


"""simple docstring"""







import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


__lowercase          = logging.getLogger(__name__)






@dataclass
class 		_A    :







    """simple docstring"""




    UpperCAmelCase			:				str      =			field(
        metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}    )
    UpperCAmelCase			:				Optional[str]      =			field(
        default=_a       ,metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}    )
    UpperCAmelCase			:				Optional[str]      =			field(
        default=_a       ,metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}    )
    UpperCAmelCase			:				bool      =			field(default=_a       ,metadata={"""help""": """Set this flag to use fast tokenization."""}    )
    # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
    # or just modify its tokenizer_config.json.
    UpperCAmelCase			:				Optional[str]      =			field(
        default=_a       ,metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}       ,)






def 	lowercase     (							)-> Dict:
  '''simple docstring'''







  a		:						Tuple 				=     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)

  if len(sys.argv			) == 2 and sys.argv[1].endswith(".json"			):
    # If we pass only one argument to the script and it's the path to a json file,
    # let's parse it to get our arguments.
    a				,						a				,						a		:						Any 				=     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
  else:
    a				,						a				,						a		:						List[str] 				=     parser.parse_args_into_dataclasses()

  if (
      os.path.exists(training_args.output_dir			)
      and os.listdir(training_args.output_dir			)
      and training_args.do_train
      and not training_args.overwrite_output_dir
  ):
    raise ValueError(
        F'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
        " --overwrite_output_dir to overcome."			)

  # Setup logging
  logging.basicConfig(
      format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"    ,  datefmt="%m/%d/%Y %H:%M:%S"    ,  level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN    ,  )
  logger.warning(
      "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"    ,  training_args.local_rank    ,  training_args.device    ,  training_args.n_gpu    ,  bool(training_args.local_rank != -1			)    ,  training_args.fpaa    ,  )
  # Set the verbosity to info of the Transformers logger (on main process only):
  if is_main_process(training_args.local_rank			):
    transformers.utils.logging.set_verbosity_info()
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()
  logger.info("Training/evaluation parameters %s"    ,  A_			)

  # Prepare Question-Answering task
  # Load pretrained model and tokenizer
  #
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.

  a		:						str 				=     AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path    ,  cache_dir=model_args.cache_dir    ,  )
  a		:						str 				=     AutoTokenizer.from_pretrained(
      model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path    ,  cache_dir=model_args.cache_dir    ,  use_fast=A_    ,  )
  a		:						List[Any] 				=     AutoModelForQuestionAnswering.from_pretrained(
      model_args.model_name_or_path    ,  from_tf=bool(".ckpt" in model_args.model_name_or_path			)    ,  config=A_    ,  cache_dir=model_args.cache_dir    ,  )

  # Get datasets
  a		:						int 				=     hasattr(model.config    ,  "lang2id"			)
  a		:						Any 				=     (
      SquadDataset(
          A_    ,  tokenizer=A_    ,  is_language_sensitive=A_    ,  cache_dir=model_args.cache_dir			)
      if training_args.do_train
      else None
  )
  a		:						Tuple 				=     (
      SquadDataset(
          A_    ,  tokenizer=A_    ,  mode="dev"    ,  is_language_sensitive=A_    ,  cache_dir=model_args.cache_dir    ,  )
      if training_args.do_eval
      else None
  )

  # Data collator
  a		:						str 				=     DataCollatorWithPadding(A_    ,  pad_to_multiple_of=8			) if training_args.fpaa else None

  # Initialize our Trainer
  a		:						Tuple 				=     Trainer(
      model=A_    ,  args=A_    ,  train_dataset=A_    ,  eval_dataset=A_    ,  data_collator=A_    ,  )

  # Training
  if training_args.do_train:
    trainer.train(
        model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path			) else None			)
    trainer.save_model()
    # For convenience, we also re-save the tokenizer to the same directory,
    # so that you can share your model easily on huggingface.co/models =)
    if trainer.is_world_master():
      tokenizer.save_pretrained(training_args.output_dir			)


def 	lowercase     (							A_			)-> int:
  '''simple docstring'''







  main()


if __name__ == "__main__":
 main()










