

import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


_SCREAMING_SNAKE_CASE  :						List[str]         =    logging.getLogger(__name__)






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      42
  a     =      42
  a     =      42






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      42
  a     =      42
  a     =      None
  a     =      None






class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  a     =      "train"
  a     =      "dev"
  a     =      "test"






class  UpperCAmelCase__	:






  """simple docstring"""


  @staticmethod
  def    lowercase_	(							__lowerCamelCase		:	List[Any]			,     __lowerCamelCase		:	Union[Split, str]      )	->							List[InputExample]:
       raise NotImplementedError






  @staticmethod
  def    lowercase_	(							__lowerCamelCase		:	str      )	->							List[str]:
       raise NotImplementedError







  @staticmethod
  def    lowercase_	(							__lowerCamelCase		:	List[InputExample]			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	PreTrainedTokenizer			,     __lowerCamelCase		:	Tuple=False			,     __lowerCamelCase		:	Tuple="[CLS]"			,     __lowerCamelCase		:	Dict=1			,     __lowerCamelCase		:	Any="[SEP]"			,     __lowerCamelCase		:	Optional[int]=False			,     __lowerCamelCase		:	Any=False			,     __lowerCamelCase		:	List[Any]=0			,     __lowerCamelCase		:	int=0			,     __lowerCamelCase		:	str=-100			,     __lowerCamelCase		:	Tuple=0			,     __lowerCamelCase		:	Tuple=True			,     )	->							List[InputFeatures]:
       SCREAMING_SNAKE_CASE__					=					{label: i for i, label in enumerate(__lowerCamelCase      )}

       SCREAMING_SNAKE_CASE__					=					[]
       for ex_index, example in enumerate(__lowerCamelCase      ):
            if ex_index % 1_0000 == 0:
                 logger.info('''Writing example %d of %d'''			,     __lowerCamelCase			,     len(__lowerCamelCase      )      )

            SCREAMING_SNAKE_CASE__					=					[]
            SCREAMING_SNAKE_CASE__					=					[]
            for word, label in zip(example.words			,     example.labels      ):
                 SCREAMING_SNAKE_CASE__					=					tokenizer.tokenize(__lowerCamelCase      )

                 # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
                 if len(__lowerCamelCase      ) > 0:
                      tokens.extend(__lowerCamelCase      )
                      # Use the real label id for the first token of the word, and padding ids for the remaining tokens
                      label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(__lowerCamelCase      ) - 1)      )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
            SCREAMING_SNAKE_CASE__					=					tokenizer.num_special_tokens_to_add()
            if len(__lowerCamelCase      ) > max_seq_length - special_tokens_count:
                 SCREAMING_SNAKE_CASE__					=					tokens[: (max_seq_length - special_tokens_count)]
                 SCREAMING_SNAKE_CASE__					=					label_ids[: (max_seq_length - special_tokens_count)]

            # The convention in BERT is:
            # (a) For sequence pairs:
            #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
            #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
            # (b) For single sequences:
            #  tokens:   [CLS] the dog is hairy . [SEP]
            #  type_ids:   0   0   0   0  0     0   0
            #
            # Where "type_ids" are used to indicate whether this is the first
            # sequence or the second sequence. The embedding vectors for `type=0` and
            # `type=1` were learned during pre-training and are added to the wordpiece
            # embedding vector (and position vector). This is not *strictly* necessary
            # since the [SEP] token unambiguously separates the sequences, but it makes
            # it easier for the model to learn the concept of sequences.
            #
            # For classification tasks, the first vector (corresponding to [CLS]) is
            # used as the "sentence vector". Note that this only makes sense because
            # the entire model is fine-tuned.
            tokens += [sep_token]
            label_ids += [pad_token_label_id]
            if sep_token_extra:
                 # roberta uses an extra separator b/w pairs of sentences
                 tokens += [sep_token]
                 label_ids += [pad_token_label_id]
            SCREAMING_SNAKE_CASE__					=					[sequence_a_segment_id] * len(__lowerCamelCase      )

            if cls_token_at_end:
                 tokens += [cls_token]
                 label_ids += [pad_token_label_id]
                 segment_ids += [cls_token_segment_id]
            else:
                 SCREAMING_SNAKE_CASE__					=					[cls_token] + tokens
                 SCREAMING_SNAKE_CASE__					=					[pad_token_label_id] + label_ids
                 SCREAMING_SNAKE_CASE__					=					[cls_token_segment_id] + segment_ids

