
'''simple docstring'''







import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
_lowercase :  Any    							=       (7_2_0, 1_2_8_0)  # Height, Width
_lowercase :  List[Any]    							=       (0.4, 0.6)  # if height or width lower than this scale, drop it.
_lowercase :  str    							=       1 / 1_0_0
_lowercase :  Any    							=       ""
_lowercase :  Union[str, Any]    							=       ""
_lowercase :  Optional[int]    							=       ""
_lowercase :  List[Any]    							=       2_5_0
def 					snake_case_		(				):



						"""simple docstring"""





						lowercase_						, lowercase_	:     Any													=       get_dataset(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							)
						for index in range(__SCREAMING_SNAKE_CASE							):
												lowercase_	:     str													=       random.sample(range(len(__SCREAMING_SNAKE_CASE							)							)      ,      4							)
												lowercase_						, lowercase_						, lowercase_	:     Any													=       update_image_and_anno(
												    __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      filter_scale=__SCREAMING_SNAKE_CASE      ,      )

												# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
												lowercase_	:     int													=       random_chars(32							)
												lowercase_	:     str													=       path.split(os.sep							)[-1].rsplit('''.'''      ,      1							)[0]
												lowercase_	:     int													=       F'''{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}'''
												cva.imwrite(F'''{file_root}.jpg'''      ,      __SCREAMING_SNAKE_CASE      ,      [cva.IMWRITE_JPEG_QUALITY, 85]							)
												print(F'''Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}'''							)
												lowercase_	:     List[Any]													=       []
												for anno in new_annos:
																		lowercase_	:     List[Any]													=       anno[3] - anno[1]
																		lowercase_	:     List[str]													=       anno[4] - anno[2]
																		lowercase_	:     Dict													=       anno[1] + width / 2
																		lowercase_	:     Dict													=       anno[2] + height / 2
																		lowercase_	:     int													=       F'''{anno[0]} {x_center} {y_center} {width} {height}'''
																		annos_list.append(__SCREAMING_SNAKE_CASE							)
												with open(F'''{file_root}.txt'''      ,      '''w'''							) as outfile:
																		outfile.write('''\n'''.join(line for line in annos_list							)							)
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: str      ,      __SCREAMING_SNAKE_CASE					: str							):



						"""simple docstring"""





						lowercase_	:     Optional[Any]													=       []
						lowercase_	:     Optional[Any]													=       []
						for label_file in glob.glob(os.path.join(__SCREAMING_SNAKE_CASE      ,      '''*.txt'''							)							):
												lowercase_	:     int													=       label_file.split(os.sep							)[-1].rsplit('''.'''      ,      1							)[0]
												with open(__SCREAMING_SNAKE_CASE							) as in_file:
																		lowercase_	:     List[str]													=       in_file.readlines()
												lowercase_	:     Optional[Any]													=       os.path.join(__SCREAMING_SNAKE_CASE      ,      F'''{label_name}.jpg'''							)

												lowercase_	:     Optional[int]													=       []
												for obj_list in obj_lists:
																		lowercase_	:     List[str]													=       obj_list.rstrip('''\n'''							).split(''' '''							)
																		lowercase_	:     Optional[int]													=       float(obj[1]							) - float(obj[3]							) / 2
																		lowercase_	:     Any													=       float(obj[2]							) - float(obj[4]							) / 2
																		lowercase_	:     str													=       float(obj[1]							) + float(obj[3]							) / 2
																		lowercase_	:     List[str]													=       float(obj[2]							) + float(obj[4]							) / 2

																		boxes.append([int(obj[0]							), xmin, ymin, xmax, ymax]							)
												if not boxes:
																		continue
												img_paths.append(__SCREAMING_SNAKE_CASE							)
												labels.append(__SCREAMING_SNAKE_CASE							)
						return img_paths, labels
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: list      ,      __SCREAMING_SNAKE_CASE					: list      ,      __SCREAMING_SNAKE_CASE					: list[int]      ,      __SCREAMING_SNAKE_CASE					: tuple[int, int]      ,      __SCREAMING_SNAKE_CASE					: tuple[float, float]      ,      __SCREAMING_SNAKE_CASE					: float = 0.0      ,      ):



