

import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					FunnelConfig.from_json_file(UpperCamelCase__   )
							print(F'''Building PyTorch model from configuration: {config}'''   )
							snake_case_           =					FunnelBaseModel(UpperCamelCase__   ) if base_model else FunnelModel(UpperCamelCase__   )

							# Load weights from tf checkpoint
							load_tf_weights_in_funnel(UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   )

							# Save pytorch-model
							print(F'''Save PyTorch model to {pytorch_dump_path}'''   )
							torch.save(model.state_dict()     ,							UpperCamelCase__   )


if __name__ == "__main__":
					_UpperCAmelCase	:     Dict		       = argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
					)
					parser.add_argument(
					    """--config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help="""The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.""",
					)
					parser.add_argument(
					    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					parser.add_argument(
					    """--base_model""", action="""store_true""", help="""Whether you want just the base model (no decoder) or not."""
					)
					_UpperCAmelCase	:     Any		       = parser.parse_args()
					convert_tf_checkpoint_to_pytorch(
					    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
					)







import json
import pathlib
import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import DetrImageProcessor







class 				lowercase       ( unittest.TestCase    ):




				def __init__(      self   ,     snake_case   ,     snake_case=7   ,     snake_case=3   ,     snake_case=30   ,     snake_case=400   ,     snake_case=True   ,     snake_case=None   ,     snake_case=True   ,     snake_case=1 / 255   ,     snake_case=True   ,     snake_case=[0.5, 0.5, 0.5]   ,     snake_case=[0.5, 0.5, 0.5]   ,     snake_case=True   ,     ):
											# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
											snake_case_           =					size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
											snake_case_           =					parent
											snake_case_           =					batch_size
											snake_case_           =					num_channels
											snake_case_           =					min_resolution
											snake_case_           =					max_resolution
											snake_case_           =					do_resize
											snake_case_           =					size
											snake_case_           =					do_rescale
											snake_case_           =					rescale_factor
											snake_case_           =					do_normalize
											snake_case_           =					image_mean
											snake_case_           =					image_std
											snake_case_           =					do_pad




				def 					a							(      self ):
											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_rescale": self.do_rescale,
											    "rescale_factor": self.rescale_factor,
											    "do_normalize": self.do_normalize,
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "do_pad": self.do_pad,
											}




				def 					a							(      self   ,     snake_case   ,     snake_case=False ):
											if not batched:
																		snake_case_           =					image_inputs[0]
																		if isinstance(snake_case   ,     Image.Image ):
																									snake_case_       ,					snake_case_           =					image.size
																		else:
																									snake_case_       ,					snake_case_           =					image.shape[1], image.shape[2]
																		if w < h:
																									snake_case_           =					int(self.size['shortest_edge'] * h / w )
																									snake_case_           =					self.size['shortest_edge']
																		elif w > h:
																									snake_case_           =					self.size['shortest_edge']
																									snake_case_           =					int(self.size['shortest_edge'] * w / h )
																		else:
																									snake_case_           =					self.size['shortest_edge']
																									snake_case_           =					self.size['shortest_edge']

											else:
																		snake_case_           =					[]
																		for image in image_inputs:
																									snake_case_       ,					snake_case_           =					self.get_expected_values([image] )
																									expected_values.append((expected_height, expected_width) )
																		snake_case_           =					max(snake_case   ,     key=lambda snake_case : item[0] )[0]
																		snake_case_           =					max(snake_case   ,     key=lambda snake_case : item[1] )[1]

											return expected_height, expected_width







@require_torch
@require_vision
class 				lowercase       ( lowercase_			,						unittest.TestCase    ):
				__SCREAMING_SNAKE_CASE      :				str     =    DetrImageProcessor if is_vision_available() else None




				def 					a							(      self ):
											snake_case_           =					DetrImageProcessingTester(self )




