
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


lowercase						:      Tuple							=							logging.get_logger(__name__)

lowercase						:      str							=							{
    """facebook/musicgen-small""": """https://huggingface.co/facebook/musicgen-small/resolve/main/config.json""",
    # See all Musicgen models at https://huggingface.co/models?filter=musicgen
}

class __snake_case	(   lowerCAmelCase  ):
    _a	:		Union[str, Any]=       "musicgen_decoder"
    _a	:		Union[str, Any]=       ["past_key_values"]
    def __init__(						self	,snake_case=2048	,snake_case=2048	,snake_case=24	,snake_case=4096	,snake_case=16	,snake_case=0.0	,snake_case=True	,snake_case="gelu"	,snake_case=1024	,snake_case=0.1	,snake_case=0.0	,snake_case=0.0	,snake_case=0.02	,snake_case=False	,snake_case=4	,snake_case=2048	,snake_case=2048	,snake_case=None	,snake_case=False	,**snake_case	,):



           '''simple docstring'''







           lowercase							:  List[str] 			=   vocab_size
           lowercase							:  Optional[Any] 			=   max_position_embeddings
           lowercase							:  Optional[int] 			=   hidden_size
           lowercase							:  str 			=   ffn_dim
           lowercase							:  Dict 			=   num_hidden_layers
           lowercase							:  str 			=   num_attention_heads
           lowercase							:  Union[str, Any] 			=   dropout
           lowercase							:  Dict 			=   attention_dropout
           lowercase							:  str 			=   activation_dropout
           lowercase							:  str 			=   activation_function
           lowercase							:  Dict 			=   initializer_factor
           lowercase							:  Tuple 			=   layerdrop
           lowercase							:  List[str] 			=   use_cache
           lowercase							:  Any 			=   scale_embedding  # scale factor will be sqrt(d_model) if True
           lowercase							:  List[Any] 			=   num_codebooks
           super().__init__(
               pad_token_id=snake_case	,bos_token_id=snake_case	,eos_token_id=snake_case	,tie_word_embeddings=snake_case	,**snake_case	,)






class __snake_case	(   lowerCAmelCase  ):
    _a	:		Any=       "musicgen"
    _a	:		Any=       True
    def __init__(						self	,**snake_case   ):



           '''simple docstring'''







           super().__init__(**snake_case   )
           if "text_encoder" not in kwargs or "audio_encoder" not in kwargs or "decoder" not in kwargs:
                  raise ValueError("""Config has to be initialized with text_encoder, audio_encoder and decoder config"""   )

           lowercase							:  Optional[int] 			=   kwargs.pop("""text_encoder"""   )
           lowercase							:  Tuple 			=   text_encoder_config.pop("""model_type"""   )

           lowercase							:  Tuple 			=   kwargs.pop("""audio_encoder"""   )
           lowercase							:  Tuple 			=   audio_encoder_config.pop("""model_type"""   )

           lowercase							:  Optional[int] 			=   kwargs.pop("""decoder"""   )

           lowercase							:  Tuple 			=   AutoConfig.for_model(snake_case	,**snake_case   )
           lowercase							:  Optional[Any] 			=   AutoConfig.for_model(snake_case	,**snake_case   )
           lowercase							:  List[str] 			=   MusicgenDecoderConfig(**snake_case   )
           lowercase							:  List[str] 			=   True
    @classmethod
    def    _SCREAMING_SNAKE_CASE (						cls	,snake_case	,snake_case	,snake_case	,**snake_case	,):



           '''simple docstring'''







           return cls(
               text_encoder=text_encoder_config.to_dict()	,audio_encoder=audio_encoder_config.to_dict()	,decoder=decoder_config.to_dict()	,**snake_case	,)
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Optional[int] 			=   copy.deepcopy(self.__dict__   )
           lowercase							:  str 			=   self.text_encoder.to_dict()
           lowercase							:  List[str] 			=   self.audio_encoder.to_dict()
           lowercase							:  List[Any] 			=   self.decoder.to_dict()
           lowercase							:  Union[str, Any] 			=   self.__class__.model_type
           return output


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_video_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import VideoMAEImageProcessor

class __snake_case	(   unittest.TestCase  ):
    def __init__(						self	,snake_case	,snake_case=7	,snake_case=3	,snake_case=10	,snake_case=18	,snake_case=30	,snake_case=400	,snake_case=True	,snake_case=None	,snake_case=True	,snake_case=[0.5, 0.5, 0.5]	,snake_case=[0.5, 0.5, 0.5]	,snake_case=None	,):