						"""simple docstring"""





						lowercase_	:     List[Any]													=       np.zeros([output_size[0], output_size[1], 3]      ,      dtype=np.uinta							)
						lowercase_	:     Tuple													=       scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
						lowercase_	:     List[Any]													=       scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
						lowercase_	:     Optional[int]													=       int(scale_x * output_size[1]							)
						lowercase_	:     Dict													=       int(scale_y * output_size[0]							)

						lowercase_	:     Union[str, Any]													=       []
						lowercase_	:     List[Any]													=       []
						for i, index in enumerate(__SCREAMING_SNAKE_CASE							):
												lowercase_	:     Union[str, Any]													=       all_img_list[index]
												path_list.append(__SCREAMING_SNAKE_CASE							)
												lowercase_	:     int													=       all_annos[index]
												lowercase_	:     Dict													=       cva.imread(__SCREAMING_SNAKE_CASE							)
												if i == 0:  # top-left
																		lowercase_	:     Optional[Any]													=       cva.resize(__SCREAMING_SNAKE_CASE      ,      (divid_point_x, divid_point_y)							)
																		lowercase_	:     Tuple													=       img
																		for bbox in img_annos:
																								lowercase_	:     Optional[int]													=       bbox[1] * scale_x
																								lowercase_	:     Optional[Any]													=       bbox[2] * scale_y
																								lowercase_	:     str													=       bbox[3] * scale_x
																								lowercase_	:     Tuple													=       bbox[4] * scale_y
																								new_anno.append([bbox[0], xmin, ymin, xmax, ymax]							)
												elif i == 1:  # top-right
																		lowercase_	:     Dict													=       cva.resize(__SCREAMING_SNAKE_CASE      ,      (output_size[1] - divid_point_x, divid_point_y)							)
																		lowercase_	:     Dict													=       img
																		for bbox in img_annos:
																								lowercase_	:     int													=       scale_x + bbox[1] * (1 - scale_x)
																								lowercase_	:     Dict													=       bbox[2] * scale_y
																								lowercase_	:     Optional[int]													=       scale_x + bbox[3] * (1 - scale_x)
																								lowercase_	:     int													=       bbox[4] * scale_y
																								new_anno.append([bbox[0], xmin, ymin, xmax, ymax]							)
												elif i == 2:  # bottom-left
																		lowercase_	:     List[Any]													=       cva.resize(__SCREAMING_SNAKE_CASE      ,      (divid_point_x, output_size[0] - divid_point_y)							)
																		lowercase_	:     List[str]													=       img
																		for bbox in img_annos:
																								lowercase_	:     Any													=       bbox[1] * scale_x
																								lowercase_	:     Optional[int]													=       scale_y + bbox[2] * (1 - scale_y)
																								lowercase_	:     str													=       bbox[3] * scale_x
																								lowercase_	:     Optional[int]													=       scale_y + bbox[4] * (1 - scale_y)
																								new_anno.append([bbox[0], xmin, ymin, xmax, ymax]							)
												else:  # bottom-right
																		lowercase_	:     int													=       cva.resize(
																		    __SCREAMING_SNAKE_CASE      ,      (output_size[1] - divid_point_x, output_size[0] - divid_point_y)							)
																		lowercase_	:     List[str]													=       img
																		for bbox in img_annos:
																								lowercase_	:     int													=       scale_x + bbox[1] * (1 - scale_x)
																								lowercase_	:     Any													=       scale_y + bbox[2] * (1 - scale_y)
																								lowercase_	:     Optional[Any]													=       scale_x + bbox[3] * (1 - scale_x)
																								lowercase_	:     int													=       scale_y + bbox[4] * (1 - scale_y)
																								new_anno.append([bbox[0], xmin, ymin, xmax, ymax]							)

    # Remove bounding box small than scale of filter
						if filter_scale > 0:
												lowercase_	:     Optional[Any]													=       [
												    anno
												    for anno in new_anno
												    if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
												]

						return output_img, new_anno, path_list[0]
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: int							):



						"""simple docstring"""





						assert number_char > 1, "The number of character should greater than 1"
						lowercase_	:     Any													=       ascii_lowercase + digits
						return "".join(random.choice(__SCREAMING_SNAKE_CASE							) for _ in range(__SCREAMING_SNAKE_CASE							)							)


if __name__ == "__main__":
							main()
							print("DONE ✅")





from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import TFResNetForImageClassification, TFResNetModel
		from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor




class  lowerCamelCase__							:




