


import argparse
import collections
import json
import os
import re
import string
import sys

import numpy as np


UpperCamelCase								=	re.compile(r"""\b(a|an|the)\b""", re.UNICODE)

UpperCamelCase								=	None


def   _SCREAMING_SNAKE_CASE	( ):
  A_      :				Tuple		     =					argparse.ArgumentParser('''Official evaluation script for SQuAD version 2.0.'''       )
  parser.add_argument('''data_file''' ,			metavar='''data.json''' ,			help='''Input data JSON file.'''       )
  parser.add_argument('''pred_file''' ,			metavar='''pred.json''' ,			help='''Model predictions.'''       )
  parser.add_argument(
      '''--out-file''' ,			'''-o''' ,			metavar='''eval.json''' ,			help='''Write accuracy metrics to file (default is stdout).'''       )
  parser.add_argument(
      '''--na-prob-file''' ,			'''-n''' ,			metavar='''na_prob.json''' ,			help='''Model estimates of probability of no answer.'''       )
  parser.add_argument(
      '''--na-prob-thresh''' ,			'''-t''' ,			type=SCREAMING_SNAKE_CASE ,			default=1.0 ,			help='''Predict "" if no-answer probability exceeds this (default = 1.0).''' ,			)
  parser.add_argument(
      '''--out-image-dir''' ,			'''-p''' ,			metavar='''out_images''' ,			default=SCREAMING_SNAKE_CASE ,			help='''Save precision-recall curves to directory.'''       )
  parser.add_argument('''--verbose''' ,			'''-v''' ,			action='''store_true'''       )
  if len(sys.argv       ) == 1:
    parser.print_help()
    sys.exit(1       )
  return parser.parse_args()


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  A_      :				Union[str, Any]		     =					{}
  for article in dataset:
    for p in article["paragraphs"]:
      for qa in p["qas"]:
        A_      :				Optional[int]		     =					bool(qa['''answers''']['''text''']       )
  return qid_to_has_ans


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):

  def remove_articles(SCREAMING_SNAKE_CASE       ):
    return ARTICLES_REGEX.sub(''' ''' ,			SCREAMING_SNAKE_CASE       )

  def white_space_fix(SCREAMING_SNAKE_CASE       ):
    return " ".join(text.split()       )

  def remove_punc(SCREAMING_SNAKE_CASE       ):
    A_      :				List[Any]		     =					set(string.punctuation       )
    return "".join(ch for ch in text if ch not in exclude       )

  def lower(SCREAMING_SNAKE_CASE       ):
    return text.lower()

  return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE       )       )       )       )


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  if not s:
    return []
  return normalize_answer(SCREAMING_SNAKE_CASE       ).split()


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  return int(normalize_answer(SCREAMING_SNAKE_CASE       ) == normalize_answer(SCREAMING_SNAKE_CASE       )       )


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  A_      :				List[str]		     =					get_tokens(SCREAMING_SNAKE_CASE       )
  A_      :				Tuple		     =					get_tokens(SCREAMING_SNAKE_CASE       )
  A_      :				str		     =					collections.Counter(SCREAMING_SNAKE_CASE       ) & collections.Counter(SCREAMING_SNAKE_CASE       )
  A_      :				Dict		     =					sum(common.values()       )
  if len(SCREAMING_SNAKE_CASE       ) == 0 or len(SCREAMING_SNAKE_CASE       ) == 0:
    # If either is no-answer, then F1 is 1 if they agree, 0 otherwise
    return int(gold_toks == pred_toks       )
  if num_same == 0:
    return 0
  A_      :				Any		     =					1.0 * num_same / len(SCREAMING_SNAKE_CASE       )
  A_      :				int		     =					1.0 * num_same / len(SCREAMING_SNAKE_CASE       )
  A_      :				Union[str, Any]		     =					(2 * precision * recall) / (precision + recall)
  return fa


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  A_      :				List[str]		     =					{}
  A_      :				List[str]		     =					{}
  for article in dataset:
    for p in article["paragraphs"]:
      for qa in p["qas"]:
        A_      :				Optional[int]		     =					qa['''id''']
        A_      :				Any		     =					[t for t in qa['''answers''']['''text'''] if normalize_answer(SCREAMING_SNAKE_CASE       )]
        if not gold_answers:
          # For unanswerable questions, only correct answer is empty string
          A_      :				Tuple		     =					['''''']
        if qid not in preds:
          print(f'''Missing prediction for {qid}'''       )
          continue
        A_      :				Optional[int]		     =					preds[qid]
        # Take max over all gold answers
        A_      :				List[str]		     =					max(compute_exact(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ) for a in gold_answers       )
        A_      :				Any		     =					max(compute_fa(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ) for a in gold_answers       )
  return exact_scores, fa_scores


