"""simple docstring"""







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 DeformableDetrImageProcessor

class 		UpperCamelCase      (   unittest.TestCase						):



				def __init__(self    :				Union[str, Any]     ,  _A    :				Union[str, Any]     ,  _A    :				Dict=7     ,  _A    :				Any=3     ,  _A    :				Optional[Any]=30     ,  _A    :				Any=4_00     ,  _A    :				Tuple=True     ,  _A    :				List[str]=None     ,  _A    :				Tuple=True     ,  _A    :				str=[0.5, 0.5, 0.5]     ,  _A    :				Tuple=[0.5, 0.5, 0.5]     ,  _A    :				List[Any]=True     ,  _A    :				str=1 / 2_55     ,  _A    :				Optional[int]=True     ,  )   ->     str:
					# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
					__snake_case      :							Tuple						=			size if size is not None else {'shortest_edge': 18, 'longest_edge': 13_33}
					__snake_case      :							int						=			parent
					__snake_case      :							List[str]						=			batch_size
					__snake_case      :							Optional[int]						=			num_channels
					__snake_case      :							Optional[Any]						=			min_resolution
					__snake_case      :							List[str]						=			max_resolution
					__snake_case      :							Optional[Any]						=			do_resize
					__snake_case      :							Optional[int]						=			size
					__snake_case      :							Tuple						=			do_normalize
					__snake_case      :							Optional[Any]						=			image_mean
					__snake_case      :							List[str]						=			image_std
					__snake_case      :							List[str]						=			do_rescale
					__snake_case      :							Union[str, Any]						=			rescale_factor
					__snake_case      :							str						=			do_pad



				def  _lowercase					(self    :				Any)   ->     Tuple:
					return {
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_normalize": self.do_normalize,
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_rescale": self.do_rescale,
					    "rescale_factor": self.rescale_factor,
					    "do_pad": self.do_pad,
					}






				def  _lowercase					(self    :				Optional[int]     ,  _A    :				int     ,  _A    :				Optional[Any]=False)   ->     str:
					if not batched:
						__snake_case      :							List[str]						=			image_inputs[0]
						if isinstance(_A     ,  Image.Image):
							__snake_case		,    __snake_case      :							Union[str, Any]						=			image.size
						else:
							__snake_case		,    __snake_case      :							str						=			image.shape[1], image.shape[2]
						if w < h:
							__snake_case      :							int						=			int(self.size['shortest_edge'] * h / w)
							__snake_case      :							Any						=			self.size['shortest_edge']
						elif w > h:
							__snake_case      :							Any						=			self.size['shortest_edge']
							__snake_case      :							Tuple						=			int(self.size['shortest_edge'] * w / h)
						else:
							__snake_case      :							str						=			self.size['shortest_edge']
							__snake_case      :							Optional[int]						=			self.size['shortest_edge']

					else:
						__snake_case      :							Union[str, Any]						=			[]
						for image in image_inputs:
							__snake_case		,    __snake_case      :							Union[str, Any]						=			self.get_expected_values([image])
							expected_values.append((expected_height, expected_width))
						__snake_case      :							Optional[Any]						=			max(_A     ,  key=lambda _A: item[0])[0]
						__snake_case      :							Any						=			max(_A     ,  key=lambda _A: item[1])[1]

					return expected_height, expected_width







@require_torch
@require_vision
class 		UpperCamelCase      (   lowercase				,     unittest.TestCase						):
				UpperCAmelCase    :				Union[str, Any]   =		DeformableDetrImageProcessor if is_vision_available() else None



				def  _lowercase					(self    :				int)   ->     int:
					__snake_case      :							Optional[int]						=			DeformableDetrImageProcessingTester(self)



				@property
				def  _lowercase					(self    :				Dict)   ->     Dict:
					return self.image_processor_tester.prepare_image_processor_dict()



				def  _lowercase					(self    :				int)   ->     Any:
					__snake_case      :							Tuple						=			self.image_processing_class(**self.image_processor_dict)
					self.assertTrue(hasattr(_A     ,  'image_mean'))
					self.assertTrue(hasattr(_A     ,  'image_std'))
					self.assertTrue(hasattr(_A     ,  'do_normalize'))
					self.assertTrue(hasattr(_A     ,  'do_resize'))
					self.assertTrue(hasattr(_A     ,  'do_rescale'))
					self.assertTrue(hasattr(_A     ,  'do_pad'))
					self.assertTrue(hasattr(_A     ,  'size'))



				def  _lowercase					(self    :				Tuple)   ->     List[str]:
					__snake_case      :							List[Any]						=			self.image_processing_class.from_dict(self.image_processor_dict)
					self.assertEqual(image_processor.size     ,  {'shortest_edge': 18, 'longest_edge': 13_33})
					self.assertEqual(image_processor.do_pad     ,  _A)