	'''simple docstring'''




	def __init__(					self		:List[Any]     ,   a		:Dict     ,   a		:Any=3     ,   a		:Any=3_2     ,   a		:Optional[Any]=3     ,   a		:str=1_0     ,   a		:Union[str, Any]=[1_0, 2_0, 3_0, 4_0]     ,   a		:Optional[Any]=[1, 1, 2, 1]     ,   a		:Optional[Any]=True     ,   a		:Dict=True     ,   a		:Tuple="relu"     ,   a		:List[str]=3     ,   a		:Tuple=None     ,   )		-> Tuple:
								__UpperCamelCase	:   Optional[Any]			   =					parent
								__UpperCamelCase	:   Dict			   =					batch_size
								__UpperCamelCase	:   int			   =					image_size
								__UpperCamelCase	:   Dict			   =					num_channels
								__UpperCamelCase	:   Optional[int]			   =					embeddings_size
								__UpperCamelCase	:   List[Any]			   =					hidden_sizes
								__UpperCamelCase	:   Optional[Any]			   =					depths
								__UpperCamelCase	:   Optional[int]			   =					is_training
								__UpperCamelCase	:   Union[str, Any]			   =					use_labels
								__UpperCamelCase	:   Optional[int]			   =					hidden_act
								__UpperCamelCase	:   Tuple			   =					num_labels
								__UpperCamelCase	:   Tuple			   =					scope
								__UpperCamelCase	:   Dict			   =					len(a						)







	def    _lowerCamelCase				(					self		:Optional[int]						)		-> Any:
								__UpperCamelCase	:   List[str]			   =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

								__UpperCamelCase	:   List[str]			   =					None
								if self.use_labels:
															__UpperCamelCase	:   Tuple			   =					ids_tensor([self.batch_size]     ,   self.num_labels						)

								__UpperCamelCase	:   List[Any]			   =					self.get_config()

								return config, pixel_values, labels







	def    _lowerCamelCase				(					self		:Union[str, Any]						)		-> int:
								return ResNetConfig(
								    num_channels=self.num_channels     ,   embeddings_size=self.embeddings_size     ,   hidden_sizes=self.hidden_sizes     ,   depths=self.depths     ,   hidden_act=self.hidden_act     ,   num_labels=self.num_labels     ,   image_size=self.image_size     ,   )







	def    _lowerCamelCase				(					self		:List[Any]     ,   a		:Dict     ,   a		:int     ,   a		:Optional[Any]						)		-> Tuple:
								__UpperCamelCase	:   str			   =					TFResNetModel(config=a						)
								__UpperCamelCase	:   Union[str, Any]			   =					model(a						)
								# expected last hidden states: B, C, H // 32, W // 32
								self.parent.assertEqual(
								    result.last_hidden_state.shape     ,   (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)     ,   )







	def    _lowerCamelCase				(					self		:Union[str, Any]     ,   a		:Optional[int]     ,   a		:List[str]     ,   a		:Optional[Any]						)		-> Any:
								__UpperCamelCase	:   str			   =					self.num_labels
								__UpperCamelCase	:   Optional[int]			   =					TFResNetForImageClassification(a						)
								__UpperCamelCase	:   List[str]			   =					model(a     ,   labels=a						)
								self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.num_labels)						)







	def    _lowerCamelCase				(					self		:Optional[int]						)		-> List[str]:
								__UpperCamelCase	:   Optional[int]			   =					self.prepare_config_and_inputs()
								__UpperCamelCase   ,						__UpperCamelCase   ,						__UpperCamelCase	:   Union[str, Any]			   =					config_and_inputs
								__UpperCamelCase	:   List[str]			   =					{"pixel_values": pixel_values}
								return config, inputs_dict




@require_tf
class  lowerCamelCase__							(				__lowercase     ,						__lowercase     ,						unittest.TestCase):




	'''simple docstring'''




	_A								=     (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
	_A								=     (
	    {'feature-extraction': TFResNetModel, 'image-classification': TFResNetForImageClassification}
	    if is_tf_available()
	    else {}
	)

	_A								=     False
	_A								=     False
	_A								=     False
	_A								=     False
	_A								=     False







	def    _lowerCamelCase				(					self		:int						)		-> List[str]:
								__UpperCamelCase	:   Union[str, Any]			   =					TFResNetModelTester(self						)
								__UpperCamelCase	:   List[Any]			   =					ConfigTester(self     ,   config_class=a     ,   has_text_modality=a						)







	def    _lowerCamelCase				(					self		:int						)		-> Dict:
								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    _lowerCamelCase				(					self		:str						)		-> Optional[Any]:
								return







