






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 DetaImageProcessor
class     lowerCamelCase_					(   unittest.TestCase			):







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







				def       A						(    self  ):
								"""simple docstring"""
								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       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase=False  ):
								"""simple docstring"""
								if not batched:
												__magic_name__		:Optional[int]   =   image_inputs[0]
												if isinstance(__lowerCAmelCase							,       Image.Image  ):
																__magic_name__  ,					__magic_name__		:List[str]   =   image.size
												else:
																__magic_name__  ,					__magic_name__		:List[Any]   =   image.shape[1], image.shape[2]
												if w < h:
																__magic_name__		:str   =   int(self.size['''shortest_edge'''] * h / w  )
																__magic_name__		:List[str]   =   self.size['''shortest_edge''']
												elif w > h:
																__magic_name__		:List[Any]   =   self.size['''shortest_edge''']
																__magic_name__		:Optional[Any]   =   int(self.size['''shortest_edge'''] * w / h  )
												else:
																__magic_name__		:str   =   self.size['''shortest_edge''']
																__magic_name__		:Optional[Any]   =   self.size['''shortest_edge''']

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

								return expected_height, expected_width







@require_torch
@require_vision
class     lowerCamelCase_					(   lowerCamelCase      ,					unittest.TestCase			):
				a__       							=	DetaImageProcessor if is_vision_available() else None







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[int]   =   DetaImageProcessingTester(self  )







				@property
				def       A						(    self  ):
								"""simple docstring"""
								return self.image_processor_tester.prepare_image_processor_dict()







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   self.image_processing_class(**self.image_processor_dict  )
								self.assertTrue(hasattr(__lowerCAmelCase							,       '''image_mean'''  )  )
								self.assertTrue(hasattr(__lowerCAmelCase							,       '''image_std'''  )  )
								self.assertTrue(hasattr(__lowerCAmelCase							,       '''do_normalize'''  )  )
								self.assertTrue(hasattr(__lowerCAmelCase							,       '''do_resize'''  )  )
								self.assertTrue(hasattr(__lowerCAmelCase							,       '''do_rescale'''  )  )
								self.assertTrue(hasattr(__lowerCAmelCase							,       '''do_pad'''  )  )
								self.assertTrue(hasattr(__lowerCAmelCase							,       '''size'''  )  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[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							,       __lowerCAmelCase  )







				def       A						(    self  ):
								"""simple docstring"""
								pass







				def       A						(    self  ):
								"""simple docstring"""
								# Initialize image_processing
								__magic_name__		:Tuple   =   self.image_processing_class(**self.image_processor_dict  )
								# create random PIL images
								__magic_name__		:Optional[int]   =   prepare_image_inputs(self.image_processor_tester							,       equal_resolution=__lowerCAmelCase  )
								for image in image_inputs:
												self.assertIsInstance(__lowerCAmelCase							,       Image.Image  )

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

								__magic_name__  ,					__magic_name__		:str   =   self.image_processor_tester.get_expected_values(__lowerCAmelCase  )

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

								# Test batched
								__magic_name__  ,					__magic_name__		:int   =   self.image_processor_tester.get_expected_values(__lowerCAmelCase							,       batched=__lowerCAmelCase  )

								__magic_name__		:Optional[Any]   =   image_processing(__lowerCAmelCase							,       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  ):
								"""simple docstring"""
								# Initialize image_processing
								__magic_name__		:Optional[int]   =   self.image_processing_class(**self.image_processor_dict  )
								# create random numpy tensors
								__magic_name__		:Optional[int]   =   prepare_image_inputs(self.image_processor_tester							,       equal_resolution=__lowerCAmelCase							,       numpify=__lowerCAmelCase  )
								for image in image_inputs:
												self.assertIsInstance(__lowerCAmelCase							,       np.ndarray  )

