
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock

import torch

from ..state import AcceleratorState, PartialState
from ..utils import (
    gather,
    is_bnb_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_mps_available,
    is_safetensors_available,
    is_tensorboard_available,
    is_torch_version,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__=False		):
					'''simple docstring'''
					try:
										lowercase							=						os.environ[key]
					except KeyError:
										# KEY isn't set, default to `default`.
										lowercase							=						default
					else:
										# KEY is set, convert it to True or False.
										try:
															lowercase							=						strtobool(lowerCAmelCase__		)
										except ValueError:
															# More values are supported, but let's keep the message simple.
															raise ValueError(f'If set, {key} must be yes or no.'		)
					return _value


lowercase__		:Tuple   =    parse_flag_from_env("RUN_SLOW", default=False)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return unittest.skip('''Test was skipped'''		)(lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return unittest.skipUnless(_run_slow_tests ,			'''test is slow'''		)(lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return unittest.skipUnless(not torch.cuda.is_available() ,			'''test requires only a CPU'''		)(lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return unittest.skipUnless(torch.cuda.is_available() ,			'''test requires a GPU'''		)(lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return unittest.skipUnless(is_xpu_available() ,			'''test requires a XPU'''		)(lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return unittest.skipUnless(is_mps_available() ,			'''test requires a `mps` backend support in `torch`'''		)(lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return unittest.skipUnless(
					    is_transformers_available() and is_datasets_available() ,			'''test requires the Hugging Face suite'''		)(lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return unittest.skipUnless(is_bnb_available() ,			'''test requires the bitsandbytes library'''		)(lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return unittest.skipUnless(is_tpu_available() ,			'''test requires TPU'''		)(lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return unittest.skipUnless(torch.cuda.device_count() == 1 ,			'''test requires a GPU'''		)(lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return unittest.skipUnless(torch.xpu.device_count() == 1 ,			'''test requires a XPU'''		)(lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return unittest.skipUnless(torch.cuda.device_count() > 1 ,			'''test requires multiple GPUs'''		)(lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return unittest.skipUnless(torch.xpu.device_count() > 1 ,			'''test requires multiple XPUs'''		)(lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return unittest.skipUnless(is_safetensors_available() ,			'''test requires safetensors'''		)(lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return unittest.skipUnless(is_deepspeed_available() ,			'''test requires DeepSpeed'''		)(lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return unittest.skipUnless(is_torch_version('''>=''' ,			'''1.12.0'''		) ,			'''test requires torch version >= 1.12.0'''		)(lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__=None ,			lowerCAmelCase__=None		):
					'''simple docstring'''
					if test_case is None:
										return partial(lowerCAmelCase__ ,			version=lowerCAmelCase__		)
					return unittest.skipUnless(is_torch_version('''>=''' ,			lowerCAmelCase__		) ,			f'test requires torch version >= {version}'		)(lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return unittest.skipUnless(is_tensorboard_available() ,			'''test requires Tensorboard'''		)(lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return unittest.skipUnless(is_wandb_available() ,			'''test requires wandb'''		)(lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return unittest.skipUnless(is_comet_ml_available() ,			'''test requires comet_ml'''		)(lowerCAmelCase__		)


lowercase__		:Union[str, Any]   =    (
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return unittest.skipUnless(
					    _atleast_one_tracker_available ,			'''test requires at least one tracker to be available and for `comet_ml` to not be installed''' ,			)(lowerCAmelCase__		)







class 				lowercase		(					unittest.TestCase		):
							lowercase_       :       Dict       					=True




							@classmethod
							def  A__      (				cls):
												lowercase							=						tempfile.mkdtemp()