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  A_      :				str		     =					{}
  for qid, s in scores.items():
    A_      :				Optional[Any]		     =					na_probs[qid] > na_prob_thresh
    if pred_na:
      A_      :				int		     =					float(not qid_to_has_ans[qid]       )
    else:
      A_      :				int		     =					s
  return new_scores


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE=None       ):
  if not qid_list:
    A_      :				Dict		     =					len(SCREAMING_SNAKE_CASE       )
    return collections.OrderedDict(
        [
            ('''exact''', 1_0_0.0 * sum(exact_scores.values()       ) / total),
            ('''f1''', 1_0_0.0 * sum(fa_scores.values()       ) / total),
            ('''total''', total),
        ]       )
  else:
    A_      :				Optional[Any]		     =					len(SCREAMING_SNAKE_CASE       )
    return collections.OrderedDict(
        [
            ('''exact''', 1_0_0.0 * sum(exact_scores[k] for k in qid_list       ) / total),
            ('''f1''', 1_0_0.0 * sum(fa_scores[k] for k in qid_list       ) / total),
            ('''total''', total),
        ]       )


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  for k in new_eval:
    A_      :				Any		     =					new_eval[k]


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  plt.step(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			color='''b''' ,			alpha=0.2 ,			where='''post'''       )
  plt.fill_between(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			step='''post''' ,			alpha=0.2 ,			color='''b'''       )
  plt.xlabel('''Recall'''       )
  plt.ylabel('''Precision'''       )
  plt.xlim([0.0, 1.0_5]       )
  plt.ylim([0.0, 1.0_5]       )
  plt.title(SCREAMING_SNAKE_CASE       )
  plt.savefig(SCREAMING_SNAKE_CASE       )
  plt.clf()


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE=None ,			SCREAMING_SNAKE_CASE=None       ):
  A_      :				int		     =					sorted(SCREAMING_SNAKE_CASE ,			key=lambda SCREAMING_SNAKE_CASE       : na_probs[k]       )
  A_      :				str		     =					0.0
  A_      :				List[Any]		     =					1.0
  A_      :				Optional[int]		     =					0.0
  A_      :				Union[str, Any]		     =					[1.0]
  A_      :				Any		     =					[0.0]
  A_      :				Optional[Any]		     =					0.0
  for i, qid in enumerate(SCREAMING_SNAKE_CASE       ):
    if qid_to_has_ans[qid]:
      true_pos += scores[qid]
    A_      :				Tuple		     =					true_pos / float(i + 1       )
    A_      :				Optional[int]		     =					true_pos / float(SCREAMING_SNAKE_CASE       )
    if i == len(SCREAMING_SNAKE_CASE       ) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
      # i.e., if we can put a threshold after this point
      avg_prec += cur_p * (cur_r - recalls[-1])
      precisions.append(SCREAMING_SNAKE_CASE       )
      recalls.append(SCREAMING_SNAKE_CASE       )
  if out_image:
    plot_pr_curve(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )
  return {"ap": 1_0_0.0 * avg_prec}


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  if out_image_dir and not os.path.exists(SCREAMING_SNAKE_CASE       ):
    os.makedirs(SCREAMING_SNAKE_CASE       )
  A_      :				Any		     =					sum(1 for v in qid_to_has_ans.values() if v       )
  if num_true_pos == 0:
    return
  A_      :				Any		     =					make_precision_recall_eval(
      SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			out_image=os.path.join(SCREAMING_SNAKE_CASE ,			'''pr_exact.png'''       ) ,			title='''Precision-Recall curve for Exact Match score''' ,			)
  A_      :				Dict		     =					make_precision_recall_eval(
      SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			out_image=os.path.join(SCREAMING_SNAKE_CASE ,			'''pr_f1.png'''       ) ,			title='''Precision-Recall curve for F1 score''' ,			)
  A_      :				Optional[int]		     =					{k: float(SCREAMING_SNAKE_CASE       ) for k, v in qid_to_has_ans.items()}
  A_      :				Optional[Any]		     =					make_precision_recall_eval(
      SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			out_image=os.path.join(SCREAMING_SNAKE_CASE ,			'''pr_oracle.png'''       ) ,			title='''Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)''' ,			)
  merge_eval(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			'''pr_exact'''       )
  merge_eval(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			'''pr_f1'''       )
  merge_eval(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			'''pr_oracle'''       )


