

import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch






class      lowerCamelCase_      (	unittest.TestCase	):
	_lowercase		:   List[str]     					=				JukeboxTokenizer
	_lowercase		:   Any     					=				{
	    '''artist''': '''Zac Brown Band''',
	    '''genres''': '''Country''',
	    '''lyrics''': '''I met a traveller from an antique land,
        Who said "Two vast and trunkless legs of stone
        Stand in the desert. . . . Near them, on the sand,
        Half sunk a shattered visage lies, whose frown,
        And wrinkled lip, and sneer of cold command,
        Tell that its sculptor well those passions read
        Which yet survive, stamped on these lifeless things,
        The hand that mocked them, and the heart that fed;
        And on the pedestal, these words appear:
        My name is Ozymandias, King of Kings;
        Look on my Works, ye Mighty, and despair!
        Nothing beside remains. Round the decay
        Of that colossal Wreck, boundless and bare
        The lone and level sands stretch far away
        ''',
	}




	@require_torch
	def        lowerCAmelCase_       (     self						:     List[Any]							):
		import torch

		__A :			Union[str, Any]		    =			JukeboxTokenizer.from_pretrained("""openai/jukebox-1b-lyrics"""							)
		__A :			Tuple		    =			tokenizer(**self.metas							)["""input_ids"""]
		# fmt: off
		__A :			Any		    =			[
		    torch.tensor([[
		        0, 0, 0, 7169, 507, 9, 76, 39, 31, 46, 76, 27,
		        76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
		        44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
		        47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
		        76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
		        30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
		        27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
		        45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
		        41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
		        19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
		        76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
		        76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
		        64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
		        30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
		        27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
		        34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
		        27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
		        41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
		        76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
		        44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
		        76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
		        32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
		        40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
		        20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
		        45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
		        31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
		        45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
		        76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
		        34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
		        31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
		        40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
		        38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
		        78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
		        76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
		        41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
		        27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
		        46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
		        76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
		        41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
		        46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
		        41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
		        78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
		        40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
		        27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
		        76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
		        76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
		        41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
		        76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
		        27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
		        78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
		        34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
		        44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
		        40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
		        78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
		        46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
		        38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
		        40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
		        27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
		        20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
		        76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
		        76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
		        76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
		        76, 76]]							),
		    torch.tensor([[0, 0, 0, 1069, 11]]							),
		    torch.tensor([[0, 0, 0, 1069, 11]]							),
		]
		# fmt: on
		self.assertTrue(torch.allclose(tokens[0]					,      EXPECTED_OUTPUT[0]							)							)
		self.assertTrue(torch.allclose(tokens[1]					,      EXPECTED_OUTPUT[1]							)							)
		self.assertTrue(torch.allclose(tokens[2]					,      EXPECTED_OUTPUT[2]							)							)




	@require_torch
	def        lowerCAmelCase_       (     self						:     Optional[Any]							):
		import torch

		__A :			int		    =			JukeboxTokenizer.from_pretrained("""openai/jukebox-5b-lyrics"""							)
		__A :			List[str]		    =			tokenizer(**self.metas							)["""input_ids"""]
		# fmt: off
		__A :			Dict		    =			[
		    torch.tensor([[
		        0, 0, 0, 1069, 11, -1, -1, -1, -1, 9, 77, 39,
		        31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
		        31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
		        40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
		        79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
		        77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
		        27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
		        37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
		        32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
		        77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
		        77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
		        77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
		        46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
		        77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
		        77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
		        77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
		        77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
		        64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
		        40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
		        40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
		        38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
		        31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
		        41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
		        77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
		        46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
		        41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
		        31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
		        31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
		        23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
		        44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
		        31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
		        38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
		        40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
		        77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
		        27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
		        31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
		        34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
		        31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
		        1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
		        31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
		        45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
		        31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
		        77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
		        15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
		        11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
		        45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
		        41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
		        44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
		        46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
		        27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
		        77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
		        35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
		        77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
		        31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
		        77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
		        41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
		        77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
		        40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
		        77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
		        77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
		        27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
		        77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
		        77, 77, 77, 77, 77, 77]]							),
		    torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]							),
		    torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]							),
		]
		# fmt: on
		self.assertTrue(torch.allclose(tokens[0]					,      EXPECTED_OUTPUT[0]							)							)
		self.assertTrue(torch.allclose(tokens[1]					,      EXPECTED_OUTPUT[1]							)							)
		self.assertTrue(torch.allclose(tokens[2]					,      EXPECTED_OUTPUT[2]							)							)








