docstring"""






    a							=		NatModel(config=__lowerCAmelCase				)
    model.to(__lowerCAmelCase				)
    model.eval()
    a							=		model(__lowerCAmelCase				)

    a							=		a							=		(config.image_size // config.patch_size) // (2 ** (len(config.depths				) - 1))
    a							=		int(config.embed_dim * 2 ** (len(config.depths				) - 1)				)

    self.parent.assertEqual(
        result.last_hidden_state.shape				, (self.batch_size, expected_height, expected_width, expected_dim)				)

   def 		A				(   self      :							Dict				, __lowerCAmelCase      :							Optional[Any]				, __lowerCAmelCase      :							Any				, __lowerCAmelCase      :							Any				)							->					Optional[int]:


    """simple docstring"""






    a							=		NatForImageClassification(__lowerCAmelCase				)
    model.to(__lowerCAmelCase				)
    model.eval()
    a							=		model(__lowerCAmelCase				, labels=__lowerCAmelCase				)
    self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.num_labels)				)

    # test greyscale images
    a							=		1
    a							=		NatForImageClassification(__lowerCAmelCase				)
    model.to(__lowerCAmelCase				)
    model.eval()

    a							=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
    a							=		model(__lowerCAmelCase				)
    self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.num_labels)				)

   def 		A				(   self      :							str				, __lowerCAmelCase      :							List[Any]				, __lowerCAmelCase      :							Optional[int]				, __lowerCAmelCase      :							Dict				)							->					Optional[int]:


    """simple docstring"""






    a							=		NatBackbone(config=__lowerCAmelCase				)
    model.to(__lowerCAmelCase				)
    model.eval()
    a							=		model(__lowerCAmelCase				)

    # verify hidden states
    self.parent.assertEqual(len(result.feature_maps				)				, len(config.out_features				)				)
    self.parent.assertListEqual(list(result.feature_maps[0].shape				)				, [self.batch_size, model.channels[0], 16, 16]				)

    # verify channels
    self.parent.assertEqual(len(model.channels				)				, len(config.out_features				)				)

    # verify backbone works with out_features=None
    a							=		None
    a							=		NatBackbone(config=__lowerCAmelCase				)
    model.to(__lowerCAmelCase				)
    model.eval()
    a							=		model(__lowerCAmelCase				)

    # verify feature maps
    self.parent.assertEqual(len(result.feature_maps				)				, 1				)
    self.parent.assertListEqual(list(result.feature_maps[0].shape				)				, [self.batch_size, model.channels[-1], 4, 4]				)

    # verify channels
    self.parent.assertEqual(len(model.channels				)				, 1				)




   def 		A				(   self      :							int				)							->					int:


    """simple docstring"""






    a							=		self.prepare_config_and_inputs()
    a		,	a		,	a							=		config_and_inputs
    a							=		{"pixel_values": pixel_values}
    return config, inputs_dict



@require_natten
@require_torch
class 		_lowercase		(							UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase						):
   _UpperCAmelCase  						=      (
       (
           NatModel,
           NatForImageClassification,
           NatBackbone,
       )
       if is_torch_available()
       else ()
   )
   _UpperCAmelCase  						=      (
       {'''feature-extraction''': NatModel, '''image-classification''': NatForImageClassification}
       if is_torch_available()
       else {}
   )
   _UpperCAmelCase  						=      False

   _UpperCAmelCase  						=      False
   _UpperCAmelCase  						=      False
   _UpperCAmelCase  						=      False
   _UpperCAmelCase  						=      False

   def 		A				(   self      :							Dict				)							->					str:


    """simple docstring"""






    a							=		NatModelTester(self				)
    a							=		ConfigTester(self				, config_class=__lowerCAmelCase				, embed_dim=37				)

   def 		A				(   self      :							Optional[Any]				)							->					Dict:


    """simple docstring"""






    self.create_and_test_config_common_properties()
    self.config_tester.create_and_test_config_to_json_string()
    self.config_tester.create_and_test_config_to_json_file()
    self.config_tester.create_and_test_config_from_and_save_pretrained()
    self.config_tester.create_and_test_config_with_num_labels()
    self.config_tester.check_config_can_be_init_without_params()
    self.config_tester.check_config_arguments_init()

   def 		A				(   self      :							Optional[Any]				)							->					List[str]:


    """simple docstring"""






    return

   def 		A				(   self      :							int				)							->					Dict:


