




import argparse
import inspect
import logging
from typing import Tuple

import numpy as np
import torch

from transformers import (
    AutoTokenizer,
    BloomForCausalLM,
    BloomTokenizerFast,
    CTRLLMHeadModel,
    CTRLTokenizer,
    GenerationMixin,
    GPTaLMHeadModel,
    GPTaTokenizer,
    GPTJForCausalLM,
    LlamaForCausalLM,
    LlamaTokenizer,
    OpenAIGPTLMHeadModel,
    OpenAIGPTTokenizer,
    OPTForCausalLM,
    TransfoXLLMHeadModel,
    TransfoXLTokenizer,
    XLMTokenizer,
    XLMWithLMHeadModel,
    XLNetLMHeadModel,
    XLNetTokenizer,
)
from transformers.modeling_outputs import CausalLMOutputWithPast


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)
_lowerCamelCase    				=		logging.getLogger(__name__)

_lowerCamelCase    				=		int(1_00_00)  # Hardcoded max length to avoid infinite loop

_lowerCamelCase    				=		{
    'gpt2': (GPTaLMHeadModel, GPTaTokenizer),
    'ctrl': (CTRLLMHeadModel, CTRLTokenizer),
    'openai-gpt': (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
    'xlnet': (XLNetLMHeadModel, XLNetTokenizer),
    'transfo-xl': (TransfoXLLMHeadModel, TransfoXLTokenizer),
    'xlm': (XLMWithLMHeadModel, XLMTokenizer),
    'gptj': (GPTJForCausalLM, AutoTokenizer),
    'bloom': (BloomForCausalLM, BloomTokenizerFast),
    'llama': (LlamaForCausalLM, LlamaTokenizer),
    'opt': (OPTForCausalLM, GPTaTokenizer),
}

# Padding text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia
# in https://github.com/rusiaaman/XLNet-gen#methodology
# and https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e
_lowerCamelCase    				=		'In 1991, the remains of Russian Tsar Nicholas II and his family\n(except for Alexei and Maria) are discovered.\nThe voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the\nremainder of the story. 1883 Western Siberia,\na young Grigori Rasputin is asked by his father and a group of men to perform magic.\nRasputin has a vision and denounces one of the men as a horse thief. Although his\nfather initially slaps him for making such an accusation, Rasputin watches as the\nman is chased outside and beaten. Twenty years later, Rasputin sees a vision of\nthe Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,\nwith people, even a bishop, begging for his blessing. <eod> </s> <eos>'




def       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		List[str]   )						->							Optional[int]:
    np.random.seed(args.seed   )
    torch.manual_seed(args.seed   )
    if args.n_gpu > 0:
        torch.cuda.manual_seed_all(args.seed   )




def       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		int ,  __UpperCamelCase    :		int ,  __UpperCamelCase    :		str ,  __UpperCamelCase    :		Dict   )						->							Tuple:
    if args.temperature > 0.7:
        logger.info('''CTRL typically works better with lower temperatures (and lower top_k).'''   )

    UpperCAmelCase_											=  tokenizer.encode(__UpperCamelCase ,  add_special_tokens=__UpperCamelCase   )
    if not any(encoded_prompt[0] == x for x in tokenizer.control_codes.values()   ):
        logger.info('''WARNING! You are not starting your generation from a control code so you won\'t get good results'''   )
    return prompt_text




def       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		List[str] ,  __UpperCamelCase    :		Any ,  __UpperCamelCase    :		Union[str, Any] ,  __UpperCamelCase    :		int   )						->							Union[str, Any]:
    # kwargs = {"language": None, "mask_token_id": None}

    # Set the language
    UpperCAmelCase_											=  hasattr(model.config ,  '''use_lang_emb'''   ) and model.config.use_lang_emb
    if hasattr(model.config ,  '''lang2id'''   ) and use_lang_emb:
        UpperCAmelCase_											=  model.config.langaid.keys()
        if args.xlm_language in available_languages:
            UpperCAmelCase_											=  args.xlm_language
        else:
            UpperCAmelCase_											=  None
            while language not in available_languages:
                UpperCAmelCase_											=  input('''Using XLM. Select language in ''' + str(list(__UpperCamelCase   )   ) + ''' >>> '''   )

        UpperCAmelCase_											=  model.config.langaid[language]
        # kwargs["language"] = tokenizer.lang2id[language]

