





import json
import os
import shutil
import sys
import tempfile
import unittest
from unittest import TestCase
from unittest.mock import patch

import faiss
import numpy as np
from datasets import Dataset

from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available
from transformers.integrations import is_ray_available
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_ray


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip

if is_torch_available():
      from distributed_pytorch_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
else:
      UpperCAmelCase_       : List[str]							=    None

if is_ray_available():
      import ray  # noqa: E402 # isort:skip
      from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
      UpperCAmelCase_       : Tuple							=    None
      UpperCAmelCase_       : List[Any]							=    None
      UpperCAmelCase_       : Dict							=    None






def 				_lowerCAmelCase			(	_a :       Tuple      )						->      List[Any]:
     if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
          lowerCAmelCase_       :							int			 =		unittest.skip("""test requires Datasets, Faiss, psutil"""      )(_a      )
     return test_case







@require_distributed_retrieval
class   lowercase__     (				__A	):







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							Tuple			 =		tempfile.mkdtemp()
       lowerCAmelCase_       :							int			 =		8

       # DPR tok
       lowerCAmelCase_       :							List[Any]			 =		[
           """[UNK]""",
           """[CLS]""",
           """[SEP]""",
           """[PAD]""",
           """[MASK]""",
           """want""",
           """##want""",
           """##ed""",
           """wa""",
           """un""",
           """runn""",
           """##ing""",
           """,""",
           """low""",
           """lowest""",
       ]
       lowerCAmelCase_       :							Optional[Any]			 =		os.path.join(self.tmpdirname     ,    """dpr_tokenizer"""    )
       os.makedirs(_lowercase     ,    exist_ok=_lowercase    )
       lowerCAmelCase_       :							Optional[Any]			 =		os.path.join(_lowercase     ,    DPR_VOCAB_FILES_NAMES["""vocab_file"""]    )
       with open(self.vocab_file     ,    """w"""     ,    encoding="""utf-8"""    ) as vocab_writer:
            vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]    )    )

       # BART tok
       lowerCAmelCase_       :							Tuple			 =		[
           """l""",
           """o""",
           """w""",
           """e""",
           """r""",
           """s""",
           """t""",
           """i""",
           """d""",
           """n""",
           """\u0120""",
           """\u0120l""",
           """\u0120n""",
           """\u0120lo""",
           """\u0120low""",
           """er""",
           """\u0120lowest""",
           """\u0120newer""",
           """\u0120wider""",
           """<unk>""",
       ]
       lowerCAmelCase_       :							int			 =		dict(zip(_lowercase     ,    range(len(_lowercase    )    )    )    )
       lowerCAmelCase_       :							int			 =		["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
       lowerCAmelCase_       :							Union[str, Any]			 =		{"""unk_token""": """<unk>"""}

       lowerCAmelCase_       :							Tuple			 =		os.path.join(self.tmpdirname     ,    """bart_tokenizer"""    )
       os.makedirs(_lowercase     ,    exist_ok=_lowercase    )
       lowerCAmelCase_       :							List[Any]			 =		os.path.join(_lowercase     ,    BART_VOCAB_FILES_NAMES["""vocab_file"""]    )
       lowerCAmelCase_       :							Tuple			 =		os.path.join(_lowercase     ,    BART_VOCAB_FILES_NAMES["""merges_file"""]    )
       with open(self.vocab_file     ,    """w"""     ,    encoding="""utf-8"""    ) as fp:
            fp.write(json.dumps(_lowercase    ) + """\n"""    )
       with open(self.merges_file     ,    """w"""     ,    encoding="""utf-8"""    ) as fp:
            fp.write("""\n""".join(_lowercase    )    )







  def 						UpperCAmelCase__		(						self    ):
       return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname     ,    """dpr_tokenizer"""    )    )







  def 						UpperCAmelCase__		(						self    ):
       return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname     ,    """bart_tokenizer"""    )    )







  def 						UpperCAmelCase__		(						self    ):
       shutil.rmtree(self.tmpdirname    )