            SCREAMING_SNAKE_CASE__					=					tokenizer.convert_tokens_to_ids(__lowerCamelCase      )

            # The mask has 1 for real tokens and 0 for padding tokens. Only real
            # tokens are attended to.
            SCREAMING_SNAKE_CASE__					=					[1 if mask_padding_with_zero else 0] * len(__lowerCamelCase      )

            # Zero-pad up to the sequence length.
            SCREAMING_SNAKE_CASE__					=					max_seq_length - len(__lowerCamelCase      )
            if pad_on_left:
                 SCREAMING_SNAKE_CASE__					=					([pad_token] * padding_length) + input_ids
                 SCREAMING_SNAKE_CASE__					=					([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                 SCREAMING_SNAKE_CASE__					=					([pad_token_segment_id] * padding_length) + segment_ids
                 SCREAMING_SNAKE_CASE__					=					([pad_token_label_id] * padding_length) + label_ids
            else:
                 input_ids += [pad_token] * padding_length
                 input_mask += [0 if mask_padding_with_zero else 1] * padding_length
                 segment_ids += [pad_token_segment_id] * padding_length
                 label_ids += [pad_token_label_id] * padding_length

            assert len(__lowerCamelCase      ) == max_seq_length
            assert len(__lowerCamelCase      ) == max_seq_length
            assert len(__lowerCamelCase      ) == max_seq_length
            assert len(__lowerCamelCase      ) == max_seq_length

            if ex_index < 5:
                 logger.info('''*** Example ***'''      )
                 logger.info('''guid: %s'''			,     example.guid      )
                 logger.info('''tokens: %s'''			,     ''' '''.join([str(__lowerCamelCase      ) for x in tokens]      )      )
                 logger.info('''input_ids: %s'''			,     ''' '''.join([str(__lowerCamelCase      ) for x in input_ids]      )      )
                 logger.info('''input_mask: %s'''			,     ''' '''.join([str(__lowerCamelCase      ) for x in input_mask]      )      )
                 logger.info('''segment_ids: %s'''			,     ''' '''.join([str(__lowerCamelCase      ) for x in segment_ids]      )      )
                 logger.info('''label_ids: %s'''			,     ''' '''.join([str(__lowerCamelCase      ) for x in label_ids]      )      )

            if "token_type_ids" not in tokenizer.model_input_names:
                 SCREAMING_SNAKE_CASE__					=					None

            features.append(
                InputFeatures(
                    input_ids=__lowerCamelCase			,     attention_mask=__lowerCamelCase			,     token_type_ids=__lowerCamelCase			,     label_ids=__lowerCamelCase      )      )
       return features


if is_torch_available():
       import torch
       from torch import nn
       from torch.utils.data import Dataset






       class  UpperCAmelCase__	( A__ ):






         """simple docstring"""


         a     =      42
         a     =      nn.CrossEntropyLoss().ignore_index






         def __init__(							self		:	str			,     __lowerCamelCase		:	TokenClassificationTask			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	PreTrainedTokenizer			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	Optional[int] = None			,     __lowerCamelCase		:	Optional[Any]=False			,     __lowerCamelCase		:	Split = Split.train			,     )	->							List[str]:
              # Load data features from cache or dataset file
              SCREAMING_SNAKE_CASE__					=					os.path.join(
                  __lowerCamelCase			,     '''cached_{}_{}_{}'''.format(mode.value			,     tokenizer.__class__.__name__			,     str(__lowerCamelCase      )      )			,     )