    # TODO fix mask_token_id setup when configurations will be synchronized between models and tokenizers
    # XLM masked-language modeling (MLM) models need masked token
    # is_xlm_mlm = "mlm" in args.model_name_or_path
    # if is_xlm_mlm:
    #     kwargs["mask_token_id"] = tokenizer.mask_token_id

    return prompt_text




def       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		int ,  __UpperCamelCase    :		List[Any] ,  __UpperCamelCase    :		Dict ,  __UpperCamelCase    :		List[str]   )						->							Union[str, Any]:
    UpperCAmelCase_											=  args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
    UpperCAmelCase_											=  prefix + prompt_text
    return prompt_text




def       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		Optional[int] ,  __UpperCamelCase    :		Optional[Any] ,  __UpperCamelCase    :		Any ,  __UpperCamelCase    :		Optional[int]   )						->							int:
    UpperCAmelCase_											=  args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
    UpperCAmelCase_											=  prefix + prompt_text
    return prompt_text


_lowerCamelCase    				=		{
    'ctrl': prepare_ctrl_input,
    'xlm': prepare_xlm_input,
    'xlnet': prepare_xlnet_input,
    'transfo-xl': prepare_transfoxl_input,
}




def       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		Optional[int] ,  __UpperCamelCase    :		Tuple   )						->							List[Any]:
    if length < 0 and max_sequence_length > 0:
        UpperCAmelCase_											=  max_sequence_length
    elif 0 < max_sequence_length < length:
        UpperCAmelCase_											=  max_sequence_length  # No generation bigger than model size
    elif length < 0:
        UpperCAmelCase_											=  MAX_LENGTH  # avoid infinite loop
    return length




def       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		Optional[int]   )						->							str:
    UpperCAmelCase_											=  None
    if hasattr(__UpperCamelCase ,  '''hidden_size'''   ):
        UpperCAmelCase_											=  model_config.hidden_size
    elif hasattr(__UpperCamelCase ,  '''n_embed'''   ):
        UpperCAmelCase_											=  model_config.n_embed
    elif hasattr(__UpperCamelCase ,  '''n_embd'''   ):
        UpperCAmelCase_											=  model_config.n_embd

    UpperCAmelCase_											=  None
    if hasattr(__UpperCamelCase ,  '''num_attention_heads'''   ):
        UpperCAmelCase_											=  model_config.num_attention_heads
    elif hasattr(__UpperCamelCase ,  '''n_head'''   ):
        UpperCAmelCase_											=  model_config.n_head

    if embedding_size is None or num_head is None or num_head == 0:
        raise ValueError('''Check the model config'''   )

    UpperCAmelCase_											=  int(embedding_size / num_head   )
    if hasattr(__UpperCamelCase ,  '''n_layer'''   ):
        UpperCAmelCase_											=  model_config.n_layer
    elif hasattr(__UpperCamelCase ,  '''num_hidden_layers'''   ):
        UpperCAmelCase_											=  model_config.num_hidden_layers
    else:
        raise ValueError('''Number of hidden layers couldn\'t be determined from the model config'''   )

    return num_layer, num_head, num_embedding_size_per_head




def       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		List[str] ,  __UpperCamelCase    :		List[Any] ,  __UpperCamelCase    :		Optional[Any]   )						->							Union[str, Any]:
    UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_											=  sparse_model_config(model.config   )
    if model.config.model_type == "bloom":
        UpperCAmelCase_											=  tuple(
            (
                torch.empty(int(num_attention_heads * batch_size   ) ,  __UpperCamelCase ,  __UpperCamelCase   )
                .to(model.dtype   )
                .to(model.device   ),
                torch.empty(int(num_attention_heads * batch_size   ) ,  __UpperCamelCase ,  __UpperCamelCase   )
                .to(model.dtype   )
                .to(model.device   ),
            )
            for _ in range(__UpperCamelCase   )   )
    else:
        UpperCAmelCase_											=  tuple(
            (
                torch.empty(__UpperCamelCase ,  __UpperCamelCase ,  __UpperCamelCase ,  __UpperCamelCase   )
                .to(model.dtype   )
                .to(model.device   ),
                torch.empty(__UpperCamelCase ,  __UpperCamelCase ,  __UpperCamelCase ,  __UpperCamelCase   )
                .to(model.dtype   )
                .to(model.device   ),
            )
            for _ in range(__UpperCamelCase   )   )
    return past_key_values




