






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

from packaging import version


if TYPE_CHECKING:
 from ... import PreTrainedTokenizer, TensorType

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


_a    :   Optional[Any]        =			logging.get_logger(__name__)

_a    :   Dict        =			{
    """bigscience/bloom""": """https://huggingface.co/bigscience/bloom/resolve/main/config.json""",
    """bigscience/bloom-560m""": """https://huggingface.co/bigscience/bloom-560m/blob/main/config.json""",
    """bigscience/bloom-1b1""": """https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json""",
    """bigscience/bloom-1b7""": """https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json""",
    """bigscience/bloom-3b""": """https://huggingface.co/bigscience/bloom-3b/blob/main/config.json""",
    """bigscience/bloom-7b1""": """https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json""",
}







class 	_UpperCAmelCase  (  _A		):




      """simple docstring"""



      A          =    '''bloom'''
      A          =    ['''past_key_values''']
      A          =    {
          '''num_hidden_layers''': '''n_layer''',
          '''num_attention_heads''': '''n_head''',
      }
      def __init__(   self    ,	_lowerCAmelCase=250_880    ,	_lowerCAmelCase=64    ,	_lowerCAmelCase=2    ,	_lowerCAmelCase=8    ,	_lowerCAmelCase=1e-5    ,	_lowerCAmelCase=0.02    ,	_lowerCAmelCase=True    ,	_lowerCAmelCase=1    ,	_lowerCAmelCase=2    ,	_lowerCAmelCase=False    ,	_lowerCAmelCase=0.0    ,	_lowerCAmelCase=0.0    ,	_lowerCAmelCase=1    ,	_lowerCAmelCase=False    ,	**_lowerCAmelCase    ,	):



          '''simple docstring'''
          lowerCAmelCase__   :Optional[Any]      							=				vocab_size
          # Backward compatibility with n_embed kwarg
          lowerCAmelCase__   :Optional[int]      							=				kwargs.pop("n_embed"    ,	_lowerCAmelCase		)
          lowerCAmelCase__   :int      							=				hidden_size if n_embed is None else n_embed
          lowerCAmelCase__   :Tuple      							=				n_layer
          lowerCAmelCase__   :int      							=				n_head
          lowerCAmelCase__   :Dict      							=				layer_norm_epsilon
          lowerCAmelCase__   :Union[str, Any]      							=				initializer_range
          lowerCAmelCase__   :str      							=				use_cache
          lowerCAmelCase__   :Any      							=				pretraining_tp
          lowerCAmelCase__   :Optional[int]      							=				apply_residual_connection_post_layernorm
          lowerCAmelCase__   :Dict      							=				hidden_dropout
          lowerCAmelCase__   :Dict      							=				attention_dropout

          lowerCAmelCase__   :Optional[Any]      							=				bos_token_id
          lowerCAmelCase__   :str      							=				eos_token_id
          lowerCAmelCase__   :Tuple      							=				slow_but_exact

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







class 	_UpperCAmelCase  (  _A		):




      """simple docstring"""



      A          =    version.parse('''1.12'''		)
      def __init__(   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase = "default"    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = 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?
              lowerCAmelCase__   :str      							=				0
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Any      							=				OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}		)
          if self.use_past:
              # BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
              self.fill_with_past_key_values_(_lowerCAmelCase    ,	direction="inputs"    ,	inverted_values_shape=_lowerCAmelCase		)
              lowerCAmelCase__   :List[Any]      							=				{0: "batch", 1: "past_sequence + sequence"}
          else:
              lowerCAmelCase__   :Optional[Any]      							=				{0: "batch", 1: "sequence"}

          return common_inputs
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return self._config.n_layer
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return self._config.n_head
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return 1e-3
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase = -1    ,	_lowerCAmelCase = -1    ,	_lowerCAmelCase = False    ,	_lowerCAmelCase = None    ,	):



          '''simple docstring'''
          lowerCAmelCase__   :Dict      							=				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()
          lowerCAmelCase__   :List[str]      							=				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

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

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

          return ordered_inputs



      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return 13








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 	_UpperCAmelCase  (  _A		):




      """simple docstring"""



      A          =    ['''vqvae''']
      def __init__(   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	):



          '''simple docstring'''
          super().__init__()
          self.register_modules(unet=_lowerCAmelCase    ,	scheduler=_lowerCAmelCase    ,	mel=_lowerCAmelCase    ,	vqvae=_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return 50 if isinstance(self.scheduler    ,	_lowerCAmelCase		) else 1_000
      @torch.no_grad()
      def __call__(   self    ,	_lowerCAmelCase = 1    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = 0    ,	_lowerCAmelCase = 0    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = 0    ,	_lowerCAmelCase = 0    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = 0    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase=True    ,	):



