from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


_SCREAMING_SNAKE_CASE      						=	logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      						=	{
    "EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class SCREAMING_SNAKE_CASE_						(	__lowerCAmelCase						):
   __lowerCAmelCase      =							"""gptj"""
   __lowerCAmelCase      =							{
       """max_position_embeddings""": """n_positions""",
       """hidden_size""": """n_embd""",
       """num_attention_heads""": """n_head""",
       """num_hidden_layers""": """n_layer""",
   }







   def __init__(			self		:		List[str]					,					lowerCamelCase_		:		List[Any]=5_0400					,					lowerCamelCase_		:		Tuple=2048					,					lowerCamelCase_		:		List[str]=4096					,					lowerCamelCase_		:		Dict=28					,					lowerCamelCase_		:		List[Any]=16					,					lowerCamelCase_		:		str=64					,					lowerCamelCase_		:		Optional[int]=None					,					lowerCamelCase_		:		Tuple="gelu_new"					,					lowerCamelCase_		:		List[str]=0.0					,					lowerCamelCase_		:		Optional[Any]=0.0					,					lowerCamelCase_		:		List[str]=0.0					,					lowerCamelCase_		:		str=1E-5					,					lowerCamelCase_		:		List[Any]=0.0_2					,					lowerCamelCase_		:		int=True					,					lowerCamelCase_		:		List[Any]=5_0256					,					lowerCamelCase_		:		Any=5_0256					,					lowerCamelCase_		:		Tuple=False					,					**lowerCamelCase_		:		Any					,					):





    """simple docstring"""


    UpperCamelCase          =	vocab_size
    UpperCamelCase          =	n_positions
    UpperCamelCase          =	n_embd
    UpperCamelCase          =	n_layer
    UpperCamelCase          =	n_head
    UpperCamelCase          =	n_inner
    UpperCamelCase          =	rotary_dim
    UpperCamelCase          =	activation_function
    UpperCamelCase          =	resid_pdrop
    UpperCamelCase          =	embd_pdrop
    UpperCamelCase          =	attn_pdrop
    UpperCamelCase          =	layer_norm_epsilon
    UpperCamelCase          =	initializer_range
    UpperCamelCase          =	use_cache

    UpperCamelCase          =	bos_token_id
    UpperCamelCase          =	eos_token_id

    super().__init__(
        bos_token_id=_lowerCAmelCase					,					eos_token_id=_lowerCAmelCase					,					tie_word_embeddings=_lowerCAmelCase					,					**_lowerCAmelCase							)





class SCREAMING_SNAKE_CASE_						(	__lowerCAmelCase						):







   def __init__(			self		:		int					,					lowerCamelCase_		:		PretrainedConfig					,					lowerCamelCase_		:		str = "default"					,					lowerCamelCase_		:		List[PatchingSpec] = None					,					lowerCamelCase_		:		bool = False					,					):





    """simple docstring"""


    super().__init__(_lowerCAmelCase					,					task=_lowerCAmelCase					,					patching_specs=_lowerCAmelCase					,					use_past=_lowerCAmelCase							)
    if not getattr(self._config					,					"""pad_token_id"""					,					_lowerCAmelCase							):
     # TODO: how to do that better?
     UpperCamelCase          =	0







   @property
   def 		lowerCamelCase_     (			self		:		List[str]							):





    """simple docstring"""


    UpperCamelCase          =	OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}							)
    if self.use_past:
     self.fill_with_past_key_values_(_lowerCAmelCase					,					direction="""inputs"""							)
     UpperCamelCase          =	{0: "batch", 1: "past_sequence + sequence"}
    else:
     UpperCamelCase          =	{0: "batch", 1: "sequence"}

    return common_inputs







   @property
   def 		lowerCamelCase_     (			self		:		Optional[Any]							):





    """simple docstring"""


    return self._config.n_layer







   @property
   def 		lowerCamelCase_     (			self		:		Dict							):





    """simple docstring"""


    return self._config.n_head







   def 		lowerCamelCase_     (			self		:		Tuple					,					lowerCamelCase_		:		PreTrainedTokenizer					,					lowerCamelCase_		:		int = -1					,					lowerCamelCase_		:		int = -1					,					lowerCamelCase_		:		bool = False					,					lowerCamelCase_		:		Optional[TensorType] = None					,					):





    """simple docstring"""


