


import pytest

from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs







@pytest.mark.parametrize(
    '''kwargs, expected'''							,   [
        ({'''num_shards''': 0, '''max_num_jobs''': 1}, []),
        ({'''num_shards''': 10, '''max_num_jobs''': 1}, [range(10				)]),
        ({'''num_shards''': 10, '''max_num_jobs''': 10}, [range(__lowerCAmelCase							,   i + 1				) for i in range(10				)]),
        ({'''num_shards''': 1, '''max_num_jobs''': 10}, [range(1				)]),
        ({'''num_shards''': 10, '''max_num_jobs''': 3}, [range(0							,   4				), range(4							,   7				), range(7							,   10				)]),
        ({'''num_shards''': 3, '''max_num_jobs''': 10}, [range(0							,   1				), range(1							,   2				), range(2							,   3				)]),
    ]							,   )
def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase				)    ->				Optional[int]:
  snake_case__						= _distribute_shards(**__lowerCAmelCase				)
  assert out == expected







@pytest.mark.parametrize(
    '''gen_kwargs, max_num_jobs, expected'''							,   [
        ({'''foo''': 0}, 10, [{'''foo''': 0}]),
        ({'''shards''': [0, 1, 2, 3]}, 1, [{'''shards''': [0, 1, 2, 3]}]),
        ({'''shards''': [0, 1, 2, 3]}, 4, [{'''shards''': [0]}, {'''shards''': [1]}, {'''shards''': [2]}, {'''shards''': [3]}]),
        ({'''shards''': [0, 1]}, 4, [{'''shards''': [0]}, {'''shards''': [1]}]),
        ({'''shards''': [0, 1, 2, 3]}, 2, [{'''shards''': [0, 1]}, {'''shards''': [2, 3]}]),
    ]							,   )
def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)    ->				Dict:
  snake_case__						= _split_gen_kwargs(__lowerCAmelCase							,   __lowerCAmelCase				)
  assert out == expected







@pytest.mark.parametrize(
    '''gen_kwargs, expected'''							,   [
        ({'''foo''': 0}, 1),
        ({'''shards''': [0]}, 1),
        ({'''shards''': [0, 1, 2, 3]}, 4),
        ({'''shards''': [0, 1, 2, 3], '''foo''': 0}, 4),
        ({'''shards''': [0, 1, 2, 3], '''other''': (0, 1)}, 4),
        ({'''shards''': [0, 1, 2, 3], '''shards2''': [0, 1]}, RuntimeError),
    ]							,   )
def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase				)    ->				List[Any]:
  if expected is RuntimeError:
    with pytest.raises(__lowerCAmelCase				):
      _number_of_shards_in_gen_kwargs(__lowerCAmelCase				)
  else:
    snake_case__						= _number_of_shards_in_gen_kwargs(__lowerCAmelCase				)
    assert out == expected





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 		__magic_name__						:





     '''simple docstring'''



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

     def 	SCREAMING_SNAKE_CASE__						(					self:int   ):
       snake_case__						= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

       snake_case__						= self.get_config()

       return config, pixel_values, labels

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       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 	SCREAMING_SNAKE_CASE__						(					self:Any     ,		_a:Optional[int]     ,		_a:Tuple     ,		_a:int   ):
       snake_case__						= BitModel(config=_a   )
       model.to(_a   )
       model.eval()
       snake_case__						= model(_a   )
       self.parent.assertEqual(
           result.last_hidden_state.shape     ,		(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)     ,		)

     def 	SCREAMING_SNAKE_CASE__						(					self:int     ,		_a:Tuple     ,		_a:Any     ,		_a:Union[str, Any]   ):
       snake_case__						= self.num_labels
       snake_case__						= BitForImageClassification(_a   )
       model.to(_a   )
       model.eval()
       snake_case__						= model(_a     ,		labels=_a   )
       self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.num_labels)   )

     def 	SCREAMING_SNAKE_CASE__						(					self:str     ,		_a:str     ,		_a:List[str]     ,		_a:Any   ):
       snake_case__						= BitBackbone(config=_a   )
       model.to(_a   )
       model.eval()
       snake_case__						= model(_a   )

