"""simple docstring"""
import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__       =	logging.get_logger(__name__)



class 	lowerCAmelCase_     (      lowerCAmelCase       ):



				"""simple docstring"""
				_lowerCAmelCase  :     str      	=    """semantic"""

				def __init__(					self		,     lowerCAmelCase=1_00_00		,     lowerCAmelCase=True		,     lowerCAmelCase=7_68		,     lowerCAmelCase=False		,     lowerCAmelCase=False		,     lowerCAmelCase=False		,     lowerCAmelCase=False		,     lowerCAmelCase=0.7		,     lowerCAmelCase=True		,     lowerCAmelCase=1_00_48		,     lowerCAmelCase=12_95_95		,     lowerCAmelCase=12_95_99		,     lowerCAmelCase=1_00_00		,     lowerCAmelCase=2_56		,     lowerCAmelCase=49.9		,     **lowerCAmelCase		,     ):






										"""simple docstring"""

										super().__init__(
										    temperature=lowerCAmelCase		,     do_sample=lowerCAmelCase		,     eos_token_id=lowerCAmelCase		,     renormalize_logits=lowerCAmelCase		,     max_new_tokens=lowerCAmelCase		,     output_scores=lowerCAmelCase		,     return_dict_in_generate=lowerCAmelCase		,     output_hidden_states=lowerCAmelCase		,     output_attentions=lowerCAmelCase		,     **lowerCAmelCase		,     )

										snake_case             =      text_encoding_offset
										snake_case             =      text_pad_token
										snake_case             =      eos_token_id
										snake_case             =      semantic_infer_token
										snake_case             =      semantic_vocab_size
										snake_case             =      max_input_semantic_length
										snake_case             =      semantic_rate_hz





class 	lowerCAmelCase_     (      lowerCAmelCase       ):



				"""simple docstring"""
				_lowerCAmelCase  :     int      	=    """coarse_acoustics"""

				def __init__(					self		,     lowerCAmelCase=True		,     lowerCAmelCase=False		,     lowerCAmelCase=False		,     lowerCAmelCase=False		,     lowerCAmelCase=False		,     lowerCAmelCase=0.7		,     lowerCAmelCase=True		,     lowerCAmelCase=1_20_48		,     lowerCAmelCase=75		,     lowerCAmelCase=2		,     lowerCAmelCase=1_20_50		,     lowerCAmelCase=2_56		,     lowerCAmelCase = 6_30		,     lowerCAmelCase = 60		,     **lowerCAmelCase		,     ):






										"""simple docstring"""

										super().__init__(
										    temperature=lowerCAmelCase		,     do_sample=lowerCAmelCase		,     renormalize_logits=lowerCAmelCase		,     output_scores=lowerCAmelCase		,     return_dict_in_generate=lowerCAmelCase		,     output_hidden_states=lowerCAmelCase		,     output_attentions=lowerCAmelCase		,     **lowerCAmelCase		,     )

										snake_case             =      coarse_semantic_pad_token
										snake_case             =      coarse_rate_hz
										snake_case             =      n_coarse_codebooks
										snake_case             =      coarse_infer_token
										snake_case             =      max_coarse_input_length
										snake_case             =      max_coarse_history
										snake_case             =      sliding_window_len





class 	lowerCAmelCase_     (      lowerCAmelCase       ):



				"""simple docstring"""
				_lowerCAmelCase  :     str      	=    """fine_acoustics"""

				def __init__(					self		,     lowerCAmelCase=0.5		,     lowerCAmelCase=5_12		,     lowerCAmelCase=10_24		,     lowerCAmelCase=8		,     **lowerCAmelCase		,     ):






										"""simple docstring"""

										super().__init__(temperature=lowerCAmelCase     )

										snake_case             =      max_fine_history_length
										snake_case             =      max_fine_input_length
										snake_case             =      n_fine_codebooks





class 	lowerCAmelCase_     (      lowerCAmelCase       ):



				"""simple docstring"""
				_lowerCAmelCase  :     int      	=    """bark"""
				_lowerCAmelCase  :     Dict      	=    True

				def __init__(					self		,     lowerCAmelCase = None		,     lowerCAmelCase = None		,     lowerCAmelCase = None		,     lowerCAmelCase=2_40_00		,     lowerCAmelCase=10_24		,     **lowerCAmelCase		,     ):






										"""simple docstring"""

										if semantic_config is None:
																snake_case             =      {}
																logger.info('semantic_config is None. initializing the semantic model with default values.'     )

										if coarse_acoustics_config is None:
																snake_case             =      {}
																logger.info('coarse_acoustics_config is None. initializing the coarse model with default values.'     )

										if fine_acoustics_config is None:
																snake_case             =      {}
																logger.info('fine_acoustics_config is None. initializing the fine model with default values.'     )

										snake_case             =      BarkSemanticGenerationConfig(**lowerCAmelCase     )
										snake_case             =      BarkCoarseGenerationConfig(**lowerCAmelCase     )
										snake_case             =      BarkFineGenerationConfig(**lowerCAmelCase     )

										snake_case             =      sample_rate
										snake_case             =      codebook_size

				@classmethod
				def 		snake_case			(					cls		,     lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase		,     **lowerCAmelCase		,     ):






										"""simple docstring"""

										return cls(
										    semantic_config=semantic_config.to_dict()		,     coarse_acoustics_config=coarse_acoustics_config.to_dict()		,     fine_acoustics_config=fine_acoustics_config.to_dict()		,     **lowerCAmelCase		,     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      copy.deepcopy(self.__dict__     )

										snake_case             =      self.semantic_config.to_dict()
										snake_case             =      self.coarse_acoustics_config.to_dict()
										snake_case             =      self.fine_acoustics_config.to_dict()

										snake_case             =      self.__class__.model_type
										return output



"""simple docstring"""
import unittest

import numpy as np

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


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import SwinaSRImageProcessor
					from transformers.image_transforms import get_image_size



class 	lowerCAmelCase_     (      unittest.TestCase       ):



