


import random
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, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					import PIL

					from transformers import FlavaImageProcessor
					from transformers.image_utils import PILImageResampling
					from transformers.models.flava.image_processing_flava import (
					    FLAVA_CODEBOOK_MEAN,
					    FLAVA_CODEBOOK_STD,
					    FLAVA_IMAGE_MEAN,
					    FLAVA_IMAGE_STD,
					)
else:
					snake_case       :					Optional[Any]        =  None







class 	_snake_case						(   unittest.TestCase							):




	def __init__(	self      ,						_lowerCamelCase      ,						_lowerCamelCase=7      ,						_lowerCamelCase=3      ,						_lowerCamelCase=30      ,						_lowerCamelCase=400      ,						_lowerCamelCase=True      ,						_lowerCamelCase=None      ,						_lowerCamelCase=True      ,						_lowerCamelCase=None      ,						_lowerCamelCase=None      ,						_lowerCamelCase=True      ,						_lowerCamelCase=1 / 255      ,						_lowerCamelCase=True      ,						_lowerCamelCase=FLAVA_IMAGE_MEAN      ,						_lowerCamelCase=FLAVA_IMAGE_STD      ,						_lowerCamelCase=14      ,						_lowerCamelCase=75      ,						_lowerCamelCase=None      ,						_lowerCamelCase=16      ,						_lowerCamelCase=0.3      ,						_lowerCamelCase=None      ,						_lowerCamelCase=True      ,						_lowerCamelCase=None      ,						_lowerCamelCase=None      ,						_lowerCamelCase=True      ,						_lowerCamelCase=None      ,						_lowerCamelCase=True      ,						_lowerCamelCase=True      ,						_lowerCamelCase=FLAVA_CODEBOOK_MEAN      ,						_lowerCamelCase=FLAVA_CODEBOOK_STD      ,						):
							a		:List[str]											= size if size is not None else {'''height''': 224, '''width''': 224}
							a		:int											= crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
							a		:int											= codebook_size if codebook_size is not None else {'''height''': 112, '''width''': 112}
							a		:Tuple											= codebook_crop_size if codebook_crop_size is not None else {'''height''': 112, '''width''': 112}

							a		:List[Any]											= parent
							a		:List[str]											= batch_size
							a		:Tuple											= num_channels
							a		:Optional[Any]											= do_resize
							a		:Union[str, Any]											= do_rescale
							a		:Dict											= rescale_factor
							a		:Dict											= min_resolution
							a		:Any											= max_resolution
							a		:Dict											= size
							a		:List[str]											= resample if resample is not None else PILImageResampling.BICUBIC
							a		:Dict											= do_normalize
							a		:Any											= image_mean
							a		:Optional[int]											= image_std
							a		:Tuple											= do_center_crop
							a		:int											= crop_size

							a		:Optional[Any]											= input_size_patches
							a		:Any											= total_mask_patches
							a		:int											= mask_group_max_patches
							a		:Any											= mask_group_min_patches
							a		:Tuple											= mask_group_min_aspect_ratio
							a		:Union[str, Any]											= mask_group_max_aspect_ratio

							a		:Optional[int]											= codebook_do_resize
							a		:Tuple											= codebook_size
							a		:List[str]											= codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
							a		:List[str]											= codebook_do_center_crop
							a		:Dict											= codebook_crop_size
							a		:List[Any]											= codebook_do_map_pixels
							a		:Dict											= codebook_do_normalize
							a		:Tuple											= codebook_image_mean
							a		:Dict											= codebook_image_std




	def  SCREAMING_SNAKE_CASE__       (	self					):
							return {
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_normalize": self.do_normalize,
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "resample": self.resample,
							    "do_rescale": self.do_rescale,
							    "rescale_factor": self.rescale_factor,
							    "do_center_crop": self.do_center_crop,
							    "crop_size": self.crop_size,
							    "input_size_patches": self.input_size_patches,
							    "total_mask_patches": self.total_mask_patches,
							    "mask_group_max_patches": self.mask_group_max_patches,
							    "mask_group_min_patches": self.mask_group_min_patches,
							    "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
							    "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
							    "codebook_do_resize": self.codebook_do_resize,
							    "codebook_size": self.codebook_size,
							    "codebook_resample": self.codebook_resample,
							    "codebook_do_center_crop": self.codebook_do_center_crop,
							    "codebook_crop_size": self.codebook_crop_size,
							    "codebook_do_map_pixels": self.codebook_do_map_pixels,
							    "codebook_do_normalize": self.codebook_do_normalize,
							    "codebook_image_mean": self.codebook_image_mean,
							    "codebook_image_std": self.codebook_image_std,
							}




	def  SCREAMING_SNAKE_CASE__       (	self					):
							return (self.size["height"], self.size["width"])




	def  SCREAMING_SNAKE_CASE__       (	self					):
							return (
							    (self.input_size_patches, self.input_size_patches)
							    if not isinstance(self.input_size_patches      ,						_lowerCamelCase					)
							    else self.input_size_patches
							)




	def  SCREAMING_SNAKE_CASE__       (	self					):
							return (self.codebook_size["height"], self.codebook_size["width"])







