



"""simple docstring"""




import unittest

import numpy as np

from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torchaudio,
    slow,
)

from .test_pipelines_common import ANY






@is_pipeline_test
class      UpperCAmelCase_  (    unittest.TestCase						):
      __SCREAMING_SNAKE_CASE						:       Dict								=			MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
      __SCREAMING_SNAKE_CASE						:       List[str]								=			TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING



      def 						snake_case_ (					self					:     str					,     A					:     Union[str, Any]					,     A					:     int					,     A					:     Dict						):
           _UpperCAmelCase      :     List[str]        =   AudioClassificationPipeline(model=A					,     feature_extractor=A						)

           # test with a raw waveform
           _UpperCAmelCase      :     List[str]        =   np.zeros((3_4_0_0_0,)						)
           _UpperCAmelCase      :     int        =   np.zeros((1_4_0_0_0,)						)
           return audio_classifier, [audioa, audio]



      def 						snake_case_ (					self					:     Union[str, Any]					,     A					:     str					,     A					:     Optional[int]						):
           _UpperCAmelCase    ,	_UpperCAmelCase      :     Tuple        =   examples
           _UpperCAmelCase      :     List[Any]        =   audio_classifier(A						)
           # by default a model is initialized with num_labels=2
           self.assertEqual(
               A					,     [
                   {"score": ANY(A						), "label": ANY(A						)},
                   {"score": ANY(A						), "label": ANY(A						)},
               ]					,     )
           _UpperCAmelCase      :     List[str]        =   audio_classifier(A					,     top_k=1						)
           self.assertEqual(
               A					,     [
                   {"score": ANY(A						), "label": ANY(A						)},
               ]					,     )

           self.run_torchaudio(A						)



      @require_torchaudio
      def 						snake_case_ (					self					:     List[str]					,     A					:     Dict						):
           import datasets

           # test with a local file
           _UpperCAmelCase      :     Dict        =   datasets.load_dataset("hf-internal-testing/librispeech_asr_dummy"					,     "clean"					,     split="validation"						)
           _UpperCAmelCase      :     Tuple        =   dataset[0]["audio"]["array"]
           _UpperCAmelCase      :     Optional[Any]        =   audio_classifier(A						)
           self.assertEqual(
               A					,     [
                   {"score": ANY(A						), "label": ANY(A						)},
                   {"score": ANY(A						), "label": ANY(A						)},
               ]					,     )



      @require_torch
      def 						snake_case_ (					self					:     Optional[int]						):
           _UpperCAmelCase      :     Any        =   "anton-l/wav2vec2-random-tiny-classifier"

           _UpperCAmelCase      :     int        =   pipeline("audio-classification"					,     model=A						)

           _UpperCAmelCase      :     Tuple        =   np.ones((8_0_0_0,)						)
           _UpperCAmelCase      :     List[Any]        =   audio_classifier(A					,     top_k=4						)

           _UpperCAmelCase      :     Optional[int]        =   [
               {"score": 0.0_842, "label": "no"},
               {"score": 0.0_838, "label": "up"},
               {"score": 0.0_837, "label": "go"},
               {"score": 0.0_834, "label": "right"},
           ]
           _UpperCAmelCase      :     Union[str, Any]        =   [
               {"score": 0.0_845, "label": "stop"},
               {"score": 0.0_844, "label": "on"},
               {"score": 0.0_841, "label": "right"},
               {"score": 0.0_834, "label": "left"},
           ]
           self.assertIn(nested_simplify(A					,     decimals=4						)					,     [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]						)

           _UpperCAmelCase      :     Union[str, Any]        =   {"array": np.ones((8_0_0_0,)						), "sampling_rate": audio_classifier.feature_extractor.sampling_rate}
           _UpperCAmelCase      :     Any        =   audio_classifier(A					,     top_k=4						)
           self.assertIn(nested_simplify(A					,     decimals=4						)					,     [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]						)