					__snake_case      :							Optional[int]						=			self.image_processing_class.from_dict(
					    self.image_processor_dict     ,  size=42     ,  max_size=84     ,  pad_and_return_pixel_mask=_A)
					self.assertEqual(image_processor.size     ,  {'shortest_edge': 42, 'longest_edge': 84})
					self.assertEqual(image_processor.do_pad     ,  _A)



				def  _lowercase					(self    :				str)   ->     List[Any]:
					pass



				def  _lowercase					(self    :				Dict)   ->     Optional[int]:
					# Initialize image_processing
					__snake_case      :							Union[str, Any]						=			self.image_processing_class(**self.image_processor_dict)
					# create random PIL images
					__snake_case      :							Dict						=			prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=_A)
					for image in image_inputs:
						self.assertIsInstance(_A     ,  Image.Image)

					# Test not batched input
					__snake_case      :							Optional[Any]						=			image_processing(image_inputs[0]     ,  return_tensors='pt').pixel_values

					__snake_case		,    __snake_case      :							Dict						=			self.image_processor_tester.get_expected_values(_A)

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

					# Test batched
					__snake_case		,    __snake_case      :							str						=			self.image_processor_tester.get_expected_values(_A     ,  batched=_A)

					__snake_case      :							Union[str, Any]						=			image_processing(_A     ,  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  _lowercase					(self    :				List[str])   ->     Optional[int]:
					# Initialize image_processing
					__snake_case      :							Tuple						=			self.image_processing_class(**self.image_processor_dict)
					# create random numpy tensors
					__snake_case      :							Union[str, Any]						=			prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=_A     ,  numpify=_A)
					for image in image_inputs:
						self.assertIsInstance(_A     ,  np.ndarray)

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

					__snake_case		,    __snake_case      :							Dict						=			self.image_processor_tester.get_expected_values(_A)

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

					# Test batched
					__snake_case      :							Any						=			image_processing(_A     ,  return_tensors='pt').pixel_values

					__snake_case		,    __snake_case      :							Optional[int]						=			self.image_processor_tester.get_expected_values(_A     ,  batched=_A)

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



				def  _lowercase					(self    :				Any)   ->     Union[str, Any]:
					# Initialize image_processing
					__snake_case      :							Union[str, Any]						=			self.image_processing_class(**self.image_processor_dict)
					# create random PyTorch tensors
					__snake_case      :							Any						=			prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=_A     ,  torchify=_A)
					for image in image_inputs:
						self.assertIsInstance(_A     ,  torch.Tensor)

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

					__snake_case		,    __snake_case      :							Any						=			self.image_processor_tester.get_expected_values(_A)

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

					# Test batched
					__snake_case      :							int						=			image_processing(_A     ,  return_tensors='pt').pixel_values

					__snake_case		,    __snake_case      :							List[Any]						=			self.image_processor_tester.get_expected_values(_A     ,  batched=_A)

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



				@slow
				def  _lowercase					(self    :				Dict)   ->     Dict:
					# prepare image and target
					__snake_case      :							Optional[int]						=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
					with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'     ,  'r') as f:
						__snake_case      :							Dict						=			json.loads(f.read())

					__snake_case      :							Dict						=			{'image_id': 3_97_69, 'annotations': target}

					# encode them
					__snake_case      :							Dict						=			DeformableDetrImageProcessor()
					__snake_case      :							List[str]						=			image_processing(images=_A     ,  annotations=_A     ,  return_tensors='pt')

					# verify pixel values
					__snake_case      :							str						=			torch.Size([1, 3, 8_00, 10_66])
					self.assertEqual(encoding['pixel_values'].shape     ,  _A)

					__snake_case      :							str						=			torch.tensor([0.2_796, 0.3_138, 0.3_481])
					self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]     ,  _A     ,  atol=1E-4))

