
'''simple docstring'''






import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import BeitImageProcessor




class     __snake_case		(   unittest.TestCase):
					def __init__(     self,    A,    A=7,    A=3,    A=18,    A=30,    A=400,    A=True,    A=None,    A=True,    A=None,    A=True,    A=[0.5, 0.5, 0.5],    A=[0.5, 0.5, 0.5],    A=False,    ):

									"""simple docstring"""


									lowerCamelCase					:			int									= size if size is not None else {'height': 20, 'width': 20}
									lowerCamelCase					:			List[Any]									= crop_size if crop_size is not None else {'height': 18, 'width': 18}
									lowerCamelCase					:			Union[str, Any]									= parent
									lowerCamelCase					:			Any									= batch_size
									lowerCamelCase					:			str									= num_channels
									lowerCamelCase					:			Optional[Any]									= image_size
									lowerCamelCase					:			Any									= min_resolution
									lowerCamelCase					:			str									= max_resolution
									lowerCamelCase					:			List[str]									= do_resize
									lowerCamelCase					:			Any									= size
									lowerCamelCase					:			int									= do_center_crop
									lowerCamelCase					:			Dict									= crop_size
									lowerCamelCase					:			Any									= do_normalize
									lowerCamelCase					:			List[Any]									= image_mean
									lowerCamelCase					:			Tuple									= image_std
									lowerCamelCase					:			Any									= do_reduce_labels




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_center_crop": self.do_center_crop,
									    "crop_size": self.crop_size,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_reduce_labels": self.do_reduce_labels,
									}





def    UpperCAmelCase							(  ):
				lowerCamelCase					:			List[str]									= load_dataset('hf-internal-testing/fixtures_ade20k'       ,							split='test')

				lowerCamelCase					:			Any									= Image.open(dataset[0]['file'])
				lowerCamelCase					:			str									= Image.open(dataset[1]['file'])

				return image, map





def    UpperCAmelCase							(  ):
				lowerCamelCase					:			List[str]									= load_dataset('hf-internal-testing/fixtures_ade20k'       ,							split='test')

				lowerCamelCase					:			Tuple									= Image.open(ds[0]['file'])
				lowerCamelCase					:			Optional[int]									= Image.open(ds[1]['file'])
				lowerCamelCase					:			int									= Image.open(ds[2]['file'])
				lowerCamelCase					:			Any									= Image.open(ds[3]['file'])

				return [imagea, imagea], [mapa, mapa]




@require_torch
@require_vision
class     __snake_case		(   snake_case__    ,   unittest.TestCase):
					_lowerCAmelCase			     =	BeitImageProcessor if is_vision_available() else None
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= BeitImageProcessingTester(self      )
					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return self.image_processor_tester.prepare_image_processor_dict()
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= self.image_processing_class(**self.image_processor_dict      )
									self.assertTrue(hasattr(UpperCAmelCase_,    'do_resize'      )      )
									self.assertTrue(hasattr(UpperCAmelCase_,    'size'      )      )
									self.assertTrue(hasattr(UpperCAmelCase_,    'do_center_crop'      )      )
									self.assertTrue(hasattr(UpperCAmelCase_,    'center_crop'      )      )
									self.assertTrue(hasattr(UpperCAmelCase_,    'do_normalize'      )      )
									self.assertTrue(hasattr(UpperCAmelCase_,    'image_mean'      )      )
									self.assertTrue(hasattr(UpperCAmelCase_,    'image_std'      )      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= self.image_processing_class.from_dict(self.image_processor_dict      )
									self.assertEqual(image_processor.size,    {'height': 20, 'width': 20}      )
									self.assertEqual(image_processor.crop_size,    {'height': 18, 'width': 18}      )
									self.assertEqual(image_processor.do_reduce_labels,    UpperCAmelCase_      )

									lowerCamelCase					:			Optional[Any]									= self.image_processing_class.from_dict(
									    self.image_processor_dict,    size=42,    crop_size=84,    reduce_labels=UpperCAmelCase_      )
									self.assertEqual(image_processor.size,    {'height': 42, 'width': 42}      )
									self.assertEqual(image_processor.crop_size,    {'height': 84, 'width': 84}      )
									self.assertEqual(image_processor.do_reduce_labels,    UpperCAmelCase_      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									pass
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= self.image_processing_class(**self.image_processor_dict      )
									# create random PIL images
									lowerCamelCase					:			Any									= prepare_image_inputs(self.image_processor_tester,    equal_resolution=UpperCAmelCase_      )
									for image in image_inputs:
													self.assertIsInstance(UpperCAmelCase_,    Image.Image      )

