



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 ConditionalDetrImageProcessor




class 							lowerCAmelCase__  (	unittest.TestCase						):







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







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

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

											return expected_height, expected_width




@require_torch
@require_vision
class 							lowerCAmelCase__  (	_UpperCamelCase					,							unittest.TestCase						):
					__A     :    Optional[int]           =						ConditionalDetrImageProcessor if is_vision_available() else None







					def  _lowercase			(       self      :     str):
											A__	:							str						    = ConditionalDetrImageProcessingTester(self)







					@property
					def  _lowercase			(       self      :     Tuple):
											return self.image_processor_tester.prepare_image_processor_dict()







					def  _lowercase			(       self      :     List[Any]):
											A__	:							List[Any]						    = 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			,		"size"))







					def  _lowercase			(       self      :     Union[str, Any]):
											A__	:							List[Any]						    = 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			,		_A)

											A__	:							Tuple						    = 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      :     Optional[int]):
											pass







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

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

											A__	:							int						    = 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
											A__	:							str						    = self.image_processor_tester.get_expected_values(_A			,		batched=_A)

											A__	:							Dict						    = 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      :     Any):
											# Initialize image_processing
											A__	:							List[str]						    = self.image_processing_class(**self.image_processor_dict)
											# create random numpy tensors
											A__	:							int						    = 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
											A__	:							Tuple						    = image_processing(image_inputs[0]			,		return_tensors="pt").pixel_values

											A__	:							List[str]						    = 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
											A__	:							List[Any]						    = image_processing(_A			,		return_tensors="pt").pixel_values

											A__	:							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      :     List[str]):
											# Initialize image_processing
											A__	:							Dict						    = self.image_processing_class(**self.image_processor_dict)
											# create random PyTorch tensors
											A__	:							Union[str, 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
											A__	:							Union[str, Any]						    = image_processing(image_inputs[0]			,		return_tensors="pt").pixel_values

											A__	:							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
											A__	:							Optional[Any]						    = image_processing(_A			,		return_tensors="pt").pixel_values

											A__	:							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,
											    )			,		)







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

											A__	:							Dict						    = {"image_id": 3_9769, "annotations": target}

											# encode them
											A__	:							Union[str, Any]						    = ConditionalDetrImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50")
											A__	:							List[str]						    = image_processing(images=_A			,		annotations=_A			,		return_tensors="pt")

											# verify pixel values
											A__	:							Union[str, Any]						    = torch.Size([1, 3, 800, 1066])
											self.assertEqual(encoding["pixel_values"].shape			,		_A)

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

											# verify area
											A__	:							Any						    = torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438])
											self.assertTrue(torch.allclose(encoding["labels"][0]["area"]			,		_A))
											# verify boxes
											A__	:							Union[str, Any]						    = torch.Size([6, 4])
											self.assertEqual(encoding["labels"][0]["boxes"].shape			,		_A)
											A__	:							List[Any]						    = torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15])
											self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]			,		_A			,		atol=1e-3))
											# verify image_id
											A__	:							str						    = torch.tensor([3_9769])
											self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]			,		_A))
											# verify is_crowd
											A__	:							Optional[Any]						    = torch.tensor([0, 0, 0, 0, 0, 0])
											self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]			,		_A))
											# verify class_labels
											A__	:							str						    = torch.tensor([75, 75, 63, 65, 17, 17])
											self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]			,		_A))
											# verify orig_size
											A__	:							str						    = torch.tensor([480, 640])
											self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]			,		_A))
											# verify size
											A__	:							Optional[Any]						    = torch.tensor([800, 1066])
											self.assertTrue(torch.allclose(encoding["labels"][0]["size"]			,		_A))







					@slow
					def  _lowercase			(       self      :     Dict):
											# prepare image, target and masks_path
											A__	:							Any						    = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
											with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"			,		"r") as f:
																	A__	:							str						    = json.loads(f.read())

											A__	:							Tuple						    = {"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}

											A__	:							str						    = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic")

