
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:
   _A  					= _A  					= _A  					= _A  					= None







class      lowerCamelCase		(  unittest.TestCase  ):





     def __init__(self      :    Any				,    _A      :    Tuple				,    _A      :    Optional[int]=7				,    _A      :    List[str]=3				,    _A      :    List[str]=3_0				,    _A      :    str=4_0_0				,    _A      :    str=True				,    _A      :    List[str]=None				,    _A      :    Tuple=True				,    _A      :    List[str]=None				,    _A      :    Union[str, Any]=None				,    _A      :    List[str]=True				,    _A      :    Optional[Any]=1 / 2_5_5				,    _A      :    int=True				,    _A      :    Optional[int]=FLAVA_IMAGE_MEAN				,    _A      :    Union[str, Any]=FLAVA_IMAGE_STD				,    _A      :    List[str]=1_4				,    _A      :    List[Any]=7_5				,    _A      :    List[Any]=None				,    _A      :    Dict=1_6				,    _A      :    Optional[Any]=0.3				,    _A      :    Optional[Any]=None				,    _A      :    Dict=True				,    _A      :    Any=None				,    _A      :    Union[str, Any]=None				,    _A      :    List[Any]=True				,    _A      :    Any=None				,    _A      :    Optional[int]=True				,    _A      :    int=True				,    _A      :    int=FLAVA_CODEBOOK_MEAN				,    _A      :    Optional[int]=FLAVA_CODEBOOK_STD				,    )						->						List[Any]:
         snake_case      =					size if size is not None else {"height": 2_2_4, "width": 2_2_4}
         snake_case      =					crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
         snake_case      =					codebook_size if codebook_size is not None else {"height": 1_1_2, "width": 1_1_2}
         snake_case      =					codebook_crop_size if codebook_crop_size is not None else {"height": 1_1_2, "width": 1_1_2}

         snake_case      =					parent
         snake_case      =					batch_size
         snake_case      =					num_channels
         snake_case      =					do_resize
         snake_case      =					do_rescale
         snake_case      =					rescale_factor
         snake_case      =					min_resolution
         snake_case      =					max_resolution
         snake_case      =					size
         snake_case      =					resample if resample is not None else PILImageResampling.BICUBIC
         snake_case      =					do_normalize
         snake_case      =					image_mean
         snake_case      =					image_std
         snake_case      =					do_center_crop
         snake_case      =					crop_size

         snake_case      =					input_size_patches
         snake_case      =					total_mask_patches
         snake_case      =					mask_group_max_patches
         snake_case      =					mask_group_min_patches
         snake_case      =					mask_group_min_aspect_ratio
         snake_case      =					mask_group_max_aspect_ratio

         snake_case      =					codebook_do_resize
         snake_case      =					codebook_size
         snake_case      =					codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
         snake_case      =					codebook_do_center_crop
         snake_case      =					codebook_crop_size
         snake_case      =					codebook_do_map_pixels
         snake_case      =					codebook_do_normalize
         snake_case      =					codebook_image_mean
         snake_case      =					codebook_image_std





     def 		UpperCAmelCase(self      :    Union[str, Any]   )						->						List[str]:
         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 		UpperCAmelCase(self      :    int   )						->						Union[str, Any]:
         return (self.size["height"], self.size["width"])





     def 		UpperCAmelCase(self      :    str   )						->						Dict:
         return (
             (self.input_size_patches, self.input_size_patches)
             if not isinstance(self.input_size_patches				,    _A   )
             else self.input_size_patches
         )





     def 		UpperCAmelCase(self      :    Dict   )						->						Union[str, Any]:
         return (self.codebook_size["height"], self.codebook_size["width"])







@require_torch
@require_vision
class      lowerCamelCase		(  A_							, unittest.TestCase  ):
     UpperCAmelCase__				:							Optional[int]           =		FlavaImageProcessor if is_vision_available() else None
     UpperCAmelCase__				:							Dict           =		None





     def 		UpperCAmelCase(self      :    List[str]   )						->						List[Any]:
         snake_case      =					FlavaImageProcessingTester(self   )





     @property
     def 		UpperCAmelCase(self      :    Union[str, Any]   )						->						Optional[Any]:
         return self.image_processor_tester.prepare_image_processor_dict()





