'''simple docstring'''

import logging
import pathlib
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Callable, Dict, List, Optional, Set, Union

import datasets
import librosa
import numpy as np
import torch
from lang_trans import arabic
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaProcessor,
    is_apex_available,
    trainer_utils,
)


if is_apex_available():
       from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse("1.6"):
       lowerCAmelCase_			:			Tuple    	=   True
       from torch.cuda.amp import autocast


lowerCAmelCase_			:			str    	=   logging.getLogger(__name__)





@dataclass
class 				lowercase    :
   lowerCamelCase_            =field(
       metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}			)
   lowerCamelCase_            =field(
       default=__A			,					metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}			,					)
   lowerCamelCase_            =field(
       default=__A			,					metadata={'help': 'Whether to freeze the feature extractor layers of the model.'}			)
   lowerCamelCase_            =field(
       default=__A			,					metadata={'help': 'Whether to log verbose messages or not.'}			,					)




def __a			(						__lowerCamelCase      :    ModelArguments			,		__lowerCamelCase      :    TrainingArguments					)						->				List[str]:
      '''simple docstring'''


      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"			,		datefmt="%m/%d/%Y %H:%M:%S"			,		handlers=[logging.StreamHandler(sys.stdout					)]			,		)
      lowercase_     			=   logging.WARNING
      if model_args.verbose_logging:
            lowercase_     			=   logging.DEBUG
      elif trainer_utils.is_main_process(training_args.local_rank					):
            lowercase_     			=   logging.INFO
      logger.setLevel(_a					)





@dataclass
class 				lowercase    :
   lowerCamelCase_            =field(
       default=__A			,					metadata={'help': 'The name of the dataset to use (via the datasets library).'}			)
   lowerCamelCase_            =field(
       default=__A			,					metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}			)
   lowerCamelCase_            =field(
       default='train'			,					metadata={
           'help': 'The name of the training data set split to use (via the datasets library). Defaults to \'train\''
       }			,					)
   lowerCamelCase_            =field(
       default='validation'			,					metadata={
           'help': (
               'The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''
           )
       }			,					)
   lowerCamelCase_            =field(
       default='text'			,					metadata={'help': 'Column in the dataset that contains label (target text). Defaults to \'text\''}			,					)
   lowerCamelCase_            =field(
       default='file'			,					metadata={'help': 'Column in the dataset that contains speech file path. Defaults to \'file\''}			,					)
   lowerCamelCase_            =field(
       default=__A			,					metadata={'help': 'Resample loaded audio to target feature extractor\'s sampling rate or not.'}			,					)
   lowerCamelCase_            =field(
       default=__A			,					metadata={'help': 'Filters out examples longer than specified. Defaults to no filtering.'}			,					)
   lowerCamelCase_            =field(
       default='librispeech'			,					metadata={
           'help': (
               'Orthography used for normalization and tokenization: \'librispeech\' (default), \'timit\', or'
               ' \'buckwalter\'.'
           )
       }			,					)
   lowerCamelCase_            =field(
       default=__A			,					metadata={'help': 'Overwrite the cached preprocessed datasets or not.'}			)
   lowerCamelCase_            =field(
       default=__A			,					metadata={'help': 'The number of processes to use for the preprocessing.'}			,					)





@dataclass
class 				lowercase    :
   lowerCamelCase_            =False
   lowerCamelCase_            =None
   lowerCamelCase_            ='|'
   lowerCamelCase_            =field(default_factory=__A			)
   lowerCamelCase_            =field(default_factory=__A			)
   lowerCamelCase_            =None


   @classmethod
   def __UpperCAmelCase							( cls					:						str ,		__lowerCAmelCase					:						List[Any])   ->       Tuple:
         if name == "librispeech":
               return cls()
         if name == "timit":
               return cls(
                   do_lower_case=_lowercase ,		translation_table=str.maketrans({"-": " "}) ,		)
         if name == "buckwalter":
               lowercase_     			=   {
                   """-""": """ """,  # sometimes used to represent pauses
                   """^""": """v""",  # fixing "tha" in arabic_speech_corpus dataset
               }
               return cls(
                   vocab_file=pathlib.Path(_lowercase).parent.joinpath("vocab/buckwalter.json") ,		word_delimiter_token="/" ,		translation_table=str.maketrans(_lowercase) ,		words_to_remove={"sil"} ,		untransliterator=arabic.buckwalter.untransliterate ,		)
         raise ValueError(F'Unsupported orthography: \'{name}\'.')