           '''simple docstring'''







           lowercase							:  Dict 			=   size if size is not None else {"""shortest_edge""": 18}
           lowercase							:  Tuple 			=   crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

           lowercase							:  Optional[int] 			=   parent
           lowercase							:  Any 			=   batch_size
           lowercase							:  int 			=   num_channels
           lowercase							:  Dict 			=   num_frames
           lowercase							:  Any 			=   image_size
           lowercase							:  str 			=   min_resolution
           lowercase							:  Any 			=   max_resolution
           lowercase							:  Tuple 			=   do_resize
           lowercase							:  Optional[Any] 			=   size
           lowercase							:  str 			=   do_normalize
           lowercase							:  Optional[Any] 			=   image_mean
           lowercase							:  Optional[Any] 			=   image_std
           lowercase							:  Union[str, Any] 			=   crop_size
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







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






@require_torch
@require_vision
class __snake_case	(   lowerCAmelCase      ,			unittest.TestCase  ):
    _a	:		Optional[Any]=       VideoMAEImageProcessor if is_vision_available() else None
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Any 			=   VideoMAEImageProcessingTester(self   )
    @property
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







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



           '''simple docstring'''







           lowercase							:  Any 			=   self.image_processing_class(**self.image_processor_dict   )
           self.assertTrue(hasattr(snake_case	,"""image_mean"""   )   )
           self.assertTrue(hasattr(snake_case	,"""image_std"""   )   )
           self.assertTrue(hasattr(snake_case	,"""do_normalize"""   )   )
           self.assertTrue(hasattr(snake_case	,"""do_resize"""   )   )
           self.assertTrue(hasattr(snake_case	,"""do_center_crop"""   )   )
           self.assertTrue(hasattr(snake_case	,"""size"""   )   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  List[str] 			=   self.image_processing_class.from_dict(self.image_processor_dict   )
           self.assertEqual(image_processor.size	,{"""shortest_edge""": 18}   )
           self.assertEqual(image_processor.crop_size	,{"""height""": 18, """width""": 18}   )

           lowercase							:  Any 			=   self.image_processing_class.from_dict(self.image_processor_dict	,size=42	,crop_size=84   )
           self.assertEqual(image_processor.size	,{"""shortest_edge""": 42}   )
           self.assertEqual(image_processor.crop_size	,{"""height""": 84, """width""": 84}   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           pass
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Tuple 			=   self.image_processing_class(**self.image_processor_dict   )
           # create random PIL videos
           lowercase							:  Optional[int] 			=   prepare_video_inputs(self.image_processor_tester	,equal_resolution=snake_case   )
           for video in video_inputs:
                  self.assertIsInstance(snake_case	,snake_case   )
                  self.assertIsInstance(video[0]	,Image.Image   )

           # Test not batched input
           lowercase							:  Optional[Any] 			=   image_processing(video_inputs[0]	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   1,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,)

           # Test batched
           lowercase							:  Union[str, Any] 			=   image_processing(snake_case	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,)
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Dict 			=   self.image_processing_class(**self.image_processor_dict   )
           # create random numpy tensors
           lowercase							:  Optional[Any] 			=   prepare_video_inputs(self.image_processor_tester	,equal_resolution=snake_case	,numpify=snake_case   )
           for video in video_inputs:
                  self.assertIsInstance(snake_case	,snake_case   )
                  self.assertIsInstance(video[0]	,np.ndarray   )

           # Test not batched input
           lowercase							:  int 			=   image_processing(video_inputs[0]	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   1,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,)

           # Test batched
           lowercase							:  Optional[int] 			=   image_processing(snake_case	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,)
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Dict 			=   self.image_processing_class(**self.image_processor_dict   )
           # create random PyTorch tensors
           lowercase							:  List[str] 			=   prepare_video_inputs(self.image_processor_tester	,equal_resolution=snake_case	,torchify=snake_case   )
           for video in video_inputs:
                  self.assertIsInstance(snake_case	,snake_case   )
                  self.assertIsInstance(video[0]	,torch.Tensor   )

           # Test not batched input
           lowercase							:  Optional[Any] 			=   image_processing(video_inputs[0]	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   1,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,)

           # Test batched
           lowercase							:  Union[str, Any] 			=   image_processing(snake_case	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,)
