
'''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, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast






@require_vision
class 	snake_case__       (      unittest.TestCase					):







		"""simple docstring"""

		def       lowerCAmelCase				(      self				:			List[Any]      )						->			Optional[int]:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						tempfile.mkdtemp()

				snake_case  :       int     			=						BlipImageProcessor()
				snake_case  :       Optional[int]     			=						BertTokenizer.from_pretrained('''hf-internal-testing/tiny-random-BertModel'''      )

				snake_case  :       int     			=						BlipProcessor(__UpperCAmelCase  ,	__UpperCAmelCase      )

				processor.save_pretrained(self.tmpdirname      )



		def       lowerCAmelCase				(      self				:			Optional[int]  ,	**UpperCamelCase__				:			int      )						->			Tuple:






				"""simple docstring"""







				return AutoProcessor.from_pretrained(self.tmpdirname  ,	**__UpperCAmelCase      ).tokenizer



		def       lowerCAmelCase				(      self				:			Dict  ,	**UpperCamelCase__				:			Dict      )						->			Optional[Any]:






				"""simple docstring"""







				return AutoProcessor.from_pretrained(self.tmpdirname  ,	**__UpperCAmelCase      ).image_processor



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






				"""simple docstring"""







				shutil.rmtree(self.tmpdirname      )



		def       lowerCAmelCase				(      self				:			List[Any]      )						->			Dict:






				"""simple docstring"""







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

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

				return image_inputs



		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			List[str]:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						BlipProcessor(tokenizer=self.get_tokenizer()  ,	image_processor=self.get_image_processor()      )
				processor.save_pretrained(self.tmpdirname      )

				snake_case  :       str     			=						self.get_tokenizer(bos_token='''(BOS)'''  ,	eos_token='''(EOS)'''      )
				snake_case  :       Optional[Any]     			=						self.get_image_processor(do_normalize=__UpperCAmelCase  ,	padding_value=1.0      )

				snake_case  :       Any     			=						BlipProcessor.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       lowerCAmelCase				(      self				:			int      )						->			str:






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						self.get_image_processor()
				snake_case  :       Any     			=						self.get_tokenizer()

				snake_case  :       List[str]     			=						BlipProcessor(tokenizer=__UpperCAmelCase  ,	image_processor=__UpperCAmelCase      )

				snake_case  :       Union[str, Any]     			=						self.prepare_image_inputs()

				snake_case  :       List[str]     			=						image_processor(__UpperCAmelCase  ,	return_tensors='''np'''      )
				snake_case  :       Union[str, Any]     			=						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       lowerCAmelCase				(      self				:			List[Any]      )						->			List[Any]:






				"""simple docstring"""







				snake_case  :       Dict     			=						self.get_image_processor()
				snake_case  :       Union[str, Any]     			=						self.get_tokenizer()

				snake_case  :       List[Any]     			=						BlipProcessor(tokenizer=__UpperCAmelCase  ,	image_processor=__UpperCAmelCase      )

				snake_case  :       Dict     			=						'''lower newer'''

				snake_case  :       Any     			=						processor(text=__UpperCAmelCase      )

				snake_case  :       Union[str, Any]     			=						tokenizer(__UpperCAmelCase  ,	return_token_type_ids=__UpperCAmelCase      )

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



		def       lowerCAmelCase				(      self				:			List[Any]      )						->			str:






				"""simple docstring"""







				snake_case  :       List[str]     			=						self.get_image_processor()
				snake_case  :       Any     			=						self.get_tokenizer()

				snake_case  :       Tuple     			=						BlipProcessor(tokenizer=__UpperCAmelCase  ,	image_processor=__UpperCAmelCase      )

				snake_case  :       Optional[Any]     			=						'''lower newer'''
				snake_case  :       List[Any]     			=						self.prepare_image_inputs()

				snake_case  :       int     			=						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       lowerCAmelCase				(      self				:			Union[str, Any]      )						->			List[Any]:






				"""simple docstring"""







				snake_case  :       List[Any]     			=						self.get_image_processor()
				snake_case  :       Union[str, Any]     			=						self.get_tokenizer()

				snake_case  :       Optional[Any]     			=						BlipProcessor(tokenizer=__UpperCAmelCase  ,	image_processor=__UpperCAmelCase      )

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

				snake_case  :       Dict     			=						processor.batch_decode(__UpperCAmelCase      )
				snake_case  :       Any     			=						tokenizer.batch_decode(__UpperCAmelCase      )

				self.assertListEqual(__UpperCAmelCase  ,	__UpperCAmelCase      )



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






				"""simple docstring"""







				snake_case  :       List[str]     			=						self.get_image_processor()
				snake_case  :       Tuple     			=						self.get_tokenizer()

				snake_case  :       List[Any]     			=						BlipProcessor(tokenizer=__UpperCAmelCase  ,	image_processor=__UpperCAmelCase      )

				snake_case  :       List[Any]     			=						'''lower newer'''
				snake_case  :       Optional[Any]     			=						self.prepare_image_inputs()

				snake_case  :       Optional[Any]     			=						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''']      )










from __future__ import annotations






class 						lowercase_   :






      '''simple docstring'''




      def __init__( self :		List[str]					,   __UpperCAmelCase :		int    )       ->None:




       """simple docstring"""


       a						    = order

       # a_{0} ... a_{k}
       a						    = [1.0] + [0.0] * order
       # b_{0} ... b_{k}
       a						    = [1.0] + [0.0] * order

       # x[n-1] ... x[n-k]
       a						    = [0.0] * self.order
       # y[n-1] ... y[n-k]
       a						    = [0.0] * self.order







      def  __lowerCAmelCase   ( self :		Union[str, Any]					,   __UpperCAmelCase :		list[float]					,   __UpperCAmelCase :		list[float]    )       ->None:




       """simple docstring"""


       if len(__UpperCAmelCase    ) < self.order:
        a						    = [1.0, *a_coeffs]

       if len(__UpperCAmelCase    ) != self.order + 1:
        a						    = (
            F"""Expected a_coeffs to have {self.order + 1} elements """
            F"""for {self.order}-order filter, got {len(__UpperCAmelCase    )}"""
        )
        raise ValueError(__UpperCAmelCase    )

       if len(__UpperCAmelCase    ) != self.order + 1:
        a						    = (
            F"""Expected b_coeffs to have {self.order + 1} elements """
            F"""for {self.order}-order filter, got {len(__UpperCAmelCase    )}"""
        )
        raise ValueError(__UpperCAmelCase    )

       a						    = a_coeffs
       a						    = b_coeffs







      def  __lowerCAmelCase   ( self :		Any					,   __UpperCAmelCase :		float    )       ->float:




       """simple docstring"""


       a						    = 0.0

       # Start at index 1 and do index 0 at the end.
       for i in range(1					,   self.order + 1    ):
        result += (
            self.b_coeffs[i] * self.input_history[i - 1]
            - self.a_coeffs[i] * self.output_history[i - 1]
        )

       a						    = (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

       a						    = self.input_history[:-1]
       a						    = self.output_history[:-1]

       a						    = sample
       a						    = result

       return result


