
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 CLIPSegProcessor, ViTImageProcessor

@require_vision
class        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       List[Any]      ):
							UpperCamelCase_:							Any	=						tempfile.mkdtemp()

							# fmt: off
							UpperCamelCase_:							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
							UpperCamelCase_:							Optional[Any]	=						dict(zip(snake_case_						,	range(len(snake_case_      )      )      )      )
							UpperCamelCase_:							Dict	=						["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
							UpperCamelCase_:							Tuple	=						{"""unk_token""": """<unk>"""}

							UpperCamelCase_:							Tuple	=						os.path.join(self.tmpdirname						,	VOCAB_FILES_NAMES["""vocab_file"""]      )
							UpperCamelCase_:							Tuple	=						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(snake_case_      ) + """\n"""      )
							with open(self.merges_file						,	"""w"""						,	encoding="""utf-8"""      ) as fp:
											fp.write("""\n""".join(snake_case_      )      )

							UpperCamelCase_:							int	=						{
							    """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],
							}
							UpperCamelCase_:							Dict	=						os.path.join(self.tmpdirname						,	snake_case_      )
							with open(self.image_processor_file						,	"""w"""						,	encoding="""utf-8"""      ) as fp:
											json.dump(snake_case_						,	snake_case_      )

			def       lowerCAmelCase__	(       self :       str						,	**snake_case_ :       Tuple      ):
							return CLIPTokenizer.from_pretrained(self.tmpdirname						,	**snake_case_      )

			def       lowerCAmelCase__	(       self :       List[str]						,	**snake_case_ :       Tuple      ):
							return CLIPTokenizerFast.from_pretrained(self.tmpdirname						,	**snake_case_      )

			def       lowerCAmelCase__	(       self :       List[str]						,	**snake_case_ :       Optional[int]      ):
							return ViTImageProcessor.from_pretrained(self.tmpdirname						,	**snake_case_      )

			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							shutil.rmtree(self.tmpdirname      )

			def       lowerCAmelCase__	(       self :       Dict      ):
							UpperCamelCase_:							Any	=						[np.random.randint(255						,	size=(3, 30, 400)						,	dtype=np.uinta      )]

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

							return image_inputs

			def       lowerCAmelCase__	(       self :       str      ):
							UpperCamelCase_:							Tuple	=						self.get_tokenizer()
							UpperCamelCase_:							int	=						self.get_rust_tokenizer()
							UpperCamelCase_:							List[Any]	=						self.get_image_processor()

							UpperCamelCase_:							int	=						CLIPSegProcessor(tokenizer=snake_case_						,	image_processor=snake_case_      )
							processor_slow.save_pretrained(self.tmpdirname      )
							UpperCamelCase_:							List[str]	=						CLIPSegProcessor.from_pretrained(self.tmpdirname						,	use_fast=snake_case_      )

							UpperCamelCase_:							Optional[int]	=						CLIPSegProcessor(tokenizer=snake_case_						,	image_processor=snake_case_      )
							processor_fast.save_pretrained(self.tmpdirname      )
							UpperCamelCase_:							Union[str, Any]	=						CLIPSegProcessor.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						,	snake_case_      )
							self.assertIsInstance(processor_fast.tokenizer						,	snake_case_      )

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

			def       lowerCAmelCase__	(       self :       Any      ):
							UpperCamelCase_:							Any	=						CLIPSegProcessor(tokenizer=self.get_tokenizer()						,	image_processor=self.get_image_processor()      )
							processor.save_pretrained(self.tmpdirname      )

							UpperCamelCase_:							Optional[Any]	=						self.get_tokenizer(bos_token="""(BOS)"""						,	eos_token="""(EOS)"""      )
							UpperCamelCase_:							Union[str, Any]	=						self.get_image_processor(do_normalize=snake_case_						,	padding_value=1.0      )