							@classmethod
							def  A__      (				cls):
												if os.path.exists(cls.tmpdir):
																	shutil.rmtree(cls.tmpdir)





							def  A__      (				self):
												if self.clear_on_setup:
																	for path in Path(self.tmpdir).glob('''**/*'''):
																						if path.is_file():
																											path.unlink()
																						elif path.is_dir():
																											shutil.rmtree(A__)







class 				lowercase		(					unittest.TestCase		):




							def  A__      (				self):
												super().tearDown()
												# Reset the state of the AcceleratorState singleton.
												AcceleratorState._reset_state()
												PartialState._reset_state()







class 				lowercase		(					unittest.TestCase		):




							def  A__      (				self   ,A__):
												lowercase							=						mocks if isinstance(A__   ,(tuple, list)) else [mocks]
												for m in self.mocks:
																	m.start()
																	self.addCleanup(m.stop)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						AcceleratorState()
					lowercase							=						tensor[None].clone().to(state.device		)
					lowercase							=						gather(lowerCAmelCase__		).cpu()
					lowercase							=						tensor[0].cpu()
					for i in range(tensors.shape[0]		):
										if not torch.equal(tensors[i] ,			lowerCAmelCase__		):
															return False
					return True







class 				lowercase		:




							def __init__(				self   ,A__   ,A__   ,A__):
												lowercase							=						returncode
												lowercase							=						stdout
												lowercase							=						stderr



async def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					while True:
										lowercase							=						await stream.readline()
										if line:
															callback(lowerCAmelCase__		)
										else:
															break



async def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__=None ,			lowerCAmelCase__=None ,			lowerCAmelCase__=None ,			lowerCAmelCase__=False ,			lowerCAmelCase__=False		):
					'''simple docstring'''
					if echo:
										print('''\nRunning: ''' ,			''' '''.join(lowerCAmelCase__		)		)

					lowercase							=						await asyncio.create_subprocess_exec(
					    cmd[0] ,			*cmd[1:] ,			stdin=lowerCAmelCase__ ,			stdout=asyncio.subprocess.PIPE ,			stderr=asyncio.subprocess.PIPE ,			env=lowerCAmelCase__ ,			)

					# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
					# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
					#
					# If it starts hanging, will need to switch to the following code. The problem is that no data
					# will be seen until it's done and if it hangs for example there will be no debug info.
					# out, err = await p.communicate()
					# return _RunOutput(p.returncode, out, err)

					lowercase							=						[]
					lowercase							=						[]

					def tee(lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__=""		):
										lowercase							=						line.decode('''utf-8'''		).rstrip()
										sink.append(lowerCAmelCase__		)
										if not quiet:
															print(lowerCAmelCase__ ,			lowerCAmelCase__ ,			file=lowerCAmelCase__		)

    # XXX: the timeout doesn't seem to make any difference here
					await asyncio.wait(
					    [
					        asyncio.create_task(_read_stream(p.stdout ,			lambda lowerCAmelCase__		: tee(lowerCAmelCase__ ,			lowerCAmelCase__ ,			sys.stdout ,			label='''stdout:'''		)		)		),
					        asyncio.create_task(_read_stream(p.stderr ,			lambda lowerCAmelCase__		: tee(lowerCAmelCase__ ,			lowerCAmelCase__ ,			sys.stderr ,			label='''stderr:'''		)		)		),
					    ] ,			timeout=lowerCAmelCase__ ,			)
					return _RunOutput(await p.wait() ,			lowerCAmelCase__ ,			lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__=None ,			lowerCAmelCase__=None ,			lowerCAmelCase__=180 ,			lowerCAmelCase__=False ,			lowerCAmelCase__=True		):
					'''simple docstring'''
					lowercase							=						asyncio.get_event_loop()
					lowercase							=						loop.run_until_complete(
					    _stream_subprocess(lowerCAmelCase__ ,			env=lowerCAmelCase__ ,			stdin=lowerCAmelCase__ ,			timeout=lowerCAmelCase__ ,			quiet=lowerCAmelCase__ ,			echo=lowerCAmelCase__		)		)

