




import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


a_   		=     "bert-base-cased"
a_   		=     "google/pegasus-xsum"
a_   		=     [" Sam ate lunch today.", "Sams lunch ingredients."]
a_   		=     ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
a_   		=     "patrickvonplaten/t5-tiny-random"
a_   		=     "sshleifer/bart-tiny-random"
a_   		=     "sshleifer/tiny-mbart"
a_   		=     "sshleifer/tiny-marian-en-de"




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	Dict:

    """simple docstring"""


    __UpperCamelCase  		=						'''\n'''.join(lowercase_      )
    Path(lowercase_      ).open('''w'''      ).writelines(lowercase_      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	Optional[int]:

    """simple docstring"""


    for split in ["train", "val", "test"]:
        _dump_articles(os.path.join(lowercase_					,     F"{split}.source"      )					,     lowercase_      )
        _dump_articles(os.path.join(lowercase_					,     F"{split}.target"      )					,     lowercase_      )
    return tmp_dir


class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      @parameterized.expand(
          [
              MBART_TINY,
              MARIAN_TINY,
              T5_TINY,
              BART_TINY,
              PEGASUS_XSUM,
          ] ,       )
      @slow
      def       snake_case	(     self					: Dict ,       snake_case					: Dict				):
          __UpperCamelCase  		=						AutoTokenizer.from_pretrained(snake_case				)
          __UpperCamelCase  		=						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
          __UpperCamelCase  		=						max(len(tokenizer.encode(snake_case				)				) for a in ARTICLES				)
          __UpperCamelCase  		=						max(len(tokenizer.encode(snake_case				)				) for a in SUMMARIES				)
          __UpperCamelCase  		=						4
          __UpperCamelCase  		=						8
          assert max_len_target > max_src_len  # Will be truncated
          assert max_len_source > max_src_len  # Will be truncated
          __UpperCamelCase		,      __UpperCamelCase  		=						'''ro_RO''', '''de_DE'''  # ignored for all but mbart, but never causes error.
          __UpperCamelCase  		=						SeqaSeqDataset(
              snake_case ,       data_dir=snake_case ,       type_path='''train''' ,       max_source_length=snake_case ,       max_target_length=snake_case ,       src_lang=snake_case ,       tgt_lang=snake_case ,       )
          __UpperCamelCase  		=						DataLoader(snake_case ,       batch_size=2 ,       collate_fn=train_dataset.collate_fn				)
          for batch in dataloader:
              assert isinstance(snake_case ,       snake_case				)
              assert batch["attention_mask"].shape == batch["input_ids"].shape
              # show that articles were trimmed.
              assert batch["input_ids"].shape[1] == max_src_len
              # show that targets are the same len
              assert batch["labels"].shape[1] == max_tgt_len
              if tok_name != MBART_TINY:
                  continue
              # check language codes in correct place
              __UpperCamelCase  		=						shift_tokens_right(batch['''labels'''] ,       tokenizer.pad_token_id				)
              assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
              assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
              assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
              assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

              break  # No need to test every batch







      @parameterized.expand([BART_TINY, BERT_BASE_CASED]				)
      def       snake_case	(     self					: List[str] ,       snake_case					: Dict				):
          __UpperCamelCase  		=						AutoTokenizer.from_pretrained(snake_case				)
          __UpperCamelCase  		=						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
          __UpperCamelCase  		=						max(len(tokenizer.encode(snake_case				)				) for a in ARTICLES				)
          __UpperCamelCase  		=						max(len(tokenizer.encode(snake_case				)				) for a in SUMMARIES				)
          __UpperCamelCase  		=						4
          __UpperCamelCase  		=						LegacySeqaSeqDataset(
              snake_case ,       data_dir=snake_case ,       type_path='''train''' ,       max_source_length=20 ,       max_target_length=snake_case ,       )
          __UpperCamelCase  		=						DataLoader(snake_case ,       batch_size=2 ,       collate_fn=train_dataset.collate_fn				)
          for batch in dataloader:
              assert batch["attention_mask"].shape == batch["input_ids"].shape
              # show that articles were trimmed.
              assert batch["input_ids"].shape[1] == max_len_source
              assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
              # show that targets were truncated
              assert batch["labels"].shape[1] == trunc_target  # Truncated
              assert max_len_target > trunc_target  # Truncated
              break  # No need to test every batch







      def       snake_case	(     self					: Optional[Any]				):
          __UpperCamelCase  		=						AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25'''				)

