from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin

class    snake_case       (   UpperCamelCase_ ,    UpperCamelCase_ ,    UpperCamelCase_    ):
  lowercase_								=  [r'h\.\d+\.attn\.bias', r'h\.\d+\.attn\.masked_bias']




  @register_to_config
  def __init__(   self  :						str  ,			a_  :						int  ,			a_  :						int  ,			a_  :						Optional[int] = None  ,			a_  :						int = 5_0257  ,			a_  :						int = 1024  ,			a_  :						int = 768  ,			a_  :						int = 12  ,			a_  :						int = 12  ,			a_  :						Optional[int] = None  ,			a_  :						str = "gelu_new"  ,			a_  :						float = 0.1  ,			a_  :						float = 0.1  ,			a_  :						float = 0.1  ,			a_  :						float = 1e-5  ,			a_  :						float = 0.02  ,			a_  :						bool = True  ,			a_  :						bool = True  ,			a_  :						bool = False  ,			a_  :						bool = False  ,			)->							Optional[int]:




       """simple docstring"""
       super().__init__()

       SCREAMING_SNAKE_CASE__					:					List[Any]      =		prefix_length

       if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
            raise ValueError(
                F'''`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and'''
                F''' `n_embd`: {n_embd} are not equal.'''					)

       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		prefix_inner_dim
       SCREAMING_SNAKE_CASE__					:					str      =		prefix_hidden_dim

       SCREAMING_SNAKE_CASE__					:					str      =		(
           nn.Linear(self.prefix_inner_dim  ,			self.prefix_hidden_dim					)
           if self.prefix_hidden_dim is not None
           else nn.Identity()
       )
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		(
           nn.Linear(self.prefix_hidden_dim  ,			a_					) if self.prefix_hidden_dim is not None else nn.Identity()
       )

       SCREAMING_SNAKE_CASE__					:					List[str]      =		GPTaConfig(
           vocab_size=a_  ,			n_positions=a_  ,			n_embd=a_  ,			n_layer=a_  ,			n_head=a_  ,			n_inner=a_  ,			activation_function=a_  ,			resid_pdrop=a_  ,			embd_pdrop=a_  ,			attn_pdrop=a_  ,			layer_norm_epsilon=a_  ,			initializer_range=a_  ,			scale_attn_weights=a_  ,			use_cache=a_  ,			scale_attn_by_inverse_layer_idx=a_  ,			reorder_and_upcast_attn=a_  ,			)
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		GPTaLMHeadModel(a_					)




  def 						__lowercase(   self  :						Any  ,			a_  :						torch.Tensor  ,			a_  :						torch.Tensor  ,			a_  :						Optional[torch.Tensor] = None  ,			a_  :						Optional[torch.Tensor] = None  ,			)->							str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Tuple      =		self.transformer.transformer.wte(a_					)
       SCREAMING_SNAKE_CASE__					:					List[str]      =		self.encode_prefix(a_					)
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self.decode_prefix(a_					)
       SCREAMING_SNAKE_CASE__					:					Any      =		torch.cat((prefix_embeds, embedding_text)  ,			dim=1					)

       if labels is not None:
            SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self.get_dummy_token(input_ids.shape[0]  ,			input_ids.device					)
            SCREAMING_SNAKE_CASE__					:					Tuple      =		torch.cat((dummy_token, input_ids)  ,			dim=1					)
       SCREAMING_SNAKE_CASE__					:					Any      =		self.transformer(inputs_embeds=a_  ,			labels=a_  ,			attention_mask=a_					)
       if self.prefix_hidden_dim is not None:
            return out, hidden
       else:
            return out




  def 						__lowercase(   self  :						str  ,			a_  :						int  ,			a_  :						torch.device					)->							torch.Tensor:




       """simple docstring"""
       return torch.zeros(a_  ,			self.prefix_length  ,			dtype=torch.intaa  ,			device=a_					)




  def 						__lowercase(   self  :						str  ,			a_  :						int					)->							Any:




       """simple docstring"""
       return self.encode_prefix(a_					)




