





"""simple docstring"""


import argparse
import math
import os
from copy import deepcopy

import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel


__magic_name__       :					Any						=			{
    """gwf-440k""": {
        """url""": """https://model-server.zqevans2.workers.dev/gwf-440k.ckpt""",
        """sample_rate""": 4_8_0_0_0,
        """sample_size""": 6_5_5_3_6,
    },
    """jmann-small-190k""": {
        """url""": """https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt""",
        """sample_rate""": 4_8_0_0_0,
        """sample_size""": 6_5_5_3_6,
    },
    """jmann-large-580k""": {
        """url""": """https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt""",
        """sample_rate""": 4_8_0_0_0,
        """sample_size""": 1_3_1_0_7_2,
    },
    """maestro-uncond-150k""": {
        """url""": """https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt""",
        """sample_rate""": 1_6_0_0_0,
        """sample_size""": 6_5_5_3_6,
    },
    """unlocked-uncond-250k""": {
        """url""": """https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt""",
        """sample_rate""": 1_6_0_0_0,
        """sample_size""": 6_5_5_3_6,
    },
    """honk-140k""": {
        """url""": """https://model-server.zqevans2.workers.dev/honk-140k.ckpt""",
        """sample_rate""": 1_6_0_0_0,
        """sample_size""": 6_5_5_3_6,
    },
}

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						return torch.atana(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ) / math.pi * 2

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Optional[Any]				   =			torch.sin(t * math.pi / 2     ) ** 2
						UpperCamelCase				:		Dict				   =			(1 - sigma**2) ** 0.5
						return alpha_sigma_to_t(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )






class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""
			pass






class 							lowercase__			(   nn.Module	):

			"""simple docstring"""







			def __init__(      self						,   _A    ):






									'''simple docstring'''







									super().__init__()

									UpperCamelCase				:		List[str]				   =			DiffusionAttnUnetaD(_A						,   n_attn_layers=4    )
									UpperCamelCase				:		Tuple				   =			deepcopy(self.diffusion    )
									UpperCamelCase				:		Dict				   =			torch.quasirandom.SobolEngine(1						,   scramble=_A    )


def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Optional[int]				   =			MODELS_MAP[model_name]["""url"""]
						os.system(f"""wget {url} ./"""     )

						return f"""./{model_name}.ckpt"""


__magic_name__       :					str						=			{
    """1""": """resnets.0""",
    """2""": """attentions.0""",
    """3""": """resnets.1""",
    """4""": """attentions.1""",
    """5""": """resnets.2""",
    """6""": """attentions.2""",
}
__magic_name__       :					Dict						=			{
    """8""": """resnets.0""",
    """9""": """attentions.0""",
    """10""": """resnets.1""",
    """11""": """attentions.1""",
    """12""": """resnets.2""",
    """13""": """attentions.2""",
}
__magic_name__       :					Any						=			{
    """1""": """resnets.0""",
    """2""": """attentions.0""",
    """3""": """resnets.1""",
    """4""": """attentions.1""",
    """5""": """resnets.2""",
    """6""": """attentions.2""",
    """8""": """resnets.3""",
    """9""": """attentions.3""",
    """10""": """resnets.4""",
    """11""": """attentions.4""",
    """12""": """resnets.5""",
    """13""": """attentions.5""",
}
__magic_name__       :					Dict						=			{
    """0""": """resnets.0""",
    """1""": """resnets.1""",
    """2""": """resnets.2""",
    """4""": """resnets.0""",
    """5""": """resnets.1""",
    """6""": """resnets.2""",
}

__magic_name__       :					Any						=			{
    """skip""": """conv_skip""",
    """main.0""": """conv_1""",
    """main.1""": """group_norm_1""",
    """main.3""": """conv_2""",
    """main.4""": """group_norm_2""",
}

__magic_name__       :					Any						=			{
    """norm""": """group_norm""",
    """qkv_proj""": ["""query""", """key""", """value"""],
    """out_proj""": ["""proj_attn"""],
}

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						if name.startswith("""skip"""     ):
												return name.replace("""skip"""							,     RES_CONV_MAP["""skip"""]     )

						# name has to be of format main.{digit}
						if not name.startswith("""main."""     ):
												raise ValueError(f"""ResConvBlock error with {name}"""     )

						return name.replace(name[:6]							,     RES_CONV_MAP[name[:6]]     )

