


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

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


_snake_case	:						Union[str, Any]         =							logging.get_logger(__name__)

_snake_case	:						Union[str, Any]         =							{
    "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json",
    # See all Marian models at https://huggingface.co/models?filter=marian
}

class 							a						(_lowerCAmelCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				Any	    =    "marian"
							__UpperCAmelCase       :				str	    =    ["past_key_values"]
							__UpperCAmelCase       :				int	    =    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}

							def __init__(				self			:    List[Any]       ,      lowerCamelCase			:    Tuple=58101       ,      lowerCamelCase			:    str=None       ,      lowerCamelCase			:    Dict=1024       ,      lowerCamelCase			:    Tuple=12       ,      lowerCamelCase			:    List[str]=4096       ,      lowerCamelCase			:    Any=16       ,      lowerCamelCase			:    Tuple=12       ,      lowerCamelCase			:    Dict=4096       ,      lowerCamelCase			:    Union[str, Any]=16       ,      lowerCamelCase			:    Optional[Any]=0.0       ,      lowerCamelCase			:    Optional[Any]=0.0       ,      lowerCamelCase			:    Tuple=True       ,      lowerCamelCase			:    str=True       ,      lowerCamelCase			:    int="gelu"       ,      lowerCamelCase			:    Any=1024       ,      lowerCamelCase			:    Optional[Any]=0.1       ,      lowerCamelCase			:    List[Any]=0.0       ,      lowerCamelCase			:    int=0.0       ,      lowerCamelCase			:    Optional[int]=0.02       ,      lowerCamelCase			:    Optional[Any]=58100       ,      lowerCamelCase			:    str=False       ,      lowerCamelCase			:    Dict=58100       ,      lowerCamelCase			:    Union[str, Any]=0       ,      lowerCamelCase			:    Tuple=0       ,      lowerCamelCase			:    List[str]=True       ,      **lowerCamelCase			:    int       ,      )      ->       Optional[Any]:
														__snake_case						:     Dict									=  vocab_size
														__snake_case						:     List[str]									=  decoder_vocab_size or vocab_size
														__snake_case						:     int									=  max_position_embeddings
														__snake_case						:     int									=  d_model
														__snake_case						:     Optional[Any]									=  encoder_ffn_dim
														__snake_case						:     str									=  encoder_layers
														__snake_case						:     Optional[int]									=  encoder_attention_heads
														__snake_case						:     Any									=  decoder_ffn_dim
														__snake_case						:     str									=  decoder_layers
														__snake_case						:     Dict									=  decoder_attention_heads
														__snake_case						:     str									=  dropout
														__snake_case						:     Any									=  attention_dropout
														__snake_case						:     int									=  activation_dropout
														__snake_case						:     Optional[Any]									=  activation_function
														__snake_case						:     str									=  init_std
														__snake_case						:     List[str]									=  encoder_layerdrop
														__snake_case						:     str									=  decoder_layerdrop
														__snake_case						:     Optional[Any]									=  use_cache
														__snake_case						:     Tuple									=  encoder_layers
														__snake_case						:     List[Any]									=  scale_embedding  # scale factor will be sqrt(d_model) if True
														__snake_case						:     Dict									=  share_encoder_decoder_embeddings
														super().__init__(
														    pad_token_id=lowerCamelCase       ,      eos_token_id=lowerCamelCase       ,      is_encoder_decoder=lowerCamelCase       ,      decoder_start_token_id=lowerCamelCase       ,      forced_eos_token_id=lowerCamelCase       ,      **lowerCamelCase       ,      )

class 							a						(_lowerCAmelCase ):




							"""simple docstring"""