              # Make sure only the first process in distributed training processes the dataset,
              # and the others will use the cache.
              SCREAMING_SNAKE_CASE__					=					cached_features_file + '''.lock'''
              with FileLock(__lowerCamelCase      ):
                   if os.path.exists(__lowerCamelCase      ) and not overwrite_cache:
                        logger.info(f'''Loading features from cached file {cached_features_file}'''      )
                        SCREAMING_SNAKE_CASE__					=					torch.load(__lowerCamelCase      )
                   else:
                        logger.info(f'''Creating features from dataset file at {data_dir}'''      )
                        SCREAMING_SNAKE_CASE__					=					token_classification_task.read_examples_from_file(__lowerCamelCase			,     __lowerCamelCase      )
                        # TODO clean up all this to leverage built-in features of tokenizers
                        SCREAMING_SNAKE_CASE__					=					token_classification_task.convert_examples_to_features(
                            __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     cls_token_at_end=bool(model_type in ['''xlnet''']      )			,     cls_token=tokenizer.cls_token			,     cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0			,     sep_token=tokenizer.sep_token			,     sep_token_extra=__lowerCamelCase			,     pad_on_left=bool(tokenizer.padding_side == '''left'''      )			,     pad_token=tokenizer.pad_token_id			,     pad_token_segment_id=tokenizer.pad_token_type_id			,     pad_token_label_id=self.pad_token_label_id			,     )
                        logger.info(f'''Saving features into cached file {cached_features_file}'''      )
                        torch.save(self.features			,     __lowerCamelCase      )






         def __len__(							self		:	Tuple      )	->							Optional[int]:
              return len(self.features      )







         def __getitem__(							self		:	Any			,     __lowerCamelCase		:	Optional[Any]      )	->							InputFeatures:
              return self.features[i]


if is_tf_available():
       import tensorflow as tf






       class  UpperCAmelCase__	:






         """simple docstring"""


         a     =      42
         a     =      -1_00






         def __init__(							self		:	Union[str, Any]			,     __lowerCamelCase		:	TokenClassificationTask			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	PreTrainedTokenizer			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	Optional[int] = None			,     __lowerCamelCase		:	Optional[int]=False			,     __lowerCamelCase		:	Split = Split.train			,     )	->							Optional[Any]:
              SCREAMING_SNAKE_CASE__					=					token_classification_task.read_examples_from_file(__lowerCamelCase			,     __lowerCamelCase      )
              # TODO clean up all this to leverage built-in features of tokenizers
              SCREAMING_SNAKE_CASE__					=					token_classification_task.convert_examples_to_features(
                  __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     cls_token_at_end=bool(model_type in ['''xlnet''']      )			,     cls_token=tokenizer.cls_token			,     cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0			,     sep_token=tokenizer.sep_token			,     sep_token_extra=__lowerCamelCase			,     pad_on_left=bool(tokenizer.padding_side == '''left'''      )			,     pad_token=tokenizer.pad_token_id			,     pad_token_segment_id=tokenizer.pad_token_type_id			,     pad_token_label_id=self.pad_token_label_id			,     )

              def gen():
                   for ex in self.features:
                        if ex.token_type_ids is None:
                             yield (
                                 {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                                 ex.label_ids,
                             )
                        else:
                             yield (
                                 {
                                     "input_ids": ex.input_ids,
                                     "attention_mask": ex.attention_mask,
                                     "token_type_ids": ex.token_type_ids,
                                 },
                                 ex.label_ids,
                             )

              if "token_type_ids" not in tokenizer.model_input_names:
                   SCREAMING_SNAKE_CASE__					=					tf.data.Dataset.from_generator(
                       __lowerCamelCase			,     ({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)			,     (
                           {'''input_ids''': tf.TensorShape([None]      ), '''attention_mask''': tf.TensorShape([None]      )},
                           tf.TensorShape([None]      ),
                       )			,     )
              else:
                   SCREAMING_SNAKE_CASE__					=					tf.data.Dataset.from_generator(
                       __lowerCamelCase			,     ({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa, '''token_type_ids''': tf.intaa}, tf.intaa)			,     (
                           {
                               '''input_ids''': tf.TensorShape([None]      ),
                               '''attention_mask''': tf.TensorShape([None]      ),
                               '''token_type_ids''': tf.TensorShape([None]      ),
                           },
                           tf.TensorShape([None]      ),
                       )			,     )