      @require_torch
      @slow
      def 						snake_case_ (					self					:     Any						):
           import datasets

           _UpperCAmelCase      :     Optional[int]        =   "superb/wav2vec2-base-superb-ks"

           _UpperCAmelCase      :     Tuple        =   pipeline("audio-classification"					,     model=A						)
           _UpperCAmelCase      :     List[Any]        =   datasets.load_dataset("anton-l/superb_dummy"					,     "ks"					,     split="test"						)

           _UpperCAmelCase      :     Any        =   np.array(dataset[3]["speech"]					,     dtype=np.floataa						)
           _UpperCAmelCase      :     Union[str, Any]        =   audio_classifier(A					,     top_k=4						)
           self.assertEqual(
               nested_simplify(A					,     decimals=3						)					,     [
                   {"score": 0.981, "label": "go"},
                   {"score": 0.007, "label": "up"},
                   {"score": 0.006, "label": "_unknown_"},
                   {"score": 0.001, "label": "down"},
               ]					,     )



      @require_tf
      @unittest.skip("Audio classification is not implemented for TF"						)
      def 						snake_case_ (					self					:     int						):
           pass





"""simple docstring"""




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 CLIPImageProcessor, CLIPProcessor






@require_vision
class      UpperCAmelCase_  (    unittest.TestCase						):



      def 						snake_case_ (					self					:     List[Any]						):
           _UpperCAmelCase      :     List[str]        =   tempfile.mkdtemp()

           # fmt: off
           _UpperCAmelCase      :     Union[str, 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      :     List[Any]        =   dict(zip(A					,     range(len(A						)						)						)						)
           _UpperCAmelCase      :     Union[str, Any]        =   ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
           _UpperCAmelCase      :     Optional[int]        =   {"unk_token": "<unk>"}

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

           _UpperCAmelCase      :     List[str]        =   {
               "do_resize": True,
               "size": 2_0,
               "do_center_crop": True,
               "crop_size": 1_8,
               "do_normalize": True,
               "image_mean": [0.48_145_466, 0.4_578_275, 0.40_821_073],
               "image_std": [0.26_862_954, 0.26_130_258, 0.27_577_711],
           }
           _UpperCAmelCase      :     Any        =   os.path.join(self.tmpdirname					,     A						)
           with open(self.image_processor_file					,     "w"					,     encoding="utf-8"						) as fp:
                json.dump(A					,     A						)



      def 						snake_case_ (					self					:     List[Any]					,     **A					:     Union[str, Any]						):
           return CLIPTokenizer.from_pretrained(self.tmpdirname					,     **A						)



      def 						snake_case_ (					self					:     int					,     **A					:     Any						):
           return CLIPTokenizerFast.from_pretrained(self.tmpdirname					,     **A						)



      def 						snake_case_ (					self					:     List[str]					,     **A					:     Optional[Any]						):
           return CLIPImageProcessor.from_pretrained(self.tmpdirname					,     **A						)



      def 						snake_case_ (					self					:     Optional[int]						):
           shutil.rmtree(self.tmpdirname						)



      def 						snake_case_ (					self					:     str						):
           _UpperCAmelCase      :     int        =   [np.random.randint(2_5_5					,     size=(3, 3_0, 4_0_0)					,     dtype=np.uinta						)]

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

           return image_inputs



      def 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase      :     int        =   self.get_tokenizer()
           _UpperCAmelCase      :     Dict        =   self.get_rust_tokenizer()
           _UpperCAmelCase      :     int        =   self.get_image_processor()

           _UpperCAmelCase      :     List[Any]        =   CLIPProcessor(tokenizer=A					,     image_processor=A						)
           processor_slow.save_pretrained(self.tmpdirname						)
           _UpperCAmelCase      :     Optional[Any]        =   CLIPProcessor.from_pretrained(self.tmpdirname					,     use_fast=A						)