    UpperCamelCase          =	super(_lowerCAmelCase					,					self							).generate_dummy_inputs(
        _lowerCAmelCase					,					batch_size=_lowerCAmelCase					,					seq_length=_lowerCAmelCase					,					is_pair=_lowerCAmelCase					,					framework=_lowerCAmelCase							)

    # We need to order the input in the way they appears in the forward()
    UpperCamelCase          =	OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}							)

    # Need to add the past_keys
    if self.use_past:
     if not is_torch_available():
      raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""							)
     else:
      import torch

      UpperCamelCase          =	common_inputs["input_ids"].shape
      # Not using the same length for past_key_values
      UpperCamelCase          =	seqlen + 2
      UpperCamelCase          =	(
          batch,
          self.num_attention_heads,
          past_key_values_length,
          self._config.hidden_size // self.num_attention_heads,
      )
      UpperCamelCase          =	[
          (torch.zeros(_lowerCAmelCase							), torch.zeros(_lowerCAmelCase							)) for _ in range(self.num_layers							)
      ]

    UpperCamelCase          =	common_inputs["attention_mask"]
    if self.use_past:
     UpperCamelCase          =	ordered_inputs["attention_mask"].dtype
     UpperCamelCase          =	torch.cat(
         [ordered_inputs["""attention_mask"""], torch.ones(_lowerCAmelCase					,					_lowerCAmelCase					,					dtype=_lowerCAmelCase							)]					,					dim=1							)

    return ordered_inputs







   @property
   def 		lowerCamelCase_     (			self		:		Optional[Any]							):





    """simple docstring"""


    return 13






import gc
import unittest

import numpy as np
import torch

from diffusers import (
    AudioDiffusionPipeline,
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    DiffusionPipeline,
    Mel,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()




class       __lowerCAmelCase				(							unittest.TestCase):






       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any] ):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    int ):
         torch.manual_seed(0 )
         lowercase			:str							=  UNetaDModel(
             sample_size=(32, 64)				,    in_channels=1				,    out_channels=1				,    layers_per_block=2				,    block_out_channels=(1_28, 1_28)				,    down_block_types=("AttnDownBlock2D", "DownBlock2D")				,    up_block_types=("UpBlock2D", "AttnUpBlock2D")				,    )
         return model






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Any ):
         torch.manual_seed(0 )
         lowercase			:Dict							=  UNetaDConditionModel(
             sample_size=(64, 32)				,    in_channels=1				,    out_channels=1				,    layers_per_block=2				,    block_out_channels=(1_28, 1_28)				,    down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")				,    up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")				,    cross_attention_dim=10				,    )
         return model






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any] ):
         torch.manual_seed(0 )
         lowercase			:List[str]							=  AutoencoderKL(
             sample_size=(1_28, 64)				,    in_channels=1				,    out_channels=1				,    latent_channels=1				,    layers_per_block=2				,    block_out_channels=(1_28, 1_28)				,    down_block_types=("DownEncoderBlock2D", "DownEncoderBlock2D")				,    up_block_types=("UpDecoderBlock2D", "UpDecoderBlock2D")				,    )
         lowercase			:List[str]							=  UNetaDModel(
             sample_size=(64, 32)				,    in_channels=1				,    out_channels=1				,    layers_per_block=2				,    block_out_channels=(1_28, 1_28)				,    down_block_types=("AttnDownBlock2D", "DownBlock2D")				,    up_block_types=("UpBlock2D", "AttnUpBlock2D")				,    )
         return vqvae, unet






       @slow
       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         lowercase			:Optional[Any]							=  "cpu"  # ensure determinism for the device-dependent torch.Generator
         lowercase			:Optional[int]							=  Mel(
             x_res=self.dummy_unet.config.sample_size[1]				,    y_res=self.dummy_unet.config.sample_size[0]				,    )

         lowercase			:List[str]							=  DDPMScheduler()
         lowercase			:Tuple							=  AudioDiffusionPipeline(vqvae=_lowerCAmelCase				,    unet=self.dummy_unet				,    mel=_lowerCAmelCase				,    scheduler=_lowerCAmelCase )
         lowercase			:Dict							=  pipe.to(_lowerCAmelCase )
         pipe.set_progress_bar_config(disable=_lowerCAmelCase )

         lowercase			:Tuple							=  torch.Generator(device=_lowerCAmelCase ).manual_seed(42 )
         lowercase			:Optional[int]							=  pipe(generator=_lowerCAmelCase				,    steps=4 )
         lowercase			:List[str]							=  output.audios[0]
         lowercase			:List[str]							=  output.images[0]