							@property
							# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
							def        __snake_case   (				self			:    str							)      ->       Mapping[str, Mapping[int, str]]:
														if self.task in ["default", "seq2seq-lm"]:
																					__snake_case						:     Optional[Any]									=  OrderedDict(
																					    [
																					        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																					        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																					    ]							)

																					if self.use_past:
																												__snake_case						:     Optional[int]									=  {0: "batch"}
																												__snake_case						:     Any									=  {0: "batch", 1: "past_decoder_sequence + sequence"}
																					else:
																												__snake_case						:     Union[str, Any]									=  {0: "batch", 1: "decoder_sequence"}
																												__snake_case						:     List[Any]									=  {0: "batch", 1: "decoder_sequence"}

																					if self.use_past:
																												self.fill_with_past_key_values_(lowerCamelCase       ,      direction="inputs"							)
														elif self.task == "causal-lm":
																					# TODO: figure this case out.
																					__snake_case						:     Dict									=  OrderedDict(
																					    [
																					        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																					        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																					    ]							)
																					if self.use_past:
																												__snake_case					,     __snake_case						:     Dict									=  self.num_layers
																												for i in range(lowerCamelCase							):
																																			__snake_case						:     List[str]									=  {0: "batch", 2: "past_sequence + sequence"}
																																			__snake_case						:     Any									=  {0: "batch", 2: "past_sequence + sequence"}
														else:
																					__snake_case						:     Dict									=  OrderedDict(
																					    [
																					        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																					        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																					        ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
																					        ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
																					    ]							)

														return common_inputs

							@property
							# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
							def        __snake_case   (				self			:    Dict							)      ->       Mapping[str, Mapping[int, str]]:
														if self.task in ["default", "seq2seq-lm"]:
																					__snake_case						:     Dict									=  super().outputs
														else:
																					__snake_case						:     Optional[int]									=  super(lowerCamelCase       ,      self							).outputs
																					if self.use_past:
																												__snake_case					,     __snake_case						:     Dict									=  self.num_layers
																												for i in range(lowerCamelCase							):
																																			__snake_case						:     Dict									=  {0: "batch", 2: "past_sequence + sequence"}
																																			__snake_case						:     List[str]									=  {0: "batch", 2: "past_sequence + sequence"}
														return common_outputs

							def        __snake_case   (				self			:    Any       ,      lowerCamelCase			:    PreTrainedTokenizer       ,      lowerCamelCase			:    int = -1       ,      lowerCamelCase			:    int = -1       ,      lowerCamelCase			:    bool = False       ,      lowerCamelCase			:    Optional[TensorType] = None       ,      )      ->       Mapping[str, Any]:
														__snake_case						:     Tuple									=  self._generate_dummy_inputs_for_encoder_and_decoder(
														    lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase							)

														# Generate decoder inputs
														__snake_case						:     Any									=  seq_length if not self.use_past else 1
														__snake_case						:     List[str]									=  self._generate_dummy_inputs_for_encoder_and_decoder(
														    lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase							)
														__snake_case						:     Optional[int]									=  {F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
														__snake_case						:     int									=  dict(**lowerCamelCase       ,      **lowerCamelCase							)

														if self.use_past:
																					if not is_torch_available():
																												raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."							)
																					else:
																												import torch
																					__snake_case					,     __snake_case						:     Any									=  common_inputs["input_ids"].shape
																					__snake_case						:     Tuple									=  common_inputs["decoder_input_ids"].shape[1]
																					__snake_case					,     __snake_case						:     Optional[Any]									=  self.num_attention_heads
																					__snake_case						:     List[Any]									=  (
																					    batch,
																					    num_encoder_attention_heads,
																					    encoder_seq_length,
																					    self._config.hidden_size // num_encoder_attention_heads,
																					)
																					__snake_case						:     int									=  decoder_seq_length + 3
																					__snake_case						:     List[str]									=  (
																					    batch,
																					    num_decoder_attention_heads,
																					    decoder_past_length,
																					    self._config.hidden_size // num_decoder_attention_heads,
																					)

																					__snake_case						:     Optional[int]									=  torch.cat(
																					    [common_inputs["decoder_attention_mask"], torch.ones(lowerCamelCase       ,      lowerCamelCase							)]       ,      dim=1							)