					lowercase							=						''' '''.join(lowerCAmelCase__		)
					if result.returncode > 0:
										lowercase							=						'''\n'''.join(result.stderr		)
										raise RuntimeError(
										    f'\'{cmd_str}\' failed with returncode {result.returncode}\n\n'
										    f'The combined stderr from workers follows:\n{stderr}'		)

					return result







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):
							pass



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__=False		):
					'''simple docstring'''
					try:
										lowercase							=						subprocess.check_output(lowerCAmelCase__ ,			stderr=subprocess.STDOUT		)
										if return_stdout:
															if hasattr(lowerCAmelCase__ ,			'''decode'''		):
																				lowercase							=						output.decode('''utf-8'''		)
															return output
					except subprocess.CalledProcessError as e:
										raise SubprocessCallException(
										    f'Command `{" ".join(lowerCAmelCase__		)}` failed with the following error:\n\n{e.output.decode()}'		) from e












import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin





class 			__a  (    A__       ,   A__       ):







 @register_to_config
 def __init__(		self					:   str							,   SCREAMING_SNAKE_CASE					:   int = 1_28							,   SCREAMING_SNAKE_CASE					:   int = 2_56							,   SCREAMING_SNAKE_CASE					:   float = 2_0_0_0.0							,   SCREAMING_SNAKE_CASE					:   int = 7_68							,   SCREAMING_SNAKE_CASE					:   int = 12							,   SCREAMING_SNAKE_CASE					:   int = 12							,   SCREAMING_SNAKE_CASE					:   int = 64							,   SCREAMING_SNAKE_CASE					:   int = 20_48							,   SCREAMING_SNAKE_CASE					:   float = 0.1							,   ):


      '''simple docstring'''






      super().__init__()

      UpperCamelCase__			:   Optional[Any]       				=  nn.Sequential(
          nn.Linear(SCREAMING_SNAKE_CASE							,   d_model * 4							,   bias=SCREAMING_SNAKE_CASE						)							,   nn.SiLU()							,   nn.Linear(d_model * 4							,   d_model * 4							,   bias=SCREAMING_SNAKE_CASE						)							,   nn.SiLU()							,   )

      UpperCamelCase__			:   Optional[int]       				=  nn.Embedding(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Optional[Any]       				=  False

      UpperCamelCase__			:   Union[str, Any]       				=  nn.Linear(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   bias=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Optional[Any]       				=  nn.Dropout(p=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Any       				=  nn.ModuleList()
      for lyr_num in range(SCREAMING_SNAKE_CASE						):
           # FiLM conditional T5 decoder
           UpperCamelCase__			:   Optional[int]       				=  DecoderLayer(d_model=SCREAMING_SNAKE_CASE							,   d_kv=SCREAMING_SNAKE_CASE							,   num_heads=SCREAMING_SNAKE_CASE							,   d_ff=SCREAMING_SNAKE_CASE							,   dropout_rate=SCREAMING_SNAKE_CASE						)
           self.decoders.append(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Dict       				=  TaLayerNorm(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Optional[Any]       				=  nn.Dropout(p=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Any       				=  nn.Linear(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   bias=SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   Tuple							,   SCREAMING_SNAKE_CASE					:   List[str]						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  torch.mul(query_input.unsqueeze(-1						)							,   key_input.unsqueeze(-2						)						)
      return mask.unsqueeze(-3						)







 def 					__lowercase					(		self					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   Optional[Any]						):


      '''simple docstring'''






      UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__			:   Tuple       				=  decoder_input_tokens.shape
      assert decoder_noise_time.shape == (batch,)

      # decoder_noise_time is in [0, 1), so rescale to expected timing range.
      UpperCamelCase__			:   List[str]       				=  get_timestep_embedding(
          decoder_noise_time * self.config.max_decoder_noise_time							,   embedding_dim=self.config.d_model							,   max_period=self.config.max_decoder_noise_time							,   ).to(dtype=self.dtype						)