					# verify area
					__snake_case      :							Dict						=			torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438])
					self.assertTrue(torch.allclose(encoding['labels'][0]['area']     ,  _A))
					# verify boxes
					__snake_case      :							List[Any]						=			torch.Size([6, 4])
					self.assertEqual(encoding['labels'][0]['boxes'].shape     ,  _A)
					__snake_case      :							int						=			torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215])
					self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]     ,  _A     ,  atol=1E-3))
					# verify image_id
					__snake_case      :							Optional[int]						=			torch.tensor([3_97_69])
					self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']     ,  _A))
					# verify is_crowd
					__snake_case      :							int						=			torch.tensor([0, 0, 0, 0, 0, 0])
					self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']     ,  _A))
					# verify class_labels
					__snake_case      :							List[str]						=			torch.tensor([75, 75, 63, 65, 17, 17])
					self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']     ,  _A))
					# verify orig_size
					__snake_case      :							Optional[Any]						=			torch.tensor([4_80, 6_40])
					self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']     ,  _A))
					# verify size
					__snake_case      :							Optional[Any]						=			torch.tensor([8_00, 10_66])
					self.assertTrue(torch.allclose(encoding['labels'][0]['size']     ,  _A))






				@slow
				def  _lowercase					(self    :				str)   ->     Union[str, Any]:
					# prepare image, target and masks_path
					__snake_case      :							List[Any]						=			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      :							List[Any]						=			json.loads(f.read())

					__snake_case      :							Dict						=			{'file_name': '000000039769.png', 'image_id': 3_97_69, 'segments_info': target}

					__snake_case      :							List[str]						=			pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic')

					# encode them
					__snake_case      :							str						=			DeformableDetrImageProcessor(format='coco_panoptic')
					__snake_case      :							List[str]						=			image_processing(images=_A     ,  annotations=_A     ,  masks_path=_A     ,  return_tensors='pt')

					# verify pixel values
					__snake_case      :							Tuple						=			torch.Size([1, 3, 8_00, 10_66])
					self.assertEqual(encoding['pixel_values'].shape     ,  _A)

					__snake_case      :							Optional[int]						=			torch.tensor([0.2_796, 0.3_138, 0.3_481])
					self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]     ,  _A     ,  atol=1E-4))

					# verify area
					__snake_case      :							List[Any]						=			torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147])
					self.assertTrue(torch.allclose(encoding['labels'][0]['area']     ,  _A))
					# verify boxes
					__snake_case      :							List[Any]						=			torch.Size([6, 4])
					self.assertEqual(encoding['labels'][0]['boxes'].shape     ,  _A)
					__snake_case      :							Tuple						=			torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625])
					self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]     ,  _A     ,  atol=1E-3))
					# verify image_id
					__snake_case      :							List[str]						=			torch.tensor([3_97_69])
					self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']     ,  _A))
					# verify is_crowd
					__snake_case      :							List[str]						=			torch.tensor([0, 0, 0, 0, 0, 0])
					self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']     ,  _A))
					# verify class_labels
					__snake_case      :							Optional[int]						=			torch.tensor([17, 17, 63, 75, 75, 93])
					self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']     ,  _A))
					# verify masks
					__snake_case      :							Optional[int]						=			82_28_73
					self.assertEqual(encoding['labels'][0]['masks'].sum().item()     ,  _A)
					# verify orig_size
					__snake_case      :							List[str]						=			torch.tensor([4_80, 6_40])
					self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']     ,  _A))
					# verify size
					__snake_case      :							Any						=			torch.tensor([8_00, 10_66])
					self.assertTrue(torch.allclose(encoding['labels'][0]['size']     ,  _A))






"""simple docstring"""







from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Namespace  )							->      Union[str, Any]:

	'''simple docstring'''





	return ConvertCommand(
	    args.model_type      ,							args.tf_checkpoint      ,							args.pytorch_dump_output      ,							args.config      ,							args.finetuning_task_name  )


_a					:					str=			"\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n"

class 		UpperCamelCase      (   lowercase						):