    """simple docstring"""






    a							=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*__lowerCAmelCase				)

   def 		A				(   self      :							List[Any]				)							->					Optional[Any]:


    """simple docstring"""






    a							=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase				)

   def 		A				(   self      :							Optional[Any]				)							->					List[Any]:


    """simple docstring"""






    a							=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_backbone(*__lowerCAmelCase				)

   @unittest.skip(reason="Nat does not use inputs_embeds"				)
   def 		A				(   self      :							int				)							->					Optional[Any]:


    """simple docstring"""






    pass

   @unittest.skip(reason="Nat does not use feedforward chunking"				)
   def 		A				(   self      :							int				)							->					Dict:


    """simple docstring"""






    pass

   def 		A				(   self      :							str				)							->					Union[str, Any]:


    """simple docstring"""






    a		,	a							=		self.model_tester.prepare_config_and_inputs_for_common()

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

   def 		A				(   self      :							Any				)							->					Tuple:


    """simple docstring"""






    a		,	a							=		self.model_tester.prepare_config_and_inputs_for_common()

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

     a							=		["pixel_values"]
     self.assertListEqual(arg_names[:1]				, __lowerCAmelCase				)

   def 		A				(   self      :							List[Any]				)							->					Optional[int]:


    """simple docstring"""






    self.skipTest("Nat's attention operation is handled entirely by NATTEN."				)

   def 		A				(   self      :							Optional[Any]				, __lowerCAmelCase      :							Optional[int]				, __lowerCAmelCase      :							Any				, __lowerCAmelCase      :							Union[str, Any]				, __lowerCAmelCase      :							List[str]				)							->					int:


    """simple docstring"""






    a							=		model_class(__lowerCAmelCase				)
    model.to(__lowerCAmelCase				)
    model.eval()

    with torch.no_grad():
     a							=		model(**self._prepare_for_class(__lowerCAmelCase				, __lowerCAmelCase				)				)

    a							=		outputs.hidden_states

    a							=		getattr(
        self.model_tester				, "expected_num_hidden_layers"				, len(self.model_tester.depths				) + 1				)
    self.assertEqual(len(__lowerCAmelCase				)				, __lowerCAmelCase				)

    # Nat has a different seq_length
    a							=		(
        config.patch_size
        if isinstance(config.patch_size				, collections.abc.Iterable				)
        else (config.patch_size, config.patch_size)
    )

    a							=		image_size[0] // patch_size[0]
    a							=		image_size[1] // patch_size[1]

    self.assertListEqual(
        list(hidden_states[0].shape[-3:]				)				, [height, width, self.model_tester.embed_dim]				, )

    if model_class.__name__ != "NatBackbone":
     a							=		outputs.reshaped_hidden_states
     self.assertEqual(len(__lowerCAmelCase				)				, __lowerCAmelCase				)

     a		,	a		,	a		,	a							=		reshaped_hidden_states[0].shape
     a							=		(
         reshaped_hidden_states[0].view(__lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				).permute(0				, 2				, 3				, 1				)
     )
     self.assertListEqual(
         list(reshaped_hidden_states.shape[-3:]				)				, [height, width, self.model_tester.embed_dim]				, )

   def 		A				(   self      :							List[Any]				)							->					Optional[Any]:


    """simple docstring"""






    a		,	a							=		self.model_tester.prepare_config_and_inputs_for_common()

    a							=		(
        self.model_tester.image_size
        if isinstance(self.model_tester.image_size				, collections.abc.Iterable				)
        else (self.model_tester.image_size, self.model_tester.image_size)
    )

    for model_class in self.all_model_classes:
     a							=		True
     self.check_hidden_states_output(__lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				)

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

     self.check_hidden_states_output(__lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				)

   @slow
   def 		A				(   self      :							Optional[Any]				)							->					List[Any]:


    """simple docstring"""






    for model_name in NAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     a							=		NatModel.from_pretrained(__lowerCAmelCase				)
     self.assertIsNotNone(__lowerCAmelCase				)




   def 		A				(   self      :							Optional[Any]				)							->					List[Any]:


    """simple docstring"""






    a		,	a							=		self.model_tester.prepare_config_and_inputs_for_common()

    a							=		_config_zero_init(__lowerCAmelCase				)
    for model_class in self.all_model_classes:
     a							=		model_class(config=__lowerCAmelCase				)
     for name, param in model.named_parameters():
      if "embeddings" not in name and 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"""				, )