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						for key, value in ATTN_MAP.items():
												if name.startswith(SCREAMING_SNAKE_CASE     ) and not isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
																		return name.replace(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
												elif name.startswith(SCREAMING_SNAKE_CASE     ):
																		return [name.replace(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ) for v in value]
						raise ValueError(f"""Attn error with {name}"""     )

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE=13     ):
						UpperCamelCase				:		Optional[Any]				   =			input_string

						if string.split("""."""     )[0] == "timestep_embed":
												return string.replace("""timestep_embed"""							,     """time_proj"""     )

						UpperCamelCase				:		int				   =			0
						if string.startswith("""net.3."""     ):
												depth += 1
												UpperCamelCase				:		List[str]				   =			string[6:]
						elif string.startswith("""net."""     ):
												UpperCamelCase				:		List[Any]				   =			string[4:]

						while string.startswith("""main.7."""     ):
												depth += 1
												UpperCamelCase				:		List[Any]				   =			string[7:]

						if string.startswith("""main."""     ):
												UpperCamelCase				:		Union[str, Any]				   =			string[5:]

						# mid block
						if string[:2].isdigit():
												UpperCamelCase				:		List[Any]				   =			string[:2]
												UpperCamelCase				:		Optional[Any]				   =			string[2:]
						else:
												UpperCamelCase				:		str				   =			string[0]
												UpperCamelCase				:		int				   =			string[1:]

						if depth == max_depth:
												UpperCamelCase				:		List[str]				   =			MID_NUM_TO_LAYER[layer_num]
												UpperCamelCase				:		Optional[Any]				   =			"""mid_block"""
						elif depth > 0 and int(SCREAMING_SNAKE_CASE     ) < 7:
												UpperCamelCase				:		List[str]				   =			DOWN_NUM_TO_LAYER[layer_num]
												UpperCamelCase				:		Dict				   =			f"""down_blocks.{depth}"""
						elif depth > 0 and int(SCREAMING_SNAKE_CASE     ) > 7:
												UpperCamelCase				:		Any				   =			UP_NUM_TO_LAYER[layer_num]
												UpperCamelCase				:		Optional[int]				   =			f"""up_blocks.{max_depth - depth - 1}"""
						elif depth == 0:
												UpperCamelCase				:		str				   =			DEPTH_0_TO_LAYER[layer_num]
												UpperCamelCase				:		int				   =			f"""up_blocks.{max_depth - 1}""" if int(SCREAMING_SNAKE_CASE     ) > 3 else """down_blocks.0"""

						if not string_left.startswith("""."""     ):
												raise ValueError(f"""Naming error with {input_string} and string_left: {string_left}."""     )

						UpperCamelCase				:		Dict				   =			string_left[1:]

						if "resnets" in new_layer:
												UpperCamelCase				:		Optional[Any]				   =			convert_resconv_naming(SCREAMING_SNAKE_CASE     )
						elif "attentions" in new_layer:
												UpperCamelCase				:		Tuple				   =			convert_attn_naming(SCREAMING_SNAKE_CASE     )
												UpperCamelCase				:		Union[str, Any]				   =			new_string_left

						if not isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
												UpperCamelCase				:		Dict				   =			prefix + """.""" + new_layer + """.""" + string_left
						else:
												UpperCamelCase				:		Tuple				   =			[prefix + """.""" + new_layer + """.""" + s for s in string_left]
						return new_string

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		List[str]				   =			{}
						for k, v in state_dict.items():
												if k.endswith("""kernel"""     ):
																		# up- and downsample layers, don't have trainable weights
																		continue

												UpperCamelCase				:		List[Any]				   =			rename(SCREAMING_SNAKE_CASE     )

												# check if we need to transform from Conv => Linear for attention
												if isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
																		UpperCamelCase				:		Any				   =			transform_conv_attns(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
												else:
																		UpperCamelCase				:		int				   =			v

						return new_state_dict

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						if len(SCREAMING_SNAKE_CASE     ) == 1:
												if len(v.shape     ) == 3:
																		# weight
																		UpperCamelCase				:		Any				   =			v[:, :, 0]
												else:
																		# bias
																		UpperCamelCase				:		List[str]				   =			v
						else:
												# qkv matrices
												UpperCamelCase				:		Union[str, Any]				   =			v.shape[0]
												UpperCamelCase				:		int				   =			trippled_shape // 3
												for i in range(3     ):
																		if len(v.shape     ) == 3:
																								UpperCamelCase				:		Tuple				   =			v[i * single_shape : (i + 1) * single_shape, :, 0]
																		else:
																								UpperCamelCase				:		str				   =			v[i * single_shape : (i + 1) * single_shape]
						return new_state_dict