          __UpperCamelCase  		=						Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)				)
          __UpperCamelCase  		=						tmp_dir.joinpath('''train.source'''				).open().readlines()
          __UpperCamelCase  		=						Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)				)
          pack_data_dir(snake_case ,       snake_case ,       128 ,       snake_case				)
          __UpperCamelCase  		=						{x.name for x in tmp_dir.iterdir()}
          __UpperCamelCase  		=						{x.name for x in save_dir.iterdir()}
          __UpperCamelCase  		=						save_dir.joinpath('''train.source'''				).open().readlines()
          # orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
          # desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
          assert len(snake_case				) < len(snake_case				)
          assert len(snake_case				) == 1
          assert len(packed_examples[0]				) == sum(len(snake_case				) for x in orig_examples				)
          assert orig_paths == new_paths







      @pytest.mark.skipif(not FAIRSEQ_AVAILABLE ,       reason='''This test requires fairseq'''				)
      def       snake_case	(     self					: str				):
          if not FAIRSEQ_AVAILABLE:
              return
          __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						self._get_dataset(max_len=64				)
          __UpperCamelCase  		=						64
          __UpperCamelCase  		=						ds.make_dynamic_sampler(snake_case ,       required_batch_size_multiple=snake_case				)
          __UpperCamelCase  		=						[len(snake_case				) for x in batch_sampler]
          assert len(set(snake_case				)				) > 1  # it's not dynamic batch size if every batch is the same length
          assert sum(snake_case				) == len(snake_case				)  # no dropped or added examples
          __UpperCamelCase  		=						DataLoader(snake_case ,       batch_sampler=snake_case ,       collate_fn=ds.collate_fn ,       num_workers=2				)
          __UpperCamelCase  		=						[]
          __UpperCamelCase  		=						[]
          for batch in data_loader:
              __UpperCamelCase  		=						batch['''input_ids'''].shape
              __UpperCamelCase  		=						src_shape[0]
              assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
              __UpperCamelCase  		=						np.product(batch['''input_ids'''].shape				)
              num_src_per_batch.append(snake_case				)
              if num_src_tokens > (max_tokens * 1.1):
                  failures.append(snake_case				)
          assert num_src_per_batch[0] == max(snake_case				)
          if failures:
              raise AssertionError(F"too many tokens in {len(snake_case				)} batches"				)







      def       snake_case	(     self					: Tuple				):
          __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						self._get_dataset(max_len=512				)
          __UpperCamelCase  		=						2
          __UpperCamelCase  		=						ds.make_sortish_sampler(snake_case ,       shuffle=snake_case				)

          __UpperCamelCase  		=						DataLoader(snake_case ,       batch_size=snake_case ,       collate_fn=ds.collate_fn ,       num_workers=2				)
          __UpperCamelCase  		=						DataLoader(snake_case ,       batch_size=snake_case ,       collate_fn=ds.collate_fn ,       num_workers=2 ,       sampler=snake_case				)

          __UpperCamelCase  		=						tokenizer.pad_token_id

          def count_pad_tokens(snake_case					: int ,       snake_case					: int="input_ids"				):
              return [batch[k].eq(snake_case				).sum().item() for batch in data_loader]

          assert sum(count_pad_tokens(snake_case ,       k='''labels'''				)				) < sum(count_pad_tokens(snake_case ,       k='''labels'''				)				)
          assert sum(count_pad_tokens(snake_case				)				) < sum(count_pad_tokens(snake_case				)				)
          assert len(snake_case				) == len(snake_case				)







      def       snake_case	(     self					: Any ,       snake_case					: Optional[int]=1000 ,       snake_case					: str=128				):
          if os.getenv('''USE_REAL_DATA''' ,       snake_case				):
              __UpperCamelCase  		=						'''examples/seq2seq/wmt_en_ro'''
              __UpperCamelCase  		=						max_len * 2 * 64
              if not Path(snake_case				).joinpath('''train.len'''				).exists():
                  save_len_file(snake_case ,       snake_case				)
          else:
              __UpperCamelCase  		=						'''examples/seq2seq/test_data/wmt_en_ro'''
              __UpperCamelCase  		=						max_len * 4
              save_len_file(snake_case ,       snake_case				)

