
import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert 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 AlignProcessor, EfficientNetImageProcessor

@require_vision
class     _UpperCamelCase			(   unittest.TestCase					):






			'''simple docstring'''

			def  lowerCAmelCase__		(    self	:    Tuple ):
							UpperCamelCase_:							Any		=  tempfile.mkdtemp()

							UpperCamelCase_:							Tuple		=  [
							    '''[UNK]''',
							    '''[CLS]''',
							    '''[SEP]''',
							    '''[PAD]''',
							    '''[MASK]''',
							    '''want''',
							    '''##want''',
							    '''##ed''',
							    '''wa''',
							    '''un''',
							    '''runn''',
							    '''##ing''',
							    ''',''',
							    '''low''',
							    '''lowest''',
							]
							UpperCamelCase_:							int		=  os.path.join(self.tmpdirname  ,		VOCAB_FILES_NAMES["""vocab_file"""] )
							with open(self.vocab_file  ,		"""w"""  ,		encoding="""utf-8""" ) as vocab_writer:
											vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )

							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_:							Optional[int]		=  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	:    Optional[Any]  ,		**snake_case_	:    Union[str, Any] ):
							return BertTokenizer.from_pretrained(self.tmpdirname  ,		**snake_case__ )

			def  lowerCAmelCase__		(    self	:    Tuple  ,		**snake_case_	:    int ):
							return BertTokenizerFast.from_pretrained(self.tmpdirname  ,		**snake_case__ )

			def  lowerCAmelCase__		(    self	:    Union[str, Any]  ,		**snake_case_	:    Union[str, Any] ):
							return EfficientNetImageProcessor.from_pretrained(self.tmpdirname  ,		**snake_case__ )

			def  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							shutil.rmtree(self.tmpdirname )

			def  lowerCAmelCase__		(    self	:    List[str] ):
							UpperCamelCase_:							Tuple		=  [np.random.randint(255  ,		size=(3, 30, 400)  ,		dtype=np.uinta )]
							UpperCamelCase_:							Union[str, Any]		=  [Image.fromarray(np.moveaxis(snake_case__  ,		0  ,		-1 ) ) for x in image_inputs]
							return image_inputs

			def  lowerCAmelCase__		(    self	:    List[str] ):
							UpperCamelCase_:							Tuple		=  self.get_tokenizer()
							UpperCamelCase_:							Dict		=  self.get_rust_tokenizer()
							UpperCamelCase_:							int		=  self.get_image_processor()

							UpperCamelCase_:							Tuple		=  AlignProcessor(tokenizer=snake_case__  ,		image_processor=snake_case__ )
							processor_slow.save_pretrained(self.tmpdirname )
							UpperCamelCase_:							Optional[int]		=  AlignProcessor.from_pretrained(self.tmpdirname  ,		use_fast=snake_case__ )

							UpperCamelCase_:							Optional[int]		=  AlignProcessor(tokenizer=snake_case__  ,		image_processor=snake_case__ )
							processor_fast.save_pretrained(self.tmpdirname )
							UpperCamelCase_:							Tuple		=  AlignProcessor.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	:    str ):
							UpperCamelCase_:							int		=  AlignProcessor(tokenizer=self.get_tokenizer()  ,		image_processor=self.get_image_processor() )
							processor.save_pretrained(self.tmpdirname )

							UpperCamelCase_:							Dict		=  self.get_tokenizer(bos_token="""(BOS)"""  ,		eos_token="""(EOS)""" )
							UpperCamelCase_:							List[Any]		=  self.get_image_processor(do_normalize=snake_case__  ,		padding_value=1.0 )

							UpperCamelCase_:							Tuple		=  AlignProcessor.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	:    List[str] ):
							UpperCamelCase_:							List[Any]		=  self.get_image_processor()
							UpperCamelCase_:							Any		=  self.get_tokenizer()

							UpperCamelCase_:							Optional[Any]		=  AlignProcessor(tokenizer=snake_case__  ,		image_processor=snake_case__ )

							UpperCamelCase_:							Any		=  self.prepare_image_inputs()

							UpperCamelCase_:							Optional[Any]		=  image_processor(snake_case__  ,		return_tensors="""np""" )
							UpperCamelCase_:							int		=  processor(images=snake_case__  ,		return_tensors="""np""" )

							for key in input_image_proc.keys():
											self.assertAlmostEqual(input_image_proc[key].sum()  ,		input_processor[key].sum()  ,		delta=1e-2 )

			def  lowerCAmelCase__		(    self	:    Any ):
							UpperCamelCase_:							Dict		=  self.get_image_processor()
							UpperCamelCase_:							List[Any]		=  self.get_tokenizer()

							UpperCamelCase_:							int		=  AlignProcessor(tokenizer=snake_case__  ,		image_processor=snake_case__ )

							UpperCamelCase_:							int		=  '''lower newer'''

							UpperCamelCase_:							str		=  processor(text=snake_case__ )