def       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		Tuple ,  __UpperCamelCase    :		Optional[int] ,  __UpperCamelCase    :		Tuple   )						->							int:
    UpperCAmelCase_											=  len(__UpperCamelCase   )
    UpperCAmelCase_											=  tokenizer.batch_encode_plus(__UpperCamelCase ,  return_tensors='''pt'''   )
    UpperCAmelCase_											=  dummy_input.to(model.device   )
    if model.config.use_cache:
        UpperCAmelCase_											=  generate_past_key_values(__UpperCamelCase ,  __UpperCamelCase ,  1   )
    UpperCAmelCase_											=  torch.cat(
        [
            torch.zeros(dummy_input['''attention_mask'''].shape[0] ,  1   )
            .to(dummy_input['''attention_mask'''].dtype   )
            .to(model.device   ),
            dummy_input['''attention_mask'''],
        ] ,  -1 ,  )
    return dummy_input



class     a  (     _A    ):

     '''simple docstring'''





     lowerCAmelCase       : Tuple     	=						('_optimized', '_default')
     def __init__(			self     :    Union[str, Any]      ,					__snake_case     :    List[Any]      ,					__snake_case     :    List[Any]       ):
         UpperCAmelCase_											=  optimized
         UpperCAmelCase_											=  default
     def __call__(			self     :    List[str]      ,					*__snake_case     :    Any      ,					**__snake_case     :    List[str]       ):
         if kwargs["past_key_values"] is None and self._default.config.use_cache:
             UpperCAmelCase_											=  generate_past_key_values(self._default      ,					kwargs['''input_ids'''].shape[0]      ,					0       )
         kwargs.pop('''position_ids'''      ,					__snake_case       )
         for k in list(kwargs.keys()       ):
             if kwargs[k] is None or isinstance(kwargs[k]      ,					__snake_case       ):
                 kwargs.pop(__snake_case       )
         UpperCAmelCase_											=  self._optimized(**__snake_case       )
         UpperCAmelCase_											=  outputs[0]
         UpperCAmelCase_											=  outputs[1]
         UpperCAmelCase_											=  CausalLMOutputWithPast(
             loss=__snake_case      ,					logits=__snake_case      ,					past_key_values=__snake_case      ,					hidden_states=__snake_case      ,					attentions=__snake_case      ,					)
         return fixed_output
     def __getattr__(			self     :    Union[str, Any]      ,					__snake_case     :    Tuple       ):
         return getattr(self._default      ,					__snake_case       )
     def 						lowerCamelCase_     (			self     :    str      ,					__snake_case     :    int      ,					__snake_case     :    List[Any]=None      ,					__snake_case     :    List[str]=None      ,					__snake_case     :    Union[str, Any]=None      ,					**__snake_case     :    int       ):
         return self._default.prepare_inputs_for_generation(
             __snake_case      ,					past_key_values=__snake_case      ,					inputs_embeds=__snake_case      ,					use_cache=__snake_case      ,					**__snake_case       )




     def 						lowerCamelCase_     (			self     :    Any      ,					__snake_case     :    Tuple[Tuple[torch.Tensor]]      ,					__snake_case     :    torch.Tensor       ):
         return self._default._reorder_cache(__snake_case      ,					__snake_case       )




def       SCREAMING_SNAKE_CASE			( )						->							List[str]:
    UpperCAmelCase_											=  argparse.ArgumentParser()
    parser.add_argument(
        '''--model_type''' ,  default=__UpperCamelCase ,  type=__UpperCamelCase ,  required=__UpperCamelCase ,  help='''Model type selected in the list: ''' + ''', '''.join(MODEL_CLASSES.keys()   ) ,  )
    parser.add_argument(
        '''--model_name_or_path''' ,  default=__UpperCamelCase ,  type=__UpperCamelCase ,  required=__UpperCamelCase ,  help='''Path to pre-trained model or shortcut name selected in the list: ''' + ''', '''.join(MODEL_CLASSES.keys()   ) ,  )

    parser.add_argument('''--prompt''' ,  type=__UpperCamelCase ,  default=''''''   )
    parser.add_argument('''--length''' ,  type=__UpperCamelCase ,  default=20   )
    parser.add_argument('''--stop_token''' ,  type=__UpperCamelCase ,  default=__UpperCamelCase ,  help='''Token at which text generation is stopped'''   )