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  if not qid_list:
    return
  A_      :				Any		     =					[na_probs[k] for k in qid_list]
  A_      :				Any		     =					np.ones_like(SCREAMING_SNAKE_CASE       ) / float(len(SCREAMING_SNAKE_CASE       )       )
  plt.hist(SCREAMING_SNAKE_CASE ,			weights=SCREAMING_SNAKE_CASE ,			bins=20 ,			range=(0.0, 1.0)       )
  plt.xlabel('''Model probability of no-answer'''       )
  plt.ylabel('''Proportion of dataset'''       )
  plt.title(f'''Histogram of no-answer probability: {name}'''       )
  plt.savefig(os.path.join(SCREAMING_SNAKE_CASE ,			f'''na_prob_hist_{name}.png'''       )       )
  plt.clf()


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  A_      :				Any		     =					sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k]       )
  A_      :				int		     =					num_no_ans
  A_      :				Dict		     =					cur_score
  A_      :				Union[str, Any]		     =					0.0
  A_      :				List[str]		     =					sorted(SCREAMING_SNAKE_CASE ,			key=lambda SCREAMING_SNAKE_CASE       : na_probs[k]       )
  for i, qid in enumerate(SCREAMING_SNAKE_CASE       ):
    if qid not in scores:
      continue
    if qid_to_has_ans[qid]:
      A_      :				str		     =					scores[qid]
    else:
      if preds[qid]:
        A_      :				Dict		     =					-1
      else:
        A_      :				List[Any]		     =					0
    cur_score += diff
    if cur_score > best_score:
      A_      :				str		     =					cur_score
      A_      :				List[Any]		     =					na_probs[qid]
  return 1_0_0.0 * best_score / len(SCREAMING_SNAKE_CASE       ), best_thresh


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  A_     ,       A_      :				Dict		     =					find_best_thresh(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )
  A_     ,       A_      :				Any		     =					find_best_thresh(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )
  A_      :				Tuple		     =					best_exact
  A_      :				Any		     =					exact_thresh
  A_      :				Dict		     =					best_fa
  A_      :				Tuple		     =					fa_thresh


def   _SCREAMING_SNAKE_CASE	( ):
  with open(OPTS.data_file       ) as f:
    A_      :				List[Any]		     =					json.load(SCREAMING_SNAKE_CASE       )
    A_      :				str		     =					dataset_json['''data''']
  with open(OPTS.pred_file       ) as f:
    A_      :				Tuple		     =					json.load(SCREAMING_SNAKE_CASE       )
  if OPTS.na_prob_file:
    with open(OPTS.na_prob_file       ) as f:
      A_      :				Tuple		     =					json.load(SCREAMING_SNAKE_CASE       )
  else:
    A_      :				Any		     =					{k: 0.0 for k in preds}
  A_      :				Any		     =					make_qid_to_has_ans(SCREAMING_SNAKE_CASE       )  # maps qid to True/False
  A_      :				Any		     =					[k for k, v in qid_to_has_ans.items() if v]
  A_      :				List[str]		     =					[k for k, v in qid_to_has_ans.items() if not v]
  A_     ,       A_      :				int		     =					get_raw_scores(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )
  A_      :				Union[str, Any]		     =					apply_no_ans_threshold(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			OPTS.na_prob_thresh       )
  A_      :				Any		     =					apply_no_ans_threshold(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			OPTS.na_prob_thresh       )
  A_      :				Optional[int]		     =					make_eval_dict(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )
  if has_ans_qids:
    A_      :				List[Any]		     =					make_eval_dict(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			qid_list=SCREAMING_SNAKE_CASE       )
    merge_eval(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			'''HasAns'''       )
  if no_ans_qids:
    A_      :				Any		     =					make_eval_dict(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			qid_list=SCREAMING_SNAKE_CASE       )
    merge_eval(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			'''NoAns'''       )
  if OPTS.na_prob_file:
    find_all_best_thresh(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )
  if OPTS.na_prob_file and OPTS.out_image_dir:
    run_precision_recall_analysis(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			OPTS.out_image_dir       )
    histogram_na_prob(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			OPTS.out_image_dir ,			'''hasAns'''       )
    histogram_na_prob(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			OPTS.out_image_dir ,			'''noAns'''       )
  if OPTS.out_file:
    with open(OPTS.out_file ,			'''w'''       ) as f:
      json.dump(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )
  else:
    print(json.dumps(SCREAMING_SNAKE_CASE ,			indent=2       )       )


if __name__ == "__main__":
     UpperCamelCase								=	parse_args()
     if OPTS.out_image_dir:
          import matplotlib

          matplotlib.use("""Agg""")
          import matplotlib.pyplot as plt
     main()








import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase								=	logging.get_logger(__name__)