     def 		UpperCAmelCase(self      :    Tuple   )						->						Optional[int]:
         snake_case      =					self.image_processing_class(**self.image_processor_dict   )
         self.assertTrue(hasattr(_A				,    "image_mean"   )   )
         self.assertTrue(hasattr(_A				,    "image_std"   )   )
         self.assertTrue(hasattr(_A				,    "do_normalize"   )   )
         self.assertTrue(hasattr(_A				,    "do_resize"   )   )
         self.assertTrue(hasattr(_A				,    "resample"   )   )
         self.assertTrue(hasattr(_A				,    "crop_size"   )   )
         self.assertTrue(hasattr(_A				,    "do_center_crop"   )   )
         self.assertTrue(hasattr(_A				,    "do_rescale"   )   )
         self.assertTrue(hasattr(_A				,    "rescale_factor"   )   )
         self.assertTrue(hasattr(_A				,    "masking_generator"   )   )
         self.assertTrue(hasattr(_A				,    "codebook_do_resize"   )   )
         self.assertTrue(hasattr(_A				,    "codebook_size"   )   )
         self.assertTrue(hasattr(_A				,    "codebook_resample"   )   )
         self.assertTrue(hasattr(_A				,    "codebook_do_center_crop"   )   )
         self.assertTrue(hasattr(_A				,    "codebook_crop_size"   )   )
         self.assertTrue(hasattr(_A				,    "codebook_do_map_pixels"   )   )
         self.assertTrue(hasattr(_A				,    "codebook_do_normalize"   )   )
         self.assertTrue(hasattr(_A				,    "codebook_image_mean"   )   )
         self.assertTrue(hasattr(_A				,    "codebook_image_std"   )   )





     def 		UpperCAmelCase(self      :    List[str]   )						->						Tuple:
         snake_case      =					self.image_processing_class.from_dict(self.image_processor_dict   )
         self.assertEqual(image_processor.size				,    {"height": 2_2_4, "width": 2_2_4}   )
         self.assertEqual(image_processor.crop_size				,    {"height": 2_2_4, "width": 2_2_4}   )
         self.assertEqual(image_processor.codebook_size				,    {"height": 1_1_2, "width": 1_1_2}   )
         self.assertEqual(image_processor.codebook_crop_size				,    {"height": 1_1_2, "width": 1_1_2}   )

         snake_case      =					self.image_processing_class.from_dict(
             self.image_processor_dict				,    size=4_2				,    crop_size=8_4				,    codebook_size=3_3				,    codebook_crop_size=6_6   )
         self.assertEqual(image_processor.size				,    {"height": 4_2, "width": 4_2}   )
         self.assertEqual(image_processor.crop_size				,    {"height": 8_4, "width": 8_4}   )
         self.assertEqual(image_processor.codebook_size				,    {"height": 3_3, "width": 3_3}   )
         self.assertEqual(image_processor.codebook_crop_size				,    {"height": 6_6, "width": 6_6}   )





     def 		UpperCAmelCase(self      :    Dict   )						->						Any:
         pass





     def 		UpperCAmelCase(self      :    List[Any]   )						->						Any:
         # Initialize image_processing
         snake_case      =					self.image_processing_class(**self.image_processor_dict   )
         # create random PIL images
         snake_case      =					prepare_image_inputs(self.image_processor_tester				,    equal_resolution=_A   )
         for image in image_inputs:
             self.assertIsInstance(_A				,    PIL.Image.Image   )

         # Test not batched input
         snake_case      =					image_processing(image_inputs[0]				,    return_tensors="pt"   )

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

         snake_case     ,							snake_case      =					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
         snake_case      =					image_processing(_A				,    return_tensors="pt"   )
         snake_case     ,							snake_case      =					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 		UpperCAmelCase(self      :    Any				,    _A      :    Tuple				,    _A      :    Dict   )						->						Tuple:
         # Initialize image_processing
         snake_case      =					self.image_processing_class(**self.image_processor_dict   )
         # create random tensors
         snake_case      =					prepare_image_inputs(self.image_processor_tester				,    equal_resolution=_A				,    **_A   )
         for image in image_inputs:
             self.assertIsInstance(_A				,    _A   )

         # Test not batched input
         snake_case      =					image_processing(image_inputs[0]				,    return_tensors="pt"   )

         snake_case     ,							snake_case      =					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)				,    )

         snake_case      =					image_processing(_A				,    return_image_mask=_A				,    return_tensors="pt"   )

         snake_case     ,							snake_case      =					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,
             )				,    )

         snake_case     ,							snake_case      =					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
         snake_case      =					image_processing(_A				,    return_tensors="pt"   ).pixel_values

         snake_case     ,							snake_case      =					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
         snake_case      =					image_processing(_A				,    return_image_mask=_A				,    return_tensors="pt"   )

         snake_case     ,							snake_case      =					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,
             )				,    )

         snake_case     ,							snake_case      =					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 		UpperCAmelCase(self      :    Dict   )						->						Any:
         self._test_call_framework(np.ndarray				,    prepare_kwargs={"numpify": True}   )





     def 		UpperCAmelCase(self      :    List[Any]   )						->						Tuple:
         self._test_call_framework(torch.Tensor				,    prepare_kwargs={"torchify": True}   )





     def 		UpperCAmelCase(self      :    int   )						->						Union[str, Any]:
         # Initialize image_processing
         random.seed(1_2_3_4   )
         snake_case      =					self.image_processing_class(**self.image_processor_dict   )
         snake_case      =					prepare_image_inputs(self.image_processor_tester				,    equal_resolution=_A				,    torchify=_A   )