          __UpperCamelCase  		=						AutoTokenizer.from_pretrained(snake_case				)
          __UpperCamelCase  		=						SeqaSeqDataset(
              snake_case ,       data_dir=snake_case ,       type_path='''train''' ,       max_source_length=snake_case ,       max_target_length=snake_case ,       n_obs=snake_case ,       )
          return ds, max_tokens, tokenizer







      def       snake_case	(     self					: List[str]				):
          __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						self._get_dataset()
          __UpperCamelCase  		=						set(DistributedSortishSampler(snake_case ,       256 ,       num_replicas=2 ,       rank=0 ,       add_extra_examples=snake_case				)				)
          __UpperCamelCase  		=						set(DistributedSortishSampler(snake_case ,       256 ,       num_replicas=2 ,       rank=1 ,       add_extra_examples=snake_case				)				)
          assert idsa.intersection(snake_case				) == set()







      @parameterized.expand(
          [
              MBART_TINY,
              MARIAN_TINY,
              T5_TINY,
              BART_TINY,
              PEGASUS_XSUM,
          ] ,       )
      def       snake_case	(     self					: List[Any] ,       snake_case					: Tuple				):
          __UpperCamelCase  		=						AutoTokenizer.from_pretrained(snake_case ,       use_fast=snake_case				)
          if tok_name == MBART_TINY:
              __UpperCamelCase  		=						SeqaSeqDataset(
                  snake_case ,       data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				) ,       type_path='''train''' ,       max_source_length=4 ,       max_target_length=8 ,       src_lang='''EN''' ,       tgt_lang='''FR''' ,       )
              __UpperCamelCase  		=						train_dataset.dataset_kwargs
              assert "src_lang" in kwargs and "tgt_lang" in kwargs
          else:
              __UpperCamelCase  		=						SeqaSeqDataset(
                  snake_case ,       data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				) ,       type_path='''train''' ,       max_source_length=4 ,       max_target_length=8 ,       )
              __UpperCamelCase  		=						train_dataset.dataset_kwargs
              assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
              assert len(snake_case				) == 1 if tok_name == BART_TINY else len(snake_case				) == 0












import collections
import inspect
import unittest

from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
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 (
     FocalNetBackbone,
     FocalNetForImageClassification,
     FocalNetForMaskedImageModeling,
     FocalNetModel,
 )
 from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor

class  _lowerCamelCase						:

      """simple docstring"""

      def __init__(     self					: Any ,       snake_case					: Optional[Any] ,       snake_case					: Tuple=13 ,       snake_case					: List[Any]=32 ,       snake_case					: Optional[Any]=2 ,       snake_case					: Dict=3 ,       snake_case					: Union[str, Any]=16 ,       snake_case					: Optional[int]=[32, 64, 128] ,       snake_case					: int=[1, 2, 1] ,       snake_case					: Any=[2, 2, 4] ,       snake_case					: Optional[int]=2 ,       snake_case					: List[str]=2.0 ,       snake_case					: Any=True ,       snake_case					: Optional[Any]=0.0 ,       snake_case					: str=0.0 ,       snake_case					: List[Any]=0.1 ,       snake_case					: List[str]="gelu" ,       snake_case					: Any=False ,       snake_case					: Optional[int]=True ,       snake_case					: List[str]=0.02 ,       snake_case					: List[Any]=1E-5 ,       snake_case					: str=True ,       snake_case					: Any=None ,       snake_case					: int=True ,       snake_case					: Optional[Any]=10 ,       snake_case					: List[str]=8 ,       snake_case					: List[str]=["stage1", "stage2"] ,       snake_case					: Optional[Any]=[1, 2] ,       ):
          __UpperCamelCase  		=						parent
          __UpperCamelCase  		=						batch_size
          __UpperCamelCase  		=						image_size
          __UpperCamelCase  		=						patch_size
          __UpperCamelCase  		=						num_channels
          __UpperCamelCase  		=						embed_dim
          __UpperCamelCase  		=						hidden_sizes
          __UpperCamelCase  		=						depths
          __UpperCamelCase  		=						num_heads
          __UpperCamelCase  		=						window_size
          __UpperCamelCase  		=						mlp_ratio
          __UpperCamelCase  		=						qkv_bias
          __UpperCamelCase  		=						hidden_dropout_prob
          __UpperCamelCase  		=						attention_probs_dropout_prob
          __UpperCamelCase  		=						drop_path_rate
          __UpperCamelCase  		=						hidden_act
          __UpperCamelCase  		=						use_absolute_embeddings
          __UpperCamelCase  		=						patch_norm
          __UpperCamelCase  		=						layer_norm_eps
          __UpperCamelCase  		=						initializer_range
          __UpperCamelCase  		=						is_training
          __UpperCamelCase  		=						scope
          __UpperCamelCase  		=						use_labels
          __UpperCamelCase  		=						type_sequence_label_size
          __UpperCamelCase  		=						encoder_stride
          __UpperCamelCase  		=						out_features
          __UpperCamelCase  		=						out_indices







