





import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list


class 						lowerCamelCase   (unittest.TestCase			):







							"""simple docstring"""




							def 					A_	(					self	:     Union[str, Any]						)      -> Dict:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   :			str      =						"<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K ' X J Q Z".split(" "						)
														SCREAMING_SNAKE_CASE__   :			Dict      =						dict(zip(__SCREAMING_SNAKE_CASE, range(len(__SCREAMING_SNAKE_CASE						)						)						)						)

														SCREAMING_SNAKE_CASE__   :			int      =						{
														    "pad_token": "<pad>",
														    "unk_token": "<unk>",
														    "bos_token": "<s>",
														    "eos_token": "</s>",
														}
														SCREAMING_SNAKE_CASE__   :			Optional[int]      =						{
														    "feature_size": 1,
														    "padding_value": 0.0,
														    "sampling_rate": 1_6_0_0_0,
														    "return_attention_mask": False,
														    "do_normalize": True,
														}

														SCREAMING_SNAKE_CASE__   :			Optional[Any]      =						tempfile.mkdtemp()
														SCREAMING_SNAKE_CASE__   :			str      =						os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]						)
														SCREAMING_SNAKE_CASE__   :			int      =						os.path.join(self.tmpdirname, __SCREAMING_SNAKE_CASE						)
														with open(self.vocab_file, "w", encoding="utf-8"						) as fp:
																					fp.write(json.dumps(__SCREAMING_SNAKE_CASE						) + "\n"						)

														with open(self.feature_extraction_file, "w", encoding="utf-8"						) as fp:
																					fp.write(json.dumps(__SCREAMING_SNAKE_CASE						) + "\n"						)






							def 					A_	(					self	:     Tuple, **_UpperCAmelCase	:     List[str]						)      -> int:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   :			Optional[int]      =						self.add_kwargs_tokens_map.copy()
														kwargs.update(__SCREAMING_SNAKE_CASE						)
														return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname, **__SCREAMING_SNAKE_CASE						)






							def 					A_	(					self	:     str, **_UpperCAmelCase	:     str						)      -> Dict:





														"""simple docstring"""
														return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname, **__SCREAMING_SNAKE_CASE						)






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





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






							def 					A_	(					self	:     Dict						)      -> List[str]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   :			Dict      =						self.get_tokenizer()
														SCREAMING_SNAKE_CASE__   :			Optional[int]      =						self.get_feature_extractor()

														SCREAMING_SNAKE_CASE__   :			int      =						WavaVecaProcessor(tokenizer=__SCREAMING_SNAKE_CASE, feature_extractor=__SCREAMING_SNAKE_CASE						)

														processor.save_pretrained(self.tmpdirname						)
														SCREAMING_SNAKE_CASE__   :			Optional[Any]      =						WavaVecaProcessor.from_pretrained(self.tmpdirname						)

														self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab()						)
														self.assertIsInstance(processor.tokenizer, __SCREAMING_SNAKE_CASE						)

														self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string()						)
														self.assertIsInstance(processor.feature_extractor, __SCREAMING_SNAKE_CASE						)






							def 					A_	(					self	:     Optional[int]						)      -> Dict:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   :			Optional[Any]      =						WavaVecaProcessor(tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor()						)
														processor.save_pretrained(self.tmpdirname						)

														SCREAMING_SNAKE_CASE__   :			Union[str, Any]      =						self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)"						)
														SCREAMING_SNAKE_CASE__   :			Optional[Any]      =						self.get_feature_extractor(do_normalize=__SCREAMING_SNAKE_CASE, padding_value=1.0						)

														SCREAMING_SNAKE_CASE__   :			Any      =						WavaVecaProcessor.from_pretrained(
														    self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=__SCREAMING_SNAKE_CASE, padding_value=1.0						)

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

														self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string()						)
														self.assertIsInstance(processor.feature_extractor, __SCREAMING_SNAKE_CASE						)






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





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   :			Dict      =						self.get_feature_extractor()
														SCREAMING_SNAKE_CASE__   :			str      =						self.get_tokenizer()

														SCREAMING_SNAKE_CASE__   :			int      =						WavaVecaProcessor(tokenizer=__SCREAMING_SNAKE_CASE, feature_extractor=__SCREAMING_SNAKE_CASE						)