from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
			import torch


UpperCAmelCase_							:					Optional[int]		 =   logging.get_logger(__name__)






class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   List[str]     					=				['''pixel_values''']




	def __init__(     self						:     Dict					,      __A						:     bool = True					,      __A						:     Optional[Dict[str, int]] = None					,      __A						:     PILImageResampling = PILImageResampling.BILINEAR					,      __A						:     bool = True					,      __A						:     Dict[str, int] = None					,      __A						:     bool = True					,      __A						:     Union[int, float] = 1 / 255					,      __A						:     bool = True					,      __A						:     Optional[Union[float, List[float]]] = None					,      __A						:     Optional[Union[float, List[float]]] = None					,      **__A						:     int					,      ):
		super().__init__(**__A							)
		__A :			Union[str, Any]		    =			size if size is not None else {"""shortest_edge""": 256}
		__A :			Dict		    =			get_size_dict(__A					,      default_to_square=__A							)
		__A :			str		    =			crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
		__A :			int		    =			get_size_dict(__A					,      param_name="""crop_size"""							)
		__A :			str		    =			do_resize
		__A :			Dict		    =			size
		__A :			Any		    =			resample
		__A :			Optional[Any]		    =			do_center_crop
		__A :			List[str]		    =			crop_size
		__A :			Optional[int]		    =			do_rescale
		__A :			int		    =			rescale_factor
		__A :			Union[str, Any]		    =			do_normalize
		__A :			int		    =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
		__A :			Dict		    =			image_std if image_std is not None else IMAGENET_STANDARD_STD




	def        lowerCAmelCase_       (     self						:     Optional[Any]					,      __A						:     np.ndarray					,      __A						:     Dict[str, int]					,      __A						:     PILImageResampling = PILImageResampling.BICUBIC					,      __A						:     Optional[Union[str, ChannelDimension]] = None					,      **__A						:     List[Any]					,      ):
		__A :			str		    =			get_size_dict(__A					,      default_to_square=__A							)
		if "shortest_edge" not in size:
			raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""							)
		__A :			Dict		    =			get_resize_output_image_size(__A					,      size=size["""shortest_edge"""]					,      default_to_square=__A							)
		return resize(__A					,      size=__A					,      resample=__A					,      data_format=__A					,      **__A							)




	def        lowerCAmelCase_       (     self						:     Tuple					,      __A						:     np.ndarray					,      __A						:     Dict[str, int]					,      __A						:     Optional[Union[str, ChannelDimension]] = None					,      **__A						:     str					,      ):
		__A :			str		    =			get_size_dict(__A							)
		if "height" not in size or "width" not in size:
			raise ValueError(F"""The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}"""							)
		return center_crop(__A					,      size=(size["""height"""], size["""width"""])					,      data_format=__A					,      **__A							)




	def        lowerCAmelCase_       (     self						:     List[str]					,      __A						:     np.ndarray					,      __A						:     float					,      __A						:     Optional[Union[str, ChannelDimension]] = None					,      **__A						:     Optional[int]							):
		return rescale(__A					,      scale=__A					,      data_format=__A					,      **__A							)




	def        lowerCAmelCase_       (     self						:     Any					,      __A						:     np.ndarray					,      __A						:     Union[float, List[float]]					,      __A						:     Union[float, List[float]]					,      __A						:     Optional[Union[str, ChannelDimension]] = None					,      **__A						:     Tuple					,      ):
		return normalize(__A					,      mean=__A					,      std=__A					,      data_format=__A					,      **__A							)




