





import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip 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 OwlViTImageProcessor, OwlViTProcessor





@require_vision
class  A__    (							unittest.TestCase	):




   """simple docstring"""



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







          '''simple docstring'''

          a__						:	Optional[int] =				tempfile.mkdtemp()

          # fmt: off
          a__						:	Optional[Any] =				['', 'l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
          # fmt: on
          a__						:	List[Any] =				dict(zip(lowercase					,  range(len(lowercase))))
          a__						:	int =				['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
          a__						:	Dict =				{'unk_token': '<unk>'}

          a__						:	Optional[int] =				os.path.join(self.tmpdirname					,  VOCAB_FILES_NAMES['vocab_file'])
          a__						:	List[Any] =				os.path.join(self.tmpdirname					,  VOCAB_FILES_NAMES['merges_file'])
          with open(self.vocab_file					,  'w'					,  encoding='utf-8') as fp:
                 fp.write(json.dumps(lowercase) + '\n')
          with open(self.merges_file					,  'w'					,  encoding='utf-8') as fp:
                 fp.write('\n'.join(lowercase))

          a__						:	List[str] =				{
              'do_resize': True,
              'size': 20,
              'do_center_crop': True,
              'crop_size': 18,
              'do_normalize': True,
              'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
              'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
          }
          a__						:	Dict =				os.path.join(self.tmpdirname					,  lowercase)
          with open(self.image_processor_file					,  'w'					,  encoding='utf-8') as fp:
                 json.dump(lowercase					,  lowercase)

   def 							__lowercase		(					self					,  **lowercase)			->						Tuple:







          '''simple docstring'''

          return CLIPTokenizer.from_pretrained(self.tmpdirname					,  pad_token='!'					,  **lowercase)

   def 							__lowercase		(					self					,  **lowercase)			->						Optional[int]:







          '''simple docstring'''

          return CLIPTokenizerFast.from_pretrained(self.tmpdirname					,  pad_token='!'					,  **lowercase)

   def 							__lowercase		(					self					,  **lowercase)			->						List[Any]:







          '''simple docstring'''

          return OwlViTImageProcessor.from_pretrained(self.tmpdirname					,  **lowercase)

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          shutil.rmtree(self.tmpdirname)

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          a__						:	List[Any] =				[np.random.randint(255					,  size=(3, 30, 400)					,  dtype=np.uinta)]

          a__						:	List[Any] =				[Image.fromarray(np.moveaxis(lowercase					,  0					,  -1)) for x in image_inputs]

          return image_inputs

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







          '''simple docstring'''

          a__						:	List[Any] =				self.get_tokenizer()
          a__						:	Tuple =				self.get_rust_tokenizer()
          a__						:	Any =				self.get_image_processor()

          a__						:	Optional[int] =				OwlViTProcessor(tokenizer=lowercase					,  image_processor=lowercase)
          processor_slow.save_pretrained(self.tmpdirname)
          a__						:	List[str] =				OwlViTProcessor.from_pretrained(self.tmpdirname					,  use_fast=lowercase)

          a__						:	Optional[int] =				OwlViTProcessor(tokenizer=lowercase					,  image_processor=lowercase)
          processor_fast.save_pretrained(self.tmpdirname)
          a__						:	List[str] =				OwlViTProcessor.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					,  lowercase)
          self.assertIsInstance(processor_fast.tokenizer					,  lowercase)

          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					,  lowercase)
          self.assertIsInstance(processor_fast.image_processor					,  lowercase)

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







          '''simple docstring'''

          a__						:	int =				OwlViTProcessor(tokenizer=self.get_tokenizer()					,  image_processor=self.get_image_processor())
          processor.save_pretrained(self.tmpdirname)

          a__						:	List[Any] =				self.get_tokenizer(bos_token='(BOS)'					,  eos_token='(EOS)')
          a__						:	List[Any] =				self.get_image_processor(do_normalize=lowercase)

          a__						:	Optional[Any] =				OwlViTProcessor.from_pretrained(
              self.tmpdirname					,  bos_token='(BOS)'					,  eos_token='(EOS)'					,  do_normalize=lowercase)

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

          self.assertEqual(processor.image_processor.to_json_string()					,  image_processor_add_kwargs.to_json_string())
          self.assertIsInstance(processor.image_processor					,  lowercase)

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          a__						:	Dict =				self.get_image_processor()
          a__						:	Any =				self.get_tokenizer()

          a__						:	Union[str, Any] =				OwlViTProcessor(tokenizer=lowercase					,  image_processor=lowercase)

          a__						:	Any =				self.prepare_image_inputs()

          a__						:	int =				image_processor(lowercase					,  return_tensors='np')
          a__						:	Optional[Any] =				processor(images=lowercase					,  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 							__lowercase		(					self)			->						Union[str, Any]:







          '''simple docstring'''

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

          a__						:	str =				OwlViTProcessor(tokenizer=lowercase					,  image_processor=lowercase)

          a__						:	List[Any] =				'lower newer'

          a__						:	str =				processor(text=lowercase					,  return_tensors='np')

          a__						:	Dict =				tokenizer(lowercase					,  return_tensors='np')

          for key in encoded_tok.keys():
                 self.assertListEqual(encoded_tok[key][0].tolist()					,  encoded_processor[key][0].tolist())

   def 							__lowercase		(					self)			->						Union[str, Any]:







          '''simple docstring'''

          a__						:	Any =				self.get_image_processor()
          a__						:	str =				self.get_tokenizer()

          a__						:	Tuple =				OwlViTProcessor(tokenizer=lowercase					,  image_processor=lowercase)

          a__						:	List[str] =				'lower newer'
          a__						:	Union[str, Any] =				self.prepare_image_inputs()

          a__						:	Tuple =				processor(text=lowercase					,  images=lowercase)

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

          # test if it raises when no input is passed
          with pytest.raises(lowercase):
                 processor()

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







          '''simple docstring'''

          a__						:	Any =				'google/owlvit-base-patch32'
          a__						:	int =				OwlViTProcessor.from_pretrained(lowercase)

          a__						:	Dict =				['cat', 'nasa badge']
          a__						:	int =				processor(text=lowercase)

          a__						:	Optional[int] =				16
          self.assertListEqual(list(inputs.keys())					,  ['input_ids', 'attention_mask'])
          self.assertEqual(inputs['input_ids'].shape					,  (2, seq_length))

          # test if it raises when no input is passed
          with pytest.raises(lowercase):
                 processor()

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          a__						:	str =				'google/owlvit-base-patch32'
          a__						:	Any =				OwlViTProcessor.from_pretrained(lowercase)

          a__						:	Optional[int] =				[['cat', 'nasa badge'], ['person']]
          a__						:	Optional[int] =				processor(text=lowercase)

          a__						:	Tuple =				16
          a__						:	List[str] =				len(lowercase)
          a__						:	Union[str, Any] =				max([len(lowercase) for texts in input_texts])

          self.assertListEqual(list(inputs.keys())					,  ['input_ids', 'attention_mask'])
          self.assertEqual(inputs['input_ids'].shape					,  (batch_size * num_max_text_queries, seq_length))

          # test if it raises when no input is passed
          with pytest.raises(lowercase):
                 processor()

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          a__						:	Dict =				'google/owlvit-base-patch32'
          a__						:	int =				OwlViTProcessor.from_pretrained(lowercase)

          a__						:	Optional[int] =				['cat', 'nasa badge']
          a__						:	str =				processor(text=lowercase)

          a__						:	Optional[Any] =				16
          a__						:	Any =				inputs['input_ids']
          a__						:	Optional[Any] =				[
              [4_9406, 2368, 4_9407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
              [4_9406, 6841, 1_1301, 4_9407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
          ]

          self.assertListEqual(list(inputs.keys())					,  ['input_ids', 'attention_mask'])
          self.assertEqual(inputs['input_ids'].shape					,  (2, seq_length))
          self.assertListEqual(list(input_ids[0])					,  predicted_ids[0])
          self.assertListEqual(list(input_ids[1])					,  predicted_ids[1])

   def 							__lowercase		(					self)			->						Union[str, Any]:







          '''simple docstring'''

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

          a__						:	List[Any] =				OwlViTProcessor(tokenizer=lowercase					,  image_processor=lowercase)

          a__						:	Optional[Any] =				self.prepare_image_inputs()
          a__						:	int =				self.prepare_image_inputs()

          a__						:	int =				processor(images=lowercase					,  query_images=lowercase)

          self.assertListEqual(list(inputs.keys())					,  ['query_pixel_values', 'pixel_values'])

          # test if it raises when no input is passed
          with pytest.raises(lowercase):
                 processor()

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







          '''simple docstring'''

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

          a__						:	List[str] =				OwlViTProcessor(tokenizer=lowercase					,  image_processor=lowercase)

          a__						:	Any =				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          a__						:	int =				processor.batch_decode(lowercase)
          a__						:	str =				tokenizer.batch_decode(lowercase)

          self.assertListEqual(lowercase					,  lowercase)







import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


_lowercase: Union[str, Any]      			=		get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
				_lowercase: List[str]      			=		json.load(f)



@require_torch
class 	_lowercase							(   unittest.TestCase	):





	"""simple docstring"""




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						return FSMTTokenizer.from_pretrained(lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   FSMTForConditionalGeneration.from_pretrained(lowerCamelCase_		).to(lowerCamelCase_		)
						if torch_device == "cuda":
											model.half()
						return model




	@parameterized.expand(
	    [
	        ["en-ru", 26.0],
	        ["ru-en", 22.0],
	        ["en-de", 22.0],
	        ["de-en", 29.0],
	    ]		)
	@slow
	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   F'''facebook/wmt19-{pair}'''
						a					     =   self.get_tokenizer(lowerCamelCase_		)
						a					     =   self.get_model(lowerCamelCase_		)

						a					     =   bleu_data[pair]["src"]
						a					     =   bleu_data[pair]["tgt"]

						a					     =   tokenizer(lowerCamelCase_		,       return_tensors="pt"		,       truncation=lowerCamelCase_		,       padding="longest"		).to(lowerCamelCase_		)
						a					     =   model.generate(
						    input_ids=batch.input_ids		,       num_beams=8		,       )
						a					     =   tokenizer.batch_decode(
						    lowerCamelCase_		,       skip_special_tokens=lowerCamelCase_		,       clean_up_tokenization_spaces=lowerCamelCase_		)
						a					     =   calculate_bleu(lowerCamelCase_		,       lowerCamelCase_		)
						print(lowerCamelCase_		)
						self.assertGreaterEqual(scores["bleu"]		,       lowerCamelCase_		)