      UpperCamelCase__			:   Any       				=  self.conditioning_emb(SCREAMING_SNAKE_CASE						).unsqueeze(1						)

      assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

      UpperCamelCase__			:   Optional[int]       				=  decoder_input_tokens.shape[1]

      # If we want to use relative positions for audio context, we can just offset
      # this sequence by the length of encodings_and_masks.
      UpperCamelCase__			:   Optional[int]       				=  torch.broadcast_to(
          torch.arange(SCREAMING_SNAKE_CASE							,   device=decoder_input_tokens.device						)							,   (batch, seq_length)							,   )

      UpperCamelCase__			:   Dict       				=  self.position_encoding(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Dict       				=  self.continuous_inputs_projection(SCREAMING_SNAKE_CASE						)
      inputs += position_encodings
      UpperCamelCase__			:   Optional[Any]       				=  self.dropout(SCREAMING_SNAKE_CASE						)

      # decoder: No padding present.
      UpperCamelCase__			:   Dict       				=  torch.ones(
          decoder_input_tokens.shape[:2]							,   device=decoder_input_tokens.device							,   dtype=inputs.dtype						)

      # Translate encoding masks to encoder-decoder masks.
      UpperCamelCase__			:   Optional[int]       				=  [(x, self.encoder_decoder_mask(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)) for x, y in encodings_and_masks]

      # cross attend style: concat encodings
      UpperCamelCase__			:   int       				=  torch.cat([x[0] for x in encodings_and_encdec_masks]							,   dim=1						)
      UpperCamelCase__			:   List[Any]       				=  torch.cat([x[1] for x in encodings_and_encdec_masks]							,   dim=-1						)

      for lyr in self.decoders:
           UpperCamelCase__			:   int       				=  lyr(
               SCREAMING_SNAKE_CASE							,   conditioning_emb=SCREAMING_SNAKE_CASE							,   encoder_hidden_states=SCREAMING_SNAKE_CASE							,   encoder_attention_mask=SCREAMING_SNAKE_CASE							,   )[0]

      UpperCamelCase__			:   Tuple       				=  self.decoder_norm(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Any       				=  self.post_dropout(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Tuple       				=  self.spec_out(SCREAMING_SNAKE_CASE						)
      return spec_out







class 			__a  (    nn.Module       ):







 def __init__(		self					:   int							,   SCREAMING_SNAKE_CASE					:   Dict							,   SCREAMING_SNAKE_CASE					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   List[Any]=1e-6						):


      '''simple docstring'''






      super().__init__()
      UpperCamelCase__			:   List[str]       				=  nn.ModuleList()

      # cond self attention: layer 0
      self.layer.append(
          TaLayerSelfAttentionCond(d_model=SCREAMING_SNAKE_CASE							,   d_kv=SCREAMING_SNAKE_CASE							,   num_heads=SCREAMING_SNAKE_CASE							,   dropout_rate=SCREAMING_SNAKE_CASE						)						)

      # cross attention: layer 1
      self.layer.append(
          TaLayerCrossAttention(
              d_model=SCREAMING_SNAKE_CASE							,   d_kv=SCREAMING_SNAKE_CASE							,   num_heads=SCREAMING_SNAKE_CASE							,   dropout_rate=SCREAMING_SNAKE_CASE							,   layer_norm_epsilon=SCREAMING_SNAKE_CASE							,   )						)

