


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

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


__lowerCamelCase        =     logging.get_logger(__name__)

__lowerCamelCase        =     {
    '''EleutherAI/gpt-neo-1.3B''': '''https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json''',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}


class   a__	(						lowerCAmelCase_    ):
       lowerCamelCase__:     List[Any]							    =     """gpt_neo"""
       lowerCamelCase__:     str							    =     ["""past_key_values"""]
       lowerCamelCase__:     Tuple							    =     {"""num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}

       def __init__(   self      :     Union[str, Any]      ,							lowerCamelCase_      :     Tuple=5_0_2_5_7      ,							lowerCamelCase_      :     Dict=2_0_4_8      ,							lowerCamelCase_      :     str=2_0_4_8      ,							lowerCamelCase_      :     Tuple=2_4      ,							lowerCamelCase_      :     str=[[["global", "local"], 1_2]]      ,							lowerCamelCase_      :     List[Any]=1_6      ,							lowerCamelCase_      :     List[Any]=None      ,							lowerCamelCase_      :     Optional[Any]=2_5_6      ,							lowerCamelCase_      :     Optional[Any]="gelu_new"      ,							lowerCamelCase_      :     Tuple=0.0      ,							lowerCamelCase_      :     List[Any]=0.0      ,							lowerCamelCase_      :     str=0.0      ,							lowerCamelCase_      :     Union[str, Any]=0.1      ,							lowerCamelCase_      :     str=1E-5      ,							lowerCamelCase_      :     int=0.0_2      ,							lowerCamelCase_      :     List[str]=True      ,							lowerCamelCase_      :     List[Any]=5_0_2_5_6      ,							lowerCamelCase_      :     Dict=5_0_2_5_6      ,							**lowerCamelCase_      :     List[Any]      ,							):
            a_    :     Optional[Any]										=    vocab_size
            a_    :     Optional[Any]										=    max_position_embeddings
            a_    :     Union[str, Any]										=    hidden_size
            a_    :     int										=    num_layers
            a_    :     int										=    num_heads
            a_    :     Tuple										=    intermediate_size
            a_    :     Any										=    window_size
            a_    :     Dict										=    activation_function
            a_    :     Tuple										=    resid_dropout
            a_    :     Any										=    embed_dropout
            a_    :     Tuple										=    attention_dropout
            a_    :     List[Any]										=    classifier_dropout
            a_    :     int										=    layer_norm_epsilon
            a_    :     List[str]										=    initializer_range
            a_    :     Union[str, Any]										=    use_cache

            a_    :     str										=    bos_token_id
            a_    :     Any										=    eos_token_id

            a_    :     str										=    attention_types
            a_    :     Optional[int]										=    self.expand_attention_types_params(lowerCamelCase_    )

            if len(self.attention_layers    ) != self.num_layers:
                 raise ValueError(
                     """Configuration for convolutional module is incorrect. """
                     """It is required that `len(config.attention_layers)` == `config.num_layers` """
                     F'''but is `len(config.attention_layers) = {len(self.attention_layers    )}`, '''
                     F'''`config.num_layers = {self.num_layers}`. '''
                     """`config.attention_layers` is prepared using `config.attention_types`. """
                     """Please verify the value of `config.attention_types` argument."""    )

            super().__init__(bos_token_id=lowerCamelCase_      ,							eos_token_id=lowerCamelCase_      ,							**lowerCamelCase_    )

       @staticmethod
       def 						UpperCAmelCase(   lowerCamelCase_      :     List[Any]    ):
            a_    :     Any										=    []
            for item in attention_types:
                 for _ in range(item[1]    ):
                      attentions.extend(item[0]    )
            return attentions



