


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 						lowerCamelCase_							(					unittest.TestCase					):



					'''simple docstring'''







					def __init__(					self			,							snake_case_			,							snake_case_=7			,							snake_case_=3			,							snake_case_=3_0			,							snake_case_=4_0_0			,							snake_case_=True			,							snake_case_=None			,							snake_case_=True			,							snake_case_=[0.5, 0.5, 0.5]			,							snake_case_=[0.5, 0.5, 0.5]			,							snake_case_=True			,							snake_case_=1 / 2_5_5			,							snake_case_=True			,							) ->      List[Any]:







								'''simple docstring'''

								__lowercase			       = size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
								__lowercase			       = parent
								__lowercase			       = batch_size
								__lowercase			       = num_channels
								__lowercase			       = min_resolution
								__lowercase			       = max_resolution
								__lowercase			       = do_resize
								__lowercase			       = size
								__lowercase			       = do_normalize
								__lowercase			       = image_mean
								__lowercase			       = image_std
								__lowercase			       = do_rescale
								__lowercase			       = rescale_factor
								__lowercase			       = do_pad






					def A   (					self    ) ->      int:







								'''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			,							snake_case_			,							snake_case_=False    ) ->      Tuple:







								'''simple docstring'''

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

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

								return expected_height, expected_width




@require_torch
@require_vision
class 						lowerCamelCase_							(					lowerCAmelCase__  ,  unittest.TestCase					):



					'''simple docstring'''







					__UpperCAmelCase							  =					DeformableDetrImageProcessor if is_vision_available() else None






					def A   (					self    ) ->      Any:







								'''simple docstring'''

								__lowercase			       = DeformableDetrImageProcessingTester(self    )






					@property
					def A   (					self    ) ->      Optional[int]:







								'''simple docstring'''

								return self.image_processor_tester.prepare_image_processor_dict()






					def A   (					self    ) ->      List[str]:







								'''simple docstring'''

								__lowercase			       = self.image_processing_class(**self.image_processor_dict    )
								self.assertTrue(hasattr(snake_case_			,							'''image_mean'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''image_std'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''do_normalize'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''do_resize'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''do_rescale'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''do_pad'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''size'''    )    )






					def A   (					self    ) ->      Optional[int]:







								'''simple docstring'''

								__lowercase			       = 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			,							snake_case_    )

								__lowercase			       = self.image_processing_class.from_dict(
								    self.image_processor_dict			,							size=4_2			,							max_size=8_4			,							pad_and_return_pixel_mask=snake_case_    )
								self.assertEqual(image_processor.size			,							{'''shortest_edge''': 4_2, '''longest_edge''': 8_4}    )
								self.assertEqual(image_processor.do_pad			,							snake_case_    )






					def A   (					self    ) ->      Tuple:







								'''simple docstring'''

								pass






					def A   (					self    ) ->      Tuple:







								'''simple docstring'''

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

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

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

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

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

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






					def A   (					self    ) ->      Optional[int]:







								'''simple docstring'''

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

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

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

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

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

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

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






					def A   (					self    ) ->      Optional[int]:







								'''simple docstring'''

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

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

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

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

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

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

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






					@slow
					def A   (					self    ) ->      Any:







								'''simple docstring'''

								__lowercase			       = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
								with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''			,							'''r'''    ) as f:
											__lowercase			       = json.loads(f.read()    )

								__lowercase			       = {'''image_id''': 3_9_7_6_9, '''annotations''': target}

								# encode them
								__lowercase			       = DeformableDetrImageProcessor()
								__lowercase			       = image_processing(images=snake_case_			,							annotations=snake_case_			,							return_tensors='''pt'''    )

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

								__lowercase			       = 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]			,							snake_case_			,							atol=1e-4    )    )

								# verify area
								__lowercase			       = 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''']			,							snake_case_    )    )
								# verify boxes
								__lowercase			       = torch.Size([6, 4]    )
								self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape			,							snake_case_    )
								__lowercase			       = 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]			,							snake_case_			,							atol=1e-3    )    )
								# verify image_id
								__lowercase			       = torch.tensor([3_9_7_6_9]    )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']			,							snake_case_    )    )
								# verify is_crowd
								__lowercase			       = torch.tensor([0, 0, 0, 0, 0, 0]    )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']			,							snake_case_    )    )
								# verify class_labels
								__lowercase			       = torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]    )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']			,							snake_case_    )    )
								# verify orig_size
								__lowercase			       = torch.tensor([4_8_0, 6_4_0]    )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']			,							snake_case_    )    )
								# verify size
								__lowercase			       = torch.tensor([8_0_0, 1_0_6_6]    )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']			,							snake_case_    )    )






					@slow
					def A   (					self    ) ->      List[Any]:







								'''simple docstring'''

								__lowercase			       = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
								with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''			,							'''r'''    ) as f:
											__lowercase			       = json.loads(f.read()    )

								__lowercase			       = {'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}

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

								# encode them
								__lowercase			       = DeformableDetrImageProcessor(format='''coco_panoptic'''    )
								__lowercase			       = image_processing(images=snake_case_			,							annotations=snake_case_			,							masks_path=snake_case_			,							return_tensors='''pt'''    )

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

								__lowercase			       = 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]			,							snake_case_			,							atol=1e-4    )    )

								# verify area
								__lowercase			       = 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''']			,							snake_case_    )    )
								# verify boxes
								__lowercase			       = torch.Size([6, 4]    )
								self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape			,							snake_case_    )
								__lowercase			       = 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]			,							snake_case_			,							atol=1e-3    )    )
								# verify image_id
								__lowercase			       = torch.tensor([3_9_7_6_9]    )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']			,							snake_case_    )    )
								# verify is_crowd
								__lowercase			       = torch.tensor([0, 0, 0, 0, 0, 0]    )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']			,							snake_case_    )    )
								# verify class_labels
								__lowercase			       = torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]    )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']			,							snake_case_    )    )
								# verify masks
								__lowercase			       = 8_2_2_8_7_3
								self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()			,							snake_case_    )
								# verify orig_size
								__lowercase			       = torch.tensor([4_8_0, 6_4_0]    )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']			,							snake_case_    )    )
								# verify size
								__lowercase			       = torch.tensor([8_0_0, 1_0_6_6]    )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']			,							snake_case_    )    )






# 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


