



import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin






class 		lowercase						(			snake_case__			,   unittest.TestCase):






	"""simple docstring"""


	a__				: Tuple		       =							TransfoXLTokenizer
	a__				: str		       =							False
	a__				: Union[str, Any]		       =							False




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			Any:
							super().setUp()

							UpperCAmelCase_=       [
							    """<unk>""",
							    """[CLS]""",
							    """[SEP]""",
							    """want""",
							    """unwanted""",
							    """wa""",
							    """un""",
							    """running""",
							    """,""",
							    """low""",
							    """l""",
							]
							UpperCAmelCase_=       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]					)					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]						,  **__UpperCAmelCase     :						List[Any]					)      ->			Union[str, Any]:
							UpperCAmelCase_=       True
							return TransfoXLTokenizer.from_pretrained(self.tmpdirname						,  **__UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]						,  __UpperCAmelCase     :						int					)      ->			List[Any]:
							UpperCAmelCase_=       """<unk> UNwanted , running"""
							UpperCAmelCase_=       """<unk> unwanted, running"""
							return input_text, output_text




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			List[str]:
							UpperCAmelCase_=       TransfoXLTokenizer(vocab_file=self.vocab_file						,  lower_case=__UpperCAmelCase					)

							UpperCAmelCase_=       tokenizer.tokenize("""<unk> UNwanted , running"""					)
							self.assertListEqual(__UpperCAmelCase						,  ["""<unk>""", """unwanted""", """,""", """running"""]					)

							self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase					)						,  [0, 4, 8, 7]					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			Optional[Any]:
							UpperCAmelCase_=       TransfoXLTokenizer(lower_case=__UpperCAmelCase					)

							self.assertListEqual(
							    tokenizer.tokenize(""" \tHeLLo ! how  \n Are yoU ?  """					)						,  ["""hello""", """!""", """how""", """are""", """you""", """?"""]					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]					)      ->			Tuple:
							UpperCAmelCase_=       TransfoXLTokenizer(lower_case=__UpperCAmelCase					)

							self.assertListEqual(
							    tokenizer.tokenize(""" \tHeLLo ! how  \n Are yoU ?  """					)						,  ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						int					)      ->			Any:
							UpperCAmelCase_=       TransfoXLTokenizer(lower_case=__UpperCAmelCase					)
							UpperCAmelCase_=       """Hello (bracket) and side-scrolled [and] Henry's $5,000 with 3.34 m. What's up!?"""
							UpperCAmelCase_=       [
							    """Hello""",
							    """(""",
							    """bracket""",
							    """)""",
							    """and""",
							    """side""",
							    """@-@""",
							    """scrolled""",
							    """[""",
							    """and""",
							    """]""",
							    """Henry""",
							    """'s""",
							    """$""",
							    """5""",
							    """@,@""",
							    """000""",
							    """with""",
							    """3""",
							    """@.@""",
							    """34""",
							    """m""",
							    """.""",
							    """What""",
							    """'s""",
							    """up""",
							    """!""",
							    """?""",
							]

							self.assertListEqual(tokenizer.tokenize(__UpperCAmelCase					)						,  __UpperCAmelCase					)

							self.assertEqual(tokenizer.convert_tokens_to_string(__UpperCAmelCase					)						,  __UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			str:
							UpperCAmelCase_=       self.get_tokenizer()
							UpperCAmelCase_=       len(__UpperCAmelCase					)

							tokenizer.add_tokens(["""new1""", """new2"""]					)
							tokenizer.move_added_token("""new1"""						,  1					)

							# Check that moved token is not copied (duplicate)
							self.assertEqual(len(__UpperCAmelCase					)						,  original_len + 2					)
							# Check that token is moved to specified id
							self.assertEqual(tokenizer.encode("""new1"""					)						,  [1]					)
							self.assertEqual(tokenizer.decode([1]					)						,  """new1"""					)









import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
	from PIL import Image

	from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast






@require_vision
class 		lowercase						(			unittest.TestCase):






	"""simple docstring"""


	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			List[Any]:
							UpperCAmelCase_=       tempfile.mkdtemp()

							UpperCAmelCase_=       BlipImageProcessor()
							UpperCAmelCase_=       GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""					)

							UpperCAmelCase_=       BlipaProcessor(__UpperCAmelCase						,  __UpperCAmelCase					)

							processor.save_pretrained(self.tmpdirname					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]						,  **__UpperCAmelCase     :						Union[str, Any]					)      ->			int:
							return AutoProcessor.from_pretrained(self.tmpdirname						,  **__UpperCAmelCase					).tokenizer