																					__snake_case						:     Optional[Any]									=  []
																					# If the number of encoder and decoder layers are present in the model configuration, both are considered
																					__snake_case					,     __snake_case						:     str									=  self.num_layers
																					__snake_case						:     Any									=  min(lowerCamelCase       ,      lowerCamelCase							)
																					__snake_case						:     Optional[Any]									=  max(lowerCamelCase       ,      lowerCamelCase							) - min_num_layers
																					__snake_case						:     List[str]									=  "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

																					for _ in range(lowerCamelCase							):
																												common_inputs["past_key_values"].append(
																												    (
																												        torch.zeros(lowerCamelCase							),
																												        torch.zeros(lowerCamelCase							),
																												        torch.zeros(lowerCamelCase							),
																												        torch.zeros(lowerCamelCase							),
																												    )							)
																					# TODO: test this.
																					__snake_case						:     List[Any]									=  encoder_shape if remaining_side_name == "encoder" else decoder_shape
																					for _ in range(lowerCamelCase       ,      lowerCamelCase							):
																												common_inputs["past_key_values"].append((torch.zeros(lowerCamelCase							), torch.zeros(lowerCamelCase							))							)
														return common_inputs

							def        __snake_case   (				self			:    int       ,      lowerCamelCase			:    PreTrainedTokenizer       ,      lowerCamelCase			:    int = -1       ,      lowerCamelCase			:    int = -1       ,      lowerCamelCase			:    bool = False       ,      lowerCamelCase			:    Optional[TensorType] = None       ,      )      ->       Mapping[str, Any]:
														__snake_case						:     Any									=  self._generate_dummy_inputs_for_encoder_and_decoder(
														    lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase							)

														if self.use_past:
																					if not is_torch_available():
																												raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."							)
																					else:
																												import torch
																					__snake_case					,     __snake_case						:     int									=  common_inputs["input_ids"].shape
																					# Not using the same length for past_key_values
																					__snake_case						:     List[Any]									=  seqlen + 2
																					__snake_case					,     __snake_case						:     List[Any]									=  self.num_layers
																					__snake_case					,     __snake_case						:     Union[str, Any]									=  self.num_attention_heads
																					__snake_case						:     Optional[Any]									=  (
																					    batch,
																					    num_encoder_attention_heads,
																					    past_key_values_length,
																					    self._config.hidden_size // num_encoder_attention_heads,
																					)

																					__snake_case						:     Any									=  common_inputs["attention_mask"].dtype
																					__snake_case						:     Optional[int]									=  torch.cat(
																					    [common_inputs["attention_mask"], torch.ones(lowerCamelCase       ,      lowerCamelCase       ,      dtype=lowerCamelCase							)]       ,      dim=1							)
																					__snake_case						:     Tuple									=  [
																					    (torch.zeros(lowerCamelCase							), torch.zeros(lowerCamelCase							)) for _ in range(lowerCamelCase							)
																					]
														return common_inputs

							def        __snake_case   (				self			:    Optional[int]       ,      lowerCamelCase			:    PreTrainedTokenizer       ,      lowerCamelCase			:    int = -1       ,      lowerCamelCase			:    int = -1       ,      lowerCamelCase			:    bool = False       ,      lowerCamelCase			:    Optional[TensorType] = None       ,      )      ->       Mapping[str, Any]:
														# Copied from OnnxConfig.generate_dummy_inputs
														# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
														# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
														__snake_case						:     Optional[int]									=  compute_effective_axis_dimension(
														    lowerCamelCase       ,      fixed_dimension=OnnxConfig.default_fixed_batch       ,      num_token_to_add=0							)

														# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
														__snake_case						:     List[Any]									=  tokenizer.num_special_tokens_to_add(lowerCamelCase							)
														__snake_case						:     Optional[int]									=  compute_effective_axis_dimension(
														    lowerCamelCase       ,      fixed_dimension=OnnxConfig.default_fixed_sequence       ,      num_token_to_add=lowerCamelCase							)

