

import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import AlignProcessor, EfficientNetImageProcessor






@require_vision
class _UpperCamelCase							(							unittest.TestCase   ):
			'''simple docstring'''







			def 							__UpperCamelCase (			self							: List[Any]	)							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       tempfile.mkdtemp()

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       [
				    "[UNK]",
				    "[CLS]",
				    "[SEP]",
				    "[PAD]",
				    "[MASK]",
				    "want",
				    "##want",
				    "##ed",
				    "wa",
				    "un",
				    "runn",
				    "##ing",
				    ",",
				    "low",
				    "lowest",
				]
				SCREAMING_SNAKE_CASE   :      List[str]				     =       os.path.join(self.tmpdirname      ,      VOCAB_FILES_NAMES["vocab_file"]	)
				with open(self.vocab_file      ,      "w"      ,      encoding="utf-8"	) as vocab_writer:
					vocab_writer.write("".join([x + "\n" for x in vocab_tokens]	)	)

				SCREAMING_SNAKE_CASE   :      str				     =       {
				    "do_resize": True,
				    "size": 20,
				    "do_center_crop": True,
				    "crop_size": 18,
				    "do_normalize": True,
				    "image_mean": [0.4814_5466, 0.457_8275, 0.4082_1073],
				    "image_std": [0.2686_2954, 0.2613_0258, 0.2757_7711],
				}
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       os.path.join(self.tmpdirname      ,      a	)
				with open(self.image_processor_file      ,      "w"      ,      encoding="utf-8"	) as fp:
					json.dump(a      ,      a	)
			def 							__UpperCamelCase (			self							: Any      ,      **a							: Tuple	)							->       List[str]:

				"""simple docstring"""

				return BertTokenizer.from_pretrained(self.tmpdirname      ,      **a	)
			def 							__UpperCamelCase (			self							: Tuple      ,      **a							: List[Any]	)							->       List[str]:

				"""simple docstring"""

				return BertTokenizerFast.from_pretrained(self.tmpdirname      ,      **a	)
			def 							__UpperCamelCase (			self							: int      ,      **a							: List[Any]	)							->       Union[str, Any]:

				"""simple docstring"""

				return EfficientNetImageProcessor.from_pretrained(self.tmpdirname      ,      **a	)
			def 							__UpperCamelCase (			self							: str	)							->       str:

				"""simple docstring"""

				shutil.rmtree(self.tmpdirname	)
			def 							__UpperCamelCase (			self							: Dict	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       [np.random.randint(255      ,      size=(3, 30, 400)      ,      dtype=np.uinta	)]
				SCREAMING_SNAKE_CASE   :      Dict				     =       [Image.fromarray(np.moveaxis(a      ,      0      ,      -1	)	) for x in image_inputs]
				return image_inputs
			def 							__UpperCamelCase (			self							: List[str]	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.get_tokenizer()
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.get_rust_tokenizer()
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.get_image_processor()

				SCREAMING_SNAKE_CASE   :      Any				     =       AlignProcessor(tokenizer=a      ,      image_processor=a	)
				processor_slow.save_pretrained(self.tmpdirname	)
				SCREAMING_SNAKE_CASE   :      Dict				     =       AlignProcessor.from_pretrained(self.tmpdirname      ,      use_fast=a	)

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       AlignProcessor(tokenizer=a      ,      image_processor=a	)
				processor_fast.save_pretrained(self.tmpdirname	)
				SCREAMING_SNAKE_CASE   :      Any				     =       AlignProcessor.from_pretrained(self.tmpdirname	)

				self.assertEqual(processor_slow.tokenizer.get_vocab()      ,      tokenizer_slow.get_vocab()	)
				self.assertEqual(processor_fast.tokenizer.get_vocab()      ,      tokenizer_fast.get_vocab()	)
				self.assertEqual(tokenizer_slow.get_vocab()      ,      tokenizer_fast.get_vocab()	)
				self.assertIsInstance(processor_slow.tokenizer      ,      a	)
				self.assertIsInstance(processor_fast.tokenizer      ,      a	)

				self.assertEqual(processor_slow.image_processor.to_json_string()      ,      image_processor.to_json_string()	)
				self.assertEqual(processor_fast.image_processor.to_json_string()      ,      image_processor.to_json_string()	)
				self.assertIsInstance(processor_slow.image_processor      ,      a	)
				self.assertIsInstance(processor_fast.image_processor      ,      a	)
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       AlignProcessor(tokenizer=self.get_tokenizer()      ,      image_processor=self.get_image_processor()	)
				processor.save_pretrained(self.tmpdirname	)

