






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    SCREAMING_SNAKE_CASE			(							unittest.TestCase					):





							def 						UpperCamelCase_					(   self		:		Any	):

											'''simple docstring'''
											__a       =		tempfile.mkdtemp()

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

											__a       =		BlipaProcessor(lowerCAmelCase_      ,				lowerCAmelCase_	)

											processor.save_pretrained(self.tmpdirname	)





							def 						UpperCamelCase_					(   self		:		int      ,				**__lowercase		:		Dict	):

											'''simple docstring'''
											return AutoProcessor.from_pretrained(self.tmpdirname      ,				**lowerCAmelCase_	).tokenizer





							def 						UpperCamelCase_					(   self		:		Any      ,				**__lowercase		:		Optional[int]	):

											'''simple docstring'''
											return AutoProcessor.from_pretrained(self.tmpdirname      ,				**lowerCAmelCase_	).image_processor





							def 						UpperCamelCase_					(   self		:		Any	):

											'''simple docstring'''
											shutil.rmtree(self.tmpdirname	)





							def 						UpperCamelCase_					(   self		:		int	):

											'''simple docstring'''
											__a       =		[np.random.randint(255      ,				size=(3, 30, 400)      ,				dtype=np.uinta	)]

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

											return image_inputs





							def 						UpperCamelCase_					(   self		:		Dict	):

											'''simple docstring'''
											__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=lowerCAmelCase_      ,				padding_value=1.0	)

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

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

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





							def 						UpperCamelCase_					(   self		:		Dict	):

											'''simple docstring'''
											__a       =		self.get_image_processor()
											__a       =		self.get_tokenizer()

											__a       =		BlipaProcessor(tokenizer=lowerCAmelCase_      ,				image_processor=lowerCAmelCase_	)

											__a       =		self.prepare_image_inputs()

											__a       =		image_processor(lowerCAmelCase_      ,				return_tensors="""np"""	)
											__a       =		processor(images=lowerCAmelCase_      ,				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 						UpperCamelCase_					(   self		:		str	):

											'''simple docstring'''
											__a       =		self.get_image_processor()
											__a       =		self.get_tokenizer()

											__a       =		BlipaProcessor(tokenizer=lowerCAmelCase_      ,				image_processor=lowerCAmelCase_	)

											__a       =		"""lower newer"""

											__a       =		processor(text=lowerCAmelCase_	)

											__a       =		tokenizer(lowerCAmelCase_      ,				return_token_type_ids=lowerCAmelCase_	)

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





							def 						UpperCamelCase_					(   self		:		Optional[Any]	):

											'''simple docstring'''
											__a       =		self.get_image_processor()
											__a       =		self.get_tokenizer()

											__a       =		BlipaProcessor(tokenizer=lowerCAmelCase_      ,				image_processor=lowerCAmelCase_	)

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

											__a       =		processor(text=lowerCAmelCase_      ,				images=lowerCAmelCase_	)

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

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





							def 						UpperCamelCase_					(   self		:		str	):

											'''simple docstring'''
											__a       =		self.get_image_processor()
											__a       =		self.get_tokenizer()

											__a       =		BlipaProcessor(tokenizer=lowerCAmelCase_      ,				image_processor=lowerCAmelCase_	)

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

											__a       =		processor.batch_decode(lowerCAmelCase_	)
											__a       =		tokenizer.batch_decode(lowerCAmelCase_	)

											self.assertListEqual(lowerCAmelCase_      ,				lowerCAmelCase_	)







							def 						UpperCamelCase_					(   self		:		str	):

											'''simple docstring'''
											__a       =		self.get_image_processor()
											__a       =		self.get_tokenizer()

											__a       =		BlipaProcessor(tokenizer=lowerCAmelCase_      ,				image_processor=lowerCAmelCase_	)

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

											__a       =		processor(text=lowerCAmelCase_      ,				images=lowerCAmelCase_	)

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





import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__			,      unittest.TestCase					):
							__lowerCamelCase	:	Optional[int]    =BlenderbotSmallTokenizer
							__lowerCamelCase	:	str    =False





							def 						UpperCamelCase_					(   self		:		Union[str, Any]	):

											'''simple docstring'''
											super().setUp()

											__a       =		["""__start__""", """adapt""", """act""", """ap@@""", """te""", """__end__""", """__unk__"""]
											__a       =		dict(zip(__lowercase      ,				range(len(__lowercase	)	)	)	)

											__a       =		["""#version: 0.2""", """a p""", """t e</w>""", """ap t</w>""", """a d""", """ad apt</w>""", """a c""", """ac t</w>""", """"""]
											__a       =		{"""unk_token""": """__unk__""", """bos_token""": """__start__""", """eos_token""": """__end__"""}

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





							def 						UpperCamelCase_					(   self		:		Optional[Any]      ,				**__lowercase		:		Dict	):

											'''simple docstring'''
											kwargs.update(self.special_tokens_map	)
											return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname      ,				**__lowercase	)





							def 						UpperCamelCase_					(   self		:		Union[str, Any]      ,				__lowercase		:		Optional[Any]	):

											'''simple docstring'''
											__a       =		"""adapt act apte"""
											__a       =		"""adapt act apte"""
											return input_text, output_text





							def 						UpperCamelCase_					(   self		:		Optional[Any]	):

											'''simple docstring'''
											__a       =		BlenderbotSmallTokenizer(self.vocab_file      ,				self.merges_file      ,				**self.special_tokens_map	)
											__a       =		"""adapt act apte"""
											__a       =		["""adapt""", """act""", """ap@@""", """te"""]
											__a       =		tokenizer.tokenize(__lowercase	)
											self.assertListEqual(__lowercase      ,				__lowercase	)

											__a       =		[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

											__a       =		[0, 1, 2, 3, 4, 5]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase	)      ,				__lowercase	)





							def 						UpperCamelCase_					(   self		:		Optional[int]	):

											'''simple docstring'''
											__a       =		BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M"""	)
											assert tok("""sam"""	).input_ids == [1384]
											__a       =		"""I am a small frog."""
											__a       =		tok([src_text]      ,				padding=__lowercase      ,				truncation=__lowercase	)["""input_ids"""]
											__a       =		tok.batch_decode(__lowercase      ,				skip_special_tokens=__lowercase      ,				clean_up_tokenization_spaces=__lowercase	)[0]
											assert src_text != decoded  # I wish it did!
											assert decoded == "i am a small frog ."







							def 						UpperCamelCase_					(   self		:		Tuple	):

											'''simple docstring'''
											__a       =		BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M"""	)
											__a       =		"""I am a small frog ."""
											__a       =		"""."""
											__a       =		tok(__lowercase	)["""input_ids"""]
											__a       =		tok(__lowercase	)["""input_ids"""]

											assert encoded[-1] == encoded_dot[0]