  @torch.no_grad()
  def 						__lowercase(   self  :						List[Any]  ,			a_  :						Tuple  ,			a_  :						int  ,			a_  :						str					)->							Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Any      =		torch.split(a_  ,			1  ,			dim=0					)
       SCREAMING_SNAKE_CASE__					:					int      =		[]
       SCREAMING_SNAKE_CASE__					:					Dict      =		[]
       for feature in features:
            SCREAMING_SNAKE_CASE__					:					Dict      =		self.decode_prefix(feature.to(a_					)					)  # back to the clip feature
            # Only support beam search for now
            SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self.generate_beam(
                input_embeds=a_  ,			device=a_  ,			eos_token_id=a_					)
            generated_tokens.append(output_tokens[0]					)
            generated_seq_lengths.append(seq_lengths[0]					)
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		torch.stack(a_					)
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		torch.stack(a_					)
       return generated_tokens, generated_seq_lengths




  @torch.no_grad()
  def 						__lowercase(   self  :						Tuple  ,			a_  :						Optional[int]=None  ,			a_  :						Optional[Any]=None  ,			a_  :						str=None  ,			a_  :						int = 5  ,			a_  :						int = 67  ,			a_  :						float = 1.0  ,			a_  :						Optional[int] = None  ,			)->							List[str]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[str]      =		eos_token_id
       SCREAMING_SNAKE_CASE__					:					Tuple      =		None
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		None
       SCREAMING_SNAKE_CASE__					:					List[str]      =		torch.ones(a_  ,			device=a_  ,			dtype=torch.int					)
       SCREAMING_SNAKE_CASE__					:					List[str]      =		torch.zeros(a_  ,			device=a_  ,			dtype=torch.bool					)

       if input_embeds is not None:
            SCREAMING_SNAKE_CASE__					:					Dict      =		input_embeds
       else:
            SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		self.transformer.transformer.wte(a_					)

       for i in range(a_					):
            SCREAMING_SNAKE_CASE__					:					Dict      =		self.transformer(inputs_embeds=a_					)
            SCREAMING_SNAKE_CASE__					:					int      =		outputs.logits
            SCREAMING_SNAKE_CASE__					:					Optional[int]      =		logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
            SCREAMING_SNAKE_CASE__					:					Tuple      =		logits.softmax(-1					).log()

            if scores is None:
                 SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					List[Any]      =		logits.topk(a_  ,			-1					)
                 SCREAMING_SNAKE_CASE__					:					List[str]      =		generated.expand(a_  ,			*generated.shape[1:]					)
                 SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		next_tokens.permute(1  ,			0					), scores.squeeze(0					)
                 if tokens is None:
                      SCREAMING_SNAKE_CASE__					:					Dict      =		next_tokens
                 else:
                      SCREAMING_SNAKE_CASE__					:					Dict      =		tokens.expand(a_  ,			*tokens.shape[1:]					)
                      SCREAMING_SNAKE_CASE__					:					List[Any]      =		torch.cat((tokens, next_tokens)  ,			dim=1					)
            else:
                 SCREAMING_SNAKE_CASE__					:					List[str]      =		-float(np.inf					)
                 SCREAMING_SNAKE_CASE__					:					Any      =		0
                 SCREAMING_SNAKE_CASE__					:					Any      =		scores[:, None] + logits
                 seq_lengths[~is_stopped] += 1
                 SCREAMING_SNAKE_CASE__					:					Dict      =		scores_sum / seq_lengths[:, None]
                 SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		scores_sum_average.view(-1					).topk(a_  ,			-1					)
                 SCREAMING_SNAKE_CASE__					:					str      =		next_tokens // scores_sum.shape[1]
                 SCREAMING_SNAKE_CASE__					:					str      =		seq_lengths[next_tokens_source]
                 SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		next_tokens % scores_sum.shape[1]
                 SCREAMING_SNAKE_CASE__					:					Any      =		next_tokens.unsqueeze(1					)
                 SCREAMING_SNAKE_CASE__					:					int      =		tokens[next_tokens_source]
                 SCREAMING_SNAKE_CASE__					:					List[str]      =		torch.cat((tokens, next_tokens)  ,			dim=1					)
                 SCREAMING_SNAKE_CASE__					:					Any      =		generated[next_tokens_source]
                 SCREAMING_SNAKE_CASE__					:					List[str]      =		scores_sum_average * seq_lengths
                 SCREAMING_SNAKE_CASE__					:					int      =		is_stopped[next_tokens_source]