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase ):
     import torch

     a_    :     Optional[Any]										=    input.size()
     a_    :     Optional[Any]										=    len(__UpperCamelCase )
     a_    :     Any										=    shape[dimension]

     a_    :     List[str]										=    torch.arange(0  ,  __UpperCamelCase  ,  __UpperCamelCase )
     a_    :     Union[str, Any]										=    torch.div(sizedim - size  ,  __UpperCamelCase  ,  rounding_mode="""floor""" ) + 1
     a_    :     Union[str, Any]										=    torch.arange(__UpperCamelCase ) + low_indices[:min_length][:, None]

     a_    :     Dict										=    [slice(__UpperCamelCase )] * rank
     a_    :     str										=    indices
     a_    :     List[Any]										=    input[s]

     a_    :     Tuple										=    list(range(0  ,  rank + 1 ) )
     perm.append(perm.pop(dimension + 1 ) )

     return sliced.permute(__UpperCamelCase )



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase ):
     import torch

     a_    :     Optional[int]										=    torch.arange(1  ,  __UpperCamelCase )
     a_    :     Dict										=    torch.remainder(__UpperCamelCase  ,  __UpperCamelCase )
     a_    :     Optional[int]										=    remainders == 0
     a_    :     Any										=    candidates[divisor_indices]
     a_    :     Union[str, Any]										=    torch.max(__UpperCamelCase )
     return largest_divisor, torch.div(__UpperCamelCase  ,  __UpperCamelCase  ,  rounding_mode="""floor""" )


class   a__	(						lowerCAmelCase_    ):

       @property
       def 						UpperCAmelCase(   self      :     int    ):
            a_    :     Union[str, Any]										=    OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}    )
            if self.use_past:
                 self.fill_with_past_key_values_(lowerCamelCase_      ,							direction="""inputs"""    )
                 a_    :     Optional[int]										=    {0: """batch""", 1: """past_sequence + sequence"""}
            else:
                 a_    :     Optional[Any]										=    {0: """batch""", 1: """sequence"""}

            return common_inputs

       @property
       def 						UpperCAmelCase(   self      :     int    ):
            return self._config.num_heads

       def 						UpperCAmelCase(   self      :     str      ,							lowerCamelCase_      :     PreTrainedTokenizer      ,							lowerCamelCase_      :     int = -1      ,							lowerCamelCase_      :     int = -1      ,							lowerCamelCase_      :     bool = False      ,							lowerCamelCase_      :     Optional[TensorType] = None      ,							):
            a_    :     Optional[Any]										=    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()
            a_    :     Optional[int]										=    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

                      a_			,			a_    :     Union[str, Any]										=    common_inputs["""input_ids"""].shape
                      # Not using the same length for past_key_values
                      a_    :     Optional[Any]										=    seqlen + 2
                      a_    :     str										=    (
                          batch,
                          self.num_attention_heads,
                          past_key_values_length,
                          self._config.hidden_size // self.num_attention_heads,
                      )
                      a_    :     Dict										=    [
                          (torch.zeros(lowerCamelCase_    ), torch.zeros(lowerCamelCase_    )) for _ in range(self.num_layers    )
                      ]

            a_    :     Optional[int]										=    common_inputs["""attention_mask"""]
            if self.use_past:
                 a_    :     Tuple										=    ordered_inputs["""attention_mask"""].dtype
                 a_    :     Any										=    torch.cat(
                     [ordered_inputs["""attention_mask"""], torch.ones(lowerCamelCase_      ,							lowerCamelCase_      ,							dtype=lowerCamelCase_    )]      ,							dim=1    )

            return ordered_inputs

       @property
       def 						UpperCAmelCase(   self      :     List[str]    ):
            return 1_3







from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel


class   a__	(						lowerCAmelCase_    ):
       lowerCamelCase__:     List[str]							    =     ["""vqvae"""]

       def __init__(   self      :     str      ,							lowerCamelCase_      :     AutoencoderKL      ,							lowerCamelCase_      :     UNetaDConditionModel      ,							lowerCamelCase_      :     Mel      ,							lowerCamelCase_      :     Union[DDIMScheduler, DDPMScheduler]      ,							):
            super().__init__()
            self.register_modules(unet=lowerCamelCase_      ,							scheduler=lowerCamelCase_      ,							mel=lowerCamelCase_      ,							vqvae=lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Any    ):
            return 5_0 if isinstance(self.scheduler      ,							lowerCamelCase_    ) else 1_0_0_0