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		List[str]				   =			torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""     )

						UpperCamelCase				:		Any				   =			args.model_path.split("""/"""     )[-1].split("""."""     )[0]
						if not os.path.isfile(args.model_path     ):
												assert (
												    model_name == args.model_path
												), f"""Make sure to provide one of the official model names {MODELS_MAP.keys()}"""
												UpperCamelCase				:		Union[str, Any]				   =			download(SCREAMING_SNAKE_CASE     )

						UpperCamelCase				:		Dict				   =			MODELS_MAP[model_name]["""sample_rate"""]
						UpperCamelCase				:		Tuple				   =			MODELS_MAP[model_name]["""sample_size"""]

						UpperCamelCase				:		List[str]				   =			Object()
						UpperCamelCase				:		Union[str, Any]				   =			sample_size
						UpperCamelCase				:		Dict				   =			sample_rate
						UpperCamelCase				:		Any				   =			0

						UpperCamelCase				:		int				   =			UNetaDModel(sample_size=SCREAMING_SNAKE_CASE							,     sample_rate=SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		Any				   =			diffusers_model.state_dict()

						UpperCamelCase				:		Optional[Any]				   =			DiffusionUncond(SCREAMING_SNAKE_CASE     )
						orig_model.load_state_dict(torch.load(args.model_path							,     map_location=SCREAMING_SNAKE_CASE     )["""state_dict"""]     )
						UpperCamelCase				:		Optional[Any]				   =			orig_model.diffusion_ema.eval()
						UpperCamelCase				:		Optional[int]				   =			orig_model.state_dict()
						UpperCamelCase				:		str				   =			rename_orig_weights(SCREAMING_SNAKE_CASE     )

						UpperCamelCase				:		Optional[Any]				   =			set(renamed_state_dict.keys()     ) - set(diffusers_state_dict.keys()     )
						UpperCamelCase				:		Optional[Any]				   =			set(diffusers_state_dict.keys()     ) - set(renamed_state_dict.keys()     )

						assert len(SCREAMING_SNAKE_CASE     ) == 0, f"""Problem with {renamed_minus_diffusers}"""
						assert all(k.endswith("""kernel"""     ) for k in list(SCREAMING_SNAKE_CASE     )     ), f"""Problem with {diffusers_minus_renamed}"""

						for key, value in renamed_state_dict.items():
												assert (
												    diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
												), f"""Shape for {key} doesn't match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}"""
												if key == "time_proj.weight":
																		UpperCamelCase				:		int				   =			value.squeeze()

												UpperCamelCase				:		str				   =			value

						diffusers_model.load_state_dict(SCREAMING_SNAKE_CASE     )

						UpperCamelCase				:		Optional[int]				   =			100
						UpperCamelCase				:		List[Any]				   =			33

						UpperCamelCase				:		Tuple				   =			IPNDMScheduler(num_train_timesteps=SCREAMING_SNAKE_CASE     )

						UpperCamelCase				:		Optional[int]				   =			torch.manual_seed(SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		List[str]				   =			torch.randn([1, 2, config.sample_size]							,     generator=SCREAMING_SNAKE_CASE     ).to(SCREAMING_SNAKE_CASE     )

						UpperCamelCase				:		str				   =			torch.linspace(1							,     0							,     steps + 1							,     device=SCREAMING_SNAKE_CASE     )[:-1]
						UpperCamelCase				:		Tuple				   =			get_crash_schedule(SCREAMING_SNAKE_CASE     )

						UpperCamelCase				:		Union[str, Any]				   =			DanceDiffusionPipeline(unet=SCREAMING_SNAKE_CASE							,     scheduler=SCREAMING_SNAKE_CASE     )

