



"""simple docstring"""






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     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE  ):



   @register_to_config
   def __init__(       self    :	str							,       __lowerCamelCase    :	int = 1_2_8							,       __lowerCamelCase    :	int = 2_5_6							,       __lowerCamelCase    :	float = 2_0_0_0.0							,       __lowerCamelCase    :	int = 7_6_8							,       __lowerCamelCase    :	int = 1_2							,       __lowerCamelCase    :	int = 1_2							,       __lowerCamelCase    :	int = 6_4							,       __lowerCamelCase    :	int = 2_0_4_8							,       __lowerCamelCase    :	float = 0.1							,       ):



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

     _snake_case   	=      nn.Sequential(
         nn.Linear(__lowerCamelCase							,       d_model * 4							,       bias=__lowerCamelCase				)							,       nn.SiLU()							,       nn.Linear(d_model * 4							,       d_model * 4							,       bias=__lowerCamelCase				)							,       nn.SiLU()							,       )

     _snake_case   	=      nn.Embedding(__lowerCamelCase							,       __lowerCamelCase				)
     _snake_case   	=      False

     _snake_case   	=      nn.Linear(__lowerCamelCase							,       __lowerCamelCase							,       bias=__lowerCamelCase				)

     _snake_case   	=      nn.Dropout(p=__lowerCamelCase				)

     _snake_case   	=      nn.ModuleList()
     for lyr_num in range(__lowerCamelCase				):
       # FiLM conditional T5 decoder
       _snake_case   	=      DecoderLayer(d_model=__lowerCamelCase							,       d_kv=__lowerCamelCase							,       num_heads=__lowerCamelCase							,       d_ff=__lowerCamelCase							,       dropout_rate=__lowerCamelCase				)
       self.decoders.append(__lowerCamelCase				)

     _snake_case   	=      TaLayerNorm(__lowerCamelCase				)

     _snake_case   	=      nn.Dropout(p=__lowerCamelCase				)
     _snake_case   	=      nn.Linear(__lowerCamelCase							,       __lowerCamelCase							,       bias=__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Optional[Any]							,       __lowerCamelCase    :	Union[str, Any]							,       __lowerCamelCase    :	str				):



     """simple docstring"""
     _snake_case   	=      torch.mul(query_input.unsqueeze(-1				)							,       key_input.unsqueeze(-2				)				)
     return mask.unsqueeze(-3				)





   def    __UpperCAmelCase    (       self    :	Union[str, Any]							,       __lowerCamelCase    :	Optional[int]							,       __lowerCamelCase    :	str							,       __lowerCamelCase    :	List[Any]				):



     """simple docstring"""
     _snake_case	,     _snake_case	,     _snake_case   	=      decoder_input_tokens.shape
     assert decoder_noise_time.shape == (batch,)

     # decoder_noise_time is in [0, 1), so rescale to expected timing range.
     _snake_case   	=      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				)

     _snake_case   	=      self.conditioning_emb(__lowerCamelCase				).unsqueeze(1				)

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

     _snake_case   	=      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.
     _snake_case   	=      torch.broadcast_to(
         torch.arange(__lowerCamelCase							,       device=decoder_input_tokens.device				)							,       (batch, seq_length)							,       )

     _snake_case   	=      self.position_encoding(__lowerCamelCase				)

     _snake_case   	=      self.continuous_inputs_projection(__lowerCamelCase				)
     inputs += position_encodings
     _snake_case   	=      self.dropout(__lowerCamelCase				)

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

     # Translate encoding masks to encoder-decoder masks.
     _snake_case   	=      [(x, self.encoder_decoder_mask(__lowerCamelCase							,       __lowerCamelCase				)) for x, y in encodings_and_masks]

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

     for lyr in self.decoders:
       _snake_case   	=      lyr(
           __lowerCamelCase							,       conditioning_emb=__lowerCamelCase							,       encoder_hidden_states=__lowerCamelCase							,       encoder_attention_mask=__lowerCamelCase							,       )[0]

     _snake_case   	=      self.decoder_norm(__lowerCamelCase				)
     _snake_case   	=      self.post_dropout(__lowerCamelCase				)