      # Film Cond MLP + dropout: last layer
      self.layer.append(
          TaLayerFFCond(d_model=SCREAMING_SNAKE_CASE							,   d_ff=SCREAMING_SNAKE_CASE							,   dropout_rate=SCREAMING_SNAKE_CASE							,   layer_norm_epsilon=SCREAMING_SNAKE_CASE						)						)







 def 					__lowercase					(		self					:   int							,   SCREAMING_SNAKE_CASE					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   Any=None							,   SCREAMING_SNAKE_CASE					:   List[str]=None							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]=None							,   SCREAMING_SNAKE_CASE					:   Any=None							,   SCREAMING_SNAKE_CASE					:   Optional[Any]=None							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   List[Any]       				=  self.layer[0](
          SCREAMING_SNAKE_CASE							,   conditioning_emb=SCREAMING_SNAKE_CASE							,   attention_mask=SCREAMING_SNAKE_CASE							,   )

      if encoder_hidden_states is not None:
           UpperCamelCase__			:   int       				=  torch.where(encoder_attention_mask > 0							,   0							,   -1e1_0						).to(
               encoder_hidden_states.dtype						)

           UpperCamelCase__			:   Tuple       				=  self.layer[1](
               SCREAMING_SNAKE_CASE							,   key_value_states=SCREAMING_SNAKE_CASE							,   attention_mask=SCREAMING_SNAKE_CASE							,   )

      # Apply Film Conditional Feed Forward layer
      UpperCamelCase__			:   Any       				=  self.layer[-1](SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)

      return (hidden_states,)







class 			__a  (    nn.Module       ):







 def __init__(		self					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   int							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   str							,   SCREAMING_SNAKE_CASE					:   List[str]						):


      '''simple docstring'''






      super().__init__()
      UpperCamelCase__			:   Union[str, Any]       				=  TaLayerNorm(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[Any]       				=  TaFiLMLayer(in_features=d_model * 4							,   out_features=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[Any]       				=  Attention(query_dim=SCREAMING_SNAKE_CASE							,   heads=SCREAMING_SNAKE_CASE							,   dim_head=SCREAMING_SNAKE_CASE							,   out_bias=SCREAMING_SNAKE_CASE							,   scale_qk=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Any       				=  nn.Dropout(SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   List[str]							,   SCREAMING_SNAKE_CASE					:   Any							,   SCREAMING_SNAKE_CASE					:   List[str]=None							,   SCREAMING_SNAKE_CASE					:   List[Any]=None							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   str       				=  self.layer_norm(SCREAMING_SNAKE_CASE						)

      if conditioning_emb is not None:
           UpperCamelCase__			:   List[Any]       				=  self.FiLMLayer(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)

      # Self-attention block
      UpperCamelCase__			:   Optional[Any]       				=  self.attention(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Tuple       				=  hidden_states + self.dropout(SCREAMING_SNAKE_CASE						)

      return hidden_states







class 			__a  (    nn.Module       ):







 def __init__(		self					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   Any							,   SCREAMING_SNAKE_CASE					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   str							,   SCREAMING_SNAKE_CASE					:   Any							,   SCREAMING_SNAKE_CASE					:   List[Any]						):


      '''simple docstring'''






      super().__init__()
      UpperCamelCase__			:   str       				=  Attention(query_dim=SCREAMING_SNAKE_CASE							,   heads=SCREAMING_SNAKE_CASE							,   dim_head=SCREAMING_SNAKE_CASE							,   out_bias=SCREAMING_SNAKE_CASE							,   scale_qk=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Union[str, Any]       				=  TaLayerNorm(SCREAMING_SNAKE_CASE							,   eps=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Optional[Any]       				=  nn.Dropout(SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   List[str]							,   SCREAMING_SNAKE_CASE					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   Optional[Any]=None							,   SCREAMING_SNAKE_CASE					:   Optional[int]=None							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   str       				=  self.layer_norm(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[str]       				=  self.attention(
          SCREAMING_SNAKE_CASE							,   encoder_hidden_states=SCREAMING_SNAKE_CASE							,   attention_mask=attention_mask.squeeze(1						)							,   )
      UpperCamelCase__			:   Optional[Any]       				=  hidden_states + self.dropout(SCREAMING_SNAKE_CASE						)
      return layer_output







class 			__a  (    nn.Module       ):







 def __init__(		self					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   List[str]						):