    parser.add_argument(
        '''--temperature''' ,  type=__UpperCamelCase ,  default=1.0 ,  help='''temperature of 1.0 has no effect, lower tend toward greedy sampling''' ,  )
    parser.add_argument(
        '''--repetition_penalty''' ,  type=__UpperCamelCase ,  default=1.0 ,  help='''primarily useful for CTRL model; in that case, use 1.2'''   )
    parser.add_argument('''--k''' ,  type=__UpperCamelCase ,  default=0   )
    parser.add_argument('''--p''' ,  type=__UpperCamelCase ,  default=0.9   )

    parser.add_argument('''--prefix''' ,  type=__UpperCamelCase ,  default='''''' ,  help='''Text added prior to input.'''   )
    parser.add_argument('''--padding_text''' ,  type=__UpperCamelCase ,  default='''''' ,  help='''Deprecated, the use of `--prefix` is preferred.'''   )
    parser.add_argument('''--xlm_language''' ,  type=__UpperCamelCase ,  default='''''' ,  help='''Optional language when used with the XLM model.'''   )

    parser.add_argument('''--seed''' ,  type=__UpperCamelCase ,  default=42 ,  help='''random seed for initialization'''   )
    parser.add_argument('''--no_cuda''' ,  action='''store_true''' ,  help='''Avoid using CUDA when available'''   )
    parser.add_argument('''--num_return_sequences''' ,  type=__UpperCamelCase ,  default=1 ,  help='''The number of samples to generate.'''   )
    parser.add_argument(
        '''--fp16''' ,  action='''store_true''' ,  help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit''' ,  )
    parser.add_argument('''--jit''' ,  action='''store_true''' ,  help='''Whether or not to use jit trace to accelerate inference'''   )
    UpperCAmelCase_											=  parser.parse_args()

    UpperCAmelCase_											=  torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''   )
    UpperCAmelCase_											=  0 if args.no_cuda else torch.cuda.device_count()

    logger.warning(f'device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}'   )

    set_seed(__UpperCamelCase   )

    # Initialize the model and tokenizer
    try:
        UpperCAmelCase_											=  args.model_type.lower()
        UpperCAmelCase_				,							UpperCAmelCase_											=  MODEL_CLASSES[args.model_type]
    except KeyError:
        raise KeyError('''the model {} you specified is not supported. You are welcome to add it and open a PR :)'''   )

    UpperCAmelCase_											=  tokenizer_class.from_pretrained(args.model_name_or_path   )
    if tokenizer.pad_token is None:
        UpperCAmelCase_											=  tokenizer.eos_token
    UpperCAmelCase_											=  model_class.from_pretrained(args.model_name_or_path   )
    model.to(args.device   )

    if args.fpaa:
        model.half()
    UpperCAmelCase_											=  getattr(model.config ,  '''max_position_embeddings''' ,  0   )
    UpperCAmelCase_											=  adjust_length_to_model(args.length ,  max_sequence_length=__UpperCamelCase   )
    logger.info(__UpperCamelCase   )

    UpperCAmelCase_											=  args.prompt if args.prompt else input('''Model prompt >>> '''   )

    # Different models need different input formatting and/or extra arguments
    UpperCAmelCase_											=  args.model_type in PREPROCESSING_FUNCTIONS.keys()
    if requires_preprocessing:
        UpperCAmelCase_											=  PREPROCESSING_FUNCTIONS.get(args.model_type   )
        UpperCAmelCase_											=  prepare_input(__UpperCamelCase ,  __UpperCamelCase ,  __UpperCamelCase ,  __UpperCamelCase   )

        if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
            UpperCAmelCase_											=  {'''add_space_before_punct_symbol''': True}
        else:
            UpperCAmelCase_											=  {}