     _snake_case   	=      self.spec_out(__lowerCamelCase				)
     return spec_out



class     UpperCAmelCase    (	nn.Module  ):



   def __init__(       self    :	int							,       __lowerCamelCase    :	Tuple							,       __lowerCamelCase    :	Optional[int]							,       __lowerCamelCase    :	int							,       __lowerCamelCase    :	Optional[int]							,       __lowerCamelCase    :	str							,       __lowerCamelCase    :	str=1E-6				):



     """simple docstring"""
     super().__init__()
     _snake_case   	=      nn.ModuleList()

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

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

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





   def    __UpperCAmelCase    (       self    :	int							,       __lowerCamelCase    :	Dict							,       __lowerCamelCase    :	int=None							,       __lowerCamelCase    :	List[str]=None							,       __lowerCamelCase    :	List[str]=None							,       __lowerCamelCase    :	Dict=None							,       __lowerCamelCase    :	Optional[Any]=None							,       ):



     """simple docstring"""
     _snake_case   	=      self.layer[0](
         __lowerCamelCase							,       conditioning_emb=__lowerCamelCase							,       attention_mask=__lowerCamelCase							,       )

     if encoder_hidden_states is not None:
       _snake_case   	=      torch.where(encoder_attention_mask > 0							,       0							,       -1E10				).to(
           encoder_hidden_states.dtype				)

       _snake_case   	=      self.layer[1](
           __lowerCamelCase							,       key_value_states=__lowerCamelCase							,       attention_mask=__lowerCamelCase							,       )

     # Apply Film Conditional Feed Forward layer
     _snake_case   	=      self.layer[-1](__lowerCamelCase							,       __lowerCamelCase				)

     return (hidden_states,)



class     UpperCAmelCase    (	nn.Module  ):



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



     """simple docstring"""
     super().__init__()
     _snake_case   	=      TaLayerNorm(__lowerCamelCase				)
     _snake_case   	=      TaFiLMLayer(in_features=d_model * 4							,       out_features=__lowerCamelCase				)
     _snake_case   	=      Attention(query_dim=__lowerCamelCase							,       heads=__lowerCamelCase							,       dim_head=__lowerCamelCase							,       out_bias=__lowerCamelCase							,       scale_qk=__lowerCamelCase				)
     _snake_case   	=      nn.Dropout(__lowerCamelCase				)





   def    __UpperCAmelCase    (       self    :	Tuple							,       __lowerCamelCase    :	Dict							,       __lowerCamelCase    :	Dict=None							,       __lowerCamelCase    :	Union[str, Any]=None							,       ):



     """simple docstring"""
     # pre_self_attention_layer_norm
     _snake_case   	=      self.layer_norm(__lowerCamelCase				)

     if conditioning_emb is not None:
       _snake_case   	=      self.FiLMLayer(__lowerCamelCase							,       __lowerCamelCase				)

     # Self-attention block
     _snake_case   	=      self.attention(__lowerCamelCase				)

     _snake_case   	=      hidden_states + self.dropout(__lowerCamelCase				)

     return hidden_states



class     UpperCAmelCase    (	nn.Module  ):



   def __init__(       self    :	int							,       __lowerCamelCase    :	List[str]							,       __lowerCamelCase    :	Dict							,       __lowerCamelCase    :	str							,       __lowerCamelCase    :	List[str]							,       __lowerCamelCase    :	List[Any]				):



     """simple docstring"""
     super().__init__()
     _snake_case   	=      Attention(query_dim=__lowerCamelCase							,       heads=__lowerCamelCase							,       dim_head=__lowerCamelCase							,       out_bias=__lowerCamelCase							,       scale_qk=__lowerCamelCase				)
     _snake_case   	=      TaLayerNorm(__lowerCamelCase							,       eps=__lowerCamelCase				)
     _snake_case   	=      nn.Dropout(__lowerCamelCase				)





   def    __UpperCAmelCase    (       self    :	Tuple							,       __lowerCamelCase    :	Optional[Any]							,       __lowerCamelCase    :	str=None							,       __lowerCamelCase    :	Optional[int]=None							,       ):