   def __UpperCAmelCase							( self					:						Optional[Any] ,		__lowerCAmelCase					:						List[str])   ->       Union[str, Any]:
         # TODO(elgeish) return a pipeline (e.g., from jiwer) instead? Or rely on branch predictor as is
         if len(self.translation_table) > 0:
               lowercase_     			=   text.translate(self.translation_table)
         if len(self.words_to_remove) == 0:
               lowercase_     			=   """ """.join(text.split())  # clean up whitespaces
         else:
               lowercase_     			=   """ """.join(w for w in text.split() if w not in self.words_to_remove)  # and clean up whilespaces
         return text


   def __UpperCAmelCase							( self					:						List[str] ,		__lowerCAmelCase					:						List[str])   ->       Tuple:
         lowercase_     			=   WavaVecaFeatureExtractor.from_pretrained(
             model_args.model_name_or_path ,		cache_dir=model_args.cache_dir)
         if self.vocab_file:
               lowercase_     			=   WavaVecaCTCTokenizer(
                   self.vocab_file ,		cache_dir=model_args.cache_dir ,		do_lower_case=self.do_lower_case ,		word_delimiter_token=self.word_delimiter_token ,		)
         else:
               lowercase_     			=   WavaVecaCTCTokenizer.from_pretrained(
                   model_args.model_name_or_path ,		cache_dir=model_args.cache_dir ,		do_lower_case=self.do_lower_case ,		word_delimiter_token=self.word_delimiter_token ,		)
         return WavaVecaProcessor(_lowercase ,		_lowercase)





@dataclass
class 				lowercase    :
   lowerCamelCase_            =4_2
   lowerCamelCase_            =True
   lowerCamelCase_            =None
   lowerCamelCase_            =None
   lowerCamelCase_            =None
   lowerCamelCase_            =None


   def __call__( self					:						Optional[int] ,		__lowerCAmelCase					:						List[str])   ->       str:
         # split inputs and labels since they have to be of different lenghts and need
         # different padding methods
         lowercase_     			=   [{"""input_values""": feature["""input_values"""]} for feature in features]
         lowercase_     			=   [{"""input_ids""": feature["""labels"""]} for feature in features]

         lowercase_     			=   self.processor.pad(
             _lowercase ,		padding=self.padding ,		max_length=self.max_length ,		pad_to_multiple_of=self.pad_to_multiple_of ,		return_tensors="pt" ,		)
         lowercase_     			=   self.processor.pad(
             labels=_lowercase ,		padding=self.padding ,		max_length=self.max_length_labels ,		pad_to_multiple_of=self.pad_to_multiple_of_labels ,		return_tensors="pt" ,		)

         # replace padding with -100 to ignore loss correctly
         lowercase_     			=   labels_batch["""input_ids"""].masked_fill(labels_batch.attention_mask.ne(1) ,		-100)

         lowercase_     			=   labels

         return batch





class 				lowercase    (    __A			):


   def __UpperCAmelCase							( self					:						Any ,		__lowerCAmelCase					:						Union[str, Any] ,		__lowerCAmelCase					:						Optional[Any])   ->       Tuple:
         model.train()
         lowercase_     			=   self._prepare_inputs(_lowercase)

         if self.use_amp:
               with autocast():
                     lowercase_     			=   self.compute_loss(_lowercase ,		_lowercase)
         else:
               lowercase_     			=   self.compute_loss(_lowercase ,		_lowercase)

         if self.args.n_gpu > 1:
               if model.module.config.ctc_loss_reduction == "mean":
                     lowercase_     			=   loss.mean()
               elif model.module.config.ctc_loss_reduction == "sum":
                     lowercase_     			=   loss.sum() / (inputs["""labels"""] >= 0).sum()
               else:
                     raise ValueError(F'{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']')

         if self.args.gradient_accumulation_steps > 1:
               lowercase_     			=   loss / self.args.gradient_accumulation_steps