							UpperCamelCase_:							str	=						CLIPSegProcessor.from_pretrained(
							    self.tmpdirname						,	bos_token="""(BOS)"""						,	eos_token="""(EOS)"""						,	do_normalize=snake_case_						,	padding_value=1.0      )

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

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

			def       lowerCAmelCase__	(       self :       Any      ):
							UpperCamelCase_:							Any	=						self.get_image_processor()
							UpperCamelCase_:							Any	=						self.get_tokenizer()

							UpperCamelCase_:							Optional[Any]	=						CLIPSegProcessor(tokenizer=snake_case_						,	image_processor=snake_case_      )

							UpperCamelCase_:							Optional[Any]	=						self.prepare_image_inputs()

							UpperCamelCase_:							Union[str, Any]	=						image_processor(snake_case_						,	return_tensors="""np"""      )
							UpperCamelCase_:							List[str]	=						processor(images=snake_case_						,	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       lowerCAmelCase__	(       self :       Optional[int]      ):
							UpperCamelCase_:							str	=						self.get_image_processor()
							UpperCamelCase_:							Optional[int]	=						self.get_tokenizer()

							UpperCamelCase_:							str	=						CLIPSegProcessor(tokenizer=snake_case_						,	image_processor=snake_case_      )

							UpperCamelCase_:							str	=						"""lower newer"""

							UpperCamelCase_:							int	=						processor(text=snake_case_      )

							UpperCamelCase_:							List[str]	=						tokenizer(snake_case_      )

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

			def       lowerCAmelCase__	(       self :       Dict      ):
							UpperCamelCase_:							str	=						self.get_image_processor()
							UpperCamelCase_:							Optional[Any]	=						self.get_tokenizer()

							UpperCamelCase_:							Optional[Any]	=						CLIPSegProcessor(tokenizer=snake_case_						,	image_processor=snake_case_      )

							UpperCamelCase_:							List[str]	=						"""lower newer"""
							UpperCamelCase_:							Optional[int]	=						self.prepare_image_inputs()

							UpperCamelCase_:							Union[str, Any]	=						processor(text=snake_case_						,	images=snake_case_      )

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

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

			def       lowerCAmelCase__	(       self :       Any      ):
							UpperCamelCase_:							Any	=						self.get_image_processor()
							UpperCamelCase_:							Union[str, Any]	=						self.get_tokenizer()

							UpperCamelCase_:							Optional[int]	=						CLIPSegProcessor(tokenizer=snake_case_						,	image_processor=snake_case_      )

							UpperCamelCase_:							Tuple	=						self.prepare_image_inputs()
							UpperCamelCase_:							int	=						self.prepare_image_inputs()

							UpperCamelCase_:							Any	=						processor(images=snake_case_						,	visual_prompt=snake_case_      )

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

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




			def       lowerCAmelCase__	(       self :       int      ):
							UpperCamelCase_:							List[Any]	=						self.get_image_processor()
							UpperCamelCase_:							str	=						self.get_tokenizer()

							UpperCamelCase_:							Tuple	=						CLIPSegProcessor(tokenizer=snake_case_						,	image_processor=snake_case_      )

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

							UpperCamelCase_:							Any	=						processor.batch_decode(snake_case_      )
							UpperCamelCase_:							Union[str, Any]	=						tokenizer.batch_decode(snake_case_      )

							self.assertListEqual(snake_case_						,	snake_case_      )








import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar

class        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       str      ):
							UpperCamelCase_:							Optional[int]	=						logging.get_logger()

							# the current default level is logging.WARNING
							UpperCamelCase_:							Dict	=						logging.get_verbosity()

							logging.set_verbosity_error()
							self.assertEqual(logger.getEffectiveLevel()						,	logging.get_verbosity()      )

							logging.set_verbosity_warning()
							self.assertEqual(logger.getEffectiveLevel()						,	logging.get_verbosity()      )

							logging.set_verbosity_info()
							self.assertEqual(logger.getEffectiveLevel()						,	logging.get_verbosity()      )

							logging.set_verbosity_debug()
							self.assertEqual(logger.getEffectiveLevel()						,	logging.get_verbosity()      )

