
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 warnings

from ..trainer import Trainer
from ..utils import logging


lowerCamelCase_						: Dict         =  logging.get_logger(__name__)

class        _UpperCamelCase				(					_A  ):






			'''simple docstring'''

			def __init__(       self :       List[str]						,	snake_case_ :       Tuple=None						,	**snake_case_ :       List[str]      ):
							warnings.warn(
							    """`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` """
							    """instead."""						,	snake_case_						,	)
							super().__init__(args=snake_case_						,	**snake_case_      )