         if self.use_amp:
               self.scaler.scale(_lowercase).backward()
         elif self.use_apex:
               with amp.scale_loss(_lowercase ,		self.optimizer) as scaled_loss:
                     scaled_loss.backward()
         elif self.deepspeed:
               self.deepspeed.backward(_lowercase)
         else:
               loss.backward()

         return loss.detach()




def __a			(						)						->				List[Any]:
      '''simple docstring'''


      lowercase_     			=   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)

      lowercase_     			=   parser.parse_args_into_dataclasses()
      configure_logger(_a			,		_a					)

      lowercase_     			=   Orthography.from_name(data_args.orthography.lower()					)
      lowercase_     			=   orthography.create_processor(_a					)
      lowercase_     			=   WavaVecaForCTC.from_pretrained(
          model_args.model_name_or_path			,		cache_dir=model_args.cache_dir			,		gradient_checkpointing=training_args.gradient_checkpointing			,		vocab_size=len(processor.tokenizer					)			,		)

      lowercase_     			=   datasets.load_dataset(
          data_args.dataset_name			,		data_args.dataset_config_name			,		split=data_args.train_split_name					)
      lowercase_     			=   datasets.load_dataset(
          data_args.dataset_name			,		data_args.dataset_config_name			,		split=data_args.validation_split_name					)

      lowercase_     			=   datasets.load_metric("wer"					)
      lowercase_     			=   processor.feature_extractor.sampling_rate if data_args.target_feature_extractor_sampling_rate else None
      lowercase_     			=   """""".join(t for t in processor.tokenizer.get_vocab().keys() if len(_a					) == 1					)
      lowercase_     			=   re.compile(  # remove characters not in vocabulary
          Rf'[^\s{re.escape(_a					)}]'			,		flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0			,		)
      lowercase_     			=   []

      def prepare_example(__lowerCamelCase      :    Optional[Any]					):  # TODO(elgeish) make use of multiprocessing?
            lowercase_     			=   librosa.load(example[data_args.speech_file_column]			,		sr=_a					)
            if data_args.max_duration_in_seconds is not None:
                  lowercase_     			=   len(example["speech"]					) / example["""sampling_rate"""]
            # Normalize and clean up text; order matters!
            lowercase_     			=   orthography.preprocess_for_training(example[data_args.target_text_column]					)
            lowercase_     			=   vocabulary_text_cleaner.sub(""			,		_a					)
            if updated_text != example[data_args.target_text_column]:
                  text_updates.append((example[data_args.target_text_column], updated_text)					)
                  lowercase_     			=   updated_text
            return example

      lowercase_     			=   train_dataset.map(_a			,		remove_columns=[data_args.speech_file_column]					)
      lowercase_     			=   val_dataset.map(_a			,		remove_columns=[data_args.speech_file_column]					)

      if data_args.max_duration_in_seconds is not None:

            def filter_by_max_duration(__lowerCamelCase      :    Optional[Any]					):
                  return example["duration_in_seconds"] <= data_args.max_duration_in_seconds

            lowercase_     			=   len(_a					)
            lowercase_     			=   len(_a					)
            lowercase_     			=   train_dataset.filter(_a			,		remove_columns=["duration_in_seconds"]					)
            lowercase_     			=   val_dataset.filter(_a			,		remove_columns=["duration_in_seconds"]					)
            if len(_a					) > old_train_size:
                  logger.warning(
                      f'Filtered out {len(_a					) - old_train_size} train example(s) longer than'
                      f' {data_args.max_duration_in_seconds} second(s).'					)
            if len(_a					) > old_val_size:
                  logger.warning(
                      f'Filtered out {len(_a					) - old_val_size} validation example(s) longer than'
                      f' {data_args.max_duration_in_seconds} second(s).'					)
      logger.info(f'Split sizes: {len(_a					)} train and {len(_a					)} validation.'					)

      logger.warning(f'Updated {len(_a					)} transcript(s) using \'{data_args.orthography}\' orthography rules.'					)
      if logger.isEnabledFor(logging.DEBUG					):
            for original_text, updated_text in text_updates:
                  logger.debug(f'Updated text: "{original_text}" -> "{updated_text}"'					)
      lowercase_     			=   None

      def prepare_dataset(__lowerCamelCase      :    List[str]					):
            # check that all files have the correct sampling rate
            assert (
                len(set(batch["sampling_rate"]					)					) == 1
            ), f'Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}.'