						UpperCamelCase				:		Optional[Any]				   =			torch.manual_seed(33     )
						UpperCamelCase				:		Dict				   =			pipe(num_inference_steps=SCREAMING_SNAKE_CASE							,     generator=SCREAMING_SNAKE_CASE     ).audios

						UpperCamelCase				:		Optional[Any]				   =			sampling.iplms_sample(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     {}     )
						UpperCamelCase				:		Any				   =			generated.clamp(-1							,     1     )

						UpperCamelCase				:		List[Any]				   =			(generated - audio).abs().sum()
						UpperCamelCase				:		Optional[int]				   =			(generated - audio).abs().max()

						if args.save:
												pipe.save_pretrained(args.checkpoint_path     )

						print("""Diff sum"""							,     SCREAMING_SNAKE_CASE     )
						print("""Diff max"""							,     SCREAMING_SNAKE_CASE     )

						assert diff_max < 1E-3, f"""Diff max: {diff_max} is too much :-/"""

						print(f"""Conversion for {model_name} successful!"""     )


if __name__ == "__main__":
		__magic_name__       :					Tuple						=			argparse.ArgumentParser()

		parser.add_argument("""--model_path""", default=None, type=str, required=True, help="""Path to the model to convert.""")
		parser.add_argument(
		    """--save""", default=True, type=bool, required=False, help="""Whether to save the converted model or not."""
		)
		parser.add_argument("""--checkpoint_path""", default=None, type=str, required=True, help="""Path to the output model.""")
		__magic_name__       :					List[str]						=			parser.parse_args()

		main(args)







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

import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    BartForSequenceClassification,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    TapexTokenizer,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.17.0.dev0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")

SCREAMING_SNAKE_CASE			: Dict				   =					logging.getLogger(__name__)






@dataclass
class _lowerCamelCase:
       lowercase_					:      Optional[str]       					=     field(
           default="""tab_fact""",					metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}  )
       lowercase_					:      Optional[str]       					=     field(
           default="""tab_fact""",					metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""},					)
       lowercase_					:      int       					=     field(
           default=10_24,					metadata={
               """help""": (
                   """The maximum total input sequence length after tokenization. Sequences longer """
                   """than this will be truncated, sequences shorter will be padded."""
               )
           },					)
       lowercase_					:      bool       					=     field(
           default=_a,					metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""}  )
       lowercase_					:      bool       					=     field(
           default=_a,					metadata={
               """help""": (
                   """Whether to pad all samples to `max_seq_length`. """
                   """If False, will pad the samples dynamically when batching to the maximum length in the batch."""
               )
           },					)
       lowercase_					:      Optional[int]       					=     field(
           default=_a,					metadata={
               """help""": (
                   """For debugging purposes or quicker training, truncate the number of training examples to this """
                   """value if set."""
               )
           },					)
       lowercase_					:      Optional[int]       					=     field(
           default=_a,					metadata={
               """help""": (
                   """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
                   """value if set."""
               )
           },					)
       lowercase_					:      Optional[int]       					=     field(
           default=_a,					metadata={
               """help""": (
                   """For debugging purposes or quicker training, truncate the number of prediction examples to this """
                   """value if set."""
               )
           },					)
       lowercase_					:      Optional[str]       					=     field(
           default=_a,					metadata={"""help""": """A csv or a json file containing the training data."""}  )
       lowercase_					:      Optional[str]       					=     field(
           default=_a,					metadata={"""help""": """A csv or a json file containing the validation data."""}  )
       lowercase_					:      Optional[str]       					=     field(default=_a,					metadata={"""help""": """A csv or a json file containing the test data."""}  )
       def      UpperCamelCase	(   self)			->  Dict:




           """simple docstring"""

           if self.dataset_name is not None:
               pass
           elif self.train_file is None or self.validation_file is None:
               raise ValueError('Need either a GLUE task, a training/validation file or a dataset name.')
           else:
               _lowercase			:				int 						=						self.train_file.split('.')[-1]
               assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
               _lowercase			:				Tuple 						=						self.validation_file.split('.')[-1]
               assert (
                   validation_extension == train_extension
               ), "`validation_file` should have the same extension (csv or json) as `train_file`."