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							Tuple			 =		Dataset.from_dict(
           {
               """id""": ["""0""", """1"""],
               """text""": ["""foo""", """bar"""],
               """title""": ["""Foo""", """Bar"""],
               """embeddings""": [np.ones(self.retrieval_vector_size    ), 2 * np.ones(self.retrieval_vector_size    )],
           }    )
       dataset.add_faiss_index("""embeddings"""     ,    string_factory="""Flat"""     ,    metric_type=faiss.METRIC_INNER_PRODUCT    )
       return dataset







  def 						UpperCAmelCase__		(						self     ,    _lowercase     ,    _lowercase=12_345    ):
       lowerCAmelCase_       :							str			 =		self.get_dummy_dataset()
       lowerCAmelCase_       :							Dict			 =		RagConfig(
           retrieval_vector_size=self.retrieval_vector_size     ,    question_encoder=DPRConfig().to_dict()     ,    generator=BartConfig().to_dict()     ,    )
       with patch("""transformers.models.rag.retrieval_rag.load_dataset"""    ) as mock_load_dataset:
            lowerCAmelCase_       :							Tuple			 =		dataset
            lowerCAmelCase_       :							List[Any]			 =		RagPyTorchDistributedRetriever(
                _lowercase     ,    question_encoder_tokenizer=self.get_dpr_tokenizer()     ,    generator_tokenizer=self.get_bart_tokenizer()     ,    )
            if init_retrieval:
                 retriever.init_retrieval(_lowercase    )
       return retriever







  def 						UpperCAmelCase__		(						self     ,    _lowercase    ):
       # Have to run in local mode because sys.path modifications at top of
       # file are not propogated to remote workers.
       # https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
       ray.init(local_mode=_lowercase    )
       lowerCAmelCase_       :							List[Any]			 =		RagConfig(
           retrieval_vector_size=self.retrieval_vector_size     ,    question_encoder=DPRConfig().to_dict()     ,    generator=BartConfig().to_dict()     ,    )
       lowerCAmelCase_       :							str			 =		ray.remote(_lowercase    )
       lowerCAmelCase_       :							Optional[Any]			 =		[remote_cls.remote() for _ in range(1    )]
       with patch("""transformers.models.rag.retrieval_rag.load_dataset"""    ) as mock_load_dataset:
            lowerCAmelCase_       :							List[str]			 =		self.get_dummy_dataset()
            lowerCAmelCase_       :							Tuple			 =		RagRayDistributedRetriever(
                _lowercase     ,    question_encoder_tokenizer=self.get_dpr_tokenizer()     ,    generator_tokenizer=self.get_bart_tokenizer()     ,    retrieval_workers=_lowercase     ,    )
            if init_retrieval:
                 retriever.init_retrieval()
       return retriever







  def 						UpperCAmelCase__		(						self     ,    _lowercase     ,    _lowercase     ,    _lowercase=12_345    ):
       lowerCAmelCase_       :							Any			 =		self.get_dummy_dataset()
       lowerCAmelCase_       :							str			 =		RagConfig(
           retrieval_vector_size=self.retrieval_vector_size     ,    question_encoder=DPRConfig().to_dict()     ,    generator=BartConfig().to_dict()     ,    index_name="""custom"""     ,    )
       if from_disk:
            lowerCAmelCase_       :							List[str]			 =		os.path.join(self.tmpdirname     ,    """dataset"""    )
            lowerCAmelCase_       :							List[str]			 =		os.path.join(self.tmpdirname     ,    """index.faiss"""    )
            dataset.get_index("""embeddings"""    ).save(os.path.join(self.tmpdirname     ,    """index.faiss"""    )    )
            dataset.drop_index("""embeddings"""    )
            dataset.save_to_disk(os.path.join(self.tmpdirname     ,    """dataset"""    )    )
            del dataset
            lowerCAmelCase_       :							List[Any]			 =		RagPyTorchDistributedRetriever(
                _lowercase     ,    question_encoder_tokenizer=self.get_dpr_tokenizer()     ,    generator_tokenizer=self.get_bart_tokenizer()     ,    )
       else:
            lowerCAmelCase_       :							Union[str, Any]			 =		RagPyTorchDistributedRetriever(
                _lowercase     ,    question_encoder_tokenizer=self.get_dpr_tokenizer()     ,    generator_tokenizer=self.get_bart_tokenizer()     ,    index=CustomHFIndex(config.retrieval_vector_size     ,    _lowercase    )     ,    )
       if init_retrieval:
            retriever.init_retrieval(_lowercase    )
       return retriever