        UpperCAmelCase_											=  tokenizer.encode(
            __UpperCamelCase ,  add_special_tokens=__UpperCamelCase ,  return_tensors='''pt''' ,  **__UpperCamelCase   )
    else:
        UpperCAmelCase_											=  args.prefix if args.prefix else args.padding_text
        UpperCAmelCase_											=  tokenizer.encode(prefix + prompt_text ,  add_special_tokens=__UpperCamelCase ,  return_tensors='''pt'''   )
    UpperCAmelCase_											=  encoded_prompt.to(args.device   )

    if encoded_prompt.size()[-1] == 0:
        UpperCAmelCase_											=  None
    else:
        UpperCAmelCase_											=  encoded_prompt

    if args.jit:
        UpperCAmelCase_											=  ['''enable jit''']
        UpperCAmelCase_											=  prepare_jit_inputs(__UpperCamelCase ,  __UpperCamelCase ,  __UpperCamelCase   )
        torch._C._jit_set_texpr_fuser_enabled(__UpperCamelCase   )
        UpperCAmelCase_											=  False
        if hasattr(__UpperCamelCase ,  '''forward'''   ):
            UpperCAmelCase_											=  inspect.signature(model.forward   )
        else:
            UpperCAmelCase_											=  inspect.signature(model.__call__   )
        UpperCAmelCase_											=  tuple(jit_inputs[key] for key in sig.parameters if jit_inputs.get(__UpperCamelCase ,  __UpperCamelCase   ) is not None   )
        UpperCAmelCase_											=  torch.jit.trace(__UpperCamelCase ,  __UpperCamelCase ,  strict=__UpperCamelCase   )
        UpperCAmelCase_											=  torch.jit.freeze(traced_model.eval()   )
        traced_model(*__UpperCamelCase   )
        traced_model(*__UpperCamelCase   )

        UpperCAmelCase_											=  _ModelFallbackWrapper(__UpperCamelCase ,  __UpperCamelCase   )

    UpperCAmelCase_											=  model.generate(
        input_ids=__UpperCamelCase ,  max_length=args.length + len(encoded_prompt[0]   ) ,  temperature=args.temperature ,  top_k=args.k ,  top_p=args.p ,  repetition_penalty=args.repetition_penalty ,  do_sample=__UpperCamelCase ,  num_return_sequences=args.num_return_sequences ,  )

    # Remove the batch dimension when returning multiple sequences
    if len(output_sequences.shape   ) > 2:
        output_sequences.squeeze_()

    UpperCAmelCase_											=  []

    for generated_sequence_idx, generated_sequence in enumerate(__UpperCamelCase   ):
        print(f'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='   )
        UpperCAmelCase_											=  generated_sequence.tolist()

        # Decode text
        UpperCAmelCase_											=  tokenizer.decode(__UpperCamelCase ,  clean_up_tokenization_spaces=__UpperCamelCase   )

        # Remove all text after the stop token
        UpperCAmelCase_											=  text[: text.find(args.stop_token   ) if args.stop_token else None]

        # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
        UpperCAmelCase_											=  (
            prompt_text + text[len(tokenizer.decode(encoded_prompt[0] ,  clean_up_tokenization_spaces=__UpperCamelCase   )   ) :]
        )

        generated_sequences.append(__UpperCamelCase   )
        print(__UpperCamelCase   )

    return generated_sequences


if __name__ == "__main__":
 main()











from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block



@dataclass
class     a  (     _A    ):

     '''simple docstring'''





     lowerCAmelCase       : torch.FloatTensor



class     a  (     _A			,     _A    ):

     '''simple docstring'''





     @register_to_config
     def __init__(			self     :    Optional[Any]      ,					__snake_case     :    Optional[Union[int, Tuple[int, int]]] = None      ,					__snake_case     :    int = 3      ,					__snake_case     :    int = 3      ,					__snake_case     :    bool = False      ,					__snake_case     :    str = "positional"      ,					__snake_case     :    int = 0      ,					__snake_case     :    bool = True      ,					__snake_case     :    Tuple[str] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")      ,					__snake_case     :    Tuple[str] = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")      ,					__snake_case     :    Tuple[int] = (2_24, 4_48, 6_72, 8_96)      ,					__snake_case     :    int = 2      ,					__snake_case     :    float = 1      ,					__snake_case     :    int = 1      ,					__snake_case     :    str = "conv"      ,					__snake_case     :    str = "conv"      ,					__snake_case     :    str = "silu"      ,					__snake_case     :    Optional[int] = 8      ,					__snake_case     :    int = 32      ,					__snake_case     :    float = 1E-5      ,					__snake_case     :    str = "default"      ,					__snake_case     :    bool = True      ,					__snake_case     :    Optional[str] = None      ,					__snake_case     :    Optional[int] = None      ,					):
         super().__init__()