				@property
				def 					a							(      self ):
											return self.image_processor_tester.prepare_image_processor_dict()




				def 					a							(      self ):
											snake_case_           =					self.image_processing_class(**self.image_processor_dict )
											self.assertTrue(hasattr(snake_case   ,     'image_mean' ) )
											self.assertTrue(hasattr(snake_case   ,     'image_std' ) )
											self.assertTrue(hasattr(snake_case   ,     'do_normalize' ) )
											self.assertTrue(hasattr(snake_case   ,     'do_rescale' ) )
											self.assertTrue(hasattr(snake_case   ,     'rescale_factor' ) )
											self.assertTrue(hasattr(snake_case   ,     'do_resize' ) )
											self.assertTrue(hasattr(snake_case   ,     'size' ) )
											self.assertTrue(hasattr(snake_case   ,     'do_pad' ) )




				def 					a							(      self ):
											snake_case_           =					self.image_processing_class.from_dict(self.image_processor_dict )
											self.assertEqual(image_processor.size   ,     {'shortest_edge': 18, 'longest_edge': 1333} )
											self.assertEqual(image_processor.do_pad   ,     snake_case )

											snake_case_           =					self.image_processing_class.from_dict(
											    self.image_processor_dict   ,     size=42   ,     max_size=84   ,     pad_and_return_pixel_mask=snake_case )
											self.assertEqual(image_processor.size   ,     {'shortest_edge': 42, 'longest_edge': 84} )
											self.assertEqual(image_processor.do_pad   ,     snake_case )




				def 					a							(      self ):
											pass




				def 					a							(      self ):
											# Initialize image_processing
											snake_case_           =					self.image_processing_class(**self.image_processor_dict )
											# create random PIL images
											snake_case_           =					prepare_image_inputs(self.image_processor_tester   ,     equal_resolution=snake_case )
											for image in image_inputs:
																		self.assertIsInstance(snake_case   ,     Image.Image )

											# Test not batched input
											snake_case_           =					image_processing(image_inputs[0]   ,     return_tensors='pt' ).pixel_values

											snake_case_       ,					snake_case_           =					self.image_processor_tester.get_expected_values(snake_case )

											self.assertEqual(
											    encoded_images.shape   ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,     )

											# Test batched
											snake_case_       ,					snake_case_           =					self.image_processor_tester.get_expected_values(snake_case   ,     batched=snake_case )

											snake_case_           =					image_processing(snake_case   ,     return_tensors='pt' ).pixel_values
											self.assertEqual(
											    encoded_images.shape   ,     (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )   ,     )




				def 					a							(      self ):
											# Initialize image_processing
											snake_case_           =					self.image_processing_class(**self.image_processor_dict )
											# create random numpy tensors
											snake_case_           =					prepare_image_inputs(self.image_processor_tester   ,     equal_resolution=snake_case   ,     numpify=snake_case )
											for image in image_inputs:
																		self.assertIsInstance(snake_case   ,     np.ndarray )

											# Test not batched input
											snake_case_           =					image_processing(image_inputs[0]   ,     return_tensors='pt' ).pixel_values

											snake_case_       ,					snake_case_           =					self.image_processor_tester.get_expected_values(snake_case )

											self.assertEqual(
											    encoded_images.shape   ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,     )

											# Test batched
											snake_case_           =					image_processing(snake_case   ,     return_tensors='pt' ).pixel_values

											snake_case_       ,					snake_case_           =					self.image_processor_tester.get_expected_values(snake_case   ,     batched=snake_case )

											self.assertEqual(
											    encoded_images.shape   ,     (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )   ,     )




				def 					a							(      self ):
											# Initialize image_processing
											snake_case_           =					self.image_processing_class(**self.image_processor_dict )
											# create random PyTorch tensors
											snake_case_           =					prepare_image_inputs(self.image_processor_tester   ,     equal_resolution=snake_case   ,     torchify=snake_case )
											for image in image_inputs:
																		self.assertIsInstance(snake_case   ,     torch.Tensor )

											# Test not batched input
											snake_case_           =					image_processing(image_inputs[0]   ,     return_tensors='pt' ).pixel_values

											snake_case_       ,					snake_case_           =					self.image_processor_tester.get_expected_values(snake_case )

											self.assertEqual(
											    encoded_images.shape   ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,     )