            SCREAMING_SNAKE_CASE__					:					int      =		self.transformer.transformer.wte(next_tokens.squeeze()					).view(generated.shape[0]  ,			1  ,			-1					)
            SCREAMING_SNAKE_CASE__					:					List[Any]      =		torch.cat((generated, next_token_embed)  ,			dim=1					)
            SCREAMING_SNAKE_CASE__					:					Tuple      =		is_stopped + next_tokens.eq(a_					).squeeze()
            if is_stopped.all():
                 break

       SCREAMING_SNAKE_CASE__					:					Dict      =		scores / seq_lengths
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		scores.argsort(descending=a_					)
       # tokens tensors are already padded to max_seq_length
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		[tokens[i] for i in order]
       SCREAMING_SNAKE_CASE__					:					Any      =		torch.stack(a_  ,			dim=0					)
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		torch.tensor([seq_lengths[i] for i in order]  ,			dtype=seq_lengths.dtype					)
       return output_texts, seq_lengths





import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np


def 				__A(lowerCAmelCase    ,	lowerCAmelCase			)		->					np.array:

		"""simple docstring"""






		_UpperCamelCase         =						F'{sampling_rate}'
		_UpperCamelCase         =						"""1"""
		_UpperCamelCase         =						"""f32le"""
		_UpperCamelCase         =						[
		    """ffmpeg""",
		    """-i""",
		    """pipe:0""",
		    """-ac""",
		    ac,
		    """-ar""",
		    ar,
		    """-f""",
		    format_for_conversion,
		    """-hide_banner""",
		    """-loglevel""",
		    """quiet""",
		    """pipe:1""",
		]

		try:
				with subprocess.Popen(lowerCAmelCase    ,	stdin=subprocess.PIPE    ,	stdout=subprocess.PIPE			) as ffmpeg_process:
						_UpperCamelCase         =						ffmpeg_process.communicate(lowerCAmelCase			)
		except FileNotFoundError as error:
				raise ValueError("""ffmpeg was not found but is required to load audio files from filename"""			) from error
		_UpperCamelCase         =						output_stream[0]
		_UpperCamelCase         =						np.frombuffer(lowerCAmelCase    ,	np.floataa			)
		if audio.shape[0] == 0:
				raise ValueError("""Malformed soundfile"""			)
		return audio


def 				__A(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = "f32le"    ,	)		->					Union[str, Any]:

		"""simple docstring"""






		_UpperCamelCase         =						F'{sampling_rate}'
		_UpperCamelCase         =						"""1"""
		if format_for_conversion == "s16le":
				_UpperCamelCase         =						2
		elif format_for_conversion == "f32le":
				_UpperCamelCase         =						4
		else:
				raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'			)

		_UpperCamelCase         =						platform.system()
		if system == "Linux":
				_UpperCamelCase         =						"""alsa"""
				_UpperCamelCase         =						"""default"""
		elif system == "Darwin":
				_UpperCamelCase         =						"""avfoundation"""
				_UpperCamelCase         =						""":0"""
		elif system == "Windows":
				_UpperCamelCase         =						"""dshow"""
				_UpperCamelCase         =						"""default"""

		_UpperCamelCase         =						[
		    """ffmpeg""",
		    """-f""",
		    format_,
		    """-i""",
		    input_,
		    """-ac""",
		    ac,
		    """-ar""",
		    ar,
		    """-f""",
		    format_for_conversion,
		    """-fflags""",
		    """nobuffer""",
		    """-hide_banner""",
		    """-loglevel""",
		    """quiet""",
		    """pipe:1""",
		]
		_UpperCamelCase         =						int(round(sampling_rate * chunk_length_s			)			) * size_of_sample
		_UpperCamelCase         =						_ffmpeg_stream(lowerCAmelCase    ,	lowerCAmelCase			)
		for item in iterator:
				yield item


def 				__A(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = "f32le"    ,	)		->					Any:

		"""simple docstring"""






		if stream_chunk_s is not None:
				_UpperCamelCase         =						stream_chunk_s
		else:
				_UpperCamelCase         =						chunk_length_s

		_UpperCamelCase         =						ffmpeg_microphone(lowerCAmelCase    ,	lowerCAmelCase    ,	format_for_conversion=lowerCAmelCase			)
		if format_for_conversion == "s16le":
				_UpperCamelCase         =						np.intaa
				_UpperCamelCase         =						2
		elif format_for_conversion == "f32le":
				_UpperCamelCase         =						np.floataa
				_UpperCamelCase         =						4
		else:
				raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'			)

		if stride_length_s is None:
				_UpperCamelCase         =						chunk_length_s / 6
		_UpperCamelCase         =						int(round(sampling_rate * chunk_length_s			)			) * size_of_sample
		if isinstance(lowerCAmelCase    ,	(int, float)			):
				_UpperCamelCase         =						[stride_length_s, stride_length_s]

		_UpperCamelCase         =						int(round(sampling_rate * stride_length_s[0]			)			) * size_of_sample
		_UpperCamelCase         =						int(round(sampling_rate * stride_length_s[1]			)			) * size_of_sample
		_UpperCamelCase         =						datetime.datetime.now()
		_UpperCamelCase         =						datetime.timedelta(seconds=lowerCAmelCase			)
		for item in chunk_bytes_iter(lowerCAmelCase    ,	lowerCAmelCase    ,	stride=(stride_left, stride_right)    ,	stream=lowerCAmelCase			):
				# Put everything back in numpy scale
				_UpperCamelCase         =						np.frombuffer(item["""raw"""]    ,	dtype=lowerCAmelCase			)
				_UpperCamelCase         =						(
				    item["""stride"""][0] // size_of_sample,
				    item["""stride"""][1] // size_of_sample,
				)
				_UpperCamelCase         =						sampling_rate
				audio_time += delta
				if datetime.datetime.now() > audio_time + 1_0 * delta:
						# We're late !! SKIP
						continue
				yield item


def 				__A(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = False			)		->					str:

		"""simple docstring"""






		_UpperCamelCase         =						b""""""
		_UpperCamelCase     ,						_UpperCamelCase         =						stride
		if stride_left + stride_right >= chunk_len:
				raise ValueError(
				    F'Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}'			)
		_UpperCamelCase         =						0
		for raw in iterator:
				acc += raw
				if stream and len(lowerCAmelCase			) < chunk_len:
						_UpperCamelCase         =						(_stride_left, 0)
						yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
				else:
						while len(lowerCAmelCase			) >= chunk_len:
								# We are flushing the accumulator
								_UpperCamelCase         =						(_stride_left, stride_right)
								_UpperCamelCase         =						{"""raw""": acc[:chunk_len], """stride""": stride}
								if stream:
										_UpperCamelCase         =						False
								yield item
								_UpperCamelCase         =						stride_left
								_UpperCamelCase         =						acc[chunk_len - stride_left - stride_right :]
    # Last chunk
		if len(lowerCAmelCase			) > stride_left:
				_UpperCamelCase         =						{"""raw""": acc, """stride""": (_stride_left, 0)}
				if stream:
						_UpperCamelCase         =						False
				yield item







def 				__A(lowerCAmelCase    ,	lowerCAmelCase			)		->					Tuple:

		"""simple docstring"""






		_UpperCamelCase         =						2**2_4  # 16Mo
		try:
				with subprocess.Popen(lowerCAmelCase    ,	stdout=subprocess.PIPE    ,	bufsize=lowerCAmelCase			) as ffmpeg_process:
						while True:
								_UpperCamelCase         =						ffmpeg_process.stdout.read(lowerCAmelCase			)
								if raw == b"":
										break
								yield raw
		except FileNotFoundError as error:
				raise ValueError("""ffmpeg was not found but is required to stream audio files from filename"""			) from error



