






# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper

import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname

import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary

from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

__lowerCamelCase      :    int   		=   2

# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
__lowerCamelCase      :    List[str]   		=   {
    # fairseq:
    """wmt19-ru-en""": {"""length_penalty""": 1.1},
    """wmt19-en-ru""": {"""length_penalty""": 1.15},
    """wmt19-en-de""": {"""length_penalty""": 1.0},
    """wmt19-de-en""": {"""length_penalty""": 1.1},
    # allenai:
    """wmt16-en-de-dist-12-1""": {"""length_penalty""": 0.6},
    """wmt16-en-de-dist-6-1""": {"""length_penalty""": 0.6},
    """wmt16-en-de-12-1""": {"""length_penalty""": 0.8},
    """wmt19-de-en-6-6-base""": {"""length_penalty""": 0.6},
    """wmt19-de-en-6-6-big""": {"""length_penalty""": 0.6},
}

# this remaps the different models to their organization names
__lowerCamelCase      :    Tuple   		=   {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
	__lowerCamelCase      :    Tuple   		=   """facebook"""
for m in [
    "wmt16-en-de-dist-12-1",
    "wmt16-en-de-dist-6-1",
    "wmt16-en-de-12-1",
    "wmt19-de-en-6-6-base",
    "wmt19-de-en-6-6-big",
]:
	__lowerCamelCase      :    List[Any]   		=   """allenai"""







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							int     ):
	# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
	# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
	snake_case__				:   Any     =						dict((re.sub(R"@@$"  ,						""  ,						snake_case_     ), v) if k.endswith("@@"     ) else (re.sub(R"$"  ,						"</w>"  ,						snake_case_     ), v) for k, v in d.items()     )
	snake_case__				:   List[Any]     =						"<s> <pad> </s> <unk>".split()
	# restore the special tokens
	for k in keep_keys:
		del da[F'''{k}</w>''']
		snake_case__				:   List[str]     =						d[k]  # restore
	return da







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Tuple  ,						snake_case_      :							Union[str, Any]     ):
	# prep
	assert os.path.exists(snake_case_     )
	os.makedirs(snake_case_  ,						exist_ok=snake_case_     )
	print(F'''Writing results to {pytorch_dump_folder_path}'''     )

	# handle various types of models

	snake_case__				:   Any     =						basename(snake_case_     )
	snake_case__				:   int     =						dirname(snake_case_     )

	snake_case__				:   Optional[Any]     =						fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
	snake_case__				:   List[Any]     =						cls.hub_models()
	snake_case__				:   List[str]     =						{"bpe": "fastbpe", "tokenizer": "moses"}
	snake_case__				:   Union[str, Any]     =						"."
	# note: since the model dump is old, fairseq has upgraded its model some
	# time later, and it does a whole lot of rewrites and splits on the saved
	# weights, therefore we can't use torch.load() directly on the model file.
	# see: upgrade_state_dict(state_dict) in fairseq_model.py
	print(F'''using checkpoint {checkpoint_file}'''     )
	snake_case__				:   List[str]     =						hub_utils.from_pretrained(
	    snake_case_  ,						snake_case_  ,						snake_case_  ,						archive_map=snake_case_  ,						**snake_case_     )

	snake_case__				:   Union[str, Any]     =						vars(chkpt["args"]["model"]     )

	snake_case__				:   Any     =						args["source_lang"]
	snake_case__				:   Union[str, Any]     =						args["target_lang"]

	snake_case__				:   Tuple     =						dirname(snake_case_     )
	snake_case__				:   List[Any]     =						basename(snake_case_     )

	# dicts
	snake_case__				:   List[Any]     =						os.path.join(snake_case_  ,						F'''dict.{src_lang}.txt'''     )
	snake_case__				:   Optional[int]     =						os.path.join(snake_case_  ,						F'''dict.{tgt_lang}.txt'''     )

	snake_case__				:   Tuple     =						Dictionary.load(snake_case_     )
	snake_case__				:   Optional[int]     =						rewrite_dict_keys(src_dict.indices     )
	snake_case__				:   Optional[int]     =						len(snake_case_     )
	snake_case__				:   Optional[int]     =						os.path.join(snake_case_  ,						"vocab-src.json"     )
	print(F'''Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records'''     )
	with open(snake_case_  ,						"w"  ,						encoding="utf-8"     ) as f:
		f.write(json.dumps(snake_case_  ,						ensure_ascii=snake_case_  ,						indent=snake_case_     )     )