         def    lowercase_	(							self		:	Optional[int]      )	->							int:
              SCREAMING_SNAKE_CASE__					=					self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features      )      )      )

              return self.dataset






         def __len__(							self		:	Tuple      )	->							Optional[int]:
              return len(self.features      )







         def __getitem__(							self		:	List[str]			,     __lowerCamelCase		:	Tuple      )	->							InputFeatures:
              return self.features[i]





import inspect
import unittest

from transformers import BitConfig
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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
       from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image






class  UpperCAmelCase__	:






  """simple docstring"""


  def __init__(							self		:	Optional[Any]			,     __lowerCamelCase		:	Union[str, Any]			,     __lowerCamelCase		:	Union[str, Any]=3			,     __lowerCamelCase		:	int=32			,     __lowerCamelCase		:	Tuple=3			,     __lowerCamelCase		:	Optional[int]=10			,     __lowerCamelCase		:	List[Any]=[8, 16, 32, 64]			,     __lowerCamelCase		:	Dict=[1, 1, 2, 1]			,     __lowerCamelCase		:	int=True			,     __lowerCamelCase		:	Dict=True			,     __lowerCamelCase		:	Optional[int]="relu"			,     __lowerCamelCase		:	Optional[Any]=3			,     __lowerCamelCase		:	Any=None			,     __lowerCamelCase		:	Optional[int]=["stage2", "stage3", "stage4"]			,     __lowerCamelCase		:	Tuple=[2, 3, 4]			,     __lowerCamelCase		:	List[Any]=1			,     )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					parent
       SCREAMING_SNAKE_CASE__					=					batch_size
       SCREAMING_SNAKE_CASE__					=					image_size
       SCREAMING_SNAKE_CASE__					=					num_channels
       SCREAMING_SNAKE_CASE__					=					embeddings_size
       SCREAMING_SNAKE_CASE__					=					hidden_sizes
       SCREAMING_SNAKE_CASE__					=					depths
       SCREAMING_SNAKE_CASE__					=					is_training
       SCREAMING_SNAKE_CASE__					=					use_labels
       SCREAMING_SNAKE_CASE__					=					hidden_act
       SCREAMING_SNAKE_CASE__					=					num_labels
       SCREAMING_SNAKE_CASE__					=					scope
       SCREAMING_SNAKE_CASE__					=					len(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					out_features
       SCREAMING_SNAKE_CASE__					=					out_indices
       SCREAMING_SNAKE_CASE__					=					num_groups






  def    lowercase_	(							self		:	Optional[Any]      )	->							str:
       SCREAMING_SNAKE_CASE__					=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

       SCREAMING_SNAKE_CASE__					=					None
       if self.use_labels:
            SCREAMING_SNAKE_CASE__					=					ids_tensor([self.batch_size]			,     self.num_labels      )

       SCREAMING_SNAKE_CASE__					=					self.get_config()

       return config, pixel_values, labels






  def    lowercase_	(							self		:	List[str]      )	->							Dict:
       return BitConfig(
           num_channels=self.num_channels			,     embeddings_size=self.embeddings_size			,     hidden_sizes=self.hidden_sizes			,     depths=self.depths			,     hidden_act=self.hidden_act			,     num_labels=self.num_labels			,     out_features=self.out_features			,     out_indices=self.out_indices			,     num_groups=self.num_groups			,     )






  def    lowercase_	(							self		:	Optional[Any]			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	Dict			,     __lowerCamelCase		:	Union[str, Any]      )	->							int:
       SCREAMING_SNAKE_CASE__					=					BitModel(config=__lowerCamelCase      )
       model.to(__lowerCamelCase      )
       model.eval()
       SCREAMING_SNAKE_CASE__					=					model(__lowerCamelCase      )
       self.parent.assertEqual(
           result.last_hidden_state.shape			,     (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)			,     )