  def 						UpperCAmelCase__		(						self     ,    _lowercase     ,    _lowercase    ):
       # Have to run in local mode because sys.path modifications at top of
       # file are not propogated to remote workers.
       # https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
       ray.init(local_mode=_lowercase    )
       lowerCAmelCase_       :							Tuple			 =		self.get_dummy_dataset()
       lowerCAmelCase_       :							List[Any]			 =		RagConfig(
           retrieval_vector_size=self.retrieval_vector_size     ,    question_encoder=DPRConfig().to_dict()     ,    generator=BartConfig().to_dict()     ,    index_name="""custom"""     ,    )
       lowerCAmelCase_       :							Optional[int]			 =		ray.remote(_lowercase    )
       lowerCAmelCase_       :							Optional[int]			 =		[remote_cls.remote() for _ in range(1    )]
       if from_disk:
            lowerCAmelCase_       :							Tuple			 =		os.path.join(self.tmpdirname     ,    """dataset"""    )
            lowerCAmelCase_       :							Tuple			 =		os.path.join(self.tmpdirname     ,    """index.faiss"""    )
            dataset.get_index("""embeddings"""    ).save(os.path.join(self.tmpdirname     ,    """index.faiss"""    )    )
            dataset.drop_index("""embeddings"""    )
            dataset.save_to_disk(os.path.join(self.tmpdirname     ,    """dataset"""    )    )
            del dataset
            lowerCAmelCase_       :							List[Any]			 =		RagRayDistributedRetriever(
                _lowercase     ,    question_encoder_tokenizer=self.get_dpr_tokenizer()     ,    generator_tokenizer=self.get_bart_tokenizer()     ,    retrieval_workers=_lowercase     ,    index=CustomHFIndex.load_from_disk(
                    vector_size=config.retrieval_vector_size     ,    dataset_path=config.passages_path     ,    index_path=config.index_path     ,    )     ,    )
       else:
            lowerCAmelCase_       :							Any			 =		RagRayDistributedRetriever(
                _lowercase     ,    question_encoder_tokenizer=self.get_dpr_tokenizer()     ,    generator_tokenizer=self.get_bart_tokenizer()     ,    retrieval_workers=_lowercase     ,    index=CustomHFIndex(config.retrieval_vector_size     ,    _lowercase    )     ,    )
       if init_retrieval:
            retriever.init_retrieval()
       return retriever







  def 						UpperCAmelCase__		(						self     ,    _lowercase     ,    _lowercase     ,    _lowercase    ):
       lowerCAmelCase_ ,	lowerCAmelCase_ ,	lowerCAmelCase_       :							Dict			 =		retriever.retrieve(_lowercase     ,    n_docs=_lowercase    )
       self.assertEqual(retrieved_doc_embeds.shape     ,    (2, n_docs, self.retrieval_vector_size)    )
       self.assertEqual(len(_lowercase    )     ,    2    )
       self.assertEqual(sorted(doc_dicts[0]    )     ,    ["""embeddings""", """id""", """text""", """title"""]    )
       self.assertEqual(len(doc_dicts[0]["""id"""]    )     ,    _lowercase    )
       self.assertEqual(doc_dicts[0]["""id"""][0]     ,    """1"""    )  # max inner product is reached with second doc
       self.assertEqual(doc_dicts[1]["""id"""][0]     ,    """0"""    )  # max inner product is reached with first doc
       self.assertListEqual(doc_ids.tolist()     ,    [[1], [0]]    )







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							Any			 =		1
       lowerCAmelCase_       :							Tuple			 =		np.array(
           [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]     ,    dtype=np.floataa    )

       self.distributed_retriever_check(
           self.get_dummy_pytorch_distributed_retriever(init_retrieval=_lowercase    )     ,    _lowercase     ,    _lowercase    )







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							str			 =		1
       lowerCAmelCase_       :							Dict			 =		np.array(
           [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]     ,    dtype=np.floataa    )

       self.distributed_retriever_check(
           self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=_lowercase     ,    from_disk=_lowercase    )     ,    _lowercase     ,    _lowercase     ,    )







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							Any			 =		1
       lowerCAmelCase_       :							List[Any]			 =		np.array(
           [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]     ,    dtype=np.floataa    )

       self.distributed_retriever_check(
           self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=_lowercase     ,    from_disk=_lowercase    )     ,    _lowercase     ,    _lowercase     ,    )







  @require_ray
  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							Optional[Any]			 =		1
       lowerCAmelCase_       :							Optional[Any]			 =		np.array(
           [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]     ,    dtype=np.floataa    )

       self.distributed_retriever_check(
           self.get_dummy_ray_distributed_retriever(init_retrieval=_lowercase    )     ,    _lowercase     ,    _lowercase    )
       ray.shutdown()







  @require_ray
  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							int			 =		1
       lowerCAmelCase_       :							str			 =		np.array(
           [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]     ,    dtype=np.floataa    )
       with self.assertRaises(_lowercase    ):
            self.distributed_retriever_check(
                self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=_lowercase     ,    from_disk=_lowercase    )     ,    _lowercase     ,    _lowercase     ,    )
       ray.shutdown()







  @require_ray
  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							Tuple			 =		1
       lowerCAmelCase_       :							int			 =		np.array(
           [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]     ,    dtype=np.floataa    )

       self.distributed_retriever_check(
           self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=_lowercase     ,    from_disk=_lowercase    )     ,    _lowercase     ,    _lowercase    )
       ray.shutdown()







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'     ,    )