            lowercase_     			=   processor(
                audio=batch["speech"]			,		text=batch[data_args.target_text_column]			,		sampling_rate=batch["sampling_rate"][0]					)
            batch.update(_a					)
            return batch

      lowercase_     			=   train_dataset.map(
          _a			,		batch_size=training_args.per_device_train_batch_size			,		batched=_a			,		num_proc=data_args.preprocessing_num_workers			,		)
      lowercase_     			=   val_dataset.map(
          _a			,		batch_size=training_args.per_device_train_batch_size			,		batched=_a			,		num_proc=data_args.preprocessing_num_workers			,		)

      lowercase_     			=   DataCollatorCTCWithPadding(processor=_a			,		padding=_a					)

      def compute_metrics(__lowerCamelCase      :    int					):
            lowercase_     			=   pred.predictions
            lowercase_     			=   np.argmax(_a			,		axis=-1					)

            lowercase_     			=   processor.tokenizer.pad_token_id

            lowercase_     			=   processor.batch_decode(_a					)
            # we do not want to group tokens when computing the metrics
            lowercase_     			=   processor.batch_decode(pred.label_ids			,		group_tokens=_a					)
            if logger.isEnabledFor(logging.DEBUG					):
                  for reference, predicted in zip(_a			,		_a					):
                        logger.debug(f'reference: "{reference}"'					)
                        logger.debug(f'predicted: "{predicted}"'					)
                        if orthography.untransliterator is not None:
                              logger.debug(f'reference (untransliterated): "{orthography.untransliterator(_a					)}"'					)
                              logger.debug(f'predicted (untransliterated): "{orthography.untransliterator(_a					)}"'					)

            lowercase_     			=   wer_metric.compute(predictions=_a			,		references=_a					)

            return {"wer": wer}

      if model_args.freeze_feature_extractor:
            model.freeze_feature_extractor()

      lowercase_     			=   CTCTrainer(
          model=_a			,		data_collator=_a			,		args=_a			,		compute_metrics=_a			,		train_dataset=_a			,		eval_dataset=_a			,		tokenizer=processor.feature_extractor			,		)

      trainer.train()


if __name__ == "__main__":
       main()













import collections
import inspect
import unittest

from transformers import DonutSwinConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch
      from torch import nn

      from transformers import DonutSwinModel
      from transformers.models.donut.modeling_donut_swin import DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST





class   lowercase__     :







  def __init__(						self     ,    _lowercase     ,    _lowercase=13     ,    _lowercase=32     ,    _lowercase=2     ,    _lowercase=3     ,    _lowercase=16     ,    _lowercase=[1, 2, 1]     ,    _lowercase=[2, 2, 4]     ,    _lowercase=2     ,    _lowercase=2.0     ,    _lowercase=True     ,    _lowercase=0.0     ,    _lowercase=0.0     ,    _lowercase=0.1     ,    _lowercase="gelu"     ,    _lowercase=False     ,    _lowercase=True     ,    _lowercase=0.02     ,    _lowercase=1e-5     ,    _lowercase=True     ,    _lowercase=None     ,    _lowercase=True     ,    _lowercase=10     ,    _lowercase=8     ,    ):
       lowerCAmelCase_       :							List[str]			 =		parent
       lowerCAmelCase_       :							int			 =		batch_size
       lowerCAmelCase_       :							str			 =		image_size
       lowerCAmelCase_       :							Any			 =		patch_size
       lowerCAmelCase_       :							Dict			 =		num_channels
       lowerCAmelCase_       :							str			 =		embed_dim
       lowerCAmelCase_       :							Tuple			 =		depths
       lowerCAmelCase_       :							int			 =		num_heads
       lowerCAmelCase_       :							str			 =		window_size
       lowerCAmelCase_       :							str			 =		mlp_ratio
       lowerCAmelCase_       :							List[Any]			 =		qkv_bias
       lowerCAmelCase_       :							Tuple			 =		hidden_dropout_prob
       lowerCAmelCase_       :							int			 =		attention_probs_dropout_prob
       lowerCAmelCase_       :							Union[str, Any]			 =		drop_path_rate
       lowerCAmelCase_       :							List[str]			 =		hidden_act
       lowerCAmelCase_       :							str			 =		use_absolute_embeddings
       lowerCAmelCase_       :							Union[str, Any]			 =		patch_norm
       lowerCAmelCase_       :							Tuple			 =		layer_norm_eps
       lowerCAmelCase_       :							Optional[Any]			 =		initializer_range
       lowerCAmelCase_       :							Optional[int]			 =		is_training
       lowerCAmelCase_       :							Optional[int]			 =		scope
       lowerCAmelCase_       :							Any			 =		use_labels
       lowerCAmelCase_       :							Dict			 =		type_sequence_label_size
       lowerCAmelCase_       :							str			 =		encoder_stride