      def       snake_case	(     self					: Union[str, Any]				):
          __UpperCamelCase  		=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

          __UpperCamelCase  		=						None
          if self.use_labels:
              __UpperCamelCase  		=						ids_tensor([self.batch_size] ,       self.type_sequence_label_size				)

          __UpperCamelCase  		=						self.get_config()

          return config, pixel_values, labels







      def       snake_case	(     self					: List[str]				):
          return FocalNetConfig(
              image_size=self.image_size ,       patch_size=self.patch_size ,       num_channels=self.num_channels ,       embed_dim=self.embed_dim ,       hidden_sizes=self.hidden_sizes ,       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 ,       out_features=self.out_features ,       out_indices=self.out_indices ,       )







      def       snake_case	(     self					: int ,       snake_case					: Optional[int] ,       snake_case					: List[str] ,       snake_case					: Optional[int]				):
          __UpperCamelCase  		=						FocalNetModel(config=snake_case				)
          model.to(snake_case				)
          model.eval()
          __UpperCamelCase  		=						model(snake_case				)

          __UpperCamelCase  		=						((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths				) - 1))
          __UpperCamelCase  		=						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       snake_case	(     self					: Optional[Any] ,       snake_case					: Any ,       snake_case					: Dict ,       snake_case					: int				):
          __UpperCamelCase  		=						FocalNetBackbone(config=snake_case				)
          model.to(snake_case				)
          model.eval()
          __UpperCamelCase  		=						model(snake_case				)

          # verify feature maps
          self.parent.assertEqual(len(result.feature_maps				) ,       len(config.out_features				)				)
          self.parent.assertListEqual(list(result.feature_maps[0].shape				) ,       [self.batch_size, self.image_size, 8, 8]				)

          # verify channels
          self.parent.assertEqual(len(model.channels				) ,       len(config.out_features				)				)
          self.parent.assertListEqual(model.channels ,       config.hidden_sizes[:-1]				)

          # verify backbone works with out_features=None
          __UpperCamelCase  		=						None
          __UpperCamelCase  		=						FocalNetBackbone(config=snake_case				)
          model.to(snake_case				)
          model.eval()
          __UpperCamelCase  		=						model(snake_case				)

          # verify feature maps
          self.parent.assertEqual(len(result.feature_maps				) ,       1				)
          self.parent.assertListEqual(list(result.feature_maps[0].shape				) ,       [self.batch_size, self.image_size * 2, 4, 4]				)

          # verify channels
          self.parent.assertEqual(len(model.channels				) ,       1				)
          self.parent.assertListEqual(model.channels ,       [config.hidden_sizes[-1]]				)







      def       snake_case	(     self					: Optional[int] ,       snake_case					: Union[str, Any] ,       snake_case					: int ,       snake_case					: Any				):
          __UpperCamelCase  		=						FocalNetForMaskedImageModeling(config=snake_case				)
          model.to(snake_case				)
          model.eval()
          __UpperCamelCase  		=						model(snake_case				)
          self.parent.assertEqual(
              result.reconstruction.shape ,       (self.batch_size, self.num_channels, self.image_size, self.image_size)				)

          # test greyscale images
          __UpperCamelCase  		=						1
          __UpperCamelCase  		=						FocalNetForMaskedImageModeling(snake_case				)
          model.to(snake_case				)
          model.eval()

          __UpperCamelCase  		=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
          __UpperCamelCase  		=						model(snake_case				)
          self.parent.assertEqual(result.reconstruction.shape ,       (self.batch_size, 1, self.image_size, self.image_size)				)







      def       snake_case	(     self					: str ,       snake_case					: Tuple ,       snake_case					: List[str] ,       snake_case					: Union[str, Any]				):
          __UpperCamelCase  		=						self.type_sequence_label_size
          __UpperCamelCase  		=						FocalNetForImageClassification(snake_case				)
          model.to(snake_case				)
          model.eval()
          __UpperCamelCase  		=						model(snake_case ,       labels=snake_case				)
          self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.type_sequence_label_size)				)