														SCREAMING_SNAKE_CASE__   :			Any      =						floats_list((3, 1_0_0_0)						)

														SCREAMING_SNAKE_CASE__   :			Any      =						feature_extractor(__SCREAMING_SNAKE_CASE, return_tensors="np"						)
														SCREAMING_SNAKE_CASE__   :			Optional[int]      =						processor(__SCREAMING_SNAKE_CASE, 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	:     List[Any]						)      -> Tuple:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   :			Dict      =						self.get_feature_extractor()
														SCREAMING_SNAKE_CASE__   :			Tuple      =						self.get_tokenizer()

														SCREAMING_SNAKE_CASE__   :			Optional[int]      =						WavaVecaProcessor(tokenizer=__SCREAMING_SNAKE_CASE, feature_extractor=__SCREAMING_SNAKE_CASE						)

														SCREAMING_SNAKE_CASE__   :			List[Any]      =						"This is a test string"

														SCREAMING_SNAKE_CASE__   :			Any      =						processor(text=__SCREAMING_SNAKE_CASE						)

														SCREAMING_SNAKE_CASE__   :			Union[str, Any]      =						tokenizer(__SCREAMING_SNAKE_CASE						)

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






							def 					A_	(					self	:     int						)      -> str:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   :			List[str]      =						self.get_feature_extractor()
														SCREAMING_SNAKE_CASE__   :			Optional[Any]      =						self.get_tokenizer()

														SCREAMING_SNAKE_CASE__   :			Any      =						WavaVecaProcessor(tokenizer=__SCREAMING_SNAKE_CASE, feature_extractor=__SCREAMING_SNAKE_CASE						)

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

														SCREAMING_SNAKE_CASE__   :			Optional[Any]      =						processor.batch_decode(__SCREAMING_SNAKE_CASE						)
														SCREAMING_SNAKE_CASE__   :			Union[str, Any]      =						tokenizer.batch_decode(__SCREAMING_SNAKE_CASE						)

														self.assertListEqual(__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE						)






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





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   :			Tuple      =						self.get_feature_extractor()
														SCREAMING_SNAKE_CASE__   :			Tuple      =						self.get_tokenizer()

														SCREAMING_SNAKE_CASE__   :			Any      =						WavaVecaProcessor(tokenizer=__SCREAMING_SNAKE_CASE, feature_extractor=__SCREAMING_SNAKE_CASE						)

														self.assertListEqual(
														    processor.model_input_names, feature_extractor.model_input_names, msg="`processor` and `feature_extractor` model input names do not match", )













"""simple docstring"""


from typing import List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION



class    lowercase_ (   __lowerCAmelCase ,__lowerCAmelCase							):

       def __init__(	self   ):






        '''simple docstring'''

        super().__init__()

        self.register_buffer("watermark_image"      ,    torch.zeros((62, 62, 4)   )   )
        snake_case__										= None

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE=None   ):






        '''simple docstring'''

        snake_case__										= images[0].height
        snake_case__										= images[0].width

        snake_case__										= sample_size or h

        snake_case__										= min(h / sample_size      ,    w / sample_size   )
        snake_case__							,      snake_case__										= (int(h / coef   ), int(w / coef   )) if coef < 1 else (h, w)

        snake_case__							,      snake_case__										= 1024**2, img_w * img_h
        snake_case__										= (Sa / Sa) ** 0.5
        snake_case__							,      snake_case__							,      snake_case__										= int(K * 62   ), img_w - int(14 * K   ), img_h - int(14 * K   )

        if self.watermark_image_as_pil is None:
         snake_case__										= self.watermark_image.to(torch.uinta   ).cpu().numpy()
         snake_case__										= Image.fromarray(__SCREAMING_SNAKE_CASE      ,    mode="RGBA"   )
         snake_case__										= watermark_image

        snake_case__										= self.watermark_image_as_pil.resize(
            (wm_size, wm_size)      ,    PIL_INTERPOLATION["bicubic"]      ,    reducing_gap=__SCREAMING_SNAKE_CASE   )

        for pil_img in images:
         pil_img.paste(__SCREAMING_SNAKE_CASE      ,    box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y)      ,    mask=wm_img.split()[-1]   )

        return images