         # Test not batched input
         snake_case      =					image_processing(image_inputs[0]				,    return_image_mask=_A				,    return_tensors="pt"   )
         self.assertEqual(encoded_images.bool_masked_pos.sum().item()				,    7_5   )





     def 		UpperCAmelCase(self      :    Union[str, Any]   )						->						Optional[Any]:
         # Initialize image_processing
         snake_case      =					self.image_processing_class(**self.image_processor_dict   )
         # create random PIL images
         snake_case      =					prepare_image_inputs(self.image_processor_tester				,    equal_resolution=_A   )
         for image in image_inputs:
             self.assertIsInstance(_A				,    PIL.Image.Image   )

         # Test not batched input
         snake_case      =					image_processing(image_inputs[0]				,    return_codebook_pixels=_A				,    return_tensors="pt"   )
         snake_case     ,							snake_case      =					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
         snake_case      =					image_processing(_A				,    return_codebook_pixels=_A				,    return_tensors="pt"   )
         snake_case     ,							snake_case      =					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


_A  					= logging.get_logger(__name__)







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							Tuple           =		"semantic"





     def __init__(self      :    Dict				,    _A      :    Optional[Any]=1_0_0_0_0				,    _A      :    List[Any]=True				,    _A      :    Any=7_6_8				,    _A      :    Union[str, Any]=False				,    _A      :    List[Any]=False				,    _A      :    Union[str, Any]=False				,    _A      :    Union[str, Any]=False				,    _A      :    str=0.7				,    _A      :    Optional[int]=True				,    _A      :    Any=1_0_0_4_8				,    _A      :    Optional[int]=1_2_9_5_9_5				,    _A      :    Union[str, Any]=1_2_9_5_9_9				,    _A      :    Any=1_0_0_0_0				,    _A      :    str=2_5_6				,    _A      :    int=49.9				,    **_A      :    Union[str, Any]				,    )						->						Any:
         super().__init__(
             temperature=_A				,    do_sample=_A				,    eos_token_id=_A				,    renormalize_logits=_A				,    max_new_tokens=_A				,    output_scores=_A				,    return_dict_in_generate=_A				,    output_hidden_states=_A				,    output_attentions=_A				,    **_A				,    )

         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		(  A_  ):
     UpperCAmelCase__				:							List[Any]           =		"coarse_acoustics"





     def __init__(self      :    Any				,    _A      :    Union[str, Any]=True				,    _A      :    List[Any]=False				,    _A      :    Any=False				,    _A      :    Union[str, Any]=False				,    _A      :    Optional[Any]=False				,    _A      :    str=0.7				,    _A      :    int=True				,    _A      :    List[str]=1_2_0_4_8				,    _A      :    Optional[int]=7_5				,    _A      :    Any=2				,    _A      :    Optional[Any]=1_2_0_5_0				,    _A      :    Tuple=2_5_6				,    _A      :    int = 6_3_0				,    _A      :    int = 6_0				,    **_A      :    int				,    )						->						Union[str, Any]:
         super().__init__(
             temperature=_A				,    do_sample=_A				,    renormalize_logits=_A				,    output_scores=_A				,    return_dict_in_generate=_A				,    output_hidden_states=_A				,    output_attentions=_A				,    **_A				,    )

         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		(  A_  ):
     UpperCAmelCase__				:							int           =		"fine_acoustics"





     def __init__(self      :    Tuple				,    _A      :    Optional[int]=0.5				,    _A      :    List[str]=5_1_2				,    _A      :    Union[str, Any]=1_0_2_4				,    _A      :    Tuple=8				,    **_A      :    Tuple				,    )						->						Tuple:
         super().__init__(temperature=_A   )

         snake_case      =					max_fine_history_length
         snake_case      =					max_fine_input_length
         snake_case      =					n_fine_codebooks







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							Optional[Any]           =		"bark"
     UpperCAmelCase__				:							Union[str, Any]           =		True





     def __init__(self      :    Union[str, Any]				,    _A      :    Dict = None				,    _A      :    Dict = None				,    _A      :    Dict = None				,    _A      :    Optional[int]=2_4_0_0_0				,    _A      :    Any=1_0_2_4				,    **_A      :    List[str]				,    )						->						Optional[Any]:
         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(**_A   )
         snake_case      =					BarkCoarseGenerationConfig(**_A   )
         snake_case      =					BarkFineGenerationConfig(**_A   )

         snake_case      =					sample_rate
         snake_case      =					codebook_size





     @classmethod
     def 		UpperCAmelCase(cls      :    List[Any]				,    _A      :    BarkSemanticGenerationConfig				,    _A      :    BarkCoarseGenerationConfig				,    _A      :    BarkFineGenerationConfig				,    **_A      :    Optional[int]				,    )						->						Union[str, Any]:
         return cls(
             semantic_config=semantic_config.to_dict()				,    coarse_acoustics_config=coarse_acoustics_config.to_dict()				,    fine_acoustics_config=fine_acoustics_config.to_dict()				,    **_A				,    )





     def 		UpperCAmelCase(self      :    List[str]   )						->						int:
         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

