"""simple docstring"""





import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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


if is_torch_available():
				import torch
				from torch import nn

				from transformers import RegNetForImageClassification, RegNetModel
				from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor
class  _snake_case     :






						def __init__(       self      :			Any							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			Optional[int]=3							,					UpperCAmelCase      :			Optional[int]=32							,					UpperCAmelCase      :			Optional[int]=3							,					UpperCAmelCase      :			Any=10							,					UpperCAmelCase      :			Tuple=[10, 20, 30, 40]							,					UpperCAmelCase      :			Optional[int]=[1, 1, 2, 1]							,					UpperCAmelCase      :			Union[str, Any]=True							,					UpperCAmelCase      :			Any=True							,					UpperCAmelCase      :			str="relu"							,					UpperCAmelCase      :			Dict=3							,					UpperCAmelCase      :			List[Any]=None							,					):
													__lowerCamelCase					:		List[str]		   =     parent
													__lowerCamelCase					:		List[Any]		   =     batch_size
													__lowerCamelCase					:		Dict		   =     image_size
													__lowerCamelCase					:		Union[str, Any]		   =     num_channels
													__lowerCamelCase					:		Dict		   =     embeddings_size
													__lowerCamelCase					:		Optional[Any]		   =     hidden_sizes
													__lowerCamelCase					:		int		   =     depths
													__lowerCamelCase					:		str		   =     is_training
													__lowerCamelCase					:		List[str]		   =     use_labels
													__lowerCamelCase					:		List[Any]		   =     hidden_act
													__lowerCamelCase					:		List[str]		   =     num_labels
													__lowerCamelCase					:		Dict		   =     scope
													__lowerCamelCase					:		Dict		   =     len(__a    )






						def       lowerCamelCase__     (       self      :			List[Any]    ):
													__lowerCamelCase					:		Tuple		   =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

													__lowerCamelCase					:		Union[str, Any]		   =     None
													if self.use_labels:
																				__lowerCamelCase					:		Optional[Any]		   =     ids_tensor([self.batch_size]							,					self.num_labels    )

													__lowerCamelCase					:		int		   =     self.get_config()

													return config, pixel_values, labels






						def       lowerCamelCase__     (       self      :			int    ):
													return RegNetConfig(
													    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							,					)






						def       lowerCamelCase__     (       self      :			int							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			int    ):
													__lowerCamelCase					:		Tuple		   =     RegNetModel(config=__a    )
													model.to(__a    )
													model.eval()
													__lowerCamelCase					:		Optional[int]		   =     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 // 32, self.image_size // 32)							,					)






						def       lowerCamelCase__     (       self      :			Union[str, Any]							,					UpperCAmelCase      :			List[Any]							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			List[Any]    ):
													__lowerCamelCase					:		str		   =     self.num_labels
													__lowerCamelCase					:		List[Any]		   =     RegNetForImageClassification(__a    )
													model.to(__a    )
													model.eval()
													__lowerCamelCase					:		int		   =     model(__a							,					labels=__a    )
													self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.num_labels)    )







						def       lowerCamelCase__     (       self      :			Optional[int]    ):
													__lowerCamelCase					:		Union[str, Any]		   =     self.prepare_config_and_inputs()
													__lowerCamelCase					:		List[Any]		   =     config_and_inputs
													__lowerCamelCase					:		str		   =     {'pixel_values': pixel_values}
													return config, inputs_dict

@require_torch
class  _snake_case     (					UpperCamelCase_				, UpperCamelCase_				, unittest.TestCase		):
						snake_case__			  =			(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
						snake_case__			  =			(
						    {"feature-extraction": RegNetModel, "image-classification": RegNetForImageClassification}
						    if is_torch_available()
						    else {}
						)

						snake_case__			  =			False
						snake_case__			  =			False
						snake_case__			  =			False
						snake_case__			  =			False






						def       lowerCamelCase__     (       self      :			str    ):
													__lowerCamelCase					:		Optional[Any]		   =     RegNetModelTester(self    )
													__lowerCamelCase					:		Optional[Any]		   =     ConfigTester(self							,					config_class=__a							,					has_text_modality=__a    )






						def       lowerCamelCase__     (       self      :			Optional[Any]    ):
													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      :			Optional[int]    ):
													return






						@unittest.skip(reason="RegNet does not use inputs_embeds"    )
						def       lowerCamelCase__     (       self      :			List[str]    ):
													pass






						@unittest.skip(reason="RegNet does not support input and output embeddings"    )
						def       lowerCamelCase__     (       self      :			Any    ):
													pass






						def       lowerCamelCase__     (       self      :			List[str]    ):
													__lowerCamelCase					:		str		   =     self.model_tester.prepare_config_and_inputs_for_common()

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

																				__lowerCamelCase					:		Any		   =     ['pixel_values']
																				self.assertListEqual(arg_names[:1]							,					__a    )






						def       lowerCamelCase__     (       self      :			Dict    ):
													__lowerCamelCase					:		List[str]		   =     self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*__a    )






						def       lowerCamelCase__     (       self      :			Tuple    ):
													__lowerCamelCase					:		Optional[int]		   =     self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				__lowerCamelCase					:		Tuple		   =     model_class(config=__a    )
																				for name, module in model.named_modules():
																											if isinstance(__a							,					(nn.BatchNormad, nn.GroupNorm)    ):
																																		self.assertTrue(
																																		    torch.all(module.weight == 1    )							,					msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""							,					)
																																		self.assertTrue(
																																		    torch.all(module.bias == 0    )							,					msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""							,					)






						def       lowerCamelCase__     (       self      :			List[str]    ):






													def check_hidden_states_output(UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			Optional[int]    ):
																				__lowerCamelCase					:		Optional[int]		   =     model_class(__a    )
																				model.to(__a    )
																				model.eval()

																				with torch.no_grad():
																											__lowerCamelCase					:		Optional[Any]		   =     model(**self._prepare_for_class(__a							,					__a    )    )