"""simple docstring"""







from dataclasses import dataclass
from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..models.embeddings import ImagePositionalEmbeddings
from ..utils import BaseOutput, deprecate
from .attention import BasicTransformerBlock
from .embeddings import PatchEmbed
from .modeling_utils import ModelMixin






@dataclass
class 		_A    (    _a    ):







    """simple docstring"""




    UpperCAmelCase			:				torch.FloatTensor






class 		_A    (    _a       ,_a    ):







    """simple docstring"""




    @register_to_config
    def __init__(   self							:       List[Any]							,					__UpperCAmelCase							:       int = 16							,					__UpperCAmelCase							:       int = 88							,					__UpperCAmelCase							:       Optional[int] = None							,					__UpperCAmelCase							:       Optional[int] = None							,					__UpperCAmelCase							:       int = 1							,					__UpperCAmelCase							:       float = 0.0							,					__UpperCAmelCase							:       int = 32							,					__UpperCAmelCase							:       Optional[int] = None							,					__UpperCAmelCase							:       bool = False							,					__UpperCAmelCase							:       Optional[int] = None							,					__UpperCAmelCase							:       Optional[int] = None							,					__UpperCAmelCase							:       Optional[int] = None							,					__UpperCAmelCase							:       str = "geglu"							,					__UpperCAmelCase							:       Optional[int] = None							,					__UpperCAmelCase							:       bool = False							,					__UpperCAmelCase							:       bool = False							,					__UpperCAmelCase							:       bool = False							,					__UpperCAmelCase							:       str = "layer_norm"							,					__UpperCAmelCase							:       bool = True							,					):
      super().__init__()
      a		:						Optional[Any] 				=     use_linear_projection
      a		:						Any 				=     num_attention_heads
      a		:						Dict 				=     attention_head_dim
      a		:						str 				=     num_attention_heads * attention_head_dim

      # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
      # Define whether input is continuous or discrete depending on configuration
      a		:						int 				=     (in_channels is not None) and (patch_size is None)
      a		:						str 				=     num_vector_embeds is not None
      a		:						Dict 				=     in_channels is not None and patch_size is not None

      if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
        a		:						Dict 				=     (
            f'''The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or'''
            " incorrectly set to `'layer_norm'`.Make sure to set `norm_type` to `'ada_norm'` in the config."
            " Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect"
            " results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it"
            " would be very nice if you could open a Pull request for the `transformer/config.json` file"
        )
        deprecate("norm_type!=num_embeds_ada_norm"							,					"1.0.0"							,					__UpperCAmelCase							,					standard_warn=__UpperCAmelCase)
        a		:						Tuple 				=     "ada_norm"

      if self.is_input_continuous and self.is_input_vectorized:
        raise ValueError(
            f'''Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make'''
            " sure that either `in_channels` or `num_vector_embeds` is None.")
      elif self.is_input_vectorized and self.is_input_patches:
        raise ValueError(
            f'''Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make'''
            " sure that either `num_vector_embeds` or `num_patches` is None.")
      elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
        raise ValueError(
            f'''Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:'''
            f''' {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None.''')

      # 2. Define input layers
      if self.is_input_continuous:
        a		:						Union[str, Any] 				=     in_channels

        a		:						Dict 				=     torch.nn.GroupNorm(num_groups=__UpperCAmelCase							,					num_channels=__UpperCAmelCase							,					eps=1e-6							,					affine=__UpperCAmelCase)
        if use_linear_projection:
          a		:						Tuple 				=     nn.Linear(__UpperCAmelCase							,					__UpperCAmelCase)
        else:
          a		:						str 				=     nn.Convad(__UpperCAmelCase							,					__UpperCAmelCase							,					kernel_size=1							,					stride=1							,					padding=0)
      elif self.is_input_vectorized:
        assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
        assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"