@require_natten
@require_vision
@require_torch
class 		_lowercase		(							unittest.TestCase						):

   @cached_property
   def 		A				(   self      :							int				)							->					Optional[int]:


    """simple docstring"""






    return AutoImageProcessor.from_pretrained("shi-labs/nat-mini-in1k-224"				) if is_vision_available() else None




   @slow
   def 		A				(   self      :							Dict				)							->					Union[str, Any]:


    """simple docstring"""






    a							=		NatForImageClassification.from_pretrained("shi-labs/nat-mini-in1k-224"				).to(__lowerCAmelCase				)
    a							=		self.default_image_processor

    a							=		Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
    a							=		image_processor(images=__lowerCAmelCase				, return_tensors="pt"				).to(__lowerCAmelCase				)

    # forward pass
    with torch.no_grad():
     a							=		model(**__lowerCAmelCase				)

    # verify the logits
    a							=		torch.Size((1, 1000)				)
    self.assertEqual(outputs.logits.shape				, __lowerCAmelCase				)
    a							=		torch.tensor([0.3_8_0_5, -0.8_6_7_6, -0.3_9_1_2]				).to(__lowerCAmelCase				)
    self.assertTrue(torch.allclose(outputs.logits[0, :3]				, __lowerCAmelCase				, atol=1E-4				)				)



@require_torch
@require_natten
class 		_lowercase		(							unittest.TestCase, UpperCAmelCase__						):
   _UpperCAmelCase  						=      (NatBackbone,) if is_torch_available() else ()
   _UpperCAmelCase  						=      NatConfig

   def 		A				(   self      :							str				)							->					Tuple:


    """simple docstring"""






    a							=		NatModelTester(self				)




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							:  Any     =				False
_lowerCamelCase							:  int     =				Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
_lowerCamelCase							:  Union[str, 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    __UpperCAmelCase	:



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

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

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



   def 			__magic_name__       (				self				:	Union[str, Any],						__A				:	int		):
     UpperCAmelCase			:				List[Any]										=					int(file.split('''.'''		)[0].split('''_'''		)[-1]		)
     UpperCAmelCase			:				Optional[Any]										=					os.path.join(self.inputdir,						__A		)
     return (img_id, filepath)



   @property
   def 			__magic_name__       (				self				:	str		):
     UpperCAmelCase			:				Tuple										=					[]
     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(__A		)		)
       if len(__A		) == self.batch:
         UpperCAmelCase			:				Tuple										=					batch
         UpperCAmelCase			:				str										=					[]
         yield list(map(__A,						zip(*__A		)		)		)

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





   def __call__(				self				:	Optional[Any]		):
     # make writer
     if not TEST:
       UpperCAmelCase			:				int										=					datasets.ArrowWriter(features=self.schema,						path=self.outputfile		)
     # do file generator
     for i, (img_ids, filepaths) in enumerate(self.file_generator		):
       UpperCAmelCase					,		UpperCAmelCase					,		UpperCAmelCase			:				int										=					self.preprocess(__A		)
       UpperCAmelCase			:				Tuple										=					self.model(
           __A,						__A,						scales_yx=__A,						padding='''max_detections''',						max_detections=self.config.MAX_DETECTIONS,						pad_value=0,						return_tensors='''np''',						location='''cpu''',						)
       UpperCAmelCase			:				List[Any]										=					output_dict.pop('''normalized_boxes'''		)
       if not TEST:
         UpperCAmelCase			:				Tuple										=					np.array(__A		)
         UpperCAmelCase			:				Union[str, Any]										=					self.schema.encode_batch(__A		)
         writer.write_batch(__A		)
       if TEST:
         break
       # finalizer the writer
     if not TEST:
       UpperCAmelCase					,		UpperCAmelCase			:				Any										=					writer.finalize()
       print(F'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb'''		)






def      a__	(						UpperCAmelCase				:     List[Any]     )    ->		int:
  try:
    UpperCAmelCase			:				List[str]										=					json.load(UpperCAmelCase     )
    try:
      UpperCAmelCase			:				Optional[Any]										=					list(data.keys()     )
    except Exception:
      UpperCAmelCase			:				Dict										=					[d['''img_id'''] for d in data]
  except Exception:
    try:
      UpperCAmelCase			:				List[Any]										=					eval(stream.read()     )
    except Exception:
      UpperCAmelCase			:				List[Any]										=					stream.read().split('''\n'''     )
  return data


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