																				__lowerCamelCase					:		Any		   =     outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																				__lowerCamelCase					:		List[str]		   =     self.model_tester.num_stages
																				self.assertEqual(len(__a    )							,					expected_num_stages + 1    )

																				# RegNet'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 // 2, self.model_tester.image_size // 2]							,					)

													__lowerCamelCase					:		List[str]		   =     self.model_tester.prepare_config_and_inputs_for_common()
													__lowerCamelCase					:		str		   =     ['basic', 'bottleneck']
													for model_class in self.all_model_classes:
																				for layer_type in layers_type:
																											__lowerCamelCase					:		List[Any]		   =     layer_type
																											__lowerCamelCase					:		Tuple		   =     True
																											check_hidden_states_output(__a							,					__a							,					__a    )

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

																											check_hidden_states_output(__a							,					__a							,					__a    )






						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													__lowerCamelCase					:		Optional[Any]		   =     self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_image_classification(*__a    )







						@slow
						def       lowerCamelCase__     (       self      :			Optional[int]    ):






													for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				__lowerCamelCase					:		Tuple		   =     RegNetModel.from_pretrained(__a    )
																				self.assertIsNotNone(__a    )


def    lowercase_   (   ) ->      str:



							'''simple docstring'''

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







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






						@cached_property
						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													return (
													    AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]    )
													    if is_vision_available()
													    else None
													)







						@slow
						def       lowerCamelCase__     (       self      :			Tuple    ):
													__lowerCamelCase					:		Dict		   =     RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]    ).to(__a    )

													__lowerCamelCase					:		List[str]		   =     self.default_image_processor
													__lowerCamelCase					:		List[str]		   =     prepare_img()
													__lowerCamelCase					:		int		   =     image_processor(images=__a							,					return_tensors="pt"    ).to(__a    )

													# forward pass
													with torch.no_grad():
																				__lowerCamelCase					:		List[str]		   =     model(**__a    )

													# verify the logits
													__lowerCamelCase					:		str		   =     torch.Size((1, 1000)    )
													self.assertEqual(outputs.logits.shape							,					__a    )

													__lowerCamelCase					:		Optional[Any]		   =     torch.tensor([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]    ).to(__a    )

													self.assertTrue(torch.allclose(outputs.logits[0, :3]							,					__a							,					atol=1E-4    )    )
"""simple docstring"""





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

import cva
import numpy as np

# Parrameters
__A  =				(720, 1280)  # Height, Width
__A  =				(0.4, 0.6)  # if height or width lower than this scale, drop it.
__A  =				1 / 100
__A  =				''''''
__A  =				''''''
__A  =				''''''
__A  =				250


def    lowercase_   (   ) ->      None:



							'''simple docstring'''

							__lowerCamelCase   ,  __lowerCamelCase					:		List[Any]		   =     get_dataset(_lowerCamelCase						,   _lowerCamelCase      )
							for index in range(_lowerCamelCase      ):
														__lowerCamelCase					:		Optional[Any]		   =     random.sample(range(len(_lowerCamelCase      )      )						,   4      )
														__lowerCamelCase   ,  __lowerCamelCase   ,  __lowerCamelCase					:		Optional[Any]		   =     update_image_and_anno(
														    _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   filter_scale=_lowerCamelCase						,   )

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


def    lowercase_   (   _lowerCamelCase:	str						,   _lowerCamelCase:	str      ) ->      tuple[list, list]:



							'''simple docstring'''

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

														__lowerCamelCase					:		Union[str, Any]		   =     []
														for obj_list in obj_lists:
																					__lowerCamelCase					:		str		   =     obj_list.rstrip("\n"      ).split(" "      )
																					__lowerCamelCase					:		Union[str, Any]		   =     float(obj[1]      ) - float(obj[3]      ) / 2
																					__lowerCamelCase					:		Tuple		   =     float(obj[2]      ) - float(obj[4]      ) / 2
																					__lowerCamelCase					:		Union[str, Any]		   =     float(obj[1]      ) + float(obj[3]      ) / 2
																					__lowerCamelCase					:		Any		   =     float(obj[2]      ) + float(obj[4]      ) / 2

																					boxes.append([int(obj[0]      ), xmin, ymin, xmax, ymax]      )
														if not boxes:
																					continue
														img_paths.append(_lowerCamelCase      )
														labels.append(_lowerCamelCase      )
							return img_paths, labels


def    lowercase_   (   _lowerCamelCase:	list						,   _lowerCamelCase:	list						,   _lowerCamelCase:	list[int]						,   _lowerCamelCase:	tuple[int, int]						,   _lowerCamelCase:	tuple[float, float]						,   _lowerCamelCase:	float = 0.0						,   ) ->      tuple[list, list, str]:



							'''simple docstring'''

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

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



							'''simple docstring'''

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


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