           _UpperCAmelCase      :     Optional[Any]        =   CLIPProcessor(tokenizer=A					,     image_processor=A						)
           processor_fast.save_pretrained(self.tmpdirname						)
           _UpperCAmelCase      :     Optional[int]        =   CLIPProcessor.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					,     A						)
           self.assertIsInstance(processor_fast.tokenizer					,     A						)

           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					,     A						)
           self.assertIsInstance(processor_fast.image_processor					,     A						)



      def 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase      :     List[str]        =   CLIPProcessor(tokenizer=self.get_tokenizer()					,     image_processor=self.get_image_processor()						)
           processor.save_pretrained(self.tmpdirname						)

           _UpperCAmelCase      :     Any        =   self.get_tokenizer(bos_token="(BOS)"					,     eos_token="(EOS)"						)
           _UpperCAmelCase      :     Any        =   self.get_image_processor(do_normalize=A					,     padding_value=1.0						)

           _UpperCAmelCase      :     Any        =   CLIPProcessor.from_pretrained(
               self.tmpdirname					,     bos_token="(BOS)"					,     eos_token="(EOS)"					,     do_normalize=A					,     padding_value=1.0						)

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

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



      def 						snake_case_ (					self					:     List[Any]						):
           _UpperCAmelCase      :     str        =   self.get_image_processor()
           _UpperCAmelCase      :     List[str]        =   self.get_tokenizer()

           _UpperCAmelCase      :     Any        =   CLIPProcessor(tokenizer=A					,     image_processor=A						)

           _UpperCAmelCase      :     Dict        =   self.prepare_image_inputs()

           _UpperCAmelCase      :     Optional[int]        =   image_processor(A					,     return_tensors="np"						)
           _UpperCAmelCase      :     Any        =   processor(images=A					,     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 						snake_case_ (					self					:     str						):
           _UpperCAmelCase      :     Tuple        =   self.get_image_processor()
           _UpperCAmelCase      :     int        =   self.get_tokenizer()

           _UpperCAmelCase      :     List[str]        =   CLIPProcessor(tokenizer=A					,     image_processor=A						)

           _UpperCAmelCase      :     Optional[int]        =   "lower newer"

           _UpperCAmelCase      :     Union[str, Any]        =   processor(text=A						)

           _UpperCAmelCase      :     Optional[int]        =   tokenizer(A						)

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



      def 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase      :     Union[str, Any]        =   self.get_image_processor()
           _UpperCAmelCase      :     Tuple        =   self.get_tokenizer()

           _UpperCAmelCase      :     str        =   CLIPProcessor(tokenizer=A					,     image_processor=A						)

           _UpperCAmelCase      :     Tuple        =   "lower newer"
           _UpperCAmelCase      :     Union[str, Any]        =   self.prepare_image_inputs()

           _UpperCAmelCase      :     str        =   processor(text=A					,     images=A						)

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

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



      def 						snake_case_ (					self					:     int						):
           _UpperCAmelCase      :     List[str]        =   self.get_image_processor()
           _UpperCAmelCase      :     Dict        =   self.get_tokenizer()

           _UpperCAmelCase      :     List[Any]        =   CLIPProcessor(tokenizer=A					,     image_processor=A						)

           _UpperCAmelCase      :     str        =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           _UpperCAmelCase      :     List[str]        =   processor.batch_decode(A						)
           _UpperCAmelCase      :     int        =   tokenizer.batch_decode(A						)

           self.assertListEqual(A					,     A						)



      def 						snake_case_ (					self					:     Optional[int]						):
           _UpperCAmelCase      :     Optional[Any]        =   self.get_image_processor()
           _UpperCAmelCase      :     int        =   self.get_tokenizer()

           _UpperCAmelCase      :     int        =   CLIPProcessor(tokenizer=A					,     image_processor=A						)

           _UpperCAmelCase      :     str        =   "lower newer"
           _UpperCAmelCase      :     int        =   self.prepare_image_inputs()

           _UpperCAmelCase      :     Optional[Any]        =   processor(text=A					,     images=A						)

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