											# encode them
											A__	:							Union[str, Any]						    = ConditionalDetrImageProcessor(format="coco_panoptic")
											A__	:							Dict						    = image_processing(images=_A			,		annotations=_A			,		masks_path=_A			,		return_tensors="pt")

											# verify pixel values
											A__	:							Optional[int]						    = torch.Size([1, 3, 800, 1066])
											self.assertEqual(encoding["pixel_values"].shape			,		_A)

											A__	:							Union[str, Any]						    = torch.tensor([0.27_96, 0.31_38, 0.34_81])
											self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]			,		_A			,		atol=1e-4))

											# verify area
											A__	:							Tuple						    = torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147])
											self.assertTrue(torch.allclose(encoding["labels"][0]["area"]			,		_A))
											# verify boxes
											A__	:							Optional[Any]						    = torch.Size([6, 4])
											self.assertEqual(encoding["labels"][0]["boxes"].shape			,		_A)
											A__	:							List[str]						    = torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25])
											self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]			,		_A			,		atol=1e-3))
											# verify image_id
											A__	:							int						    = torch.tensor([3_9769])
											self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]			,		_A))
											# verify is_crowd
											A__	:							int						    = torch.tensor([0, 0, 0, 0, 0, 0])
											self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]			,		_A))
											# verify class_labels
											A__	:							Union[str, Any]						    = torch.tensor([17, 17, 63, 75, 75, 93])
											self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]			,		_A))
											# verify masks
											A__	:							Union[str, Any]						    = 82_2873
											self.assertEqual(encoding["labels"][0]["masks"].sum().item()			,		_A)
											# verify orig_size
											A__	:							int						    = torch.tensor([480, 640])
											self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]			,		_A))
											# verify size
											A__	:							str						    = torch.tensor([800, 1066])
											self.assertTrue(torch.allclose(encoding["labels"][0]["size"]			,		_A))




import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase       )		->		List[Any]:







						"""simple docstring"""

						def get_masked_lm_array(__lowercase       ):
												A__	:							int						    = F'masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'
												A__	:							Dict						    = tf.train.load_variable(__lowercase							,     __lowercase       )

												if "kernel" in name:
																		A__	:							Any						    = array.transpose()

												return torch.from_numpy(__lowercase       )

						def get_encoder_array(__lowercase       ):
												A__	:							Dict						    = F'encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'
												A__	:							Tuple						    = tf.train.load_variable(__lowercase							,     __lowercase       )

												if "kernel" in name:
																		A__	:							str						    = array.transpose()

												return torch.from_numpy(__lowercase       )

						def get_encoder_layer_array(__lowercase							,     __lowercase       ):
												A__	:							int						    = F'encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'
												A__	:							Dict						    = tf.train.load_variable(__lowercase							,     __lowercase       )

												if "kernel" in name:
																		A__	:							Any						    = array.transpose()

												return torch.from_numpy(__lowercase       )

						def get_encoder_attention_layer_array(__lowercase							,     __lowercase							,     __lowercase       ):
												A__	:							List[str]						    = F'encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'
												A__	:							List[Any]						    = tf.train.load_variable(__lowercase							,     __lowercase       )
												A__	:							int						    = array.reshape(__lowercase       )

												if "kernel" in name:
																		A__	:							Union[str, Any]						    = array.transpose()

												return torch.from_numpy(__lowercase       )

						print(F'Loading model based on config from {config_path}...'       )
						A__	:							Tuple						    = BertConfig.from_json_file(__lowercase       )
						A__	:							List[str]						    = BertForMaskedLM(__lowercase       )

						# Layers
						for layer_index in range(0							,     config.num_hidden_layers       ):
												A__	:							BertLayer						    = model.bert.encoder.layer[layer_index]