	@unittest.skip(reason="ResNet does not use inputs_embeds"						)
	def    _lowerCamelCase				(					self		:Tuple						)		-> Tuple:
								pass







	@unittest.skip(reason="ResNet does not support input and output embeddings"						)
	def    _lowerCamelCase				(					self		:List[Any]						)		-> List[str]:
								pass







	def    _lowerCamelCase				(					self		:Optional[int]						)		-> Tuple:
								__UpperCamelCase   ,						__UpperCamelCase	:   Union[str, Any]			   =					self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															__UpperCamelCase	:   Dict			   =					model_class(a						)
															__UpperCamelCase	:   Union[str, Any]			   =					inspect.signature(model.call						)
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															__UpperCamelCase	:   Dict			   =					[*signature.parameters.keys()]

															__UpperCamelCase	:   Union[str, Any]			   =					["pixel_values"]
															self.assertListEqual(arg_names[:1]     ,   a						)







	def    _lowerCamelCase				(					self		:List[str]						)		-> List[str]:
								__UpperCamelCase	:   Union[str, Any]			   =					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*a						)







	def    _lowerCamelCase				(					self		:Optional[Any]						)		-> Tuple:
								def check_hidden_states_output(a		:Optional[Any]     ,   a		:Optional[int]     ,   a		:List[str]						):
															__UpperCamelCase	:   int			   =					model_class(a						)
															__UpperCamelCase	:   int			   =					model(**self._prepare_for_class(a     ,   a						)						)

															__UpperCamelCase	:   Optional[Any]			   =					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

															__UpperCamelCase	:   Optional[int]			   =					self.model_tester.num_stages
															self.assertEqual(len(a						)     ,   expected_num_stages + 1						)

															# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
															self.assertListEqual(
															    list(hidden_states[0].shape[-2:]						)     ,   [self.model_tester.image_size // 4, self.model_tester.image_size // 4]     ,   )

								__UpperCamelCase   ,						__UpperCamelCase	:   Optional[Any]			   =					self.model_tester.prepare_config_and_inputs_for_common()
								__UpperCamelCase	:   str			   =					["basic", "bottleneck"]
								for model_class in self.all_model_classes:
															for layer_type in layers_type:
																						__UpperCamelCase	:   int			   =					layer_type
																						__UpperCamelCase	:   int			   =					True
																						check_hidden_states_output(a     ,   a     ,   a						)

																						# check that output_hidden_states also work using config
																						del inputs_dict["output_hidden_states"]
																						__UpperCamelCase	:   int			   =					True

																						check_hidden_states_output(a     ,   a     ,   a						)







	def    _lowerCamelCase				(					self		:Union[str, Any]						)		-> Dict:
								__UpperCamelCase	:   int			   =					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*a						)







	@slow
	def    _lowerCamelCase				(					self		:Dict						)		-> Dict:
								for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															__UpperCamelCase	:   Optional[Any]			   =					TFResNetModel.from_pretrained(a						)
															self.assertIsNotNone(a						)




def 	_SCREAMING_SNAKE_CASE					(		)					->    int:






							'''simple docstring'''





							__UpperCamelCase	:   Tuple			   =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
							return image




@require_tf
@require_vision
class  lowerCamelCase__							(				unittest.TestCase):




	'''simple docstring'''




	@cached_property
	def    _lowerCamelCase				(					self		:Optional[Any]						)		-> Tuple:
								return (
								    AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
								    if is_vision_available()
								    else None
								)







	@slow
	def    _lowerCamelCase				(					self		:Optional[int]						)		-> Optional[int]:
								__UpperCamelCase	:   int			   =					TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)

								__UpperCamelCase	:   List[Any]			   =					self.default_image_processor
								__UpperCamelCase	:   List[str]			   =					prepare_img()
								__UpperCamelCase	:   List[str]			   =					image_processor(images=a     ,   return_tensors="tf"						)

								# forward pass
								__UpperCamelCase	:   Dict			   =					model(**a						)

								# verify the logits
								__UpperCamelCase	:   Dict			   =					tf.TensorShape((1, 1_0_0_0)						)
								self.assertEqual(outputs.logits.shape     ,   a						)

								__UpperCamelCase	:   Union[str, Any]			   =					tf.constant([-11.1069, -9.7877, -8.3777]						)

								self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()     ,   a     ,   atol=1E-4						)						)