"""simple docstring"""







from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin

@dataclass
class 	UpperCamelCase    (					lowercase		):
				UpperCAmelCase       :   torch.FloatTensor







class 	UpperCamelCase    (					lowercase   ,			lowercase		):



				@register_to_config
				def __init__(self       :   str			,						_A       :   int = 16			,						_A       :   int = 88			,						_A       :   Optional[int] = None			,						_A       :   Optional[int] = None			,						_A       :   int = 1			,						_A       :   float = 0.0			,						_A       :   int = 32			,						_A       :   Optional[int] = None			,						_A       :   bool = False			,						_A       :   Optional[int] = None			,						_A       :   str = "geglu"			,						_A       :   bool = True			,						_A       :   bool = True			,						)					->			Union[str, Any]:
					super().__init__()
					__snake_case    :							Dict   	=				num_attention_heads
					__snake_case    :							List[str]   	=				attention_head_dim
					__snake_case    :							int   	=				num_attention_heads * attention_head_dim

					__snake_case    :							str   	=				in_channels

					__snake_case    :							str   	=				torch.nn.GroupNorm(num_groups=_A			,						num_channels=_A			,						eps=1E-6			,						affine=_A)
					__snake_case    :							Optional[int]   	=				nn.Linear(_A			,						_A)

					# 3. Define transformers blocks
					__snake_case    :							Union[str, Any]   	=				nn.ModuleList(
					    [
					        BasicTransformerBlock(
					            _A			,						_A			,						_A			,						dropout=_A			,						cross_attention_dim=_A			,						activation_fn=_A			,						attention_bias=_A			,						double_self_attention=_A			,						norm_elementwise_affine=_A			,						)
					        for d in range(_A)
					    ])

					__snake_case    :							str   	=				nn.Linear(_A			,						_A)






				def       _lowercase  (self       :   Union[str, Any]			,						_A       :   List[str]			,						_A       :   Tuple=None			,						_A       :   Tuple=None			,						_A       :   Tuple=None			,						_A       :   Union[str, Any]=1			,						_A       :   Tuple=None			,						_A       :   bool = True			,						)					->			List[Any]:
					__snake_case	,       __snake_case	,       __snake_case	,       __snake_case    :							List[str]   	=				hidden_states.shape
					__snake_case    :							str   	=				batch_frames // num_frames

					__snake_case    :							List[Any]   	=				hidden_states

					__snake_case    :							Optional[Any]   	=				hidden_states[None, :].reshape(_A			,						_A			,						_A			,						_A			,						_A)
					__snake_case    :							Optional[Any]   	=				hidden_states.permute(0			,						2			,						1			,						3			,						4)

					__snake_case    :							Union[str, Any]   	=				self.norm(_A)
					__snake_case    :							Tuple   	=				hidden_states.permute(0			,						3			,						4			,						2			,						1).reshape(batch_size * height * width			,						_A			,						_A)

					__snake_case    :							Dict   	=				self.proj_in(_A)

					# 2. Blocks
					for block in self.transformer_blocks:
						__snake_case    :							List[str]   	=				block(
						    _A			,						encoder_hidden_states=_A			,						timestep=_A			,						cross_attention_kwargs=_A			,						class_labels=_A			,						)

					# 3. Output
					__snake_case    :							List[Any]   	=				self.proj_out(_A)
					__snake_case    :							Union[str, Any]   	=				(
					    hidden_states[None, None, :]
					    .reshape(_A			,						_A			,						_A			,						_A			,						_A)
					    .permute(0			,						3			,						4			,						1			,						2)
					    .contiguous()
					)
					__snake_case    :							Any   	=				hidden_states.reshape(_A			,						_A			,						_A			,						_A)

					__snake_case    :							Tuple   	=				hidden_states + residual

					if not return_dict:
						return (output,)

					return TransformerTemporalModelOutput(sample=_A)






"""simple docstring"""







from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
_a  :  Tuple=				HfArgumentParser(InitializationArguments)
_a  :  str=				parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
_a  :  int=				AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
_a  :  Optional[Any]=				{
    "vocab_size": len(tokenizer),
    "scale_attn_by_inverse_layer_idx": True,
    "reorder_and_upcast_attn": True,
}

# Load model config (GPT-2 large in this case)
_a  :  Optional[int]=				AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
_a  :  str=				AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)





