




"""simple docstring"""

import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin

class     lowercase (      __UpperCAmelCase       ,		unittest.TestCase):
				__lowerCAmelCase      :      Tuple					    =				SpeechaTextaTokenizer
				__lowerCAmelCase      :      List[Any]					    =				False







				def    a_					(							self				:   Dict    ):



					"""simple docstring"""

					super().setUp()

					A_					:			Tuple						=						'''<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab'''.split(''' '''    )
					A_					:			Any						=						['''he re</w> 123''', '''here a 1456''']
					A_					:			List[str]						=						dict(zip(_lowerCamelCase ,       range(len(_lowerCamelCase    )    )    )    )

					A_					:			Tuple						=						{'''pad_token''': '''<pad>''', '''unk_token''': '''<unk>''', '''bos_token''': '''<s>''', '''eos_token''': '''</s>'''}

					A_					:			int						=						tempfile.mkdtemp()
					A_					:			List[Any]						=						os.path.join(self.tmpdirname ,       VOCAB_FILES_NAMES['''vocab_file''']    )
					A_					:			List[Any]						=						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(_lowerCamelCase    ) + '''\n'''    )

					with open(self.merges_file ,       '''w'''    ) as fp:
						fp.write('''\n'''.join(_lowerCamelCase    )    )







				def    a_					(							self				:   List[str]    ):



					"""simple docstring"""

					A_					:			Dict						=						list(self.get_tokenizer().get_vocab().keys()    )

					self.assertEqual(vocab_keys[0] ,       '''<s>'''    )
					self.assertEqual(vocab_keys[1] ,       '''<pad>'''    )
					self.assertEqual(vocab_keys[-1] ,       '''vocab'''    )
					self.assertEqual(len(_lowerCamelCase    ) ,       14    )







				def    a_					(							self				:   Optional[int]    ):



					"""simple docstring"""

					self.assertEqual(self.get_tokenizer().vocab_size ,       14    )







				def    a_					(							self				:   Tuple    ):



					"""simple docstring"""

					A_					:			Any						=						SpeechaTextaTokenizer.from_pretrained(self.tmpdirname    )

					# make sure @@ is correctly concatenated
					A_					:			Optional[int]						=						[4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
					A_					:			Dict						=						tokenizer.decode(_lowerCamelCase    )

					self.assertTrue(output_string == '''herecouple words ofthe'''    )







				def    a_					(							self				:   Tuple    ):



					"""simple docstring"""

					A_					:			Any						=						SpeechaTextaTokenizer.from_pretrained(self.tmpdirname    )

					with tempfile.TemporaryDirectory() as tmp_dirname:
						tokenizer.save_pretrained(_lowerCamelCase    )
						os.remove(os.path.join(_lowerCamelCase ,       '''merges.txt'''    )    )

						# load tokenizer without merges file should not throw an error
						A_					:			Optional[int]						=						SpeechaTextaTokenizer.from_pretrained(_lowerCamelCase    )

					with tempfile.TemporaryDirectory() as tmp_dirname:
						# save tokenizer and load again
						tokenizer.save_pretrained(_lowerCamelCase    )
						A_					:			List[str]						=						SpeechaTextaTokenizer.from_pretrained(_lowerCamelCase    )

					self.assertIsNotNone(_lowerCamelCase    )







				def    a_					(							self				:   Optional[int]    ):



					"""simple docstring"""

					if not self.test_slow_tokenizer:
						return

					A_					:			Tuple						=						inspect.signature(self.tokenizer_class.__init__    )
					A_					:			Optional[Any]						=						self.get_tokenizer()

					for parameter_name, parameter in signature.parameters.items():
						if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
							self.assertIn(_lowerCamelCase ,       tokenizer.init_kwargs    )











"""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, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast

@require_vision
class     lowercase (      unittest.TestCase):







				def    a_					(							self				:   Any    ):



					"""simple docstring"""

					A_					:			List[str]						=						tempfile.mkdtemp()