       # 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
       snake_case__						= None
       snake_case__						= BitBackbone(config=_a   )
       model.to(_a   )
       model.eval()
       snake_case__						= model(_a   )

       # 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 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       snake_case__						= self.prepare_config_and_inputs()
       snake_case__				,				snake_case__				,				snake_case__						= config_and_inputs
       snake_case__						= {'''pixel_values''': pixel_values}
       return config, inputs_dict




@require_torch
class 		__magic_name__						(snake_case_   ,snake_case_   ,unittest.TestCase				):





     '''simple docstring'''



     __lowercase				:							Any	   =       (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
     __lowercase				:							int	   =       (
         {'feature-extraction': BitModel, 'image-classification': BitForImageClassification}
         if is_torch_available()
         else {}
     )

     __lowercase				:							Tuple	   =       False
     __lowercase				:							Optional[Any]	   =       False
     __lowercase				:							str	   =       False
     __lowercase				:							Tuple	   =       False
     __lowercase				:							Tuple	   =       False

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       snake_case__						= BitModelTester(self   )
       snake_case__						= ConfigTester(self     ,		config_class=_a     ,		has_text_modality=_a   )

     def 	SCREAMING_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 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       return

     @unittest.skip(reason='''Bit does not output attentions'''   )
     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       pass

     @unittest.skip(reason='''Bit does not use inputs_embeds'''   )
     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       pass

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

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       snake_case__				,				snake_case__						= self.model_tester.prepare_config_and_inputs_for_common()

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

         snake_case__						= ['''pixel_values''']
         self.assertListEqual(arg_names[:1]     ,		_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       snake_case__						= self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       snake_case__						= self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       snake_case__				,				snake_case__						= self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         snake_case__						= model_class(config=_a   )
         for name, module in model.named_modules():
           if isinstance(_a     ,		(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 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       def check_hidden_states_output(_a:List[Any]     ,		_a:int     ,		_a:Union[str, Any]   ):
         snake_case__						= model_class(_a   )
         model.to(_a   )
         model.eval()

         with torch.no_grad():
           snake_case__						= model(**self._prepare_for_class(_a     ,		_a   )   )

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

         snake_case__						= self.model_tester.num_stages
         self.assertEqual(len(_a   )     ,		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]     ,		)

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

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

           check_hidden_states_output(_a     ,		_a     ,		_a   )

     @unittest.skip(reason='''Bit does not use feedforward chunking'''   )
     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       pass

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       snake_case__						= self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_a   )







     @slow
     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         snake_case__						= BitModel.from_pretrained(_a   )
         self.assertIsNotNone(_a   )







def SCREAMING_SNAKE_CASE	(			)    ->				Any:
  snake_case__						= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
  return image
@require_torch
@require_vision
class 		__magic_name__						(unittest.TestCase				):





     '''simple docstring'''



     @cached_property
     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       return (
           BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ) if is_vision_available() else None
       )







     @slow
     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       snake_case__						= BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ).to(_a   )

       snake_case__						= self.default_image_processor
       snake_case__						= prepare_img()
       snake_case__						= image_processor(images=_a     ,		return_tensors='''pt'''   ).to(_a   )

       # forward pass
       with torch.no_grad():
         snake_case__						= model(**_a   )

       # verify the logits
       snake_case__						= torch.Size((1, 10_00)   )
       self.assertEqual(outputs.logits.shape     ,		_a   )

       snake_case__						= torch.tensor([[-0.6526, -0.5263, -1.4398]]   ).to(_a   )

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




@require_torch
class 		__magic_name__						(snake_case_   ,unittest.TestCase				):





     '''simple docstring'''



     __lowercase				:							Optional[Any]	   =       (BitBackbone,) if is_torch_available() else ()
     __lowercase				:							int	   =       BitConfig

     __lowercase				:							Any	   =       False

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       snake_case__						= BitModelTester(self   )