	# detect whether this is a do_lower_case situation, which can be derived by checking whether we
	# have at least one uppercase letter in the source vocab
	snake_case__				:   int     =						True
	for k in src_vocab.keys():
		if not k.islower():
			snake_case__				:   int     =						False
			break

	snake_case__				:   str     =						Dictionary.load(snake_case_     )
	snake_case__				:   Dict     =						rewrite_dict_keys(tgt_dict.indices     )
	snake_case__				:   List[Any]     =						len(snake_case_     )
	snake_case__				:   Any     =						os.path.join(snake_case_  ,						"vocab-tgt.json"     )
	print(F'''Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records'''     )
	with open(snake_case_  ,						"w"  ,						encoding="utf-8"     ) as f:
		f.write(json.dumps(snake_case_  ,						ensure_ascii=snake_case_  ,						indent=snake_case_     )     )

	# merges_file (bpecodes)
	snake_case__				:   Dict     =						os.path.join(snake_case_  ,						VOCAB_FILES_NAMES["merges_file"]     )
	for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
		snake_case__				:   str     =						os.path.join(snake_case_  ,						snake_case_     )
		if os.path.exists(snake_case_     ):
			break
	with open(snake_case_  ,						encoding="utf-8"     ) as fin:
		snake_case__				:   str     =						fin.read()
	snake_case__				:   str     =						re.sub(R" \d+$"  ,						""  ,						snake_case_  ,						0  ,						re.M     )  # remove frequency number
	print(F'''Generating {merges_file}'''     )
	with open(snake_case_  ,						"w"  ,						encoding="utf-8"     ) as fout:
		fout.write(snake_case_     )

	# model config
	snake_case__				:   int     =						os.path.join(snake_case_  ,						"config.json"     )

	# validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
	# may have to modify the tokenizer if a different type is used by a future model
	assert args["bpe"] == "fastbpe", F'''need to extend tokenizer to support bpe={args['bpe']}'''
	assert args["tokenizer"] == "moses", F'''need to extend tokenizer to support bpe={args['tokenizer']}'''

	snake_case__				:   Dict     =						{
	    "architectures": ["FSMTForConditionalGeneration"],
	    "model_type": "fsmt",
	    "activation_dropout": args["activation_dropout"],
	    "activation_function": "relu",
	    "attention_dropout": args["attention_dropout"],
	    "d_model": args["decoder_embed_dim"],
	    "dropout": args["dropout"],
	    "init_std": 0.02,
	    "max_position_embeddings": args["max_source_positions"],
	    "num_hidden_layers": args["encoder_layers"],
	    "src_vocab_size": src_vocab_size,
	    "tgt_vocab_size": tgt_vocab_size,
	    "langs": [src_lang, tgt_lang],
	    "encoder_attention_heads": args["encoder_attention_heads"],
	    "encoder_ffn_dim": args["encoder_ffn_embed_dim"],
	    "encoder_layerdrop": args["encoder_layerdrop"],
	    "encoder_layers": args["encoder_layers"],
	    "decoder_attention_heads": args["decoder_attention_heads"],
	    "decoder_ffn_dim": args["decoder_ffn_embed_dim"],
	    "decoder_layerdrop": args["decoder_layerdrop"],
	    "decoder_layers": args["decoder_layers"],
	    "bos_token_id": 0,
	    "pad_token_id": 1,
	    "eos_token_id": 2,
	    "is_encoder_decoder": True,
	    "scale_embedding": not args["no_scale_embedding"],
	    "tie_word_embeddings": args["share_all_embeddings"],
	}

	# good hparam defaults to start with
	snake_case__				:   Any     =						5
	snake_case__				:   Dict     =						False
	if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
		snake_case__				:   Dict     =						best_score_hparams[model_dir]["length_penalty"]
	else:
		snake_case__				:   Optional[int]     =						1.0

	print(F'''Generating {fsmt_model_config_file}'''     )
	with open(snake_case_  ,						"w"  ,						encoding="utf-8"     ) as f:
		f.write(json.dumps(snake_case_  ,						ensure_ascii=snake_case_  ,						indent=snake_case_     )     )

	# tokenizer config
	snake_case__				:   str     =						os.path.join(snake_case_  ,						snake_case_     )

	snake_case__				:   int     =						{
	    "langs": [src_lang, tgt_lang],
	    "model_max_length": 1024,
	    "do_lower_case": do_lower_case,
	}

	print(F'''Generating {fsmt_tokenizer_config_file}'''     )
	with open(snake_case_  ,						"w"  ,						encoding="utf-8"     ) as f:
		f.write(json.dumps(snake_case_  ,						ensure_ascii=snake_case_  ,						indent=snake_case_     )     )

	# model
	snake_case__				:   Tuple     =						chkpt["models"][0]
	snake_case__				:   List[Any]     =						model.state_dict()

	# rename keys to start with 'model.'
	snake_case__				:   Dict     =						OrderedDict(("model." + k, v) for k, v in model_state_dict.items()     )