@require_torch
@require_vision
class 	_snake_case						(   _snake_case		,					unittest.TestCase							):
	SCREAMING_SNAKE_CASE__   = FlavaImageProcessor if is_vision_available() else None
	SCREAMING_SNAKE_CASE__   = None




	def  SCREAMING_SNAKE_CASE__       (	self					):
							a		:Dict											= FlavaImageProcessingTester(self					)




	@property
	def  SCREAMING_SNAKE_CASE__       (	self					):
							return self.image_processor_tester.prepare_image_processor_dict()




	def  SCREAMING_SNAKE_CASE__       (	self					):
							a		:Dict											= self.image_processing_class(**self.image_processor_dict					)
							self.assertTrue(hasattr(_lowerCamelCase      ,						'''image_mean'''					)					)
							self.assertTrue(hasattr(_lowerCamelCase      ,						'''image_std'''					)					)
							self.assertTrue(hasattr(_lowerCamelCase      ,						'''do_normalize'''					)					)
							self.assertTrue(hasattr(_lowerCamelCase      ,						'''do_resize'''					)					)
							self.assertTrue(hasattr(_lowerCamelCase      ,						'''resample'''					)					)
							self.assertTrue(hasattr(_lowerCamelCase      ,						'''crop_size'''					)					)
							self.assertTrue(hasattr(_lowerCamelCase      ,						'''do_center_crop'''					)					)
							self.assertTrue(hasattr(_lowerCamelCase      ,						'''do_rescale'''					)					)
							self.assertTrue(hasattr(_lowerCamelCase      ,						'''rescale_factor'''					)					)
							self.assertTrue(hasattr(_lowerCamelCase      ,						'''masking_generator'''					)					)
							self.assertTrue(hasattr(_lowerCamelCase      ,						'''codebook_do_resize'''					)					)
							self.assertTrue(hasattr(_lowerCamelCase      ,						'''codebook_size'''					)					)
							self.assertTrue(hasattr(_lowerCamelCase      ,						'''codebook_resample'''					)					)
							self.assertTrue(hasattr(_lowerCamelCase      ,						'''codebook_do_center_crop'''					)					)
							self.assertTrue(hasattr(_lowerCamelCase      ,						'''codebook_crop_size'''					)					)
							self.assertTrue(hasattr(_lowerCamelCase      ,						'''codebook_do_map_pixels'''					)					)
							self.assertTrue(hasattr(_lowerCamelCase      ,						'''codebook_do_normalize'''					)					)
							self.assertTrue(hasattr(_lowerCamelCase      ,						'''codebook_image_mean'''					)					)
							self.assertTrue(hasattr(_lowerCamelCase      ,						'''codebook_image_std'''					)					)




	def  SCREAMING_SNAKE_CASE__       (	self					):
							a		:Union[str, Any]											= self.image_processing_class.from_dict(self.image_processor_dict					)
							self.assertEqual(image_processor.size      ,						{'''height''': 224, '''width''': 224}					)
							self.assertEqual(image_processor.crop_size      ,						{'''height''': 224, '''width''': 224}					)
							self.assertEqual(image_processor.codebook_size      ,						{'''height''': 112, '''width''': 112}					)
							self.assertEqual(image_processor.codebook_crop_size      ,						{'''height''': 112, '''width''': 112}					)

							a		:Union[str, Any]											= self.image_processing_class.from_dict(
							    self.image_processor_dict      ,						size=42      ,						crop_size=84      ,						codebook_size=33      ,						codebook_crop_size=66					)
							self.assertEqual(image_processor.size      ,						{'''height''': 42, '''width''': 42}					)
							self.assertEqual(image_processor.crop_size      ,						{'''height''': 84, '''width''': 84}					)
							self.assertEqual(image_processor.codebook_size      ,						{'''height''': 33, '''width''': 33}					)
							self.assertEqual(image_processor.codebook_crop_size      ,						{'''height''': 66, '''width''': 66}					)




	def  SCREAMING_SNAKE_CASE__       (	self					):
							pass




	def  SCREAMING_SNAKE_CASE__       (	self					):
							# Initialize image_processing
							a		:Optional[int]											= self.image_processing_class(**self.image_processor_dict					)
							# create random PIL images
							a		:Optional[Any]											= prepare_image_inputs(self.image_processor_tester      ,						equal_resolution=_lowerCamelCase					)
							for image in image_inputs:
													self.assertIsInstance(_lowerCamelCase      ,						PIL.Image.Image					)