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

								__magic_name__  ,					__magic_name__		:str   =   self.image_processor_tester.get_expected_values(__lowerCAmelCase  )

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

								# Test batched
								__magic_name__		:str   =   image_processing(__lowerCAmelCase							,       return_tensors='''pt'''  ).pixel_values

								__magic_name__  ,					__magic_name__		:Optional[int]   =   self.image_processor_tester.get_expected_values(__lowerCAmelCase							,       batched=__lowerCAmelCase  )

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







				def       A						(    self  ):
								"""simple docstring"""
								# Initialize image_processing
								__magic_name__		:Any   =   self.image_processing_class(**self.image_processor_dict  )
								# create random PyTorch tensors
								__magic_name__		:Tuple   =   prepare_image_inputs(self.image_processor_tester							,       equal_resolution=__lowerCAmelCase							,       torchify=__lowerCAmelCase  )
								for image in image_inputs:
												self.assertIsInstance(__lowerCAmelCase							,       torch.Tensor  )

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

								__magic_name__  ,					__magic_name__		:Dict   =   self.image_processor_tester.get_expected_values(__lowerCAmelCase  )

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

								# Test batched
								__magic_name__		:Dict   =   image_processing(__lowerCAmelCase							,       return_tensors='''pt'''  ).pixel_values

								__magic_name__  ,					__magic_name__		:str   =   self.image_processor_tester.get_expected_values(__lowerCAmelCase							,       batched=__lowerCAmelCase  )

								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  ):
								"""simple docstring"""
								# prepare image and target
								__magic_name__		:Union[str, Any]   =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
								with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''							,       '''r'''  ) as f:
												__magic_name__		:List[Any]   =   json.loads(f.read()  )

								__magic_name__		:Tuple   =   {'''image_id''': 3_9_7_6_9, '''annotations''': target}

								# encode them
								__magic_name__		:Tuple   =   DetaImageProcessor()
								__magic_name__		:Dict   =   image_processing(images=__lowerCAmelCase							,       annotations=__lowerCAmelCase							,       return_tensors='''pt'''  )

								# verify pixel values
								__magic_name__		:Dict   =   torch.Size([1, 3, 8_0_0, 1_0_6_6]  )
								self.assertEqual(encoding['''pixel_values'''].shape							,       __lowerCAmelCase  )

								__magic_name__		:Dict   =   torch.tensor([0.2796, 0.3138, 0.3481]  )
								self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]							,       __lowerCAmelCase							,       atol=1E-4  )  )

								# verify area
								__magic_name__		:List[str]   =   torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']							,       __lowerCAmelCase  )  )
								# verify boxes
								__magic_name__		:List[Any]   =   torch.Size([6, 4]  )
								self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape							,       __lowerCAmelCase  )
								__magic_name__		:Union[str, Any]   =   torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]							,       __lowerCAmelCase							,       atol=1E-3  )  )
								# verify image_id
								__magic_name__		:Tuple   =   torch.tensor([3_9_7_6_9]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']							,       __lowerCAmelCase  )  )
								# verify is_crowd
								__magic_name__		:Optional[Any]   =   torch.tensor([0, 0, 0, 0, 0, 0]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']							,       __lowerCAmelCase  )  )
								# verify class_labels
								__magic_name__		:Union[str, Any]   =   torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']							,       __lowerCAmelCase  )  )
								# verify orig_size
								__magic_name__		:Optional[int]   =   torch.tensor([4_8_0, 6_4_0]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']							,       __lowerCAmelCase  )  )
								# verify size
								__magic_name__		:List[Any]   =   torch.tensor([8_0_0, 1_0_6_6]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']							,       __lowerCAmelCase  )  )







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

								__magic_name__		:Optional[int]   =   {'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}

								__magic_name__		:Any   =   pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''  )

								# encode them
								__magic_name__		:List[str]   =   DetaImageProcessor(format='''coco_panoptic'''  )
								__magic_name__		:Optional[int]   =   image_processing(images=__lowerCAmelCase							,       annotations=__lowerCAmelCase							,       masks_path=__lowerCAmelCase							,       return_tensors='''pt'''  )