         UpperCAmelCase_											=  sample_size
         UpperCAmelCase_											=  block_out_channels[0] * 4

         # Check inputs
         if len(__snake_case       ) != len(__snake_case       ):
             raise ValueError(
                 F'Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}.'       )

         if len(__snake_case       ) != len(__snake_case       ):
             raise ValueError(
                 F'Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.'       )

         # input
         UpperCAmelCase_											=  nn.Convad(__snake_case      ,					block_out_channels[0]      ,					kernel_size=3      ,					padding=(1, 1)       )

         # time
         if time_embedding_type == "fourier":
             UpperCAmelCase_											=  GaussianFourierProjection(embedding_size=block_out_channels[0]      ,					scale=16       )
             UpperCAmelCase_											=  2 * block_out_channels[0]
         elif time_embedding_type == "positional":
             UpperCAmelCase_											=  Timesteps(block_out_channels[0]      ,					__snake_case      ,					__snake_case       )
             UpperCAmelCase_											=  block_out_channels[0]

         UpperCAmelCase_											=  TimestepEmbedding(__snake_case      ,					__snake_case       )

         # class embedding
         if class_embed_type is None and num_class_embeds is not None:
             UpperCAmelCase_											=  nn.Embedding(__snake_case      ,					__snake_case       )
         elif class_embed_type == "timestep":
             UpperCAmelCase_											=  TimestepEmbedding(__snake_case      ,					__snake_case       )
         elif class_embed_type == "identity":
             UpperCAmelCase_											=  nn.Identity(__snake_case      ,					__snake_case       )
         else:
             UpperCAmelCase_											=  None

         UpperCAmelCase_											=  nn.ModuleList([]       )
         UpperCAmelCase_											=  None
         UpperCAmelCase_											=  nn.ModuleList([]       )

         # down
         UpperCAmelCase_											=  block_out_channels[0]
         for i, down_block_type in enumerate(__snake_case       ):
             UpperCAmelCase_											=  output_channel
             UpperCAmelCase_											=  block_out_channels[i]
             UpperCAmelCase_											=  i == len(__snake_case       ) - 1

             UpperCAmelCase_											=  get_down_block(
                 __snake_case      ,					num_layers=__snake_case      ,					in_channels=__snake_case      ,					out_channels=__snake_case      ,					temb_channels=__snake_case      ,					add_downsample=not is_final_block      ,					resnet_eps=__snake_case      ,					resnet_act_fn=__snake_case      ,					resnet_groups=__snake_case      ,					attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel      ,					downsample_padding=__snake_case      ,					resnet_time_scale_shift=__snake_case      ,					downsample_type=__snake_case      ,					)
             self.down_blocks.append(__snake_case       )

         # mid
         UpperCAmelCase_											=  UNetMidBlockaD(
             in_channels=block_out_channels[-1]      ,					temb_channels=__snake_case      ,					resnet_eps=__snake_case      ,					resnet_act_fn=__snake_case      ,					output_scale_factor=__snake_case      ,					resnet_time_scale_shift=__snake_case      ,					attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]      ,					resnet_groups=__snake_case      ,					add_attention=__snake_case      ,					)

         # up
         UpperCAmelCase_											=  list(reversed(__snake_case       )       )
         UpperCAmelCase_											=  reversed_block_out_channels[0]
         for i, up_block_type in enumerate(__snake_case       ):
             UpperCAmelCase_											=  output_channel
             UpperCAmelCase_											=  reversed_block_out_channels[i]
             UpperCAmelCase_											=  reversed_block_out_channels[min(i + 1      ,					len(__snake_case       ) - 1       )]

             UpperCAmelCase_											=  i == len(__snake_case       ) - 1

             UpperCAmelCase_											=  get_up_block(
                 __snake_case      ,					num_layers=layers_per_block + 1      ,					in_channels=__snake_case      ,					out_channels=__snake_case      ,					prev_output_channel=__snake_case      ,					temb_channels=__snake_case      ,					add_upsample=not is_final_block      ,					resnet_eps=__snake_case      ,					resnet_act_fn=__snake_case      ,					resnet_groups=__snake_case      ,					attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel      ,					resnet_time_scale_shift=__snake_case      ,					upsample_type=__snake_case      ,					)
             self.up_blocks.append(__snake_case       )
             UpperCAmelCase_											=  output_channel