          # test greyscale images
          __UpperCamelCase  		=						1
          __UpperCamelCase  		=						FocalNetForImageClassification(snake_case				)
          model.to(snake_case				)
          model.eval()

          __UpperCamelCase  		=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
          __UpperCamelCase  		=						model(snake_case				)
          self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.type_sequence_label_size)				)







      def       snake_case	(     self					: str				):
          __UpperCamelCase  		=						self.prepare_config_and_inputs()

          __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						config_and_inputs
          __UpperCamelCase  		=						{'''pixel_values''': pixel_values}
          return config, inputs_dict







@require_torch
class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE							,			_SCREAMING_SNAKE_CASE							,			unittest.TestCase   ):

      """simple docstring"""

      lowerCAmelCase__			: int    =					(
          (
              FocalNetModel,
              FocalNetForImageClassification,
              FocalNetForMaskedImageModeling,
              FocalNetBackbone,
          )
          if is_torch_available()
          else ()
      )
      lowerCAmelCase__			: Optional[Any]    =					(
          {"feature-extraction": FocalNetModel, "image-classification": FocalNetForImageClassification}
          if is_torch_available()
          else {}
      )
      lowerCAmelCase__			: List[str]    =					False

      lowerCAmelCase__			: Any    =					False
      lowerCAmelCase__			: Tuple    =					False
      lowerCAmelCase__			: List[Any]    =					False
      lowerCAmelCase__			: Optional[Any]    =					False







      def       snake_case	(     self					: Any				):
          __UpperCamelCase  		=						FocalNetModelTester(self				)
          __UpperCamelCase  		=						ConfigTester(self ,       config_class=snake_case ,       embed_dim=37 ,       has_text_modality=snake_case				)







      def       snake_case	(     self					: Union[str, Any]				):
          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       snake_case	(     self					: List[Any]				):
          return







      def       snake_case	(     self					: List[Any]				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case				)







      def       snake_case	(     self					: List[Any]				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_backbone(*snake_case				)







      def       snake_case	(     self					: Any				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_image_modeling(*snake_case				)







      def       snake_case	(     self					: Any				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*snake_case				)







      @unittest.skip(reason='''FocalNet does not use inputs_embeds'''				)
      def       snake_case	(     self					: Optional[Any]				):
          pass







      @unittest.skip(reason='''FocalNet does not use feedforward chunking'''				)
      def       snake_case	(     self					: int				):
          pass







      def       snake_case	(     self					: Tuple				):
          __UpperCamelCase		,      __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes[:-1]:
              __UpperCamelCase  		=						model_class(snake_case				)
              self.assertIsInstance(model.get_input_embeddings() ,       (nn.Module)				)
              __UpperCamelCase  		=						model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(snake_case ,       nn.Linear				)				)







      def       snake_case	(     self					: Dict				):
          __UpperCamelCase		,      __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes[:-1]:
              __UpperCamelCase  		=						model_class(snake_case				)
              __UpperCamelCase  		=						inspect.signature(model.forward				)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              __UpperCamelCase  		=						[*signature.parameters.keys()]

              __UpperCamelCase  		=						['''pixel_values''']
              self.assertListEqual(arg_names[:1] ,       snake_case				)







      def       snake_case	(     self					: Optional[int] ,       snake_case					: Union[str, Any] ,       snake_case					: int ,       snake_case					: List[str] ,       snake_case					: Optional[int]				):
          __UpperCamelCase  		=						model_class(snake_case				)
          model.to(snake_case				)
          model.eval()

          with torch.no_grad():
              __UpperCamelCase  		=						model(**self._prepare_for_class(snake_case ,       snake_case				)				)

          __UpperCamelCase  		=						outputs.hidden_states

          __UpperCamelCase  		=						getattr(
              self.model_tester ,       '''expected_num_hidden_layers''' ,       len(self.model_tester.depths				) + 1				)
          self.assertEqual(len(snake_case				) ,       snake_case				)

          # FocalNet has a different seq_length
          __UpperCamelCase  		=						(
              config.patch_size
              if isinstance(config.patch_size ,       collections.abc.Iterable				)
              else (config.patch_size, config.patch_size)
          )