				@staticmethod
				def  _lowercase					(_A    :				ArgumentParser)   ->     Tuple:
					__snake_case      :							Optional[Any]						=			parser.add_parser(
					    'convert'     ,  help='CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.'     ,  )
					train_parser.add_argument('--model_type'     ,  type=_A     ,  required=_A     ,  help='Model\'s type.')
					train_parser.add_argument(
					    '--tf_checkpoint'     ,  type=_A     ,  required=_A     ,  help='TensorFlow checkpoint path or folder.')
					train_parser.add_argument(
					    '--pytorch_dump_output'     ,  type=_A     ,  required=_A     ,  help='Path to the PyTorch saved model output.')
					train_parser.add_argument('--config'     ,  type=_A     ,  default=''     ,  help='Configuration file path or folder.')
					train_parser.add_argument(
					    '--finetuning_task_name'     ,  type=_A     ,  default=_A     ,  help='Optional fine-tuning task name if the TF model was a finetuned model.'     ,  )
					train_parser.set_defaults(func=_A)



				def __init__(self    :				List[str]     ,  _A    :				str     ,  _A    :				str     ,  _A    :				str     ,  _A    :				str     ,  _A    :				str     ,  *_A    :				Any     ,  )   ->     Optional[Any]:
					__snake_case      :							List[Any]						=			logging.get_logger('transformers-cli/converting')

					self._logger.info(f"Loading model {model_type}")
					__snake_case      :							List[str]						=			model_type
					__snake_case      :							int						=			tf_checkpoint
					__snake_case      :							Optional[int]						=			pytorch_dump_output
					__snake_case      :							Optional[Any]						=			config
					__snake_case      :							Optional[Any]						=			finetuning_task_name






				def  _lowercase					(self    :				List[str])   ->     str:
					if self._model_type == "albert":
						try:
							from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
							    convert_tf_checkpoint_to_pytorch,
							)
						except ImportError:
							raise ImportError(_A)

						convert_tf_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._config     ,  self._pytorch_dump_output)
					elif self._model_type == "bert":
						try:
							from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
							    convert_tf_checkpoint_to_pytorch,
							)
						except ImportError:
							raise ImportError(_A)

						convert_tf_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._config     ,  self._pytorch_dump_output)
					elif self._model_type == "funnel":
						try:
							from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
							    convert_tf_checkpoint_to_pytorch,
							)
						except ImportError:
							raise ImportError(_A)

						convert_tf_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._config     ,  self._pytorch_dump_output)
					elif self._model_type == "t5":
						try:
							from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
						except ImportError:
							raise ImportError(_A)

						convert_tf_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._config     ,  self._pytorch_dump_output)
					elif self._model_type == "gpt":
						from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
						    convert_openai_checkpoint_to_pytorch,
						)

						convert_openai_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._config     ,  self._pytorch_dump_output)
					elif self._model_type == "transfo_xl":
						try:
							from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
							    convert_transfo_xl_checkpoint_to_pytorch,
							)
						except ImportError:
							raise ImportError(_A)

						if "ckpt" in self._tf_checkpoint.lower():
							__snake_case      :							Union[str, Any]						=			self._tf_checkpoint
							__snake_case      :							List[Any]						=			''
						else:
							__snake_case      :							Optional[Any]						=			self._tf_checkpoint
							__snake_case      :							List[Any]						=			''
						convert_transfo_xl_checkpoint_to_pytorch(
						    _A     ,  self._config     ,  self._pytorch_dump_output     ,  _A)
					elif self._model_type == "gpt2":
						try:
							from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
							    convert_gpta_checkpoint_to_pytorch,
							)
						except ImportError:
							raise ImportError(_A)

						convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._config     ,  self._pytorch_dump_output)
					elif self._model_type == "xlnet":
						try:
							from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
							    convert_xlnet_checkpoint_to_pytorch,
							)
						except ImportError:
							raise ImportError(_A)

						convert_xlnet_checkpoint_to_pytorch(
						    self._tf_checkpoint     ,  self._config     ,  self._pytorch_dump_output     ,  self._finetuning_task_name)
					elif self._model_type == "xlm":
						from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
						    convert_xlm_checkpoint_to_pytorch,
						)

						convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._pytorch_dump_output)
					elif self._model_type == "lxmert":
						from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
						    convert_lxmert_checkpoint_to_pytorch,
						)

						convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._pytorch_dump_output)
					elif self._model_type == "rembert":
						from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
						    convert_rembert_tf_checkpoint_to_pytorch,
						)

						convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._config     ,  self._pytorch_dump_output)
					else:
						raise ValueError(
						    '--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]')





