




'''simple docstring'''

import importlib
import shutil
import threading
import warnings
from typing import List

import fsspec
import fsspec.asyn

from . import compression
from .hffilesystem import HfFileSystem


a       :						int								=		importlib.util.find_spec('''s3fs''') is not None

if _has_safs:
						from .safilesystem import SaFileSystem  # noqa: F401

a       :						List[compression.BaseCompressedFileFileSystem]								=		[
    compression.BzaFileSystem,
    compression.GzipFileSystem,
    compression.LzaFileSystem,
    compression.XzFileSystem,
    compression.ZstdFileSystem,
]

# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
						if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
												warnings.warn(F'''A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.''')
						fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)


def __UpperCAmelCase	(   _UpperCAmelCase		:   str						)      ->      str:
			if "://" in dataset_path:
						__snake_case			=		dataset_path.split("://"						)[1]
			return dataset_path


def __UpperCAmelCase	(   _UpperCAmelCase		:   fsspec.AbstractFileSystem						)      ->      bool:
			if fs is not None and fs.protocol != "file":
						return True
			else:
						return False


def __UpperCAmelCase	(   _UpperCAmelCase		:   fsspec.AbstractFileSystem		,				_UpperCAmelCase		:   str		,				_UpperCAmelCase		:   str						)      ->      List[str]:
			__snake_case			=		not is_remote_filesystem(_UpperCAmelCase						)
			if is_local:
						# LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
						shutil.move(fs._strip_protocol(_UpperCAmelCase						)		,				fs._strip_protocol(_UpperCAmelCase						)						)
			else:
						fs.mv(_UpperCAmelCase		,				_UpperCAmelCase		,				recursive=_UpperCAmelCase						)






def __UpperCAmelCase	(   )      ->      None:
			if hasattr(fsspec.asyn		,				"reset_lock"						):
						# for future fsspec>2022.05.0
						fsspec.asyn.reset_lock()
			else:
						__snake_case			=		None
						__snake_case			=		None
						__snake_case			=		threading.Lock()










"""simple docstring"""







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       UpperCamelCase__			(   unittest.TestCase      ):

			"""simple docstring"""


			def   snake_case__	(      self		)      ->	Any:
				A__											=					tempfile.mkdtemp()

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

				A__											=					BlipaProcessor(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)

				processor.save_pretrained(self.tmpdirname		)


			def   snake_case__	(      self						,	**SCREAMING_SNAKE_CASE__		)      ->	Dict:
				return AutoProcessor.from_pretrained(self.tmpdirname						,	**SCREAMING_SNAKE_CASE__		).tokenizer


			def   snake_case__	(      self						,	**SCREAMING_SNAKE_CASE__		)      ->	Optional[int]:
				return AutoProcessor.from_pretrained(self.tmpdirname						,	**SCREAMING_SNAKE_CASE__		).image_processor


			def   snake_case__	(      self		)      ->	str:
				shutil.rmtree(self.tmpdirname		)


			def   snake_case__	(      self		)      ->	List[str]:
				A__											=					[np.random.randint(255						,	size=(3, 30, 400)						,	dtype=np.uinta		)]

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

				return image_inputs


			def   snake_case__	(      self		)      ->	str:
				A__											=					BlipaProcessor(tokenizer=self.get_tokenizer()						,	image_processor=self.get_image_processor()		)
				processor.save_pretrained(self.tmpdirname		)

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

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

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

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


			def   snake_case__	(      self		)      ->	int:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					self.prepare_image_inputs()

				A__											=					image_processor(SCREAMING_SNAKE_CASE__						,	return_tensors="np"		)
				A__											=					processor(images=SCREAMING_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   snake_case__	(      self		)      ->	Optional[int]:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					"lower newer"

				A__											=					processor(text=SCREAMING_SNAKE_CASE__		)

				A__											=					tokenizer(SCREAMING_SNAKE_CASE__						,	return_token_type_ids=SCREAMING_SNAKE_CASE__		)

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


			def   snake_case__	(      self		)      ->	Tuple:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

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

				A__											=					processor(text=SCREAMING_SNAKE_CASE__						,	images=SCREAMING_SNAKE_CASE__		)

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

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


			def   snake_case__	(      self		)      ->	List[Any]:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

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

				A__											=					processor.batch_decode(SCREAMING_SNAKE_CASE__		)
				A__											=					tokenizer.batch_decode(SCREAMING_SNAKE_CASE__		)

				self.assertListEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)







			def   snake_case__	(      self		)      ->	Tuple:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

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

				A__											=					processor(text=SCREAMING_SNAKE_CASE__						,	images=SCREAMING_SNAKE_CASE__		)

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