         lowercase			:List[str]							=  torch.Generator(device=_lowerCAmelCase ).manual_seed(42 )
         lowercase			:Optional[int]							=  pipe(generator=_lowerCAmelCase				,    steps=4				,    return_dict=_lowerCAmelCase )
         lowercase			:int							=  output[0][0]

         assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
         assert (
             image.height == self.dummy_unet.config.sample_size[0]
             and image.width == self.dummy_unet.config.sample_size[1]
         )
         lowercase			:Any							=  np.frombuffer(image.tobytes()				,    dtype="uint8" )[:10]
         lowercase			:Dict							=  np.frombuffer(image_from_tuple.tobytes()				,    dtype="uint8" )[:10]
         lowercase			:List[Any]							=  np.array([69, 2_55, 2_55, 2_55, 0, 0, 77, 1_81, 12, 1_27] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() == 0

         lowercase			:Optional[Any]							=  Mel(
             x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1]				,    y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0]				,    )

         lowercase			:List[str]							=  DDIMScheduler()
         lowercase			:Tuple							=  self.dummy_vqvae_and_unet
         lowercase			:str							=  AudioDiffusionPipeline(
             vqvae=self.dummy_vqvae_and_unet[0]				,    unet=dummy_vqvae_and_unet[1]				,    mel=_lowerCAmelCase				,    scheduler=_lowerCAmelCase )
         lowercase			:Union[str, Any]							=  pipe.to(_lowerCAmelCase )
         pipe.set_progress_bar_config(disable=_lowerCAmelCase )

         np.random.seed(0 )
         lowercase			:Dict							=  np.random.uniform(-1				,    1				,    ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,) )
         lowercase			:List[Any]							=  torch.Generator(device=_lowerCAmelCase ).manual_seed(42 )
         lowercase			:Dict							=  pipe(raw_audio=_lowerCAmelCase				,    generator=_lowerCAmelCase				,    start_step=5				,    steps=10 )
         lowercase			:str							=  output.images[0]

         assert (
             image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
             and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
         )
         lowercase			:Optional[Any]							=  np.frombuffer(image.tobytes()				,    dtype="uint8" )[:10]
         lowercase			:List[str]							=  np.array([1_20, 1_17, 1_10, 1_09, 1_38, 1_67, 1_38, 1_48, 1_32, 1_21] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() == 0

         lowercase			:Optional[Any]							=  self.dummy_unet_condition
         lowercase			:Optional[int]							=  AudioDiffusionPipeline(
             vqvae=self.dummy_vqvae_and_unet[0]				,    unet=_lowerCAmelCase				,    mel=_lowerCAmelCase				,    scheduler=_lowerCAmelCase )
         lowercase			:int							=  pipe.to(_lowerCAmelCase )
         pipe.set_progress_bar_config(disable=_lowerCAmelCase )

         np.random.seed(0 )
         lowercase			:List[str]							=  torch.rand((1, 1, 10) )
         lowercase			:Union[str, Any]							=  pipe(generator=_lowerCAmelCase				,    encoding=_lowerCAmelCase )
         lowercase			:Tuple							=  output.images[0]
         lowercase			:Optional[Any]							=  np.frombuffer(image.tobytes()				,    dtype="uint8" )[:10]
         lowercase			:Any							=  np.array([1_07, 1_03, 1_20, 1_27, 1_42, 1_22, 1_13, 1_22, 97, 1_11] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() == 0







@slow
@require_torch_gpu
class       __lowerCAmelCase				(							unittest.TestCase):






       def     SCREAMING_SNAKE_CASE			(      self:    Any ):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         lowercase			:Tuple							=  torch_device

         lowercase			:List[Any]							=  DiffusionPipeline.from_pretrained("teticio/audio-diffusion-ddim-256" )
         lowercase			:Dict							=  pipe.to(_lowerCAmelCase )
         pipe.set_progress_bar_config(disable=_lowerCAmelCase )

         lowercase			:Any							=  torch.Generator(device=_lowerCAmelCase ).manual_seed(42 )
         lowercase			:Optional[int]							=  pipe(generator=_lowerCAmelCase )
         lowercase			:List[str]							=  output.audios[0]
         lowercase			:Any							=  output.images[0]

         assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
         assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
         lowercase			:Dict							=  np.frombuffer(image.tobytes()				,    dtype="uint8" )[:10]
         lowercase			:List[str]							=  np.array([1_51, 1_67, 1_54, 1_44, 1_22, 1_34, 1_21, 1_05, 70, 26] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() == 0