											# Test batched
											snake_case_           =					image_processing(snake_case   ,     return_tensors='pt' ).pixel_values

											snake_case_       ,					snake_case_           =					self.image_processor_tester.get_expected_values(snake_case   ,     batched=snake_case )

											self.assertEqual(
											    encoded_images.shape   ,     (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )   ,     )




				@slow
				def 					a							(      self ):
											# prepare image and target
											snake_case_           =					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
											with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'   ,     'r' ) as f:
																		snake_case_           =					json.loads(f.read() )

											snake_case_           =					{'image_id': 3_9769, 'annotations': target}

											# encode them
											snake_case_           =					DetrImageProcessor.from_pretrained('facebook/detr-resnet-50' )
											snake_case_           =					image_processing(images=snake_case   ,     annotations=snake_case   ,     return_tensors='pt' )

											# verify pixel values
											snake_case_           =					torch.Size([1, 3, 800, 1066] )
											self.assertEqual(encoding['pixel_values'].shape   ,     snake_case )

											snake_case_           =					torch.tensor([0.27_96, 0.31_38, 0.34_81] )
											self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]   ,     snake_case   ,     atol=1e-4 ) )

											# verify area
											snake_case_           =					torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['area']   ,     snake_case ) )
											# verify boxes
											snake_case_           =					torch.Size([6, 4] )
											self.assertEqual(encoding['labels'][0]['boxes'].shape   ,     snake_case )
											snake_case_           =					torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]   ,     snake_case   ,     atol=1e-3 ) )
											# verify image_id
											snake_case_           =					torch.tensor([3_9769] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']   ,     snake_case ) )
											# verify is_crowd
											snake_case_           =					torch.tensor([0, 0, 0, 0, 0, 0] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']   ,     snake_case ) )
											# verify class_labels
											snake_case_           =					torch.tensor([75, 75, 63, 65, 17, 17] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']   ,     snake_case ) )
											# verify orig_size
											snake_case_           =					torch.tensor([480, 640] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']   ,     snake_case ) )
											# verify size
											snake_case_           =					torch.tensor([800, 1066] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['size']   ,     snake_case ) )




				@slow
				def 					a							(      self ):
											# prepare image, target and masks_path
											snake_case_           =					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
											with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'   ,     'r' ) as f:
																		snake_case_           =					json.loads(f.read() )

											snake_case_           =					{'file_name': '000000039769.png', 'image_id': 3_9769, 'segments_info': target}

											snake_case_           =					pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic' )

											# encode them
											snake_case_           =					DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic' )
											snake_case_           =					image_processing(images=snake_case   ,     annotations=snake_case   ,     masks_path=snake_case   ,     return_tensors='pt' )

											# verify pixel values
											snake_case_           =					torch.Size([1, 3, 800, 1066] )
											self.assertEqual(encoding['pixel_values'].shape   ,     snake_case )

											snake_case_           =					torch.tensor([0.27_96, 0.31_38, 0.34_81] )
											self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]   ,     snake_case   ,     atol=1e-4 ) )

											# verify area
											snake_case_           =					torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['area']   ,     snake_case ) )
											# verify boxes
											snake_case_           =					torch.Size([6, 4] )
											self.assertEqual(encoding['labels'][0]['boxes'].shape   ,     snake_case )
											snake_case_           =					torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]   ,     snake_case   ,     atol=1e-3 ) )
											# verify image_id
											snake_case_           =					torch.tensor([3_9769] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']   ,     snake_case ) )
											# verify is_crowd
											snake_case_           =					torch.tensor([0, 0, 0, 0, 0, 0] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']   ,     snake_case ) )
											# verify class_labels
											snake_case_           =					torch.tensor([17, 17, 63, 75, 75, 93] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']   ,     snake_case ) )
											# verify masks
											snake_case_           =					82_2873
											self.assertEqual(encoding['labels'][0]['masks'].sum().item()   ,     snake_case )
											# verify orig_size
											snake_case_           =					torch.tensor([480, 640] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']   ,     snake_case ) )
											# verify size
											snake_case_           =					torch.tensor([800, 1066] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['size']   ,     snake_case ) )