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							Any			 =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

       lowerCAmelCase_       :							str			 =		None
       if self.use_labels:
            lowerCAmelCase_       :							Dict			 =		ids_tensor([self.batch_size]     ,    self.type_sequence_label_size    )

       lowerCAmelCase_       :							List[Any]			 =		self.get_config()

       return config, pixel_values, labels







  def 						UpperCAmelCase__		(						self    ):
       return DonutSwinConfig(
           image_size=self.image_size     ,    patch_size=self.patch_size     ,    num_channels=self.num_channels     ,    embed_dim=self.embed_dim     ,    depths=self.depths     ,    num_heads=self.num_heads     ,    window_size=self.window_size     ,    mlp_ratio=self.mlp_ratio     ,    qkv_bias=self.qkv_bias     ,    hidden_dropout_prob=self.hidden_dropout_prob     ,    attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,    drop_path_rate=self.drop_path_rate     ,    hidden_act=self.hidden_act     ,    use_absolute_embeddings=self.use_absolute_embeddings     ,    path_norm=self.patch_norm     ,    layer_norm_eps=self.layer_norm_eps     ,    initializer_range=self.initializer_range     ,    encoder_stride=self.encoder_stride     ,    )







  def 						UpperCAmelCase__		(						self     ,    _lowercase     ,    _lowercase     ,    _lowercase    ):
       lowerCAmelCase_       :							Optional[Any]			 =		DonutSwinModel(config=_lowercase    )
       model.to(_lowercase    )
       model.eval()
       lowerCAmelCase_       :							Optional[Any]			 =		model(_lowercase    )

       lowerCAmelCase_       :							Optional[int]			 =		((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths    ) - 1))
       lowerCAmelCase_       :							Tuple			 =		int(config.embed_dim * 2 ** (len(config.depths    ) - 1)    )

       self.parent.assertEqual(result.last_hidden_state.shape     ,    (self.batch_size, expected_seq_len, expected_dim)    )







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							str			 =		self.prepare_config_and_inputs()
       (
           (
           lowerCAmelCase_
       ) ,	(
           lowerCAmelCase_
       ) ,	(
           lowerCAmelCase_
       ) ,	
       )       :							List[str]			 =		config_and_inputs
       lowerCAmelCase_       :							int			 =		{"""pixel_values""": pixel_values}
       return config, inputs_dict





@require_torch
class   lowercase__     (				__A  ,       __A  ,       unittest.TestCase	):
  __UpperCamelCase						  =	(DonutSwinModel,) if is_torch_available() else ()
  __UpperCamelCase						  =	{"""feature-extraction""": DonutSwinModel} if is_torch_available() else {}
  __UpperCamelCase						  =	True

  __UpperCamelCase						  =	False
  __UpperCamelCase						  =	False
  __UpperCamelCase						  =	False







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							Tuple			 =		DonutSwinModelTester(self    )
       lowerCAmelCase_       :							Optional[Any]			 =		ConfigTester(self     ,    config_class=_lowercase     ,    embed_dim=37    )







  def 						UpperCAmelCase__		(						self    ):
       self.create_and_test_config_common_properties()
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()







  def 						UpperCAmelCase__		(						self    ):
       return







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							Tuple			 =		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowercase    )







  def 						UpperCAmelCase__		(						self    ):
       # DonutSwin does not use inputs_embeds
       pass