	# remove unneeded keys
	snake_case__				:   int     =						[
	    "model.model",
	    "model.encoder.version",
	    "model.decoder.version",
	    "model.encoder_embed_tokens.weight",
	    "model.decoder_embed_tokens.weight",
	    "model.encoder.embed_positions._float_tensor",
	    "model.decoder.embed_positions._float_tensor",
	]
	for k in ignore_keys:
		model_state_dict.pop(snake_case_  ,						snake_case_     )

	snake_case__				:   Union[str, Any]     =						FSMTConfig.from_pretrained(snake_case_     )
	snake_case__				:   Tuple     =						FSMTForConditionalGeneration(snake_case_     )

	# check that it loads ok
	model_new.load_state_dict(snake_case_  ,						strict=snake_case_     )

	# save
	snake_case__				:   List[str]     =						os.path.join(snake_case_  ,						snake_case_     )
	print(F'''Generating {pytorch_weights_dump_path}'''     )
	torch.save(snake_case_  ,						snake_case_     )

	print("Conversion is done!"     )
	print("\nLast step is to upload the files to s3"     )
	print(F'''cd {data_root}'''     )
	print(F'''transformers-cli upload {model_dir}'''     )


if __name__ == "__main__":
	__lowerCamelCase      :    Optional[Any]   		=   argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--fsmt_checkpoint_path""",
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        """Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"""
	        """ bpecodes, etc."""
	    ),
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
	)
	__lowerCamelCase      :    List[Any]   		=   parser.parse_args()
	convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)











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 							SCREAMING_SNAKE_CASE__		(       unittest.TestCase		):





					"""simple docstring"""






					def __init__(      self  :						int							,	__A  :						List[str]							,	__A  :						Union[str, Any]=7							,	__A  :						Any=3							,	__A  :						Optional[Any]=3_0							,	__A  :						List[str]=4_0_0							,	__A  :						str=True							,	__A  :						Optional[Any]=None							,	__A  :						Optional[int]=True							,	__A  :						int=[0.5, 0.5, 0.5]							,	__A  :						Dict=[0.5, 0.5, 0.5]							,	__A  :						Optional[int]=True							,	__A  :						int=1 / 2_5_5							,	__A  :						List[str]=True							,	):
						# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
						snake_case__				:   List[str]     =						size if size is not None else {"shortest_edge": 1_8, "longest_edge": 1_3_3_3}
						snake_case__				:   Optional[Any]     =						parent
						snake_case__				:   str     =						batch_size
						snake_case__				:   Union[str, Any]     =						num_channels
						snake_case__				:   Optional[Any]     =						min_resolution
						snake_case__				:   List[str]     =						max_resolution
						snake_case__				:   Tuple     =						do_resize
						snake_case__				:   str     =						size
						snake_case__				:   str     =						do_normalize
						snake_case__				:   Optional[Any]     =						image_mean
						snake_case__				:   List[str]     =						image_std
						snake_case__				:   List[str]     =						do_rescale
						snake_case__				:   Tuple     =						rescale_factor
						snake_case__				:   Tuple     =						do_pad



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

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

						return expected_height, expected_width



@require_torch
@require_vision
class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_				,     unittest.TestCase		):





					"""simple docstring"""






					a_			=	DeformableDetrImageProcessor if is_vision_available() else None



					def  _lowercase  (      self  :						str ):
						snake_case__				:   Optional[Any]     =						DeformableDetrImageProcessingTester(self )



					@property
					def  _lowercase  (      self  :						List[Any] ):
						return self.image_processor_tester.prepare_image_processor_dict()



					def  _lowercase  (      self  :						Tuple ):
						snake_case__				:   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							,	"do_rescale" ) )
						self.assertTrue(hasattr(__A							,	"do_pad" ) )
						self.assertTrue(hasattr(__A							,	"size" ) )



					def  _lowercase  (      self  :						Any ):
						snake_case__				:   Union[str, Any]     =						self.image_processing_class.from_dict(self.image_processor_dict )
						self.assertEqual(image_processor.size							,	{"shortest_edge": 1_8, "longest_edge": 1_3_3_3} )
						self.assertEqual(image_processor.do_pad							,	__A )

						snake_case__				:   Tuple     =						self.image_processing_class.from_dict(
						    self.image_processor_dict							,	size=4_2							,	max_size=8_4							,	pad_and_return_pixel_mask=__A )
						self.assertEqual(image_processor.size							,	{"shortest_edge": 4_2, "longest_edge": 8_4} )
						self.assertEqual(image_processor.do_pad							,	__A )



					def  _lowercase  (      self  :						str ):
						pass



					def  _lowercase  (      self  :						List[str] ):
						# Initialize image_processing
						snake_case__				:   Any     =						self.image_processing_class(**self.image_processor_dict )
						# create random PIL images
						snake_case__				:   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
						snake_case__				:   Tuple     =						image_processing(image_inputs[0]							,	return_tensors="pt" ).pixel_values

						snake_case__,		snake_case__				:   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
						snake_case__,		snake_case__				:   List[Any]     =						self.image_processor_tester.get_expected_values(__A							,	batched=__A )