							# Test not batched input
							a		:Optional[int]											= image_processing(image_inputs[0]      ,						return_tensors='''pt'''					)

							# Test no bool masked pos
							self.assertFalse('''bool_masked_pos''' in encoded_images					)

							a		:int											= self.image_processor_tester.get_expected_image_size()

							self.assertEqual(
							    encoded_images.pixel_values.shape      ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,						)

							# Test batched
							a		:Dict											= image_processing(_lowerCamelCase      ,						return_tensors='''pt'''					)
							a		:List[str]											= self.image_processor_tester.get_expected_image_size()

							# Test no bool masked pos
							self.assertFalse('''bool_masked_pos''' in encoded_images					)

							self.assertEqual(
							    encoded_images.pixel_values.shape      ,						(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )      ,						)




	def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase					):
							# Initialize image_processing
							a		:str											= self.image_processing_class(**self.image_processor_dict					)
							# create random tensors
							a		:Union[str, Any]											= prepare_image_inputs(self.image_processor_tester      ,						equal_resolution=_lowerCamelCase      ,						**_lowerCamelCase					)
							for image in image_inputs:
													self.assertIsInstance(_lowerCamelCase      ,						_lowerCamelCase					)

							# Test not batched input
							a		:Dict											= image_processing(image_inputs[0]      ,						return_tensors='''pt'''					)

							a		:str											= self.image_processor_tester.get_expected_image_size()
							self.assertEqual(
							    encoded_images.pixel_values.shape      ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,						)

							a		:Dict											= image_processing(_lowerCamelCase      ,						return_image_mask=_lowerCamelCase      ,						return_tensors='''pt'''					)

							a		:str											= self.image_processor_tester.get_expected_image_size()
							self.assertEqual(
							    encoded_images.pixel_values.shape      ,						(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )      ,						)

							a		:int											= self.image_processor_tester.get_expected_mask_size()
							self.assertEqual(
							    encoded_images.bool_masked_pos.shape      ,						(
							        self.image_processor_tester.batch_size,
							        expected_height,
							        expected_width,
							    )      ,						)

							# Test batched
							a		:Optional[int]											= image_processing(_lowerCamelCase      ,						return_tensors='''pt'''					).pixel_values

							a		:Dict											= self.image_processor_tester.get_expected_image_size()
							self.assertEqual(
							    encoded_images.shape      ,						(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )      ,						)

							# Test masking
							a		:Any											= image_processing(_lowerCamelCase      ,						return_image_mask=_lowerCamelCase      ,						return_tensors='''pt'''					)

							a		:Optional[Any]											= self.image_processor_tester.get_expected_image_size()
							self.assertEqual(
							    encoded_images.pixel_values.shape      ,						(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )      ,						)

							a		:str											= self.image_processor_tester.get_expected_mask_size()
							self.assertEqual(
							    encoded_images.bool_masked_pos.shape      ,						(
							        self.image_processor_tester.batch_size,
							        expected_height,
							        expected_width,
							    )      ,						)




	def  SCREAMING_SNAKE_CASE__       (	self					):
							self._test_call_framework(np.ndarray      ,						prepare_kwargs={'''numpify''': True}					)




	def  SCREAMING_SNAKE_CASE__       (	self					):
							self._test_call_framework(torch.Tensor      ,						prepare_kwargs={'''torchify''': True}					)




	def  SCREAMING_SNAKE_CASE__       (	self					):
							# Initialize image_processing
							random.seed(1234					)
							a		:Tuple											= self.image_processing_class(**self.image_processor_dict					)
							a		:Optional[Any]											= prepare_image_inputs(self.image_processor_tester      ,						equal_resolution=_lowerCamelCase      ,						torchify=_lowerCamelCase					)

							# Test not batched input
							a		:Dict											= image_processing(image_inputs[0]      ,						return_image_mask=_lowerCamelCase      ,						return_tensors='''pt'''					)
							self.assertEqual(encoded_images.bool_masked_pos.sum().item()      ,						75					)




	def  SCREAMING_SNAKE_CASE__       (	self					):
							# Initialize image_processing
							a		:Any											= self.image_processing_class(**self.image_processor_dict					)
							# create random PIL images
							a		:Union[str, Any]											= prepare_image_inputs(self.image_processor_tester      ,						equal_resolution=_lowerCamelCase					)
							for image in image_inputs:
													self.assertIsInstance(_lowerCamelCase      ,						PIL.Image.Image					)