														# Generate dummy inputs according to compute batch and sequence
														__snake_case						:     List[Any]									=  [" ".join([tokenizer.unk_token]							) * seq_length] * batch_size
														__snake_case						:     List[str]									=  dict(tokenizer(lowerCamelCase       ,      return_tensors=lowerCamelCase							)							)
														return common_inputs

							def        __snake_case   (				self			:    List[str]       ,      lowerCamelCase			:    PreTrainedTokenizer       ,      lowerCamelCase			:    int = -1       ,      lowerCamelCase			:    int = -1       ,      lowerCamelCase			:    bool = False       ,      lowerCamelCase			:    Optional[TensorType] = None       ,      )      ->       Mapping[str, Any]:
														if self.task in ["default", "seq2seq-lm"]:
																					__snake_case						:     Tuple									=  self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																					    lowerCamelCase       ,      batch_size=lowerCamelCase       ,      seq_length=lowerCamelCase       ,      is_pair=lowerCamelCase       ,      framework=lowerCamelCase							)

														else:
																					__snake_case						:     str									=  self._generate_dummy_inputs_for_causal_lm(
																					    lowerCamelCase       ,      batch_size=lowerCamelCase       ,      seq_length=lowerCamelCase       ,      is_pair=lowerCamelCase       ,      framework=lowerCamelCase							)

														return common_inputs

							def        __snake_case   (				self			:    str       ,      lowerCamelCase			:    Optional[int]       ,      lowerCamelCase			:    Tuple       ,      lowerCamelCase			:    Any       ,      lowerCamelCase			:    str							)      ->       List[Any]:
														if self.task in ["default", "seq2seq-lm"]:
																					__snake_case						:     List[Any]									=  super()._flatten_past_key_values_(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase							)
														else:
																					__snake_case						:     Tuple									=  super(lowerCamelCase       ,      self							)._flatten_past_key_values_(
																					    lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase							)






							@property
							def        __snake_case   (				self			:    List[str]							)      ->       float:
														return 1E-4


'''simple docstring'''


from typing import List, Optional, Tuple, Union

import torch

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


__lowerCAmelCase						:							List[Any]    							=						logging.get_logger(__name__)  # pylint: disable=invalid-name






class     A ( UpperCAmelCase ):





							def __init__(   self					:				Dict  ,		__a					:				List[Any]  ,		__a					:				Optional[Any]						)				->						Dict:
								super().__init__()
								self.register_modules(unet=__a  ,		scheduler=__a						)






							@torch.no_grad()
							def __call__(   self					:				Tuple  ,		__a					:				int = 1  ,		__a					:				int = 1_0_0  ,		__a					:				Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,		__a					:				Optional[float] = None  ,		__a					:				bool = True  ,		)				->						Union[AudioPipelineOutput, Tuple]:

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

								__UpperCAmelCase      					=      audio_length_in_s * self.unet.config.sample_rate

								__UpperCAmelCase      					=      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}."""						)

								__UpperCAmelCase      					=      int(__a						)
								if sample_size % down_scale_factor != 0:
									__UpperCAmelCase      					=      (
									    (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.'''						)
								__UpperCAmelCase      					=      int(__a						)

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

								__UpperCAmelCase      					=      randn_tensor(__a  ,		generator=__a  ,		device=self.device  ,		dtype=__a						)

								# set step values
								self.scheduler.set_timesteps(__a  ,		device=audio.device						)
								__UpperCAmelCase      					=      self.scheduler.timesteps.to(__a						)

								for t in self.progress_bar(self.scheduler.timesteps						):
									# 1. predict noise model_output
									__UpperCAmelCase      					=      self.unet(__a  ,		__a						).sample

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

								__UpperCAmelCase      					=      audio.clamp(-1  ,		1						).float().cpu().numpy()

								__UpperCAmelCase      					=      audio[:, :, :original_sample_size]

								if not return_dict:
									return (audio,)

								return AudioPipelineOutput(audios=__a						)