				"""simple docstring"""

				def __init__(					self		,     lowerCAmelCase		,     lowerCAmelCase=7		,     lowerCAmelCase=3		,     lowerCAmelCase=18		,     lowerCAmelCase=30		,     lowerCAmelCase=4_00		,     lowerCAmelCase=True		,     lowerCAmelCase=1 / 2_55		,     lowerCAmelCase=True		,     lowerCAmelCase=8		,     ):






										"""simple docstring"""

										snake_case             =      parent
										snake_case             =      batch_size
										snake_case             =      num_channels
										snake_case             =      image_size
										snake_case             =      min_resolution
										snake_case             =      max_resolution
										snake_case             =      do_rescale
										snake_case             =      rescale_factor
										snake_case             =      do_pad
										snake_case             =      pad_size

				def 		snake_case			(					self     ):






										"""simple docstring"""

										return {
										    "do_rescale": self.do_rescale,
										    "rescale_factor": self.rescale_factor,
										    "do_pad": self.do_pad,
										    "pad_size": self.pad_size,
										}

				def 		snake_case			(					self		,     lowerCAmelCase=False		,     lowerCAmelCase=False		,     lowerCAmelCase=False     ):






										"""simple docstring"""

										assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

										if equal_resolution:
																snake_case             =      []
																for i in range(self.batch_size     ):
																						image_inputs.append(
																						    np.random.randint(
																						        2_55		,     size=(self.num_channels, self.max_resolution, self.max_resolution)		,     dtype=np.uinta     )     )
										else:
																snake_case             =      []
																for i in range(self.batch_size     ):
																						snake_case       ,snake_case             =      np.random.choice(np.arange(self.min_resolution		,     self.max_resolution     )		,     2     )
																						image_inputs.append(np.random.randint(2_55		,     size=(self.num_channels, width, height)		,     dtype=np.uinta     )     )

										if not numpify and not torchify:
																# PIL expects the channel dimension as last dimension
																snake_case             =      [Image.fromarray(np.moveaxis(lowerCAmelCase		,     0		,     -1     )     ) for x in image_inputs]

										if torchify:
																snake_case             =      [torch.from_numpy(lowerCAmelCase     ) for x in image_inputs]

										return image_inputs





@require_torch
@require_vision
class 	lowerCAmelCase_     (      lowerCAmelCase  ,     unittest.TestCase       ):



				"""simple docstring"""
				_lowerCAmelCase  :     Tuple      	=    SwinaSRImageProcessor if is_vision_available() else None

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      SwinaSRImageProcessingTester(self     )

				@property
				def 		snake_case			(					self     ):






										"""simple docstring"""

										return self.image_processor_tester.prepare_image_processor_dict()

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      self.image_processing_class(**self.image_processor_dict     )
										self.assertTrue(hasattr(lowerCAmelCase		,     'do_rescale'     )     )
										self.assertTrue(hasattr(lowerCAmelCase		,     'rescale_factor'     )     )
										self.assertTrue(hasattr(lowerCAmelCase		,     'do_pad'     )     )
										self.assertTrue(hasattr(lowerCAmelCase		,     'pad_size'     )     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

										pass

				def 		snake_case			(					self		,     lowerCAmelCase     ):






										"""simple docstring"""

										snake_case       ,snake_case             =      get_image_size(lowerCAmelCase     )
										snake_case             =      self.image_processor_tester.pad_size

										snake_case             =      (old_height // size + 1) * size - old_height
										snake_case             =      (old_width // size + 1) * size - old_width
										return old_height + pad_height, old_width + pad_width

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      self.image_processing_class(**self.image_processor_dict     )
										# create random PIL images
										snake_case             =      self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase     )
										for image in image_inputs:
																self.assertIsInstance(lowerCAmelCase		,     Image.Image     )

										# Test not batched input
										snake_case             =      image_processor(image_inputs[0]		,     return_tensors='pt'     ).pixel_values
										snake_case       ,snake_case             =      self.calculate_expected_size(np.array(image_inputs[0]     )     )
										self.assertEqual(
										    encoded_images.shape		,     (
										        1,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )		,     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      self.image_processing_class(**self.image_processor_dict     )
										# create random numpy tensors
										snake_case             =      self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase		,     numpify=lowerCAmelCase     )
										for image in image_inputs:
																self.assertIsInstance(lowerCAmelCase		,     np.ndarray     )

										# Test not batched input
										snake_case             =      image_processor(image_inputs[0]		,     return_tensors='pt'     ).pixel_values
										snake_case       ,snake_case             =      self.calculate_expected_size(image_inputs[0]     )
										self.assertEqual(
										    encoded_images.shape		,     (
										        1,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )		,     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      self.image_processing_class(**self.image_processor_dict     )
										# create random PyTorch tensors
										snake_case             =      self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase		,     torchify=lowerCAmelCase     )
										for image in image_inputs:
																self.assertIsInstance(lowerCAmelCase		,     torch.Tensor     )

										# Test not batched input
										snake_case             =      image_processor(image_inputs[0]		,     return_tensors='pt'     ).pixel_values
										snake_case       ,snake_case             =      self.calculate_expected_size(image_inputs[0]     )
										self.assertEqual(
										    encoded_images.shape		,     (
										        1,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )		,     )