									# Test not batched input
									lowerCamelCase					:			Any									= image_processing(image_inputs[0],    return_tensors='pt'      ).pixel_values
									self.assertEqual(
									    encoded_images.shape,    (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    ),    )

									# Test batched
									lowerCamelCase					:			Any									= image_processing(UpperCAmelCase_,    return_tensors='pt'      ).pixel_values
									self.assertEqual(
									    encoded_images.shape,    (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    ),    )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= self.image_processing_class(**self.image_processor_dict      )
									# create random numpy tensors
									lowerCamelCase					:			Dict									= prepare_image_inputs(self.image_processor_tester,    equal_resolution=UpperCAmelCase_,    numpify=UpperCAmelCase_      )
									for image in image_inputs:
													self.assertIsInstance(UpperCAmelCase_,    np.ndarray      )

									# Test not batched input
									lowerCamelCase					:			Optional[int]									= image_processing(image_inputs[0],    return_tensors='pt'      ).pixel_values
									self.assertEqual(
									    encoded_images.shape,    (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    ),    )

									# Test batched
									lowerCamelCase					:			Tuple									= image_processing(UpperCAmelCase_,    return_tensors='pt'      ).pixel_values
									self.assertEqual(
									    encoded_images.shape,    (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    ),    )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Dict									= self.image_processing_class(**self.image_processor_dict      )
									# create random PyTorch tensors
									lowerCamelCase					:			List[str]									= prepare_image_inputs(self.image_processor_tester,    equal_resolution=UpperCAmelCase_,    torchify=UpperCAmelCase_      )
									for image in image_inputs:
													self.assertIsInstance(UpperCAmelCase_,    torch.Tensor      )

									# Test not batched input
									lowerCamelCase					:			List[str]									= image_processing(image_inputs[0],    return_tensors='pt'      ).pixel_values
									self.assertEqual(
									    encoded_images.shape,    (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    ),    )

									# Test batched
									lowerCamelCase					:			List[str]									= image_processing(UpperCAmelCase_,    return_tensors='pt'      ).pixel_values
									self.assertEqual(
									    encoded_images.shape,    (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    ),    )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Dict									= self.image_processing_class(**self.image_processor_dict      )
									# create random PyTorch tensors
									lowerCamelCase					:			Any									= prepare_image_inputs(self.image_processor_tester,    equal_resolution=UpperCAmelCase_,    torchify=UpperCAmelCase_      )
									lowerCamelCase					:			List[Any]									= []
									for image in image_inputs:
													self.assertIsInstance(UpperCAmelCase_,    torch.Tensor      )
													maps.append(torch.zeros(image.shape[-2:]      ).long()      )

									# Test not batched input
									lowerCamelCase					:			Dict									= image_processing(image_inputs[0],    maps[0],    return_tensors='pt'      )
									self.assertEqual(
									    encoding['pixel_values'].shape,    (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    ),    )
									self.assertEqual(
									    encoding['labels'].shape,    (
									        1,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    ),    )
									self.assertEqual(encoding['labels'].dtype,    torch.long      )
									self.assertTrue(encoding['labels'].min().item() >= 0      )
									self.assertTrue(encoding['labels'].max().item() <= 255      )

									# Test batched
									lowerCamelCase					:			Dict									= image_processing(UpperCAmelCase_,    UpperCAmelCase_,    return_tensors='pt'      )
									self.assertEqual(
									    encoding['pixel_values'].shape,    (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    ),    )
									self.assertEqual(
									    encoding['labels'].shape,    (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    ),    )
									self.assertEqual(encoding['labels'].dtype,    torch.long      )
									self.assertTrue(encoding['labels'].min().item() >= 0      )
									self.assertTrue(encoding['labels'].max().item() <= 255      )