	def        lowerCAmelCase_       (     self						:     int					,      __A						:     ImageInput					,      __A						:     Optional[bool] = None					,      __A						:     Dict[str, int] = None					,      __A						:     PILImageResampling = None					,      __A						:     bool = None					,      __A						:     Dict[str, int] = None					,      __A						:     Optional[bool] = None					,      __A						:     Optional[float] = None					,      __A						:     Optional[bool] = None					,      __A						:     Optional[Union[float, List[float]]] = None					,      __A						:     Optional[Union[float, List[float]]] = None					,      __A						:     Optional[Union[str, TensorType]] = None					,      __A						:     Union[str, ChannelDimension] = ChannelDimension.FIRST					,      **__A						:     Optional[int]					,      ):
		__A :			List[str]		    =			do_resize if do_resize is not None else self.do_resize
		__A :			Any		    =			size if size is not None else self.size
		__A :			Union[str, Any]		    =			get_size_dict(__A					,      default_to_square=__A							)
		__A :			Tuple		    =			resample if resample is not None else self.resample
		__A :			Tuple		    =			do_center_crop if do_center_crop is not None else self.do_center_crop
		__A :			List[Any]		    =			crop_size if crop_size is not None else self.crop_size
		__A :			int		    =			get_size_dict(__A					,      param_name="""crop_size"""							)
		__A :			Tuple		    =			do_rescale if do_rescale is not None else self.do_rescale
		__A :			Optional[Any]		    =			rescale_factor if rescale_factor is not None else self.rescale_factor
		__A :			Optional[int]		    =			do_normalize if do_normalize is not None else self.do_normalize
		__A :			Optional[int]		    =			image_mean if image_mean is not None else self.image_mean
		__A :			List[str]		    =			image_std if image_std is not None else self.image_std

		__A :			Union[str, Any]		    =			make_list_of_images(__A							)

		if not valid_images(__A							):
			raise ValueError(
			    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
			    """torch.Tensor, tf.Tensor or jax.ndarray."""							)

		if do_resize and size is None:
			raise ValueError("""Size must be specified if do_resize is True."""							)

		if do_center_crop and crop_size is None:
			raise ValueError("""Crop size must be specified if do_center_crop is True."""							)

		if do_rescale and rescale_factor is None:
			raise ValueError("""Rescale factor must be specified if do_rescale is True."""							)

		if do_normalize and (image_mean is None or image_std is None):
			raise ValueError("""Image mean and std must be specified if do_normalize is True."""							)

		# All transformations expect numpy arrays.
		__A :			Union[str, Any]		    =			[to_numpy_array(__A							) for image in images]

		if do_resize:
			__A :			int		    =			[self.resize(image=__A					,      size=__A					,      resample=__A							) for image in images]

		if do_center_crop:
			__A :			Optional[Any]		    =			[self.center_crop(image=__A					,      size=__A							) for image in images]

		if do_rescale:
			__A :			List[Any]		    =			[self.rescale(image=__A					,      scale=__A							) for image in images]

		if do_normalize:
			__A :			Any		    =			[self.normalize(image=__A					,      mean=__A					,      std=__A							) for image in images]

		__A :			int		    =			[to_channel_dimension_format(__A					,      __A							) for image in images]

		__A :			Tuple		    =			{"""pixel_values""": images}
		return BatchFeature(data=__A					,      tensor_type=__A							)




	def        lowerCAmelCase_       (     self						:     int					,      __A						:     List[str]					,      __A						:     List[Tuple] = None							):
		__A :			Union[str, Any]		    =			outputs.logits

		# Resize logits and compute semantic segmentation maps
		if target_sizes is not None:
			if len(__A							) != len(__A							):
				raise ValueError(
				    """Make sure that you pass in as many target sizes as the batch dimension of the logits"""							)

			if is_torch_tensor(__A							):
				__A :			str		    =			target_sizes.numpy()

			__A :			int		    =			[]

			for idx in range(len(__A							)							):
				__A :			Any		    =			torch.nn.functional.interpolate(
				    logits[idx].unsqueeze(dim=0							)					,      size=target_sizes[idx]					,      mode="""bilinear"""					,      align_corners=__A							)
				__A :			Union[str, Any]		    =			resized_logits[0].argmax(dim=0							)
				semantic_segmentation.append(__A							)
		else:
			__A :			List[str]		    =			logits.argmax(dim=1							)
			__A :			Tuple		    =			[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]							)]

		return semantic_segmentation