       @torch.no_grad()
       def __call__(   self      :     List[str]      ,							lowerCamelCase_      :     int = 1      ,							lowerCamelCase_      :     str = None      ,							lowerCamelCase_      :     np.ndarray = None      ,							lowerCamelCase_      :     int = 0      ,							lowerCamelCase_      :     int = 0      ,							lowerCamelCase_      :     int = None      ,							lowerCamelCase_      :     torch.Generator = None      ,							lowerCamelCase_      :     float = 0      ,							lowerCamelCase_      :     float = 0      ,							lowerCamelCase_      :     torch.Generator = None      ,							lowerCamelCase_      :     float = 0      ,							lowerCamelCase_      :     torch.Tensor = None      ,							lowerCamelCase_      :     torch.Tensor = None      ,							lowerCamelCase_      :     List[Any]=True      ,							):
            a_    :     Optional[Any]										=    steps or self.get_default_steps()
            self.scheduler.set_timesteps(lowerCamelCase_    )
            a_    :     Optional[int]										=    step_generator or generator
            # For backwards compatibility
            if type(self.unet.config.sample_size    ) == int:
                 a_    :     str										=    (self.unet.config.sample_size, self.unet.config.sample_size)
            if noise is None:
                 a_    :     Dict										=    randn_tensor(
                     (
                         batch_size,
                         self.unet.config.in_channels,
                         self.unet.config.sample_size[0],
                         self.unet.config.sample_size[1],
                     )      ,							generator=lowerCamelCase_      ,							device=self.device      ,							)
            a_    :     Tuple										=    noise
            a_    :     Union[str, Any]										=    None

            if audio_file is not None or raw_audio is not None:
                 self.mel.load_audio(lowerCamelCase_      ,							lowerCamelCase_    )
                 a_    :     Union[str, Any]										=    self.mel.audio_slice_to_image(lowerCamelCase_    )
                 a_    :     Union[str, Any]										=    np.frombuffer(input_image.tobytes()      ,							dtype="""uint8"""    ).reshape(
                     (input_image.height, input_image.width)    )
                 a_    :     Union[str, Any]										=    (input_image / 2_5_5) * 2 - 1
                 a_    :     Tuple										=    torch.tensor(input_image[np.newaxis, :, :]      ,							dtype=torch.float    ).to(self.device    )

                 if self.vqvae is not None:
                      a_    :     Dict										=    self.vqvae.encode(torch.unsqueeze(lowerCamelCase_      ,							0    )    ).latent_dist.sample(
                          generator=lowerCamelCase_    )[0]
                      a_    :     List[Any]										=    self.vqvae.config.scaling_factor * input_images

                 if start_step > 0:
                      a_    :     Tuple										=    self.scheduler.add_noise(lowerCamelCase_      ,							lowerCamelCase_      ,							self.scheduler.timesteps[start_step - 1]    )

                 a_    :     Union[str, Any]										=    (
                     self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
                 )
                 a_    :     int										=    int(mask_start_secs * pixels_per_second    )
                 a_    :     Tuple										=    int(mask_end_secs * pixels_per_second    )
                 a_    :     Tuple										=    self.scheduler.add_noise(lowerCamelCase_      ,							lowerCamelCase_      ,							torch.tensor(self.scheduler.timesteps[start_step:]    )    )

            for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]    )    ):
                 if isinstance(self.unet      ,							lowerCamelCase_    ):
                      a_    :     Dict										=    self.unet(lowerCamelCase_      ,							lowerCamelCase_      ,							lowerCamelCase_    )["""sample"""]
                 else:
                      a_    :     Dict										=    self.unet(lowerCamelCase_      ,							lowerCamelCase_    )["""sample"""]

                 if isinstance(self.scheduler      ,							lowerCamelCase_    ):
                      a_    :     Any										=    self.scheduler.step(
                          model_output=lowerCamelCase_      ,							timestep=lowerCamelCase_      ,							sample=lowerCamelCase_      ,							eta=lowerCamelCase_      ,							generator=lowerCamelCase_      ,							)["""prev_sample"""]
                 else:
                      a_    :     Optional[Any]										=    self.scheduler.step(
                          model_output=lowerCamelCase_      ,							timestep=lowerCamelCase_      ,							sample=lowerCamelCase_      ,							generator=lowerCamelCase_      ,							)["""prev_sample"""]