							# Test not batched input
							a		:Dict											= image_processing(image_inputs[0]      ,						return_codebook_pixels=_lowerCamelCase      ,						return_tensors='''pt'''					)
							a		:Any											= self.image_processor_tester.get_expected_codebook_image_size()
							self.assertEqual(
							    encoded_images.codebook_pixel_values.shape      ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,						)

							# Test batched
							a		:Dict											= image_processing(_lowerCamelCase      ,						return_codebook_pixels=_lowerCamelCase      ,						return_tensors='''pt'''					)
							a		:Union[str, Any]											= self.image_processor_tester.get_expected_codebook_image_size()
							self.assertEqual(
							    encoded_images.codebook_pixel_values.shape      ,						(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )      ,						)









import copy
from typing import Dict

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


snake_case       :					Dict        =  logging.get_logger(__name__)







class 	_snake_case						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = 'semantic'




 def __init__(	self      ,						_lowerCamelCase=1_0000      ,						_lowerCamelCase=True      ,						_lowerCamelCase=768      ,						_lowerCamelCase=False      ,						_lowerCamelCase=False      ,						_lowerCamelCase=False      ,						_lowerCamelCase=False      ,						_lowerCamelCase=0.7      ,						_lowerCamelCase=True      ,						_lowerCamelCase=1_0048      ,						_lowerCamelCase=12_9595      ,						_lowerCamelCase=12_9599      ,						_lowerCamelCase=1_0000      ,						_lowerCamelCase=256      ,						_lowerCamelCase=49.9      ,						**_lowerCamelCase      ,						):
       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      ,						)

       a		:Dict											= text_encoding_offset
       a		:Any											= text_pad_token
       a		:List[Any]											= eos_token_id
       a		:str											= semantic_infer_token
       a		:Any											= semantic_vocab_size
       a		:Any											= max_input_semantic_length
       a		:Dict											= semantic_rate_hz







class 	_snake_case						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = 'coarse_acoustics'




 def __init__(	self      ,						_lowerCamelCase=True      ,						_lowerCamelCase=False      ,						_lowerCamelCase=False      ,						_lowerCamelCase=False      ,						_lowerCamelCase=False      ,						_lowerCamelCase=0.7      ,						_lowerCamelCase=True      ,						_lowerCamelCase=1_2048      ,						_lowerCamelCase=75      ,						_lowerCamelCase=2      ,						_lowerCamelCase=1_2050      ,						_lowerCamelCase=256      ,						_lowerCamelCase = 630      ,						_lowerCamelCase = 60      ,						**_lowerCamelCase      ,						):
       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      ,						)

       a		:Optional[Any]											= coarse_semantic_pad_token
       a		:Dict											= coarse_rate_hz
       a		:List[str]											= n_coarse_codebooks
       a		:List[Any]											= coarse_infer_token
       a		:Optional[Any]											= max_coarse_input_length
       a		:Any											= max_coarse_history
       a		:Optional[int]											= sliding_window_len







class 	_snake_case						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = 'fine_acoustics'




 def __init__(	self      ,						_lowerCamelCase=0.5      ,						_lowerCamelCase=512      ,						_lowerCamelCase=1024      ,						_lowerCamelCase=8      ,						**_lowerCamelCase      ,						):
       super().__init__(temperature=_lowerCamelCase					)

       a		:Union[str, Any]											= max_fine_history_length
       a		:int											= max_fine_input_length
       a		:str											= n_fine_codebooks







class 	_snake_case						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = 'bark'
 SCREAMING_SNAKE_CASE__   = True




 def __init__(	self      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase=2_4000      ,						_lowerCamelCase=1024      ,						**_lowerCamelCase      ,						):
       if semantic_config is None:
             a		:str											= {}
             logger.info('''semantic_config is None. initializing the semantic model with default values.'''					)

       if coarse_acoustics_config is None:
             a		:List[str]											= {}
             logger.info('''coarse_acoustics_config is None. initializing the coarse model with default values.'''					)

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

       a		:Any											= BarkSemanticGenerationConfig(**_lowerCamelCase					)
       a		:int											= BarkCoarseGenerationConfig(**_lowerCamelCase					)
       a		:Optional[int]											= BarkFineGenerationConfig(**_lowerCamelCase					)

       a		:Tuple											= sample_rate
       a		:Dict											= codebook_size




 @classmethod
 def  SCREAMING_SNAKE_CASE__       (	cls      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						**_lowerCamelCase      ,						):
       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  SCREAMING_SNAKE_CASE__       (	self					):
       a		:int											= copy.deepcopy(self.__dict__					)

       a		:List[Any]											= self.semantic_config.to_dict()
       a		:Union[str, Any]											= self.coarse_acoustics_config.to_dict()
       a		:int											= self.fine_acoustics_config.to_dict()

       a		:Tuple											= self.__class__.model_type
       return output