								# verify pixel values
								__magic_name__		:Dict   =   torch.Size([1, 3, 8_0_0, 1_0_6_6]  )
								self.assertEqual(encoding['''pixel_values'''].shape							,       __lowerCAmelCase  )

								__magic_name__		:Optional[int]   =   torch.tensor([0.2796, 0.3138, 0.3481]  )
								self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]							,       __lowerCAmelCase							,       atol=1E-4  )  )

								# verify area
								__magic_name__		:str   =   torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']							,       __lowerCAmelCase  )  )
								# verify boxes
								__magic_name__		:Optional[Any]   =   torch.Size([6, 4]  )
								self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape							,       __lowerCAmelCase  )
								__magic_name__		:int   =   torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]							,       __lowerCAmelCase							,       atol=1E-3  )  )
								# verify image_id
								__magic_name__		:int   =   torch.tensor([3_9_7_6_9]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']							,       __lowerCAmelCase  )  )
								# verify is_crowd
								__magic_name__		:Tuple   =   torch.tensor([0, 0, 0, 0, 0, 0]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']							,       __lowerCAmelCase  )  )
								# verify class_labels
								__magic_name__		:Tuple   =   torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']							,       __lowerCAmelCase  )  )
								# verify masks
								__magic_name__		:List[str]   =   8_2_2_8_7_3
								self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()							,       __lowerCAmelCase  )
								# verify orig_size
								__magic_name__		:Union[str, Any]   =   torch.tensor([4_8_0, 6_4_0]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']							,       __lowerCAmelCase  )  )
								# verify size
								__magic_name__		:List[str]   =   torch.tensor([8_0_0, 1_0_6_6]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']							,       __lowerCAmelCase  )  )










# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

a			:			Union[str, Any]												=					'''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
a			:			Tuple												=					[
    '''l''',
    '''o''',
    '''w''',
    '''e''',
    '''r''',
    '''s''',
    '''t''',
    '''i''',
    '''d''',
    '''n''',
    '''w</w>''',
    '''r</w>''',
    '''t</w>''',
    '''lo''',
    '''low''',
    '''er</w>''',
    '''low</w>''',
    '''lowest</w>''',
    '''newer</w>''',
    '''wider</w>''',
    '''<unk>''',
]
a			:			List[Any]												=					dict(zip(vocab, range(len(vocab))))
a			:			Optional[int]												=					['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
	a			:			Union[str, Any]												=					Path(tmpdirname)
	a			:			Dict												=					build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
	a			:			Optional[int]												=					build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
	a			:			Tuple												=					build_dir / VOCAB_FILES_NAMES['''merges_file''']
	with open(src_vocab_file, '''w''') as fp:
		fp.write(json.dumps(vocab_tokens))
	with open(tgt_vocab_file, '''w''') as fp:
		fp.write(json.dumps(vocab_tokens))
	with open(merges_file, '''w''') as fp:
		fp.write('''\n'''.join(merges))

	a			:			Optional[int]												=					FSMTTokenizer(
	    langs=['''en''', '''ru'''],
	    src_vocab_size=len(vocab),
	    tgt_vocab_size=len(vocab),
	    src_vocab_file=src_vocab_file,
	    tgt_vocab_file=tgt_vocab_file,
	    merges_file=merges_file,
	)

a			:			List[str]												=					FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1000,
    tgt_vocab_size=1000,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

a			:			Union[str, Any]												=					FSMTForConditionalGeneration(config)
print(f'''num of params {tiny_model.num_parameters()}''')

# Test
a			:			Dict												=					tokenizer(['''Making tiny model'''], return_tensors='''pt''')
a			:			Union[str, Any]												=					tiny_model(**batch)

print('''test output:''', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f'''Generated {mname_tiny}''')

# Upload
# transformers-cli upload tiny-wmt19-en-ru