  def    lowercase_	(							self		:	str			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	Union[str, Any]			,     __lowerCamelCase		:	Optional[int]      )	->							Optional[int]:
       SCREAMING_SNAKE_CASE__					=					self.num_labels
       SCREAMING_SNAKE_CASE__					=					BitForImageClassification(__lowerCamelCase      )
       model.to(__lowerCamelCase      )
       model.eval()
       SCREAMING_SNAKE_CASE__					=					model(__lowerCamelCase			,     labels=__lowerCamelCase      )
       self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.num_labels)      )






  def    lowercase_	(							self		:	Optional[int]			,     __lowerCamelCase		:	Optional[int]			,     __lowerCamelCase		:	Optional[int]			,     __lowerCamelCase		:	Union[str, Any]      )	->							Tuple:
       SCREAMING_SNAKE_CASE__					=					BitBackbone(config=__lowerCamelCase      )
       model.to(__lowerCamelCase      )
       model.eval()
       SCREAMING_SNAKE_CASE__					=					model(__lowerCamelCase      )

       # 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.hidden_sizes[1], 4, 4]      )

       # 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
       SCREAMING_SNAKE_CASE__					=					None
       SCREAMING_SNAKE_CASE__					=					BitBackbone(config=__lowerCamelCase      )
       model.to(__lowerCamelCase      )
       model.eval()
       SCREAMING_SNAKE_CASE__					=					model(__lowerCamelCase      )

       # verify feature maps
       self.parent.assertEqual(len(result.feature_maps      )			,     1      )
       self.parent.assertListEqual(list(result.feature_maps[0].shape      )			,     [self.batch_size, self.hidden_sizes[-1], 1, 1]      )

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







  def    lowercase_	(							self		:	Tuple      )	->							int:
       SCREAMING_SNAKE_CASE__					=					self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					config_and_inputs
       SCREAMING_SNAKE_CASE__					=					{'''pixel_values''': pixel_values}
       return config, inputs_dict






@require_torch
class  UpperCAmelCase__	( A__		,  A__		,  unittest.TestCase ):






  """simple docstring"""


  a     =      (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
  a     =      (
      {"feature-extraction": BitModel, "image-classification": BitForImageClassification}
      if is_torch_available()
      else {}
  )

  a     =      False
  a     =      False
  a     =      False
  a     =      False
  a     =      False






  def    lowercase_	(							self		:	Optional[Any]      )	->							List[Any]:
       SCREAMING_SNAKE_CASE__					=					BitModelTester(self      )
       SCREAMING_SNAKE_CASE__					=					ConfigTester(self			,     config_class=__lowerCamelCase			,     has_text_modality=__lowerCamelCase      )






  def    lowercase_	(							self		:	Union[str, Any]      )	->							str:
       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    lowercase_	(							self		:	str      )	->							Dict:
       return






  @unittest.skip(reason='''Bit does not output attentions'''      )
  def    lowercase_	(							self		:	Any      )	->							Dict:
       pass






  @unittest.skip(reason='''Bit does not use inputs_embeds'''      )
  def    lowercase_	(							self		:	Tuple      )	->							Optional[int]:
       pass






  @unittest.skip(reason='''Bit does not support input and output embeddings'''      )
  def    lowercase_	(							self		:	Optional[int]      )	->							str:
       pass






  def    lowercase_	(							self		:	str      )	->							str:
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs_for_common()