UpperCamelCase								=	{
    """google/pix2struct-textcaps-base""": (
        """https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json"""
    ),
}






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""
       snake_case      =    "pix2struct_text_model"
       snake_case      =    ["past_key_values"]
       snake_case      =    {
           "hidden_size": "hidden_size",
           "num_attention_heads": "num_heads",
           "num_hidden_layers": "num_layers",
       }



       def __init__(      self				,       _SCREAMING_SNAKE_CASE=5_0244				,       _SCREAMING_SNAKE_CASE=768				,       _SCREAMING_SNAKE_CASE=64				,       _SCREAMING_SNAKE_CASE=2048				,       _SCREAMING_SNAKE_CASE=12				,       _SCREAMING_SNAKE_CASE=12				,       _SCREAMING_SNAKE_CASE=32				,       _SCREAMING_SNAKE_CASE=128				,       _SCREAMING_SNAKE_CASE=0.1				,       _SCREAMING_SNAKE_CASE=1e-6				,       _SCREAMING_SNAKE_CASE=1.0				,       _SCREAMING_SNAKE_CASE="gelu_new"				,       _SCREAMING_SNAKE_CASE=0				,       _SCREAMING_SNAKE_CASE=False				,       _SCREAMING_SNAKE_CASE=0				,       _SCREAMING_SNAKE_CASE=1				,       _SCREAMING_SNAKE_CASE=False				,       _SCREAMING_SNAKE_CASE=True				,       **_SCREAMING_SNAKE_CASE				,       )->int:




         '''simple docstring'''





         A_      :				Optional[int]		     =					vocab_size
         A_      :				Any		     =					hidden_size
         A_      :				Optional[Any]		     =					d_kv
         A_      :				int		     =					d_ff
         A_      :				int		     =					num_layers
         A_      :				Dict		     =					num_heads
         A_      :				Any		     =					relative_attention_num_buckets
         A_      :				int		     =					relative_attention_max_distance
         A_      :				Optional[Any]		     =					dropout_rate
         A_      :				Optional[Any]		     =					layer_norm_epsilon
         A_      :				List[Any]		     =					initializer_factor
         A_      :				Optional[int]		     =					use_cache

         A_      :				Optional[Any]		     =					eos_token_id
         A_      :				List[Any]		     =					decoder_start_token_id

         # for backwards compatibility
         A_      :				int		     =					dense_act_fn

         super().__init__(
             pad_token_id=_SCREAMING_SNAKE_CASE				,       eos_token_id=_SCREAMING_SNAKE_CASE				,       decoder_start_token_id=_SCREAMING_SNAKE_CASE				,       tie_word_embeddings=_SCREAMING_SNAKE_CASE				,       is_decoder=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE				,       )







       @classmethod
       def        _snake_case       (      cls				,       _SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)->"PretrainedConfig":




         '''simple docstring'''





         cls._set_token_in_kwargs(_SCREAMING_SNAKE_CASE		)

         A_     ,       A_      :				Union[str, Any]		     =					cls.get_config_dict(_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)

         # get the text config dict if we are loading from Pix2StructConfig
         if config_dict.get('''model_type'''		) == "pix2struct":
           A_      :				Any		     =					config_dict['''text_config''']

         if "model_type" in config_dict and hasattr(cls				,       '''model_type'''		) and config_dict["model_type"] != cls.model_type:
           logger.warning(
               F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
               F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''		)

         return cls.from_dict(_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""
       snake_case      =    "pix2struct_vision_model"



       def __init__(      self				,       _SCREAMING_SNAKE_CASE=768				,       _SCREAMING_SNAKE_CASE=768				,       _SCREAMING_SNAKE_CASE=2048				,       _SCREAMING_SNAKE_CASE=64				,       _SCREAMING_SNAKE_CASE=12				,       _SCREAMING_SNAKE_CASE=12				,       _SCREAMING_SNAKE_CASE="gelu_new"				,       _SCREAMING_SNAKE_CASE=1e-6				,       _SCREAMING_SNAKE_CASE=0.0				,       _SCREAMING_SNAKE_CASE=0.0				,       _SCREAMING_SNAKE_CASE=1e-10				,       _SCREAMING_SNAKE_CASE=1.0				,       _SCREAMING_SNAKE_CASE=4096				,       _SCREAMING_SNAKE_CASE=32				,       _SCREAMING_SNAKE_CASE=128				,       **_SCREAMING_SNAKE_CASE				,       )->Optional[int]:




         '''simple docstring'''





         super().__init__(**_SCREAMING_SNAKE_CASE		)

         A_      :				Dict		     =					hidden_size
         A_      :				Union[str, Any]		     =					patch_embed_hidden_size
         A_      :				Optional[Any]		     =					d_ff
         A_      :				Optional[Any]		     =					dropout_rate
         A_      :				int		     =					num_hidden_layers
         A_      :				Tuple		     =					num_attention_heads
         A_      :				List[str]		     =					initializer_range
         A_      :				List[str]		     =					initializer_factor
         A_      :				Union[str, Any]		     =					attention_dropout
         A_      :				Union[str, Any]		     =					layer_norm_eps
         A_      :				Dict		     =					dense_act_fn
         A_      :				Union[str, Any]		     =					seq_len
         A_      :				Optional[Any]		     =					relative_attention_num_buckets
         A_      :				Tuple		     =					relative_attention_max_distance
         A_      :				List[Any]		     =					d_kv







       @classmethod
       def        _snake_case       (      cls				,       _SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)->"PretrainedConfig":




         '''simple docstring'''





         cls._set_token_in_kwargs(_SCREAMING_SNAKE_CASE		)

         A_     ,       A_      :				str		     =					cls.get_config_dict(_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)

         # get the vision config dict if we are loading from Pix2StructConfig
         if config_dict.get('''model_type'''		) == "pix2struct":
           A_      :				Any		     =					config_dict['''vision_config''']

         if "model_type" in config_dict and hasattr(cls				,       '''model_type'''		) and config_dict["model_type"] != cls.model_type:
           logger.warning(
               F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
               F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''		)

         return cls.from_dict(_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""
       snake_case      =    "pix2struct"
       snake_case      =    True



       def __init__(      self				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=1.0				,       _SCREAMING_SNAKE_CASE=0.0_2				,       _SCREAMING_SNAKE_CASE=False				,       _SCREAMING_SNAKE_CASE=False				,       _SCREAMING_SNAKE_CASE=True				,       **_SCREAMING_SNAKE_CASE				,       )->Any:




         '''simple docstring'''





         super().__init__(tie_word_embeddings=_SCREAMING_SNAKE_CASE				,       is_encoder_decoder=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)

         if text_config is None:
           A_      :				Dict		     =					{}
           logger.info('''text_config is None. Initializing the Pix2StructTextConfig with default values.'''		)

         if vision_config is None:
           A_      :				List[Any]		     =					{}
           logger.info('''vision_config is None. Initializing the Pix2StructVisionConfig with default values.'''		)

         A_      :				int		     =					PixaStructTextConfig(**_SCREAMING_SNAKE_CASE		)
         A_      :				Dict		     =					PixaStructVisionConfig(**_SCREAMING_SNAKE_CASE		)

         A_      :				Optional[Any]		     =					self.text_config.decoder_start_token_id
         A_      :				Tuple		     =					self.text_config.pad_token_id
         A_      :				Union[str, Any]		     =					self.text_config.eos_token_id

         A_      :				str		     =					initializer_factor
         A_      :				Tuple		     =					initializer_range

         A_      :				List[str]		     =					self.initializer_range
         A_      :				int		     =					self.initializer_range

         A_      :				List[Any]		     =					is_vqa



       @classmethod
       def        _snake_case       (      cls				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)->Union[str, Any]:




         '''simple docstring'''





         return cls(text_config=text_config.to_dict()				,       vision_config=vision_config.to_dict()				,       **_SCREAMING_SNAKE_CASE		)







       def        _snake_case       (      self		)->Dict:




         '''simple docstring'''





         A_      :				List[Any]		     =					copy.deepcopy(self.__dict__		)
         A_      :				Dict		     =					self.text_config.to_dict()
         A_      :				int		     =					self.vision_config.to_dict()
         A_      :				List[str]		     =					self.__class__.model_type
         return output