        a		:						Dict 				=     sample_size
        a		:						List[str] 				=     sample_size
        a		:						Any 				=     num_vector_embeds
        a		:						Union[str, Any] 				=     self.height * self.width

        a		:						Any 				=     ImagePositionalEmbeddings(
            num_embed=__UpperCAmelCase							,					embed_dim=__UpperCAmelCase							,					height=self.height							,					width=self.width)
      elif self.is_input_patches:
        assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"

        a		:						int 				=     sample_size
        a		:						Optional[int] 				=     sample_size

        a		:						Dict 				=     patch_size
        a		:						Tuple 				=     PatchEmbed(
            height=__UpperCAmelCase							,					width=__UpperCAmelCase							,					patch_size=__UpperCAmelCase							,					in_channels=__UpperCAmelCase							,					embed_dim=__UpperCAmelCase							,					)

      # 3. Define transformers blocks
      a		:						int 				=     nn.ModuleList(
          [
              BasicTransformerBlock(
                  __UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					dropout=__UpperCAmelCase							,					cross_attention_dim=__UpperCAmelCase							,					activation_fn=__UpperCAmelCase							,					num_embeds_ada_norm=__UpperCAmelCase							,					attention_bias=__UpperCAmelCase							,					only_cross_attention=__UpperCAmelCase							,					upcast_attention=__UpperCAmelCase							,					norm_type=__UpperCAmelCase							,					norm_elementwise_affine=__UpperCAmelCase							,					)
              for d in range(__UpperCAmelCase)
          ])

      # 4. Define output layers
      a		:						str 				=     in_channels if out_channels is None else out_channels
      if self.is_input_continuous:
        # TODO: should use out_channels for continuous projections
        if use_linear_projection:
          a		:						List[Any] 				=     nn.Linear(__UpperCAmelCase							,					__UpperCAmelCase)
        else:
          a		:						Optional[Any] 				=     nn.Convad(__UpperCAmelCase							,					__UpperCAmelCase							,					kernel_size=1							,					stride=1							,					padding=0)
      elif self.is_input_vectorized:
        a		:						List[Any] 				=     nn.LayerNorm(__UpperCAmelCase)
        a		:						int 				=     nn.Linear(__UpperCAmelCase							,					self.num_vector_embeds - 1)
      elif self.is_input_patches:
        a		:						int 				=     nn.LayerNorm(__UpperCAmelCase							,					elementwise_affine=__UpperCAmelCase							,					eps=1e-6)
        a		:						List[Any] 				=     nn.Linear(__UpperCAmelCase							,					2 * inner_dim)
        a		:						Union[str, Any] 				=     nn.Linear(__UpperCAmelCase							,					patch_size * patch_size * self.out_channels)






    def       __snake_case				(   self							:       Dict							,					__UpperCAmelCase							:       torch.Tensor							,					__UpperCAmelCase							:       Optional[torch.Tensor] = None							,					__UpperCAmelCase							:       Optional[torch.LongTensor] = None							,					__UpperCAmelCase							:       Optional[torch.LongTensor] = None							,					__UpperCAmelCase							:       Dict[str, Any] = None							,					__UpperCAmelCase							:       Optional[torch.Tensor] = None							,					__UpperCAmelCase							:       Optional[torch.Tensor] = None							,					__UpperCAmelCase							:       bool = True							,					):
      # ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
      #   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
      #   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
      # expects mask of shape:
      #   [batch, key_tokens]
      # adds singleton query_tokens dimension:
      #   [batch,                    1, key_tokens]
      # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
      #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
      #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
      if attention_mask is not None and attention_mask.ndim == 2:
        # assume that mask is expressed as:
        #   (1 = keep,      0 = discard)
        # convert mask into a bias that can be added to attention scores:
        #       (keep = +0,     discard = -10000.0)
        a		:						Optional[Any] 				=     (1 - attention_mask.to(hidden_states.dtype)) * -10_000.0
        a		:						Tuple 				=     attention_mask.unsqueeze(1)