          '''simple docstring'''
          lowerCAmelCase__   :str      							=				steps or self.get_default_steps()
          self.scheduler.set_timesteps(_lowerCAmelCase		)
          lowerCAmelCase__   :Dict      							=				step_generator or generator
          # For backwards compatibility
          if type(self.unet.config.sample_size		) == int:
              lowerCAmelCase__   :Dict      							=				(self.unet.config.sample_size, self.unet.config.sample_size)
          if noise is None:
              lowerCAmelCase__   :Optional[int]      							=				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    ,	)
          lowerCAmelCase__   :Union[str, Any]      							=				noise
          lowerCAmelCase__   :Any      							=				None

          if audio_file is not None or raw_audio is not None:
              self.mel.load_audio(_lowerCAmelCase    ,	_lowerCAmelCase		)
              lowerCAmelCase__   :Dict      							=				self.mel.audio_slice_to_image(_lowerCAmelCase		)
              lowerCAmelCase__   :List[str]      							=				np.frombuffer(input_image.tobytes()    ,	dtype="uint8"		).reshape(
                  (input_image.height, input_image.width)		)
              lowerCAmelCase__   :Tuple      							=				(input_image / 255) * 2 - 1
              lowerCAmelCase__   :Any      							=				torch.tensor(input_image[np.newaxis, :, :]    ,	dtype=torch.float		).to(self.device		)

              if self.vqvae is not None:
                  lowerCAmelCase__   :str      							=				self.vqvae.encode(torch.unsqueeze(_lowerCAmelCase    ,	0		)		).latent_dist.sample(
                      generator=_lowerCAmelCase		)[0]
                  lowerCAmelCase__   :Dict      							=				self.vqvae.config.scaling_factor * input_images

              if start_step > 0:
                  lowerCAmelCase__   :Dict      							=				self.scheduler.add_noise(_lowerCAmelCase    ,	_lowerCAmelCase    ,	self.scheduler.timesteps[start_step - 1]		)

              lowerCAmelCase__   :Optional[Any]      							=				(
                  self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
              )
              lowerCAmelCase__   :Dict      							=				int(mask_start_secs * pixels_per_second		)
              lowerCAmelCase__   :Tuple      							=				int(mask_end_secs * pixels_per_second		)
              lowerCAmelCase__   :str      							=				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		):
                  lowerCAmelCase__   :Optional[Any]      							=				self.unet(_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		)["sample"]
              else:
                  lowerCAmelCase__   :Dict      							=				self.unet(_lowerCAmelCase    ,	_lowerCAmelCase		)["sample"]

              if isinstance(self.scheduler    ,	_lowerCAmelCase		):
                  lowerCAmelCase__   :Any      							=				self.scheduler.step(
                      model_output=_lowerCAmelCase    ,	timestep=_lowerCAmelCase    ,	sample=_lowerCAmelCase    ,	eta=_lowerCAmelCase    ,	generator=_lowerCAmelCase    ,	)["prev_sample"]
              else:
                  lowerCAmelCase__   :List[str]      							=				self.scheduler.step(
                      model_output=_lowerCAmelCase    ,	timestep=_lowerCAmelCase    ,	sample=_lowerCAmelCase    ,	generator=_lowerCAmelCase    ,	)["prev_sample"]

              if mask is not None:
                  if mask_start > 0:
                      lowerCAmelCase__   :List[Any]      							=				mask[:, step, :, :mask_start]
                  if mask_end > 0:
                      lowerCAmelCase__   :Optional[Any]      							=				mask[:, step, :, -mask_end:]

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

          lowerCAmelCase__   :Dict      							=				(images / 2 + 0.5).clamp(0    ,	1		)
          lowerCAmelCase__   :Union[str, Any]      							=				images.cpu().permute(0    ,	2    ,	3    ,	1		).numpy()
          lowerCAmelCase__   :Optional[int]      							=				(images * 255).round().astype("uint8"		)
          lowerCAmelCase__   :Optional[int]      							=				list(
              (Image.fromarray(_[:, :, 0]		) for _ in images)
              if images.shape[3] == 1
              else (Image.fromarray(_lowerCAmelCase    ,	mode="RGB"		).convert("L"		) for _ in images)		)

          lowerCAmelCase__   :Optional[Any]      							=				[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 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase = 50		):



          '''simple docstring'''
          assert isinstance(self.scheduler    ,	_lowerCAmelCase		)
          self.scheduler.set_timesteps(_lowerCAmelCase		)
          lowerCAmelCase__   :Any      							=				np.array(
              [np.frombuffer(image.tobytes()    ,	dtype="uint8"		).reshape((1, image.height, image.width)		) for image in images]		)
          lowerCAmelCase__   :Dict      							=				(sample / 255) * 2 - 1
          lowerCAmelCase__   :Optional[Any]      							=				torch.Tensor(_lowerCAmelCase		).to(self.device		)

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

          return sample



      @staticmethod
      def 			snake_case_      (   _lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :List[str]      							=				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		)