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_ ,	lowerCAmelCase_       :							str			 =		self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            lowerCAmelCase_       :							Optional[Any]			 =		model_class(_lowercase    )
            self.assertIsInstance(model.get_input_embeddings()     ,    (nn.Module)    )
            lowerCAmelCase_       :							int			 =		model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(_lowercase     ,    nn.Linear    )    )







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_ ,	lowerCAmelCase_       :							List[Any]			 =		self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            lowerCAmelCase_       :							Dict			 =		model_class(_lowercase    )
            lowerCAmelCase_       :							Any			 =		inspect.signature(model.forward    )
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            lowerCAmelCase_       :							Dict			 =		[*signature.parameters.keys()]

            lowerCAmelCase_       :							Union[str, Any]			 =		["""pixel_values"""]
            self.assertListEqual(arg_names[:1]     ,    _lowercase    )







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_ ,	lowerCAmelCase_       :							Optional[Any]			 =		self.model_tester.prepare_config_and_inputs_for_common()
       lowerCAmelCase_       :							Tuple			 =		True

       for model_class in self.all_model_classes:
            lowerCAmelCase_       :							Dict			 =		True
            lowerCAmelCase_       :							Dict			 =		False
            lowerCAmelCase_       :							int			 =		True
            lowerCAmelCase_       :							Dict			 =		model_class(_lowercase    )
            model.to(_lowercase    )
            model.eval()
            with torch.no_grad():
                 lowerCAmelCase_       :							Dict			 =		model(**self._prepare_for_class(_lowercase     ,    _lowercase    )    )
            lowerCAmelCase_       :							Union[str, Any]			 =		outputs.attentions
            lowerCAmelCase_       :							Optional[Any]			 =		len(self.model_tester.depths    )
            self.assertEqual(len(_lowercase    )     ,    _lowercase    )

            # check that output_attentions also work using config
            del inputs_dict["output_attentions"]
            lowerCAmelCase_       :							int			 =		True
            lowerCAmelCase_       :							Union[str, Any]			 =		config.window_size**2
            lowerCAmelCase_       :							Tuple			 =		model_class(_lowercase    )
            model.to(_lowercase    )
            model.eval()
            with torch.no_grad():
                 lowerCAmelCase_       :							Any			 =		model(**self._prepare_for_class(_lowercase     ,    _lowercase    )    )
            lowerCAmelCase_       :							int			 =		outputs.attentions
            self.assertEqual(len(_lowercase    )     ,    _lowercase    )

            self.assertListEqual(
                list(attentions[0].shape[-3:]    )     ,    [self.model_tester.num_heads[0], window_size_squared, window_size_squared]     ,    )
            lowerCAmelCase_       :							Any			 =		len(_lowercase    )

            # Check attention is always last and order is fine
            lowerCAmelCase_       :							int			 =		True
            lowerCAmelCase_       :							str			 =		True
            lowerCAmelCase_       :							Tuple			 =		model_class(_lowercase    )
            model.to(_lowercase    )
            model.eval()
            with torch.no_grad():
                 lowerCAmelCase_       :							Any			 =		model(**self._prepare_for_class(_lowercase     ,    _lowercase    )    )

            if hasattr(self.model_tester     ,    """num_hidden_states_types"""    ):
                 lowerCAmelCase_       :							Optional[Any]			 =		self.model_tester.num_hidden_states_types
            else:
                 # also another +1 for reshaped_hidden_states
                 lowerCAmelCase_       :							Union[str, Any]			 =		2
            self.assertEqual(out_len + added_hidden_states     ,    len(_lowercase    )    )

            lowerCAmelCase_       :							Any			 =		outputs.attentions

            self.assertEqual(len(_lowercase    )     ,    _lowercase    )

            self.assertListEqual(
                list(self_attentions[0].shape[-3:]    )     ,    [self.model_tester.num_heads[0], window_size_squared, window_size_squared]     ,    )







  def 						UpperCAmelCase__		(						self     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase    ):
       lowerCAmelCase_       :							Optional[Any]			 =		model_class(_lowercase    )
       model.to(_lowercase    )
       model.eval()

       with torch.no_grad():
            lowerCAmelCase_       :							List[str]			 =		model(**self._prepare_for_class(_lowercase     ,    _lowercase    )    )

       lowerCAmelCase_       :							List[Any]			 =		outputs.hidden_states

       lowerCAmelCase_       :							str			 =		getattr(
           self.model_tester     ,    """expected_num_hidden_layers"""     ,    len(self.model_tester.depths    ) + 1    )
       self.assertEqual(len(_lowercase    )     ,    _lowercase    )