@dataclass
class _lowerCamelCase:
       lowercase_					:      str       					=     field(
           default=_a,					metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}  )
       lowercase_					:      Optional[str]       					=     field(
           default=_a,					metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}  )
       lowercase_					:      Optional[str]       					=     field(
           default=_a,					metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}  )
       lowercase_					:      Optional[str]       					=     field(
           default=_a,					metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""},					)
       lowercase_					:      bool       					=     field(
           default=_a,					metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""},					)
       lowercase_					:      str       					=     field(
           default="""main""",					metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""},					)
       lowercase_					:      bool       					=     field(
           default=_a,					metadata={
               """help""": (
                   """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
                   """with private models)."""
               )
           },					)



def 							UpperCamelCase_( )       ->		Optional[int]:
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    _lowercase			:				Optional[int] 						=						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.
        _lowercase						,							_lowercase						,							_lowercase			:				Tuple 						=						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
    else:
        _lowercase						,							_lowercase						,							_lowercase			:				Union[str, Any] 						=						parser.parse_args_into_dataclasses()

    # Setup logging
    logging.basicConfig(
        format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'				,					datefmt='%m/%d/%Y %H:%M:%S'				,					handlers=[logging.StreamHandler(sys.stdout					)]				,					)

    _lowercase			:				Union[str, Any] 						=						training_args.get_process_log_level()
    logger.setLevel(lowerCamelCase_					)
    datasets.utils.logging.set_verbosity(lowerCamelCase_					)
    transformers.utils.logging.set_verbosity(lowerCamelCase_					)
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()

    # Log on each process the small summary:
    logger.warning(
        F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
        + F'''distributed training: {bool(training_args.local_rank != -1					)}, 16-bits training: {training_args.fpaa}'''					)
    logger.info(F'''Training/evaluation parameters {training_args}'''					)

    # Detecting last checkpoint.
    _lowercase			:				Optional[int] 						=						None
    if os.path.isdir(training_args.output_dir					) and training_args.do_train and not training_args.overwrite_output_dir:
        _lowercase			:				Dict 						=						get_last_checkpoint(training_args.output_dir					)
        if last_checkpoint is None and len(os.listdir(training_args.output_dir					)					) > 0:
            raise ValueError(
                F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
                'Use --overwrite_output_dir to overcome.'					)
        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
            logger.info(
                F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
                'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'					)

    # Set seed before initializing model.
    set_seed(training_args.seed					)

    # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
    # or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
    #
    # For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
    #
    # If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
    # single column. You can easily tweak this behavior (see below)
    #
    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
    if data_args.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
        _lowercase			:				Dict 						=						load_dataset(
            data_args.dataset_name				,					data_args.dataset_config_name				,					cache_dir=model_args.cache_dir					)
    else:
        # Loading a dataset from your local files.
        # CSV/JSON training and evaluation files are needed.
        _lowercase			:				Optional[Any] 						=						{'train': data_args.train_file, 'validation': data_args.validation_file}

        # Get the test dataset: you can provide your own CSV/JSON test file (see below)
        # when you use `do_predict` without specifying a GLUE benchmark task.
        if training_args.do_predict:
            if data_args.test_file is not None:
                _lowercase			:				Tuple 						=						data_args.train_file.split('.'					)[-1]
                _lowercase			:				int 						=						data_args.test_file.split('.'					)[-1]
                assert (
                    test_extension == train_extension
                ), "`test_file` should have the same extension (csv or json) as `train_file`."
                _lowercase			:				Any 						=						data_args.test_file
            else:
                raise ValueError('Need either a GLUE task or a test file for `do_predict`.'					)

        for key in data_files.keys():
            logger.info(F'''load a local file for {key}: {data_files[key]}'''					)

        if data_args.train_file.endswith('.csv'					):
            # Loading a dataset from local csv files
            _lowercase			:				str 						=						load_dataset('csv'				,					data_files=lowerCamelCase_				,					cache_dir=model_args.cache_dir					)
        else:
            # Loading a dataset from local json files
            _lowercase			:				Optional[int] 						=						load_dataset('json'				,					data_files=lowerCamelCase_				,					cache_dir=model_args.cache_dir					)
    # See more about loading any type of standard or custom dataset at
    # https://huggingface.co/docs/datasets/loading_datasets.html.