												# Self-attention
												A__	:							BertSelfAttention						    = layer.attention.self

												A__	:							Optional[Any]						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_query_dense/kernel"							,     self_attn.query.weight.data.shape       )
												A__	:							List[Any]						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_query_dense/bias"							,     self_attn.query.bias.data.shape       )
												A__	:							int						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_key_dense/kernel"							,     self_attn.key.weight.data.shape       )
												A__	:							Tuple						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_key_dense/bias"							,     self_attn.key.bias.data.shape       )
												A__	:							List[Any]						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_value_dense/kernel"							,     self_attn.value.weight.data.shape       )
												A__	:							Optional[Any]						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_value_dense/bias"							,     self_attn.value.bias.data.shape       )

												# Self-attention Output
												A__	:							BertSelfOutput						    = layer.attention.output

												A__	:							Tuple						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_output_dense/kernel"							,     self_output.dense.weight.data.shape       )
												A__	:							Any						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_output_dense/bias"							,     self_output.dense.bias.data.shape       )

												A__	:							Dict						    = get_encoder_layer_array(__lowercase							,     "_attention_layer_norm/gamma"       )
												A__	:							Optional[int]						    = get_encoder_layer_array(__lowercase							,     "_attention_layer_norm/beta"       )

												# Intermediate
												A__	:							BertIntermediate						    = layer.intermediate

												A__	:							Optional[Any]						    = get_encoder_layer_array(__lowercase							,     "_intermediate_dense/kernel"       )
												A__	:							Any						    = get_encoder_layer_array(__lowercase							,     "_intermediate_dense/bias"       )

												# Output
												A__	:							BertOutput						    = layer.output

												A__	:							Dict						    = get_encoder_layer_array(__lowercase							,     "_output_dense/kernel"       )
												A__	:							List[Any]						    = get_encoder_layer_array(__lowercase							,     "_output_dense/bias"       )

												A__	:							Optional[Any]						    = get_encoder_layer_array(__lowercase							,     "_output_layer_norm/gamma"       )
												A__	:							Dict						    = get_encoder_layer_array(__lowercase							,     "_output_layer_norm/beta"       )

						# Embeddings
						A__	:							str						    = get_encoder_array("_position_embedding_layer/embeddings"       )
						A__	:							List[str]						    = get_encoder_array("_type_embedding_layer/embeddings"       )
						A__	:							List[Any]						    = get_encoder_array("_embedding_norm_layer/gamma"       )
						A__	:							Optional[int]						    = get_encoder_array("_embedding_norm_layer/beta"       )

						# LM Head
						A__	:							Optional[Any]						    = model.cls.predictions.transform

						A__	:							int						    = get_masked_lm_array("dense/kernel"       )
						A__	:							Tuple						    = get_masked_lm_array("dense/bias"       )

						A__	:							Optional[Any]						    = get_masked_lm_array("layer_norm/gamma"       )
						A__	:							Tuple						    = get_masked_lm_array("layer_norm/beta"       )

						A__	:							Any						    = get_masked_lm_array("embedding_table"       )

						# Pooling
						A__	:							Optional[Any]						    = BertPooler(config=__lowercase       )
						A__	:							BertPooler						    = get_encoder_array("_pooler_layer/kernel"       )
						A__	:							BertPooler						    = get_encoder_array("_pooler_layer/bias"       )

						# Export final model
						model.save_pretrained(__lowercase       )

						# Integration test - should load without any errors ;)
						A__	:							Union[str, Any]						    = BertForMaskedLM.from_pretrained(__lowercase       )
						print(new_model.eval()       )

						print("Model conversion was done sucessfully!"       )


if __name__ == "__main__":
		snake_case    :						str  =						argparse.ArgumentParser()
		parser.add_argument(
		    '--tf_checkpoint_path', type=str, required=True, help='Path to the TensorFlow Token Dropping checkpoint path.'
		)
		parser.add_argument(
		    '--bert_config_file',
		    type=str,
		    required=True,
		    help='The config json file corresponding to the BERT model. This specifies the model architecture.',
		)
		parser.add_argument(
		    '--pytorch_dump_path',
		    type=str,
		    required=True,
		    help='Path to the output PyTorch model.',
		)
		snake_case    :						Dict  =						parser.parse_args()
		convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)