      '''simple docstring'''






      super().__init__()
      UpperCamelCase__			:   Any       				=  TaDenseGatedActDense(d_model=SCREAMING_SNAKE_CASE							,   d_ff=SCREAMING_SNAKE_CASE							,   dropout_rate=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[Any]       				=  TaFiLMLayer(in_features=d_model * 4							,   out_features=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Dict       				=  TaLayerNorm(SCREAMING_SNAKE_CASE							,   eps=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[str]       				=  nn.Dropout(SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Any							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Dict=None						):


      '''simple docstring'''






      UpperCamelCase__			:   List[str]       				=  self.layer_norm(SCREAMING_SNAKE_CASE						)
      if conditioning_emb is not None:
           UpperCamelCase__			:   Optional[int]       				=  self.film(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Optional[int]       				=  self.DenseReluDense(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[str]       				=  hidden_states + self.dropout(SCREAMING_SNAKE_CASE						)
      return hidden_states







class 			__a  (    nn.Module       ):







 def __init__(		self					:   Any							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   str						):


      '''simple docstring'''






      super().__init__()
      UpperCamelCase__			:   Tuple       				=  nn.Linear(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   bias=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[Any]       				=  nn.Linear(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   bias=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Optional[int]       				=  nn.Linear(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   bias=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Dict       				=  nn.Dropout(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Union[str, Any]       				=  NewGELUActivation()







 def 					__lowercase					(		self					:   int							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[int]       				=  self.act(self.wi_a(SCREAMING_SNAKE_CASE						)						)
      UpperCamelCase__			:   Dict       				=  self.wi_a(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Optional[Any]       				=  hidden_gelu * hidden_linear
      UpperCamelCase__			:   int       				=  self.dropout(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Tuple       				=  self.wo(SCREAMING_SNAKE_CASE						)
      return hidden_states







class 			__a  (    nn.Module       ):







 def __init__(		self					:   Any							,   SCREAMING_SNAKE_CASE					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   str=1e-6						):


      '''simple docstring'''






      super().__init__()
      UpperCamelCase__			:   List[str]       				=  nn.Parameter(torch.ones(SCREAMING_SNAKE_CASE						)						)
      UpperCamelCase__			:   Any       				=  eps







 def 					__lowercase					(		self					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   Any						):


      '''simple docstring'''






      UpperCamelCase__			:   int       				=  hidden_states.to(torch.floataa						).pow(2						).mean(-1							,   keepdim=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   str       				=  hidden_states * torch.rsqrt(variance + self.variance_epsilon						)

      # convert into half-precision if necessary
      if self.weight.dtype in [torch.floataa, torch.bfloataa]:
           UpperCamelCase__			:   Any       				=  hidden_states.to(self.weight.dtype						)

      return self.weight * hidden_states







class 			__a  (    nn.Module       ):







 def 					__lowercase					(		self					:   int							,   SCREAMING_SNAKE_CASE					:   torch.Tensor						):


      '''simple docstring'''






      return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi						) * (input + 0.0_4_4_7_1_5 * torch.pow(SCREAMING_SNAKE_CASE							,   3.0						))						))







class 			__a  (    nn.Module       ):







 def __init__(		self					:   Tuple							,   SCREAMING_SNAKE_CASE					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]						):


      '''simple docstring'''






      super().__init__()
      UpperCamelCase__			:   int       				=  nn.Linear(SCREAMING_SNAKE_CASE							,   out_features * 2							,   bias=SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   List[str]							,   SCREAMING_SNAKE_CASE					:   Tuple						):


      '''simple docstring'''






      UpperCamelCase__			:   str       				=  self.scale_bias(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__				,     UpperCamelCase__			:   List[str]       				=  torch.chunk(SCREAMING_SNAKE_CASE							,   2							,   -1						)
      UpperCamelCase__			:   Dict       				=  x * (1 + scale) + shift
      return x