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]						,  **__UpperCAmelCase     :						str					)      ->			Optional[int]:
							return AutoProcessor.from_pretrained(self.tmpdirname						,  **__UpperCAmelCase					).image_processor




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			List[Any]:
							shutil.rmtree(self.tmpdirname					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			Any:
							UpperCAmelCase_=       [np.random.randint(255						,  size=(3, 30, 400)						,  dtype=np.uinta					)]

							UpperCAmelCase_=       [Image.fromarray(np.moveaxis(__UpperCAmelCase						,  0						,  -1					)					) for x in image_inputs]

							return image_inputs




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			Dict:
							UpperCAmelCase_=       BlipaProcessor(tokenizer=self.get_tokenizer()						,  image_processor=self.get_image_processor()					)
							processor.save_pretrained(self.tmpdirname					)

							UpperCAmelCase_=       self.get_tokenizer(bos_token="""(BOS)"""						,  eos_token="""(EOS)"""					)
							UpperCAmelCase_=       self.get_image_processor(do_normalize=__UpperCAmelCase						,  padding_value=1.0					)

							UpperCAmelCase_=       BlipaProcessor.from_pretrained(
							    self.tmpdirname						,  bos_token="""(BOS)"""						,  eos_token="""(EOS)"""						,  do_normalize=__UpperCAmelCase						,  padding_value=1.0					)

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

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			Optional[int]:
							UpperCAmelCase_=       self.get_image_processor()
							UpperCAmelCase_=       self.get_tokenizer()

							UpperCAmelCase_=       BlipaProcessor(tokenizer=__UpperCAmelCase						,  image_processor=__UpperCAmelCase					)

							UpperCAmelCase_=       self.prepare_image_inputs()

							UpperCAmelCase_=       image_processor(__UpperCAmelCase						,  return_tensors="""np"""					)
							UpperCAmelCase_=       processor(images=__UpperCAmelCase						,  return_tensors="""np"""					)

							for key in input_feat_extract.keys():
													self.assertAlmostEqual(input_feat_extract[key].sum()						,  input_processor[key].sum()						,  delta=1E-2					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			Any:
							UpperCAmelCase_=       self.get_image_processor()
							UpperCAmelCase_=       self.get_tokenizer()

							UpperCAmelCase_=       BlipaProcessor(tokenizer=__UpperCAmelCase						,  image_processor=__UpperCAmelCase					)

							UpperCAmelCase_=       """lower newer"""

							UpperCAmelCase_=       processor(text=__UpperCAmelCase					)

							UpperCAmelCase_=       tokenizer(__UpperCAmelCase						,  return_token_type_ids=__UpperCAmelCase					)

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]					)      ->			Dict:
							UpperCAmelCase_=       self.get_image_processor()
							UpperCAmelCase_=       self.get_tokenizer()

							UpperCAmelCase_=       BlipaProcessor(tokenizer=__UpperCAmelCase						,  image_processor=__UpperCAmelCase					)

							UpperCAmelCase_=       """lower newer"""
							UpperCAmelCase_=       self.prepare_image_inputs()

							UpperCAmelCase_=       processor(text=__UpperCAmelCase						,  images=__UpperCAmelCase					)

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

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			Any:
							UpperCAmelCase_=       self.get_image_processor()
							UpperCAmelCase_=       self.get_tokenizer()

							UpperCAmelCase_=       BlipaProcessor(tokenizer=__UpperCAmelCase						,  image_processor=__UpperCAmelCase					)

							UpperCAmelCase_=       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							UpperCAmelCase_=       processor.batch_decode(__UpperCAmelCase					)
							UpperCAmelCase_=       tokenizer.batch_decode(__UpperCAmelCase					)

							self.assertListEqual(__UpperCAmelCase						,  __UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			int:
							UpperCAmelCase_=       self.get_image_processor()
							UpperCAmelCase_=       self.get_tokenizer()

							UpperCAmelCase_=       BlipaProcessor(tokenizer=__UpperCAmelCase						,  image_processor=__UpperCAmelCase					)

							UpperCAmelCase_=       """lower newer"""
							UpperCAmelCase_=       self.prepare_image_inputs()

							UpperCAmelCase_=       processor(text=__UpperCAmelCase						,  images=__UpperCAmelCase					)

							# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
							self.assertListEqual(list(inputs.keys()					)						,  ["""pixel_values""", """input_ids""", """attention_mask"""]					)