				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.get_tokenizer(bos_token="(BOS)"      ,      eos_token="(EOS)"	)
				SCREAMING_SNAKE_CASE   :      int				     =       self.get_image_processor(do_normalize=a      ,      padding_value=1.0	)

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       AlignProcessor.from_pretrained(
				    self.tmpdirname      ,      bos_token="(BOS)"      ,      eos_token="(EOS)"      ,      do_normalize=a      ,      padding_value=1.0	)

				self.assertEqual(processor.tokenizer.get_vocab()      ,      tokenizer_add_kwargs.get_vocab()	)
				self.assertIsInstance(processor.tokenizer      ,      a	)

				self.assertEqual(processor.image_processor.to_json_string()      ,      image_processor_add_kwargs.to_json_string()	)
				self.assertIsInstance(processor.image_processor      ,      a	)
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.get_image_processor()
				SCREAMING_SNAKE_CASE   :      Tuple				     =       self.get_tokenizer()

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       AlignProcessor(tokenizer=a      ,      image_processor=a	)

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       self.prepare_image_inputs()

				SCREAMING_SNAKE_CASE   :      Dict				     =       image_processor(a      ,      return_tensors="np"	)
				SCREAMING_SNAKE_CASE   :      Dict				     =       processor(images=a      ,      return_tensors="np"	)

				for key in input_image_proc.keys():
					self.assertAlmostEqual(input_image_proc[key].sum()      ,      input_processor[key].sum()      ,      delta=1e-2	)
			def 							__UpperCamelCase (			self							: List[Any]	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       self.get_image_processor()
				SCREAMING_SNAKE_CASE   :      int				     =       self.get_tokenizer()

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       AlignProcessor(tokenizer=a      ,      image_processor=a	)

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       "lower newer"

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       processor(text=a	)

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       tokenizer(a      ,      padding="max_length"      ,      max_length=64	)

				for key in encoded_tok.keys():
					self.assertListEqual(encoded_tok[key]      ,      encoded_processor[key]	)
			def 							__UpperCamelCase (			self							: str	)							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.get_image_processor()
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.get_tokenizer()

				SCREAMING_SNAKE_CASE   :      str				     =       AlignProcessor(tokenizer=a      ,      image_processor=a	)

				SCREAMING_SNAKE_CASE   :      Tuple				     =       "lower newer"
				SCREAMING_SNAKE_CASE   :      Dict				     =       self.prepare_image_inputs()

				SCREAMING_SNAKE_CASE   :      Dict				     =       processor(text=a      ,      images=a	)

				self.assertListEqual(list(inputs.keys()	)      ,      ["input_ids", "token_type_ids", "attention_mask", "pixel_values"]	)

				# test if it raises when no input is passed
				with pytest.raises(a	):
					processor()
			def 							__UpperCamelCase (			self							: Tuple	)							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       self.get_image_processor()
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       self.get_tokenizer()

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       AlignProcessor(tokenizer=a      ,      image_processor=a	)

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       processor.batch_decode(a	)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       tokenizer.batch_decode(a	)

				self.assertListEqual(a      ,      a	)






			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.get_image_processor()
				SCREAMING_SNAKE_CASE   :      Tuple				     =       self.get_tokenizer()

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       AlignProcessor(tokenizer=a      ,      image_processor=a	)

				SCREAMING_SNAKE_CASE   :      str				     =       "lower newer"
				SCREAMING_SNAKE_CASE   :      int				     =       self.prepare_image_inputs()

				SCREAMING_SNAKE_CASE   :      str				     =       processor(text=a      ,      images=a	)

				self.assertListEqual(list(inputs.keys()	)      ,      processor.model_input_names	)


import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property






@unittest.skipUnless(os.path.exists(__A   )							,	'Tatoeba directory does not exist.'   )
class _UpperCamelCase							(							unittest.TestCase   ):
			'''simple docstring'''







			@cached_property
			def 							__UpperCamelCase (			self							: Any	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       tempfile.mkdtemp()
				return TatoebaConverter(save_dir=a	)
			@slow
			def 							__UpperCamelCase (			self							: List[Any]	)							->       int:

				"""simple docstring"""

				self.resolver.convert_models(["heb-eng"]	)






			@slow
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.resolver.write_model_card("opus-mt-he-en"      ,      dry_run=a	)
				assert mmeta["long_pair"] == "heb-eng"