							# restore to the original level
							logging.set_verbosity(snake_case_      )

			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							UpperCamelCase_:							Union[str, Any]	=						logging.get_verbosity()

							UpperCamelCase_:							int	=						logging.get_logger("""transformers.models.bart.tokenization_bart"""      )
							UpperCamelCase_:							Union[str, Any]	=						"""Testing 1, 2, 3"""

							# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
							if level_origin <= logging.WARNING:
											with CaptureLogger(snake_case_      ) as cl:
															logger.warning(snake_case_      )
											self.assertEqual(cl.out						,	msg + """\n"""      )

							# this is setting the level for all of `transformers.*` loggers
							logging.set_verbosity_error()

							# should not be able to log warnings
							with CaptureLogger(snake_case_      ) as cl:
											logger.warning(snake_case_      )
							self.assertEqual(cl.out						,	""""""      )

							# should be able to log warnings again
							logging.set_verbosity_warning()
							with CaptureLogger(snake_case_      ) as cl:
											logger.warning(snake_case_      )
							self.assertEqual(cl.out						,	msg + """\n"""      )

							# restore to the original level
							logging.set_verbosity(snake_case_      )

			@mockenv(TRANSFORMERS_VERBOSITY="""error"""      )
			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							# reset for the env var to take effect, next time some logger call is made
							transformers.utils.logging._reset_library_root_logger()
							# this action activates the env var
							UpperCamelCase_:							List[str]	=						logging.get_logger("""transformers.models.bart.tokenization_bart"""      )

							UpperCamelCase_:							str	=						os.getenv("""TRANSFORMERS_VERBOSITY"""						,	snake_case_      )
							UpperCamelCase_:							Any	=						logging.log_levels[env_level_str]

							UpperCamelCase_:							Dict	=						logging.get_verbosity()
							self.assertEqual(
							    snake_case_						,	snake_case_						,	f'''TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}'''						,	)

							# restore to the original level
							UpperCamelCase_:							str	=						""""""
							transformers.utils.logging._reset_library_root_logger()

			@mockenv(TRANSFORMERS_VERBOSITY="""super-error"""      )
			def       lowerCAmelCase__	(       self :       List[Any]      ):
							# reset for the env var to take effect, next time some logger call is made
							transformers.utils.logging._reset_library_root_logger()
							UpperCamelCase_:							str	=						logging.logging.getLogger()
							with CaptureLogger(snake_case_      ) as cl:
											# this action activates the env var
											logging.get_logger("""transformers.models.bart.tokenization_bart"""      )
							self.assertIn("""Unknown option TRANSFORMERS_VERBOSITY=super-error"""						,	cl.out      )

							# no need to restore as nothing was changed




			def       lowerCAmelCase__	(       self :       List[Any]      ):
							# testing `logger.warning_advice()`
							transformers.utils.logging._reset_library_root_logger()

							UpperCamelCase_:							List[str]	=						logging.get_logger("""transformers.models.bart.tokenization_bart"""      )
							UpperCamelCase_:							Any	=						"""Testing 1, 2, 3"""

							with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS="""1"""      ):
											# nothing should be logged as env var disables this method
											with CaptureLogger(snake_case_      ) as cl:
															logger.warning_advice(snake_case_      )
											self.assertEqual(cl.out						,	""""""      )

							with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=""""""      ):
											# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
											with CaptureLogger(snake_case_      ) as cl:
															logger.warning_advice(snake_case_      )
											self.assertEqual(cl.out						,	msg + """\n"""      )







def        A__					(						)  ->	Union[str, Any]:
				disable_progress_bar()
				assert are_progress_bars_disabled()

				enable_progress_bar()
				assert not are_progress_bars_disabled()