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

            SCREAMING_SNAKE_CASE__					=					['''pixel_values''']
            self.assertListEqual(arg_names[:1]			,     __lowerCamelCase      )






  def    lowercase_	(							self		:	Dict      )	->							Tuple:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__lowerCamelCase      )






  def    lowercase_	(							self		:	Any      )	->							Optional[int]:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*__lowerCamelCase      )






  def    lowercase_	(							self		:	Any      )	->							str:
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            SCREAMING_SNAKE_CASE__					=					model_class(config=__lowerCamelCase      )
            for name, module in model.named_modules():
                 if isinstance(__lowerCamelCase			,     (nn.BatchNormad, nn.GroupNorm)      ):
                      self.assertTrue(
                          torch.all(module.weight == 1      )			,     msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''			,     )
                      self.assertTrue(
                          torch.all(module.bias == 0      )			,     msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''			,     )






  def    lowercase_	(							self		:	str      )	->							Optional[Any]:
       def check_hidden_states_output(__lowerCamelCase		:	List[Any]			,     __lowerCamelCase		:	Dict			,     __lowerCamelCase		:	Optional[Any]      ):
            SCREAMING_SNAKE_CASE__					=					model_class(__lowerCamelCase      )
            model.to(__lowerCamelCase      )
            model.eval()

            with torch.no_grad():
                 SCREAMING_SNAKE_CASE__					=					model(**self._prepare_for_class(__lowerCamelCase			,     __lowerCamelCase      )      )

            SCREAMING_SNAKE_CASE__					=					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

            SCREAMING_SNAKE_CASE__					=					self.model_tester.num_stages
            self.assertEqual(len(__lowerCamelCase      )			,     expected_num_stages + 1      )

            # Bit's feature maps are of shape (batch_size, num_channels, height, width)
            self.assertListEqual(
                list(hidden_states[0].shape[-2:]      )			,     [self.model_tester.image_size // 4, self.model_tester.image_size // 4]			,     )

       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE__					=					['''preactivation''', '''bottleneck''']
       for model_class in self.all_model_classes:
            for layer_type in layers_type:
                 SCREAMING_SNAKE_CASE__					=					layer_type
                 SCREAMING_SNAKE_CASE__					=					True
                 check_hidden_states_output(__lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase      )

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

                 check_hidden_states_output(__lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase      )






  @unittest.skip(reason='''Bit does not use feedforward chunking'''      )
  def    lowercase_	(							self		:	List[str]      )	->							Dict:
       pass






  def    lowercase_	(							self		:	List[Any]      )	->							List[Any]:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase      )







  @slow
  def    lowercase_	(							self		:	Optional[Any]      )	->							Dict:
       for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            SCREAMING_SNAKE_CASE__					=					BitModel.from_pretrained(__lowerCamelCase      )
            self.assertIsNotNone(__lowerCamelCase      )



def        UpperCAmelCase_     (		):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
     return image






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






  """simple docstring"""


  @cached_property
  def    lowercase_	(							self		:	List[Any]      )	->							List[Any]:
       return (
           BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]      ) if is_vision_available() else None
       )







  @slow
  def    lowercase_	(							self		:	str      )	->							List[Any]:
       SCREAMING_SNAKE_CASE__					=					BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]      ).to(__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					self.default_image_processor
       SCREAMING_SNAKE_CASE__					=					prepare_img()
       SCREAMING_SNAKE_CASE__					=					image_processor(images=__lowerCamelCase			,     return_tensors='''pt'''      ).to(__lowerCamelCase      )

       # forward pass
       with torch.no_grad():
            SCREAMING_SNAKE_CASE__					=					model(**__lowerCamelCase      )

       # verify the logits
       SCREAMING_SNAKE_CASE__					=					torch.Size((1, 1000)      )
       self.assertEqual(outputs.logits.shape			,     __lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					torch.tensor([[-0.6526, -0.5263, -1.4398]]      ).to(__lowerCamelCase      )

       self.assertTrue(torch.allclose(outputs.logits[0, :3]			,     __lowerCamelCase			,     atol=1e-4      )      )






@require_torch
class  UpperCAmelCase__	( A__		,  unittest.TestCase ):






  """simple docstring"""


  a     =      (BitBackbone,) if is_torch_available() else ()
  a     =      BitConfig

  a     =      False






  def    lowercase_	(							self		:	Optional[Any]      )	->							Dict:
       SCREAMING_SNAKE_CASE__					=					BitModelTester(self      )