    # Labels
    _lowercase			:				Optional[Any] 						=						raw_datasets['train'].features['label'].names
    _lowercase			:				Any 						=						len(lowerCamelCase_					)

    # Load pretrained model and tokenizer
    #
    # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    _lowercase			:				List[Any] 						=						AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path				,					num_labels=lowerCamelCase_				,					cache_dir=model_args.cache_dir				,					revision=model_args.model_revision				,					use_auth_token=True if model_args.use_auth_token else None				,					)
    # load tapex tokenizer
    _lowercase			:				str 						=						TapexTokenizer.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=model_args.use_fast_tokenizer				,					revision=model_args.model_revision				,					use_auth_token=True if model_args.use_auth_token else None				,					add_prefix_space=lowerCamelCase_				,					)
    _lowercase			:				Tuple 						=						BartForSequenceClassification.from_pretrained(
        model_args.model_name_or_path				,					from_tf=bool('.ckpt' in model_args.model_name_or_path					)				,					config=lowerCamelCase_				,					cache_dir=model_args.cache_dir				,					revision=model_args.model_revision				,					use_auth_token=True if model_args.use_auth_token else None				,					)

    # Padding strategy
    if data_args.pad_to_max_length:
        _lowercase			:				int 						=						'max_length'
    else:
        # We will pad later, dynamically at batch creation, to the max sequence length in each batch
        _lowercase			:				str 						=						False

    # Some models have set the order of the labels to use, so let's make sure we do use it.
    _lowercase			:				List[Any] 						=						{'Refused': 0, 'Entailed': 1}
    _lowercase			:				Union[str, Any] 						=						{0: 'Refused', 1: 'Entailed'}

    if data_args.max_seq_length > tokenizer.model_max_length:
        logger.warning(
            F'''The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the'''
            F'''model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.'''					)
    _lowercase			:				List[str] 						=						min(data_args.max_seq_length				,					tokenizer.model_max_length					)

    def preprocess_tabfact_function(lowerCamelCase_					):
        # Tokenize the texts
        def _convert_table_text_to_pandas(lowerCamelCase_					):
            _lowercase			:				int 						=						[_table_row.split('#'					) for _table_row in _table_text.strip('\n'					).split('\n'					)]
            _lowercase			:				Any 						=						pd.DataFrame.from_records(_table_content[1:]				,					columns=_table_content[0]					)
            return _table_pd

        _lowercase			:				List[Any] 						=						examples['statement']
        _lowercase			:				Optional[Any] 						=						list(map(_convert_table_text_to_pandas				,					examples['table_text']					)					)
        _lowercase			:				Union[str, Any] 						=						tokenizer(lowerCamelCase_				,					lowerCamelCase_				,					padding=lowerCamelCase_				,					max_length=lowerCamelCase_				,					truncation=lowerCamelCase_					)

        _lowercase			:				Any 						=						examples['label']
        return result

    with training_args.main_process_first(desc='dataset map pre-processing'					):
        _lowercase			:				str 						=						raw_datasets.map(
            lowerCamelCase_				,					batched=lowerCamelCase_				,					load_from_cache_file=not data_args.overwrite_cache				,					desc='Running tokenizer on dataset'				,					)
    if training_args.do_train:
        if "train" not in raw_datasets:
            raise ValueError('--do_train requires a train dataset'					)
        _lowercase			:				Any 						=						raw_datasets['train']
        if data_args.max_train_samples is not None:
            _lowercase			:				str 						=						train_dataset.select(range(data_args.max_train_samples					)					)

    if training_args.do_eval:
        if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
            raise ValueError('--do_eval requires a validation dataset'					)
        _lowercase			:				str 						=						raw_datasets['validation']
        if data_args.max_eval_samples is not None:
            _lowercase			:				List[Any] 						=						eval_dataset.select(range(data_args.max_eval_samples					)					)

    if training_args.do_predict or data_args.test_file is not None:
        if "test" not in raw_datasets and "test_matched" not in raw_datasets:
            raise ValueError('--do_predict requires a test dataset'					)
        _lowercase			:				Optional[int] 						=						raw_datasets['test']
        if data_args.max_predict_samples is not None:
            _lowercase			:				List[str] 						=						predict_dataset.select(range(data_args.max_predict_samples					)					)

    # Log a few random samples from the training set:
    if training_args.do_train:
        for index in random.sample(range(len(lowerCamelCase_					)					)				,					3					):
            logger.info(F'''Sample {index} of the training set: {train_dataset[index]}.'''					)

    # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
    # predictions and label_ids field) and has to return a dictionary string to float.
    def compute_metrics(lowerCamelCase_					):
        _lowercase			:				Dict 						=						p.predictions[0] if isinstance(p.predictions				,					lowerCamelCase_					) else p.predictions
        _lowercase			:				Tuple 						=						np.argmax(lowerCamelCase_				,					axis=1					)
        return {"accuracy": (preds == p.label_ids).astype(np.floataa					).mean().item()}

    # Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
    if data_args.pad_to_max_length:
        _lowercase			:				Any 						=						default_data_collator
    elif training_args.fpaa:
        _lowercase			:				str 						=						DataCollatorWithPadding(lowerCamelCase_				,					pad_to_multiple_of=8					)
    else:
        _lowercase			:				Optional[Any] 						=						None

    # Initialize our Trainer
    _lowercase			:				List[str] 						=						Trainer(
        model=lowerCamelCase_				,					args=lowerCamelCase_				,					train_dataset=train_dataset if training_args.do_train else None				,					eval_dataset=eval_dataset if training_args.do_eval else None				,					compute_metrics=lowerCamelCase_				,					tokenizer=lowerCamelCase_				,					data_collator=lowerCamelCase_				,					)

    # Training
    if training_args.do_train:
        _lowercase			:				Optional[int] 						=						None
        if training_args.resume_from_checkpoint is not None:
            _lowercase			:				List[Any] 						=						training_args.resume_from_checkpoint
        elif last_checkpoint is not None:
            _lowercase			:				Optional[Any] 						=						last_checkpoint
        _lowercase			:				Optional[Any] 						=						trainer.train(resume_from_checkpoint=lowerCamelCase_					)
        _lowercase			:				List[Any] 						=						train_result.metrics
        _lowercase			:				Dict 						=						(
            data_args.max_train_samples if data_args.max_train_samples is not None else len(lowerCamelCase_					)
        )
        _lowercase			:				int 						=						min(lowerCamelCase_				,					len(lowerCamelCase_					)					)

        trainer.save_model()  # Saves the tokenizer too for easy upload

        trainer.log_metrics('train'				,					lowerCamelCase_					)
        trainer.save_metrics('train'				,					lowerCamelCase_					)
        trainer.save_state()

    # Evaluation
    if training_args.do_eval:
        logger.info('*** Evaluate ***'					)

        _lowercase			:				Tuple 						=						trainer.evaluate(eval_dataset=lowerCamelCase_					)
        _lowercase			:				Any 						=						data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowerCamelCase_					)
        _lowercase			:				Optional[int] 						=						min(lowerCamelCase_				,					len(lowerCamelCase_					)					)

        trainer.log_metrics('eval'				,					lowerCamelCase_					)
        trainer.save_metrics('eval'				,					lowerCamelCase_					)

    if training_args.do_predict:
        logger.info('*** Predict ***'					)

        # Removing the `label` columns because it contains -1 and Trainer won't like that.
        _lowercase			:				Any 						=						predict_dataset.remove_columns('label'					)
        _lowercase			:				Optional[Any] 						=						trainer.predict(lowerCamelCase_				,					metric_key_prefix='predict'					).predictions
        _lowercase			:				Union[str, Any] 						=						np.argmax(lowerCamelCase_				,					axis=1					)

        _lowercase			:				Dict 						=						os.path.join(training_args.output_dir				,					'predict_results_tabfact.txt'					)
        if trainer.is_world_process_zero():
            with open(lowerCamelCase_				,					'w'					) as writer:
                logger.info('***** Predict Results *****'					)
                writer.write('index\tprediction\n'					)
                for index, item in enumerate(lowerCamelCase_					):
                    _lowercase			:				List[str] 						=						label_list[item]
                    writer.write(F'''{index}\t{item}\n'''					)

    _lowercase			:				str 						=						{'finetuned_from': model_args.model_name_or_path, 'tasks': 'text-classification'}

    if training_args.push_to_hub:
        trainer.push_to_hub(**lowerCamelCase_					)
    else:
        trainer.create_model_card(**lowerCamelCase_					)



def 							UpperCamelCase_( lowerCamelCase_					)       ->		Dict:
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
 main()


