



from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


SCREAMING_SNAKE_CASE_:Dict					=						logging.get_logger(__name__)  # pylint: disable=invalid-name






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    def __init__(	self,		lowerCamelCase__,		lowerCamelCase__		):
        super().__init__()
        self.register_modules(unet=lowerCamelCase__,		scheduler=lowerCamelCase__		)






    @torch.no_grad()
    def __call__(	self,		lowerCamelCase__ = 1,		lowerCamelCase__ = 100,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = True,		):

        if audio_length_in_s is None:
            A			:	Dict										=       self.unet.config.sample_size / self.unet.config.sample_rate

        A			:	List[str]										=       audio_length_in_s * self.unet.config.sample_rate

        A			:	Union[str, Any]										=       2 ** len(self.unet.up_blocks		)
        if sample_size < 3 * down_scale_factor:
            raise ValueError(
                f'''{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'''
                f''' {3 * down_scale_factor / self.unet.config.sample_rate}.'''		)

        A			:	Tuple										=       int(lowerCamelCase__		)
        if sample_size % down_scale_factor != 0:
            A			:	Dict										=       (
                (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
            ) * down_scale_factor
            logger.info(
                f'''{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'''
                f''' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'''
                """ process."""		)
        A			:	Dict										=       int(lowerCamelCase__		)

        A			:	Optional[Any]										=       next(iter(self.unet.parameters()		)		).dtype
        A			:	Optional[int]										=       (batch_size, self.unet.config.in_channels, sample_size)
        if isinstance(lowerCamelCase__,		lowerCamelCase__		) and len(lowerCamelCase__		) != batch_size:
            raise ValueError(
                f'''You have passed a list of generators of length {len(lowerCamelCase__		)}, but requested an effective batch'''
                f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''		)

        A			:	int										=       randn_tensor(lowerCamelCase__,		generator=lowerCamelCase__,		device=self.device,		dtype=lowerCamelCase__		)

        # set step values
        self.scheduler.set_timesteps(lowerCamelCase__,		device=audio.device		)
        A			:	Tuple										=       self.scheduler.timesteps.to(lowerCamelCase__		)

        for t in self.progress_bar(self.scheduler.timesteps		):
            # 1. predict noise model_output
            A			:	List[Any]										=       self.unet(lowerCamelCase__,		lowerCamelCase__		).sample

            # 2. compute previous image: x_t -> t_t-1
            A			:	Any										=       self.scheduler.step(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		).prev_sample

        A			:	List[str]										=       audio.clamp(-1,		1		).float().cpu().numpy()

        A			:	str										=       audio[:, :, :original_sample_size]

        if not return_dict:
            return (audio,)

        return AudioPipelineOutput(audios=lowerCamelCase__		)







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_:Any					=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:Any					=						{
    """xlm-mlm-en-2048""": """https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json""",
    """xlm-mlm-ende-1024""": """https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-enfr-1024""": """https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json""",
    """xlm-mlm-enro-1024""": """https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json""",
    """xlm-mlm-tlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json""",
    """xlm-mlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json""",
    """xlm-clm-enfr-1024""": """https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json""",
    """xlm-clm-ende-1024""": """https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-17-1280""": """https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json""",
    """xlm-mlm-100-1280""": """https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json""",
}






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    __lowerCamelCase      :			List[str] =				"xlm"
    __lowerCamelCase      :			Tuple =				{
        "hidden_size": "emb_dim",
        "num_attention_heads": "n_heads",
        "num_hidden_layers": "n_layers",
        "n_words": "vocab_size",  # For backward compatibility
    }






    def __init__(	self,		lowerCamelCase__=3_0145,		lowerCamelCase__=2048,		lowerCamelCase__=12,		lowerCamelCase__=16,		lowerCamelCase__=0.1,		lowerCamelCase__=0.1,		lowerCamelCase__=True,		lowerCamelCase__=False,		lowerCamelCase__=False,		lowerCamelCase__=False,		lowerCamelCase__=1,		lowerCamelCase__=True,		lowerCamelCase__=512,		lowerCamelCase__=2048**-0.5,		lowerCamelCase__=1e-12,		lowerCamelCase__=0.02,		lowerCamelCase__=0,		lowerCamelCase__=1,		lowerCamelCase__=2,		lowerCamelCase__=3,		lowerCamelCase__=5,		lowerCamelCase__=True,		lowerCamelCase__="first",		lowerCamelCase__=True,		lowerCamelCase__=None,		lowerCamelCase__=True,		lowerCamelCase__=0.1,		lowerCamelCase__=5,		lowerCamelCase__=5,		lowerCamelCase__=0,		lowerCamelCase__=0,		lowerCamelCase__=2,		lowerCamelCase__=0,		**lowerCamelCase__,		):
        A			:	Dict										=       vocab_size
        A			:	int										=       emb_dim
        A			:	str										=       n_layers
        A			:	Union[str, Any]										=       n_heads
        A			:	Optional[int]										=       dropout
        A			:	Union[str, Any]										=       attention_dropout
        A			:	Optional[Any]										=       gelu_activation
        A			:	Dict										=       sinusoidal_embeddings
        A			:	int										=       causal
        A			:	Optional[Any]										=       asm
        A			:	Any										=       n_langs
        A			:	List[str]										=       use_lang_emb
        A			:	Union[str, Any]										=       layer_norm_eps
        A			:	str										=       bos_index
        A			:	int										=       eos_index
        A			:	Tuple										=       pad_index
        A			:	str										=       unk_index
        A			:	Optional[Any]										=       mask_index
        A			:	Union[str, Any]										=       is_encoder
        A			:	Tuple										=       max_position_embeddings
        A			:	List[str]										=       embed_init_std
        A			:	Tuple										=       init_std
        A			:	Tuple										=       summary_type
        A			:	int										=       summary_use_proj
        A			:	List[Any]										=       summary_activation
        A			:	Optional[Any]										=       summary_proj_to_labels
        A			:	Optional[Any]										=       summary_first_dropout
        A			:	Optional[int]										=       start_n_top
        A			:	Optional[Any]										=       end_n_top
        A			:	List[str]										=       mask_token_id
        A			:	Tuple										=       lang_id

        if "n_words" in kwargs:
            A			:	List[str]										=       kwargs["""n_words"""]

        super().__init__(pad_token_id=lowerCamelCase__,		bos_token_id=lowerCamelCase__,		**lowerCamelCase__		)






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    @property
    def       _lowerCAmelCase (	self		):
        if self.task == "multiple-choice":
            A			:	Any										=       {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            A			:	Union[str, Any]										=       {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
                ("""token_type_ids""", dynamic_axis),
            ]		)