                 if mask is not None:
                      if mask_start > 0:
                           a_    :     Any										=    mask[:, step, :, :mask_start]
                      if mask_end > 0:
                           a_    :     Dict										=    mask[:, step, :, -mask_end:]

            if self.vqvae is not None:
                 # 0.18215 was scaling factor used in training to ensure unit variance
                 a_    :     str										=    1 / self.vqvae.config.scaling_factor * images
                 a_    :     List[Any]										=    self.vqvae.decode(lowerCamelCase_    )["""sample"""]

            a_    :     Union[str, Any]										=    (images / 2 + 0.5).clamp(0      ,							1    )
            a_    :     Dict										=    images.cpu().permute(0      ,							2      ,							3      ,							1    ).numpy()
            a_    :     Any										=    (images * 2_5_5).round().astype("""uint8"""    )
            a_    :     int										=    list(
                (Image.fromarray(_[:, :, 0]    ) for _ in images)
                if images.shape[3] == 1
                else (Image.fromarray(lowerCamelCase_      ,							mode="""RGB"""    ).convert("""L"""    ) for _ in images)    )

            a_    :     str										=    [self.mel.image_to_audio(lowerCamelCase_    ) for _ in images]
            if not return_dict:
                 return images, (self.mel.get_sample_rate(), audios)

            return BaseOutput(**AudioPipelineOutput(np.array(lowerCamelCase_    )[:, np.newaxis, :]    )      ,							**ImagePipelineOutput(lowerCamelCase_    )    )

       @torch.no_grad()
       def 						UpperCAmelCase(   self      :     List[str]      ,							lowerCamelCase_      :     List[Image.Image]      ,							lowerCamelCase_      :     int = 5_0    ):
            assert isinstance(self.scheduler      ,							lowerCamelCase_    )
            self.scheduler.set_timesteps(lowerCamelCase_    )
            a_    :     Any										=    np.array(
                [np.frombuffer(image.tobytes()      ,							dtype="""uint8"""    ).reshape((1, image.height, image.width)    ) for image in images]    )
            a_    :     Union[str, Any]										=    (sample / 2_5_5) * 2 - 1
            a_    :     Optional[Any]										=    torch.Tensor(lowerCamelCase_    ).to(self.device    )

            for t in self.progress_bar(torch.flip(self.scheduler.timesteps      ,							(0,)    )    ):
                 a_    :     str										=    t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
                 a_    :     Any										=    self.scheduler.alphas_cumprod[t]
                 a_    :     Dict										=    (
                     self.scheduler.alphas_cumprod[prev_timestep]
                     if prev_timestep >= 0
                     else self.scheduler.final_alpha_cumprod
                 )
                 a_    :     Optional[Any]										=    1 - alpha_prod_t
                 a_    :     Optional[int]										=    self.unet(lowerCamelCase_      ,							lowerCamelCase_    )["""sample"""]
                 a_    :     Tuple										=    (1 - alpha_prod_t_prev) ** 0.5 * model_output
                 a_    :     Tuple										=    (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
                 a_    :     Tuple										=    sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

            return sample

       @staticmethod
       def 						UpperCAmelCase(   lowerCamelCase_      :     torch.Tensor      ,							lowerCamelCase_      :     torch.Tensor      ,							lowerCamelCase_      :     float    ):
            a_    :     Dict										=    acos(torch.dot(torch.flatten(lowerCamelCase_    )      ,							torch.flatten(lowerCamelCase_    )    ) / torch.norm(lowerCamelCase_    ) / torch.norm(lowerCamelCase_    )    )
            return sin((1 - alpha) * theta    ) * xa / sin(lowerCamelCase_    ) + sin(alpha * theta    ) * xa / sin(lowerCamelCase_    )