       # DonutSwin has a different seq_length
       lowerCAmelCase_       :							int			 =		(
           config.patch_size
           if isinstance(config.patch_size     ,    collections.abc.Iterable    )
           else (config.patch_size, config.patch_size)
       )

       lowerCAmelCase_       :							List[str]			 =		(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

       self.assertListEqual(
           list(hidden_states[0].shape[-2:]    )     ,    [num_patches, self.model_tester.embed_dim]     ,    )

       lowerCAmelCase_       :							List[Any]			 =		outputs.reshaped_hidden_states
       self.assertEqual(len(_lowercase    )     ,    _lowercase    )

       lowerCAmelCase_ ,	lowerCAmelCase_ ,	lowerCAmelCase_ ,	lowerCAmelCase_       :							Optional[Any]			 =		reshaped_hidden_states[0].shape
       lowerCAmelCase_       :							str			 =		(
           reshaped_hidden_states[0].view(_lowercase     ,    _lowercase     ,    height * width    ).permute(0     ,    2     ,    1    )
       )
       self.assertListEqual(
           list(reshaped_hidden_states.shape[-2:]    )     ,    [num_patches, self.model_tester.embed_dim]     ,    )







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_ ,	lowerCAmelCase_       :							Dict			 =		self.model_tester.prepare_config_and_inputs_for_common()

       lowerCAmelCase_       :							int			 =		(
           self.model_tester.image_size
           if isinstance(self.model_tester.image_size     ,    collections.abc.Iterable    )
           else (self.model_tester.image_size, self.model_tester.image_size)
       )

       for model_class in self.all_model_classes:
            lowerCAmelCase_       :							Any			 =		True
            self.check_hidden_states_output(_lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase    )

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            lowerCAmelCase_       :							Any			 =		True

            self.check_hidden_states_output(_lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase    )







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_ ,	lowerCAmelCase_       :							int			 =		self.model_tester.prepare_config_and_inputs_for_common()
       lowerCAmelCase_       :							int			 =		3

       lowerCAmelCase_       :							str			 =		(
           self.model_tester.image_size
           if isinstance(self.model_tester.image_size     ,    collections.abc.Iterable    )
           else (self.model_tester.image_size, self.model_tester.image_size)
       )
       lowerCAmelCase_       :							Optional[int]			 =		(
           config.patch_size
           if isinstance(config.patch_size     ,    collections.abc.Iterable    )
           else (config.patch_size, config.patch_size)
       )

       lowerCAmelCase_       :							int			 =		image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
       lowerCAmelCase_       :							int			 =		image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

       for model_class in self.all_model_classes:
            lowerCAmelCase_       :							Tuple			 =		True
            self.check_hidden_states_output(_lowercase     ,    _lowercase     ,    _lowercase     ,    (padded_height, padded_width)    )

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            lowerCAmelCase_       :							Union[str, Any]			 =		True
            self.check_hidden_states_output(_lowercase     ,    _lowercase     ,    _lowercase     ,    (padded_height, padded_width)    )







  @slow
  def 						UpperCAmelCase__		(						self    ):
       for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            lowerCAmelCase_       :							str			 =		DonutSwinModel.from_pretrained(_lowercase    )
            self.assertIsNotNone(_lowercase    )







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_ ,	lowerCAmelCase_       :							List[str]			 =		self.model_tester.prepare_config_and_inputs_for_common()

       lowerCAmelCase_       :							str			 =		_config_zero_init(_lowercase    )
       for model_class in self.all_model_classes:
            lowerCAmelCase_       :							Union[str, Any]			 =		model_class(config=_lowercase    )
            for name, param in model.named_parameters():
                 if "embeddings" not in name and param.requires_grad:
                      self.assertIn(
                          ((param.data.mean() * 1e9).round() / 1e9).item()     ,    [0.0, 1.0]     ,    msg=F'Parameter {name} of model {model_class} seems not properly initialized'     ,    )