					A_					:			List[str]						=						CLIPImageProcessor()
					A_					:			Any						=						BertTokenizer.from_pretrained(
					    '''hf-internal-testing/tiny-random-BertModel''' ,       model_input_names=['''input_ids''', '''attention_mask''']    )

					A_					:			str						=						GitProcessor(_lowerCamelCase ,       _lowerCamelCase    )

					processor.save_pretrained(self.tmpdirname    )







				def    a_					(							self				:   Tuple ,       **_lowerCamelCase				:   List[str]    ):



					"""simple docstring"""

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







				def    a_					(							self				:   List[Any] ,       **_lowerCamelCase				:   int    ):



					"""simple docstring"""

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







				def    a_					(							self				:   Optional[Any]    ):



					"""simple docstring"""

					shutil.rmtree(self.tmpdirname    )







				def    a_					(							self				:   Optional[Any]    ):



					"""simple docstring"""

					A_					:			List[str]						=						[np.random.randint(2_55 ,       size=(3, 30, 4_00) ,       dtype=np.uinta    )]

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

					return image_inputs







				def    a_					(							self				:   str    ):



					"""simple docstring"""

					A_					:			Union[str, Any]						=						GitProcessor(tokenizer=self.get_tokenizer() ,       image_processor=self.get_image_processor()    )
					processor.save_pretrained(self.tmpdirname    )

					A_					:			Union[str, Any]						=						self.get_tokenizer(bos_token='''(BOS)''' ,       eos_token='''(EOS)'''    )
					A_					:			List[Any]						=						self.get_image_processor(do_normalize=_lowerCamelCase ,       padding_value=1.0    )

					A_					:			List[str]						=						GitProcessor.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    a_					(							self				:   Optional[Any]    ):



					"""simple docstring"""

					A_					:			Tuple						=						self.get_image_processor()
					A_					:			Optional[Any]						=						self.get_tokenizer()

					A_					:			int						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			Tuple						=						self.prepare_image_inputs()

					A_					:			Any						=						image_processor(_lowerCamelCase ,       return_tensors='''np'''    )
					A_					:			Optional[Any]						=						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    a_					(							self				:   Optional[int]    ):



					"""simple docstring"""

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

					A_					:			int						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			List[str]						=						'''lower newer'''

					A_					:			Tuple						=						processor(text=_lowerCamelCase    )

					A_					:			List[str]						=						tokenizer(_lowerCamelCase ,       return_token_type_ids=_lowerCamelCase    )

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







				def    a_					(							self				:   Tuple    ):



					"""simple docstring"""

					A_					:			Optional[int]						=						self.get_image_processor()
					A_					:			Optional[Any]						=						self.get_tokenizer()

					A_					:			Optional[Any]						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			Tuple						=						'''lower newer'''
					A_					:			str						=						self.prepare_image_inputs()

					A_					:			Optional[int]						=						processor(text=_lowerCamelCase ,       images=_lowerCamelCase    )

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

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







				def    a_					(							self				:   Optional[int]    ):



					"""simple docstring"""

					A_					:			Optional[int]						=						self.get_image_processor()
					A_					:			List[Any]						=						self.get_tokenizer()

					A_					:			Optional[Any]						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			Dict						=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					A_					:			str						=						processor.batch_decode(_lowerCamelCase    )
					A_					:			int						=						tokenizer.batch_decode(_lowerCamelCase    )

					self.assertListEqual(_lowerCamelCase ,       _lowerCamelCase    )







				def    a_					(							self				:   Any    ):



					"""simple docstring"""

					A_					:			Dict						=						self.get_image_processor()
					A_					:			List[str]						=						self.get_tokenizer()

					A_					:			Any						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			int						=						'''lower newer'''
					A_					:			Union[str, Any]						=						self.prepare_image_inputs()

					A_					:			List[str]						=						processor(text=_lowerCamelCase ,       images=_lowerCamelCase    )

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