							UpperCamelCase_:							Tuple		=  tokenizer(snake_case__  ,		padding="""max_length"""  ,		max_length=64 )

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

			def  lowerCAmelCase__		(    self	:    int ):
							UpperCamelCase_:							List[str]		=  self.get_image_processor()
							UpperCamelCase_:							Tuple		=  self.get_tokenizer()

							UpperCamelCase_:							Tuple		=  AlignProcessor(tokenizer=snake_case__  ,		image_processor=snake_case__ )

							UpperCamelCase_:							int		=  '''lower newer'''
							UpperCamelCase_:							str		=  self.prepare_image_inputs()

							UpperCamelCase_:							Dict		=  processor(text=snake_case__  ,		images=snake_case__ )

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

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

			def  lowerCAmelCase__		(    self	:    List[str] ):
							UpperCamelCase_:							Dict		=  self.get_image_processor()
							UpperCamelCase_:							Union[str, Any]		=  self.get_tokenizer()

							UpperCamelCase_:							Dict		=  AlignProcessor(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_:							Dict		=  processor.batch_decode(snake_case__ )
							UpperCamelCase_:							List[Any]		=  tokenizer.batch_decode(snake_case__ )

							self.assertListEqual(snake_case__  ,		snake_case__ )




			def  lowerCAmelCase__		(    self	:    Any ):
							UpperCamelCase_:							Optional[Any]		=  self.get_image_processor()
							UpperCamelCase_:							Any		=  self.get_tokenizer()

							UpperCamelCase_:							List[str]		=  AlignProcessor(tokenizer=snake_case__  ,		image_processor=snake_case__ )

							UpperCamelCase_:							Optional[int]		=  '''lower newer'''
							UpperCamelCase_:							Dict		=  self.prepare_image_inputs()

							UpperCamelCase_:							List[str]		=  processor(text=snake_case__  ,		images=snake_case__ )

							self.assertListEqual(list(inputs.keys() )  ,		processor.model_input_names )








import unittest

from datasets import load_dataset

from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow

@is_pipeline_test
@require_torch
class     _UpperCamelCase			(   unittest.TestCase					):






			'''simple docstring'''

			@require_torch
			def  lowerCAmelCase__		(    self	:    Dict ):
							UpperCamelCase_:							Any		=  pipeline(
							    task="""zero-shot-audio-classification"""  ,		model="""hf-internal-testing/tiny-clap-htsat-unfused""" )
							UpperCamelCase_:							str		=  load_dataset("""ashraq/esc50""" )
							UpperCamelCase_:							Optional[int]		=  dataset["""train"""]["""audio"""][-1]["""array"""]
							UpperCamelCase_:							Optional[int]		=  audio_classifier(snake_case_  ,		candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""] )
							self.assertEqual(
							    nested_simplify(snake_case_ )  ,		[{"""score""": 0.501, """label""": """Sound of a dog"""}, {"""score""": 0.499, """label""": """Sound of vaccum cleaner"""}]  ,		)

			@unittest.skip("""No models are available in TF""" )
			def  lowerCAmelCase__		(    self	:    Optional[Any] ):
							pass

			@slow
			@require_torch
			def  lowerCAmelCase__		(    self	:    int ):
							UpperCamelCase_:							List[str]		=  pipeline(
							    task="""zero-shot-audio-classification"""  ,		model="""laion/clap-htsat-unfused"""  ,		)
							# This is an audio of a dog
							UpperCamelCase_:							Tuple		=  load_dataset("""ashraq/esc50""" )
							UpperCamelCase_:							Optional[int]		=  dataset["""train"""]["""audio"""][-1]["""array"""]
							UpperCamelCase_:							int		=  audio_classifier(snake_case_  ,		candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""] )

							self.assertEqual(
							    nested_simplify(snake_case_ )  ,		[
							        {"""score""": 0.999, """label""": """Sound of a dog"""},
							        {"""score""": 0.001, """label""": """Sound of vaccum cleaner"""},
							    ]  ,		)

							UpperCamelCase_:							Any		=  audio_classifier([audio] * 5  ,		candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""] )
							self.assertEqual(
							    nested_simplify(snake_case_ )  ,		[
							        [
							            {"""score""": 0.999, """label""": """Sound of a dog"""},
							            {"""score""": 0.001, """label""": """Sound of vaccum cleaner"""},
							        ],
							    ]
							    * 5  ,		)
							UpperCamelCase_:							Any		=  audio_classifier(
							    [audio] * 5  ,		candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]  ,		batch_size=5 )
							self.assertEqual(
							    nested_simplify(snake_case_ )  ,		[
							        [
							            {"""score""": 0.999, """label""": """Sound of a dog"""},
							            {"""score""": 0.001, """label""": """Sound of vaccum cleaner"""},
							        ],
							    ]
							    * 5  ,		)




			@unittest.skip("""No models are available in TF""" )
			def  lowerCAmelCase__		(    self	:    Optional[Any] ):
							pass