     """simple docstring"""
     _snake_case   	=      self.layer_norm(__lowerCamelCase				)
     _snake_case   	=      self.attention(
         __lowerCamelCase							,       encoder_hidden_states=__lowerCamelCase							,       attention_mask=attention_mask.squeeze(1				)							,       )
     _snake_case   	=      hidden_states + self.dropout(__lowerCamelCase				)
     return layer_output



class     UpperCAmelCase    (	nn.Module  ):



   def __init__(       self    :	Dict							,       __lowerCamelCase    :	List[Any]							,       __lowerCamelCase    :	Dict							,       __lowerCamelCase    :	List[str]							,       __lowerCamelCase    :	List[Any]				):



     """simple docstring"""
     super().__init__()
     _snake_case   	=      TaDenseGatedActDense(d_model=__lowerCamelCase							,       d_ff=__lowerCamelCase							,       dropout_rate=__lowerCamelCase				)
     _snake_case   	=      TaFiLMLayer(in_features=d_model * 4							,       out_features=__lowerCamelCase				)
     _snake_case   	=      TaLayerNorm(__lowerCamelCase							,       eps=__lowerCamelCase				)
     _snake_case   	=      nn.Dropout(__lowerCamelCase				)





   def    __UpperCAmelCase    (       self    :	List[str]							,       __lowerCamelCase    :	Optional[int]							,       __lowerCamelCase    :	int=None				):



     """simple docstring"""
     _snake_case   	=      self.layer_norm(__lowerCamelCase				)
     if conditioning_emb is not None:
       _snake_case   	=      self.film(__lowerCamelCase							,       __lowerCamelCase				)

     _snake_case   	=      self.DenseReluDense(__lowerCamelCase				)
     _snake_case   	=      hidden_states + self.dropout(__lowerCamelCase				)
     return hidden_states



class     UpperCAmelCase    (	nn.Module  ):



   def __init__(       self    :	str							,       __lowerCamelCase    :	Dict							,       __lowerCamelCase    :	List[Any]							,       __lowerCamelCase    :	Tuple				):



     """simple docstring"""
     super().__init__()
     _snake_case   	=      nn.Linear(__lowerCamelCase							,       __lowerCamelCase							,       bias=__lowerCamelCase				)
     _snake_case   	=      nn.Linear(__lowerCamelCase							,       __lowerCamelCase							,       bias=__lowerCamelCase				)
     _snake_case   	=      nn.Linear(__lowerCamelCase							,       __lowerCamelCase							,       bias=__lowerCamelCase				)
     _snake_case   	=      nn.Dropout(__lowerCamelCase				)
     _snake_case   	=      NewGELUActivation()





   def    __UpperCAmelCase    (       self    :	str							,       __lowerCamelCase    :	str				):



     """simple docstring"""
     _snake_case   	=      self.act(self.wi_a(__lowerCamelCase				)				)
     _snake_case   	=      self.wi_a(__lowerCamelCase				)
     _snake_case   	=      hidden_gelu * hidden_linear
     _snake_case   	=      self.dropout(__lowerCamelCase				)

     _snake_case   	=      self.wo(__lowerCamelCase				)
     return hidden_states



class     UpperCAmelCase    (	nn.Module  ):



   def __init__(       self    :	str							,       __lowerCamelCase    :	int							,       __lowerCamelCase    :	Union[str, Any]=1E-6				):



     """simple docstring"""
     super().__init__()
     _snake_case   	=      nn.Parameter(torch.ones(__lowerCamelCase				)				)
     _snake_case   	=      eps





   def    __UpperCAmelCase    (       self    :	Optional[Any]							,       __lowerCamelCase    :	Tuple				):



     """simple docstring"""
     # T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
     # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
     # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
     # half-precision inputs is done in fp32

     _snake_case   	=      hidden_states.to(torch.floataa				).pow(2				).mean(-1							,       keepdim=__lowerCamelCase				)
     _snake_case   	=      hidden_states * torch.rsqrt(variance + self.variance_epsilon				)