         # out
         UpperCAmelCase_											=  norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4      ,					32       )
         UpperCAmelCase_											=  nn.GroupNorm(num_channels=block_out_channels[0]      ,					num_groups=__snake_case      ,					eps=__snake_case       )
         UpperCAmelCase_											=  nn.SiLU()
         UpperCAmelCase_											=  nn.Convad(block_out_channels[0]      ,					__snake_case      ,					kernel_size=3      ,					padding=1       )




     def 						lowerCamelCase_     (			self     :    Dict      ,					__snake_case     :    torch.FloatTensor      ,					__snake_case     :    Union[torch.Tensor, float, int]      ,					__snake_case     :    Optional[torch.Tensor] = None      ,					__snake_case     :    bool = True      ,					):
         # 0. center input if necessary
         if self.config.center_input_sample:
             UpperCAmelCase_											=  2 * sample - 1.0

         # 1. time
         UpperCAmelCase_											=  timestep
         if not torch.is_tensor(__snake_case       ):
             UpperCAmelCase_											=  torch.tensor([timesteps]      ,					dtype=torch.long      ,					device=sample.device       )
         elif torch.is_tensor(__snake_case       ) and len(timesteps.shape       ) == 0:
             UpperCAmelCase_											=  timesteps[None].to(sample.device       )

         # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
         UpperCAmelCase_											=  timesteps * torch.ones(sample.shape[0]      ,					dtype=timesteps.dtype      ,					device=timesteps.device       )

         UpperCAmelCase_											=  self.time_proj(__snake_case       )

         # timesteps does not contain any weights and will always return f32 tensors
         # but time_embedding might actually be running in fp16. so we need to cast here.
         # there might be better ways to encapsulate this.
         UpperCAmelCase_											=  t_emb.to(dtype=self.dtype       )
         UpperCAmelCase_											=  self.time_embedding(__snake_case       )

         if self.class_embedding is not None:
             if class_labels is None:
                 raise ValueError('''class_labels should be provided when doing class conditioning'''       )

             if self.config.class_embed_type == "timestep":
                 UpperCAmelCase_											=  self.time_proj(__snake_case       )

             UpperCAmelCase_											=  self.class_embedding(__snake_case       ).to(dtype=self.dtype       )
             UpperCAmelCase_											=  emb + class_emb

         # 2. pre-process
         UpperCAmelCase_											=  sample
         UpperCAmelCase_											=  self.conv_in(__snake_case       )

         # 3. down
         UpperCAmelCase_											=  (sample,)
         for downsample_block in self.down_blocks:
             if hasattr(__snake_case      ,					'''skip_conv'''       ):
                 UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_											=  downsample_block(
                     hidden_states=__snake_case      ,					temb=__snake_case      ,					skip_sample=__snake_case       )
             else:
                 UpperCAmelCase_				,							UpperCAmelCase_											=  downsample_block(hidden_states=__snake_case      ,					temb=__snake_case       )

             down_block_res_samples += res_samples

         # 4. mid
         UpperCAmelCase_											=  self.mid_block(__snake_case      ,					__snake_case       )

         # 5. up
         UpperCAmelCase_											=  None
         for upsample_block in self.up_blocks:
             UpperCAmelCase_											=  down_block_res_samples[-len(upsample_block.resnets       ) :]
             UpperCAmelCase_											=  down_block_res_samples[: -len(upsample_block.resnets       )]

             if hasattr(__snake_case      ,					'''skip_conv'''       ):
                 UpperCAmelCase_				,							UpperCAmelCase_											=  upsample_block(__snake_case      ,					__snake_case      ,					__snake_case      ,					__snake_case       )
             else:
                 UpperCAmelCase_											=  upsample_block(__snake_case      ,					__snake_case      ,					__snake_case       )

        # 6. post-process
         UpperCAmelCase_											=  self.conv_norm_out(__snake_case       )
         UpperCAmelCase_											=  self.conv_act(__snake_case       )
         UpperCAmelCase_											=  self.conv_out(__snake_case       )

         if skip_sample is not None:
             sample += skip_sample

         if self.config.time_embedding_type == "fourier":
             UpperCAmelCase_											=  timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]       )))       )
             UpperCAmelCase_											=  sample / timesteps

         if not return_dict:
             return (sample,)

         return UNetaDOutput(sample=__snake_case       )