									# Test not batched input (PIL images)
									lowerCamelCase			, lowerCamelCase					:			str									= prepare_semantic_single_inputs()

									lowerCamelCase					:			Tuple									= image_processing(UpperCAmelCase_,    UpperCAmelCase_,    return_tensors='pt'      )
									self.assertEqual(
									    encoding['pixel_values'].shape,    (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    ),    )
									self.assertEqual(
									    encoding['labels'].shape,    (
									        1,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    ),    )
									self.assertEqual(encoding['labels'].dtype,    torch.long      )
									self.assertTrue(encoding['labels'].min().item() >= 0      )
									self.assertTrue(encoding['labels'].max().item() <= 255      )

									# Test batched input (PIL images)
									lowerCamelCase			, lowerCamelCase					:			int									= prepare_semantic_batch_inputs()

									lowerCamelCase					:			Optional[int]									= image_processing(UpperCAmelCase_,    UpperCAmelCase_,    return_tensors='pt'      )
									self.assertEqual(
									    encoding['pixel_values'].shape,    (
									        2,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    ),    )
									self.assertEqual(
									    encoding['labels'].shape,    (
									        2,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    ),    )
									self.assertEqual(encoding['labels'].dtype,    torch.long      )
									self.assertTrue(encoding['labels'].min().item() >= 0      )
									self.assertTrue(encoding['labels'].max().item() <= 255      )




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= self.image_processing_class(**self.image_processor_dict      )

									# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
									lowerCamelCase			, lowerCamelCase					:			List[Any]									= prepare_semantic_single_inputs()
									lowerCamelCase					:			str									= image_processing(UpperCAmelCase_,    UpperCAmelCase_,    return_tensors='pt'      )
									self.assertTrue(encoding['labels'].min().item() >= 0      )
									self.assertTrue(encoding['labels'].max().item() <= 150      )

									lowerCamelCase					:			Dict									= True
									lowerCamelCase					:			Any									= image_processing(UpperCAmelCase_,    UpperCAmelCase_,    return_tensors='pt'      )
									self.assertTrue(encoding['labels'].min().item() >= 0      )
									self.assertTrue(encoding['labels'].max().item() <= 255      )







'''simple docstring'''






import json
import os
from typing import Optional

import numpy as np

from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer


A			      =      logging.get_logger(__name__)




class     __snake_case		(   a__):
					_lowerCAmelCase			     =	'''AutoTokenizer'''
					_lowerCAmelCase			     =	['''tokenizer''']

					_lowerCAmelCase			     =	{
					    '''semantic_prompt''': 1,
					    '''coarse_prompt''': 2,
					    '''fine_prompt''': 2,
					}
					def __init__(     self,    A,    A=None      ):

									"""simple docstring"""


									super().__init__(A      )

									lowerCamelCase					:			Union[str, Any]									= speaker_embeddings
					@classmethod
					def     UpperCAmelCase_							(     cls,    A,    A="speaker_embeddings_path.json",    **A      ):

									"""simple docstring"""


									if speaker_embeddings_dict_path is not None:
													lowerCamelCase					:			List[Any]									= get_file_from_repo(
													    A,    A,    subfolder=kwargs.pop('subfolder',    A      ),    cache_dir=kwargs.pop('cache_dir',    A      ),    force_download=kwargs.pop('force_download',    A      ),    proxies=kwargs.pop('proxies',    A      ),    resume_download=kwargs.pop('resume_download',    A      ),    local_files_only=kwargs.pop('local_files_only',    A      ),    use_auth_token=kwargs.pop('use_auth_token',    A      ),    revision=kwargs.pop('revision',    A      ),    )
													if speaker_embeddings_path is None:
																	logger.warning(
																	    F'''`{os.path.join(A,    A      )}` does not exists
                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json
                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`.'''      )
																	lowerCamelCase					:			Union[str, Any]									= None
													else:
																	with open(A      ) as speaker_embeddings_json:
																					lowerCamelCase					:			Union[str, Any]									= json.load(A      )
									else:
													lowerCamelCase					:			Dict									= None

									lowerCamelCase					:			int									= AutoTokenizer.from_pretrained(A,    **A      )

									return cls(tokenizer=A,    speaker_embeddings=A      )
					def     UpperCAmelCase_							(     self,    A,    A="speaker_embeddings_path.json",    A="speaker_embeddings",    A = False,    **A,    ):