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

     return self.weight * hidden_states



class     UpperCAmelCase    (	nn.Module  ):



   def    __UpperCAmelCase    (       self    :	List[str]							,       __lowerCamelCase    :	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(__lowerCamelCase							,       3.0				))				))



class     UpperCAmelCase    (	nn.Module  ):



   def __init__(       self    :	List[str]							,       __lowerCamelCase    :	Tuple							,       __lowerCamelCase    :	List[str]				):



     """simple docstring"""
     super().__init__()
     _snake_case   	=      nn.Linear(__lowerCamelCase							,       out_features * 2							,       bias=__lowerCamelCase				)





   def    __UpperCAmelCase    (       self    :	Optional[int]							,       __lowerCamelCase    :	List[str]							,       __lowerCamelCase    :	str				):



     """simple docstring"""
     _snake_case   	=      self.scale_bias(__lowerCamelCase				)
     _snake_case	,     _snake_case   	=      torch.chunk(__lowerCamelCase							,       2							,       -1				)
     _snake_case   	=      x * (1 + scale) + shift
     return x






'''simple docstring'''




# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


__lowercase        =    '''Create a default config file for Accelerate with only a few flags set.'''






def 							snake_case__				(     _A:   Dict="no" ,  _A:   str = default_json_config_file ,  _A:   bool = False					)       ->      List[str]:

		'''simple docstring'''


		lowerCAmelCase     =    Path(_A					)
		path.parent.mkdir(parents=_A ,  exist_ok=_A					)
		if path.exists():
				print(
				    f"Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`."					)
				return False
		lowerCAmelCase     =    mixed_precision.lower()
		if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
				raise ValueError(
				    f"`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}"					)
		lowerCAmelCase     =    {
		    """compute_environment""": """LOCAL_MACHINE""",
		    """mixed_precision""": mixed_precision,
		}
		if torch.cuda.is_available():
				lowerCAmelCase     =    torch.cuda.device_count()
				lowerCAmelCase     =    num_gpus
				lowerCAmelCase     =    False
				if num_gpus > 1:
						lowerCAmelCase     =    """MULTI_GPU"""
				else:
						lowerCAmelCase     =    """NO"""
		elif is_xpu_available() and use_xpu:
				lowerCAmelCase     =    torch.xpu.device_count()
				lowerCAmelCase     =    num_xpus
				lowerCAmelCase     =    False
				if num_xpus > 1:
						lowerCAmelCase     =    """MULTI_XPU"""
				else:
						lowerCAmelCase     =    """NO"""
		elif is_npu_available():
				lowerCAmelCase     =    torch.npu.device_count()
				lowerCAmelCase     =    num_npus
				lowerCAmelCase     =    False
				if num_npus > 1:
						lowerCAmelCase     =    """MULTI_NPU"""
				else:
						lowerCAmelCase     =    """NO"""
		else:
				lowerCAmelCase     =    0
				lowerCAmelCase     =    True
				lowerCAmelCase     =    1
				lowerCAmelCase     =    """NO"""
		lowerCAmelCase     =    ClusterConfig(**_A					)
		config.to_json_file(_A					)
		return path






def 							snake_case__				(     _A:   str ,  _A:   Any					)       ->      Optional[Any]:

		'''simple docstring'''


		lowerCAmelCase     =    parser.add_parser("""default""" ,  parents=_A ,  help=_A ,  formatter_class=_A					)
		parser.add_argument(
		    """--config_file""" ,  default=_A ,  help=(
		        """The path to use to store the config file. Will default to a file named default_config.yaml in the cache """
		        """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
		        """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
		        """with 'huggingface'."""
		    ) ,  dest="""save_location""" ,  )

		parser.add_argument(
		    """--mixed_precision""" ,  choices=["""no""", """fp16""", """bf16"""] ,  type=_A ,  help="""Whether or not to use mixed precision training. """
		    """Choose between FP16 and BF16 (bfloat16) training. """
		    """BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.""" ,  default="""no""" ,  )
		parser.set_defaults(func=_A					)
		return parser






def 							snake_case__				(     _A:   Optional[int]					)       ->      Optional[int]:

		'''simple docstring'''


		lowerCAmelCase     =    write_basic_config(args.mixed_precision ,  args.save_location					)
		if config_file:
				print(f"accelerate configuration saved at {config_file}"					)