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

						snake_case__,		snake_case__				:   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
						snake_case__				:   Tuple     =						image_processing(__A							,	return_tensors="pt" ).pixel_values

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

						snake_case__,		snake_case__				:   Union[str, 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__				:   Tuple     =						image_processing(__A							,	return_tensors="pt" ).pixel_values

						snake_case__,		snake_case__				:   Tuple     =						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  :						Optional[Any] ):
						# prepare image and target
						snake_case__				:   Tuple     =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
						with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"							,	"r" ) as f:
							snake_case__				:   Tuple     =						json.loads(f.read() )

						snake_case__				:   Union[str, Any]     =						{"image_id": 3_9_7_6_9, "annotations": target}

						# encode them
						snake_case__				:   str     =						DeformableDetrImageProcessor()
						snake_case__				:   Tuple     =						image_processing(images=__A							,	annotations=__A							,	return_tensors="pt" )

						# verify pixel values
						snake_case__				:   Optional[Any]     =						torch.Size([1, 3, 8_0_0, 1_0_6_6] )
						self.assertEqual(encoding["pixel_values"].shape							,	__A )

						snake_case__				:   Union[str, Any]     =						torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
						self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]							,	__A							,	atol=1e-4 ) )

						# verify area
						snake_case__				:   str     =						torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["area"]							,	__A ) )
						# verify boxes
						snake_case__				:   Union[str, Any]     =						torch.Size([6, 4] )
						self.assertEqual(encoding["labels"][0]["boxes"].shape							,	__A )
						snake_case__				:   List[Any]     =						torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]							,	__A							,	atol=1e-3 ) )
						# verify image_id
						snake_case__				:   Any     =						torch.tensor([3_9_7_6_9] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]							,	__A ) )
						# verify is_crowd
						snake_case__				:   Tuple     =						torch.tensor([0, 0, 0, 0, 0, 0] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]							,	__A ) )
						# verify class_labels
						snake_case__				:   int     =						torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]							,	__A ) )
						# verify orig_size
						snake_case__				:   List[str]     =						torch.tensor([4_8_0, 6_4_0] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]							,	__A ) )
						# verify size
						snake_case__				:   Tuple     =						torch.tensor([8_0_0, 1_0_6_6] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["size"]							,	__A ) )






					@slow
					def  _lowercase  (      self  :						Optional[int] ):
						# prepare image, target and masks_path
						snake_case__				:   Dict     =						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__				:   Any     =						json.loads(f.read() )

						snake_case__				:   Dict     =						{"file_name": "000000039769.png", "image_id": 3_9_7_6_9, "segments_info": target}

						snake_case__				:   int     =						pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic" )

						# encode them
						snake_case__				:   List[str]     =						DeformableDetrImageProcessor(format="coco_panoptic" )
						snake_case__				:   List[Any]     =						image_processing(images=__A							,	annotations=__A							,	masks_path=__A							,	return_tensors="pt" )

						# verify pixel values
						snake_case__				:   List[Any]     =						torch.Size([1, 3, 8_0_0, 1_0_6_6] )
						self.assertEqual(encoding["pixel_values"].shape							,	__A )

						snake_case__				:   Optional[int]     =						torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
						self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]							,	__A							,	atol=1e-4 ) )

						# verify area
						snake_case__				:   Tuple     =						torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["area"]							,	__A ) )
						# verify boxes
						snake_case__				:   Any     =						torch.Size([6, 4] )
						self.assertEqual(encoding["labels"][0]["boxes"].shape							,	__A )
						snake_case__				:   Any     =						torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]							,	__A							,	atol=1e-3 ) )
						# verify image_id
						snake_case__				:   List[str]     =						torch.tensor([3_9_7_6_9] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]							,	__A ) )
						# verify is_crowd
						snake_case__				:   Any     =						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([1_7, 1_7, 6_3, 7_5, 7_5, 9_3] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]							,	__A ) )
						# verify masks
						snake_case__				:   Union[str, Any]     =						8_2_2_8_7_3
						self.assertEqual(encoding["labels"][0]["masks"].sum().item()							,	__A )
						# verify orig_size
						snake_case__				:   int     =						torch.tensor([4_8_0, 6_4_0] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]							,	__A ) )
						# verify size
						snake_case__				:   Union[str, Any]     =						torch.tensor([8_0_0, 1_0_6_6] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["size"]							,	__A ) )