									"""simple docstring"""


									if self.speaker_embeddings is not None:
													os.makedirs(os.path.join(A,    A,    'v2'      ),    exist_ok=A      )

													lowerCamelCase					:			Any									= {}

													lowerCamelCase					:			Tuple									= save_directory

													for prompt_key in self.speaker_embeddings:
																	if prompt_key != "repo_or_path":
																					lowerCamelCase					:			Optional[int]									= self._load_voice_preset(A      )

																					lowerCamelCase					:			Optional[Any]									= {}
																					for key in self.speaker_embeddings[prompt_key]:
																									np.save(
																									    os.path.join(
																									        embeddings_dict['repo_or_path'],    A,    F'''{prompt_key}_{key}'''      ),    voice_preset[key],    allow_pickle=A,    )
																									lowerCamelCase					:			Tuple									= os.path.join(A,    F'''{prompt_key}_{key}.npy'''      )

																					lowerCamelCase					:			Optional[Any]									= tmp_dict

													with open(os.path.join(A,    A      ),    'w'      ) as fp:
																	json.dump(A,    A      )

									super().save_pretrained(A,    A,    **A      )
					def     UpperCAmelCase_							(     self,    A = None,    **A      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= self.speaker_embeddings[voice_preset]

									lowerCamelCase					:			Optional[Any]									= {}
									for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
													if key not in voice_preset_paths:
																	raise ValueError(
																	    F'''Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}].'''      )

													lowerCamelCase					:			Optional[Any]									= get_file_from_repo(
													    self.speaker_embeddings.get('repo_or_path',    '/'      ),    voice_preset_paths[key],    subfolder=kwargs.pop('subfolder',    A      ),    cache_dir=kwargs.pop('cache_dir',    A      ),    force_download=kwargs.pop('force_download',    A      ),    proxies=kwargs.pop('proxies',    A      ),    resume_download=kwargs.pop('resume_download',    A      ),    local_files_only=kwargs.pop('local_files_only',    A      ),    use_auth_token=kwargs.pop('use_auth_token',    A      ),    revision=kwargs.pop('revision',    A      ),    )
													if path is None:
																	raise ValueError(
																	    F'''`{os.path.join(self.speaker_embeddings.get('repo_or_path',    '/'      ),    voice_preset_paths[key]      )}` does not exists
                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}
                    embeddings.'''      )

													lowerCamelCase					:			Tuple									= np.load(A      )

									return voice_preset_dict
					def     UpperCAmelCase_							(     self,    A = None      ):

									"""simple docstring"""


									for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
													if key not in voice_preset:
																	raise ValueError(F'''Voice preset unrecognized, missing {key} as a key.'''      )

													if not isinstance(voice_preset[key],    np.ndarray      ):
																	raise ValueError(F'''{key} voice preset must be a {str(self.preset_shape[key]      )}D ndarray.'''      )

													if len(voice_preset[key].shape      ) != self.preset_shape[key]:
																	raise ValueError(F'''{key} voice preset must be a {str(self.preset_shape[key]      )}D ndarray.'''      )




					def __call__(     self,    A=None,    A=None,    A="pt",    A=256,    A=False,    A=True,    A=False,    **A,    ):

									"""simple docstring"""


									if voice_preset is not None and not isinstance(A,    A      ):
													if (
													    isinstance(A,    A      )
													    and self.speaker_embeddings is not None
													    and voice_preset in self.speaker_embeddings
													):
																	lowerCamelCase					:			Dict									= self._load_voice_preset(A      )

													else:
																	if isinstance(A,    A      ) and not voice_preset.endswith('.npz'      ):
																					lowerCamelCase					:			List[Any]									= voice_preset + '.npz'

																	lowerCamelCase					:			Tuple									= np.load(A      )

									if voice_preset is not None:
													self._validate_voice_preset_dict(A,    **A      )
													lowerCamelCase					:			List[Any]									= BatchFeature(data=A,    tensor_type=A      )

									lowerCamelCase					:			str									= self.tokenizer(
									    A,    return_tensors=A,    padding='max_length',    max_length=A,    return_attention_mask=A,    return_token_type_ids=A,    add_special_tokens=A,    **A,    )

									if voice_preset is not None:
													lowerCamelCase					:			Dict									= voice_preset

									return encoded_text