          __UpperCamelCase  		=						(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] ,       )

          __UpperCamelCase  		=						outputs.reshaped_hidden_states
          self.assertEqual(len(snake_case				) ,       snake_case				)

          __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						reshaped_hidden_states[0].shape
          __UpperCamelCase  		=						(
              reshaped_hidden_states[0].view(snake_case ,       snake_case ,       height * width				).permute(0 ,       2 ,       1				)
          )
          self.assertListEqual(
              list(reshaped_hidden_states.shape[-2:]				) ,       [num_patches, self.model_tester.embed_dim] ,       )







      def       snake_case	(     self					: Union[str, Any]				):
          __UpperCamelCase		,      __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs_for_common()

          __UpperCamelCase  		=						(
              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[:-1]:
              __UpperCamelCase  		=						True
              self.check_hidden_states_output(snake_case ,       snake_case ,       snake_case ,       snake_case				)

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              __UpperCamelCase  		=						True

              self.check_hidden_states_output(snake_case ,       snake_case ,       snake_case ,       snake_case				)







      def       snake_case	(     self					: Optional[Any]				):
          __UpperCamelCase		,      __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs_for_common()
          __UpperCamelCase  		=						3

          __UpperCamelCase  		=						(
              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)
          )
          __UpperCamelCase  		=						(
              config.patch_size
              if isinstance(config.patch_size ,       collections.abc.Iterable				)
              else (config.patch_size, config.patch_size)
          )

          __UpperCamelCase  		=						image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
          __UpperCamelCase  		=						image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

          for model_class in self.all_model_classes[:-1]:
              __UpperCamelCase  		=						True
              self.check_hidden_states_output(snake_case ,       snake_case ,       snake_case ,       (padded_height, padded_width)				)

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              __UpperCamelCase  		=						True
              self.check_hidden_states_output(snake_case ,       snake_case ,       snake_case ,       (padded_height, padded_width)				)







      @slow
      def       snake_case	(     self					: Union[str, Any]				):
          for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __UpperCamelCase  		=						FocalNetModel.from_pretrained(snake_case				)
              self.assertIsNotNone(snake_case				)







      def       snake_case	(     self					: Optional[int]				):
          __UpperCamelCase		,      __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs_for_common()

          __UpperCamelCase  		=						_config_zero_init(snake_case				)
          for model_class in self.all_model_classes:
              __UpperCamelCase  		=						model_class(config=snake_case				)
              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" ,       )







@require_vision
@require_torch
class  _lowerCamelCase						(			unittest.TestCase   ):

      """simple docstring"""

      @cached_property
      def       snake_case	(     self					: str				):
          # TODO update organization
          return AutoImageProcessor.from_pretrained('''microsoft/focalnet-tiny'''				) if is_vision_available() else None







      @slow
      def       snake_case	(     self					: List[str]				):
          __UpperCamelCase  		=						FocalNetForImageClassification.from_pretrained('''microsoft/focalnet-tiny'''				).to(snake_case				)
          __UpperCamelCase  		=						self.default_image_processor

          __UpperCamelCase  		=						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
          __UpperCamelCase  		=						image_processor(images=snake_case ,       return_tensors='''pt'''				).to(snake_case				)

          # forward pass
          with torch.no_grad():
              __UpperCamelCase  		=						model(**snake_case				)

          # verify the logits
          __UpperCamelCase  		=						torch.Size((1, 1000)				)
          self.assertEqual(outputs.logits.shape ,       snake_case				)
          __UpperCamelCase  		=						torch.tensor([0.21_66, -0.43_68, 0.21_91]				).to(snake_case				)
          self.assertTrue(torch.allclose(outputs.logits[0, :3] ,       snake_case ,       atol=1E-4				)				)
          self.assertTrue(outputs.logits.argmax(dim=-1				).item() ,       281				)







@require_torch
class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE							,			unittest.TestCase   ):

      """simple docstring"""

      lowerCAmelCase__			: str    =					(FocalNetBackbone,) if is_torch_available() else ()
      lowerCAmelCase__			: Dict    =					FocalNetConfig

      lowerCAmelCase__			: List[str]    =					False







      def       snake_case	(     self					: Dict				):
          __UpperCamelCase  		=						FocalNetModelTester(self				)