      # convert encoder_attention_mask to a bias the same way we do for attention_mask
      if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
        a		:						List[Any] 				=     (1 - encoder_attention_mask.to(hidden_states.dtype)) * -10_000.0
        a		:						List[Any] 				=     encoder_attention_mask.unsqueeze(1)

      # 1. Input
      if self.is_input_continuous:
        a				,						a				,						a				,						a		:						Any 				=     hidden_states.shape
        a		:						Optional[int] 				=     hidden_states

        a		:						int 				=     self.norm(__UpperCAmelCase)
        if not self.use_linear_projection:
          a		:						str 				=     self.proj_in(__UpperCAmelCase)
          a		:						Tuple 				=     hidden_states.shape[1]
          a		:						Optional[int] 				=     hidden_states.permute(0							,					2							,					3							,					1).reshape(__UpperCAmelCase							,					height * width							,					__UpperCAmelCase)
        else:
          a		:						Union[str, Any] 				=     hidden_states.shape[1]
          a		:						Optional[Any] 				=     hidden_states.permute(0							,					2							,					3							,					1).reshape(__UpperCAmelCase							,					height * width							,					__UpperCAmelCase)
          a		:						int 				=     self.proj_in(__UpperCAmelCase)
      elif self.is_input_vectorized:
        a		:						Dict 				=     self.latent_image_embedding(__UpperCAmelCase)
      elif self.is_input_patches:
        a		:						Any 				=     self.pos_embed(__UpperCAmelCase)

      # 2. Blocks
      for block in self.transformer_blocks:
        a		:						str 				=     block(
            __UpperCAmelCase							,					attention_mask=__UpperCAmelCase							,					encoder_hidden_states=__UpperCAmelCase							,					encoder_attention_mask=__UpperCAmelCase							,					timestep=__UpperCAmelCase							,					cross_attention_kwargs=__UpperCAmelCase							,					class_labels=__UpperCAmelCase							,					)

      # 3. Output
      if self.is_input_continuous:
        if not self.use_linear_projection:
          a		:						Optional[int] 				=     hidden_states.reshape(__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase).permute(0							,					3							,					1							,					2).contiguous()
          a		:						Tuple 				=     self.proj_out(__UpperCAmelCase)
        else:
          a		:						str 				=     self.proj_out(__UpperCAmelCase)
          a		:						Optional[Any] 				=     hidden_states.reshape(__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase).permute(0							,					3							,					1							,					2).contiguous()

        a		:						Tuple 				=     hidden_states + residual
      elif self.is_input_vectorized:
        a		:						Tuple 				=     self.norm_out(__UpperCAmelCase)
        a		:						Any 				=     self.out(__UpperCAmelCase)
        # (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
        a		:						Optional[int] 				=     logits.permute(0							,					2							,					1)

        # log(p(x_0))
        a		:						str 				=     F.log_softmax(logits.double()							,					dim=1).float()
      elif self.is_input_patches:
        # TODO: cleanup!
        a		:						Optional[int] 				=     self.transformer_blocks[0].norma.emb(
            __UpperCAmelCase							,					__UpperCAmelCase							,					hidden_dtype=hidden_states.dtype)
        a				,						a		:						Optional[Any] 				=     self.proj_out_a(F.silu(__UpperCAmelCase)).chunk(2							,					dim=1)
        a		:						List[str] 				=     self.norm_out(__UpperCAmelCase) * (1 + scale[:, None]) + shift[:, None]
        a		:						Union[str, Any] 				=     self.proj_out_a(__UpperCAmelCase)

        # unpatchify
        a		:						Dict 				=     int(hidden_states.shape[1] ** 0.5)
        a		:						Optional[int] 				=     hidden_states.reshape(
            shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels))
        a		:						int 				=     torch.einsum("nhwpqc->nchpwq"							,					__UpperCAmelCase)
        a		:						List[str] 				=     hidden_states.reshape(
            shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size))

      if not return_dict:
        return (output,)

      return TransformeraDModelOutput(sample=__UpperCAmelCase)






