






"""simple docstring"""


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 					snake_case		:


   '''simple docstring'''

   def __init__(	self :     Optional[Any],       _lowerCamelCase :     List[str],       _lowerCamelCase :     int=3,       _lowerCamelCase :     Any=32,       _lowerCamelCase :     int=3,       _lowerCamelCase :     str=10,       _lowerCamelCase :     Union[str, Any]=[8, 16, 32, 64],       _lowerCamelCase :     Optional[int]=[1, 1, 2, 1],       _lowerCamelCase :     int=True,       _lowerCamelCase :     Union[str, Any]=True,       _lowerCamelCase :     Tuple="relu",       _lowerCamelCase :     Union[str, Any]=3,       _lowerCamelCase :     List[Any]=None,       _lowerCamelCase :     List[Any]=["stage2", "stage3", "stage4"],       _lowerCamelCase :     Any=[2, 3, 4],       _lowerCamelCase :     Optional[Any]=1,       ):

     '''simple docstring'''
     __A									= parent
     __A									= batch_size
     __A									= image_size
     __A									= num_channels
     __A									= embeddings_size
     __A									= hidden_sizes
     __A									= depths
     __A									= is_training
     __A									= use_labels
     __A									= hidden_act
     __A									= num_labels
     __A									= scope
     __A									= len(_lowerCamelCase  )
     __A									= out_features
     __A									= out_indices
     __A									= num_groups


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     __A									= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

     __A									= self.get_config()

     return config, pixel_values, labels


   def 				_SCREAMING_SNAKE_CASE				(	self :     int  ):

     '''simple docstring'''
     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 :     Dict,       _lowerCamelCase :     Union[str, Any],       _lowerCamelCase :     str,       _lowerCamelCase :     Dict  ):

     '''simple docstring'''
     __A									= BitModel(config=_lowerCamelCase  )
     model.to(_lowerCamelCase  )
     model.eval()
     __A									= 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 				_SCREAMING_SNAKE_CASE				(	self :     List[str],       _lowerCamelCase :     Optional[int],       _lowerCamelCase :     str,       _lowerCamelCase :     int  ):

     '''simple docstring'''
     __A									= self.num_labels
     __A									= BitForImageClassification(_lowerCamelCase  )
     model.to(_lowerCamelCase  )
     model.eval()
     __A									= model(_lowerCamelCase,       labels=_lowerCamelCase  )
     self.parent.assertEqual(result.logits.shape,       (self.batch_size, self.num_labels)  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int],       _lowerCamelCase :     Union[str, Any],       _lowerCamelCase :     Union[str, Any],       _lowerCamelCase :     Union[str, Any]  ):

     '''simple docstring'''
     __A									= BitBackbone(config=_lowerCamelCase  )
     model.to(_lowerCamelCase  )
     model.eval()
     __A									= 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
     __A									= None
     __A									= BitBackbone(config=_lowerCamelCase  )
     model.to(_lowerCamelCase  )
     model.eval()
     __A									= 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 				_SCREAMING_SNAKE_CASE				(	self :     int  ):

     '''simple docstring'''
     __A									= self.prepare_config_and_inputs()
     __A			,    __A			,    __A									= config_and_inputs
     __A									= {'''pixel_values''': pixel_values}
     return config, inputs_dict





@require_torch
class 					snake_case		(							_lowerCAmelCase   ,	_lowerCAmelCase   ,	unittest.TestCase						):


   '''simple docstring'''

   A_						:	Tuple						 = (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
   A_						:	Optional[Any]						 = (
       {"feature-extraction": BitModel, "image-classification": BitForImageClassification}
       if is_torch_available()
       else {}
   )

   A_						:	Dict						 = False
   A_						:	Any						 = False
   A_						:	Union[str, Any]						 = False
   A_						:	str						 = False
   A_						:	Dict						 = False


   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any]  ):

     '''simple docstring'''
     __A									= BitModelTester(self  )
     __A									= ConfigTester(self,       config_class=_lowerCamelCase,       has_text_modality=_lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     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 :     Union[str, Any]  ):

     '''simple docstring'''
     return


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

     '''simple docstring'''
     pass


   @unittest.skip(reason='''Bit does not use inputs_embeds'''  )
   def 				_SCREAMING_SNAKE_CASE				(	self :     List[str]  ):

     '''simple docstring'''
     pass


   @unittest.skip(reason='''Bit does not support input and output embeddings'''  )
   def 				_SCREAMING_SNAKE_CASE				(	self :     Any  ):

     '''simple docstring'''
     pass


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[str]  ):

     '''simple docstring'''
     __A			,    __A									= self.model_tester.prepare_config_and_inputs_for_common()

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

       __A									= ['''pixel_values''']
       self.assertListEqual(arg_names[:1],       _lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int]  ):

     '''simple docstring'''
     __A									= self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     __A									= self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_backbone(*_lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     __A			,    __A									= self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       __A									= 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 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     def check_hidden_states_output(_lowerCamelCase :     Tuple,       _lowerCamelCase :     Union[str, Any],       _lowerCamelCase :     List[str]  ):
       __A									= model_class(_lowerCamelCase  )
       model.to(_lowerCamelCase  )
       model.eval()

       with torch.no_grad():
         __A									= model(**self._prepare_for_class(_lowerCamelCase,       _lowerCamelCase  )  )

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

       __A									= 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],       )

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

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

         check_hidden_states_output(_lowerCamelCase,       _lowerCamelCase,       _lowerCamelCase  )


   @unittest.skip(reason='''Bit does not use feedforward chunking'''  )
   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int]  ):

     '''simple docstring'''
     pass


   def 				_SCREAMING_SNAKE_CASE				(	self :     int  ):

     '''simple docstring'''
     __A									= self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase  )






   @slow
   def 				_SCREAMING_SNAKE_CASE				(	self :     int  ):

     '''simple docstring'''
     for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __A									= BitModel.from_pretrained(_lowerCamelCase  )
       self.assertIsNotNone(_lowerCamelCase  )





def 					lowerCAmelCase				(    ):




  """simple docstring"""







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


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


   '''simple docstring'''

   @cached_property
   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple  ):

     '''simple docstring'''
     return (
         BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]  ) if is_vision_available() else None
     )






   @slow
   def 				_SCREAMING_SNAKE_CASE				(	self :     List[str]  ):

     '''simple docstring'''
     __A									= BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]  ).to(_lowerCamelCase  )

     __A									= self.default_image_processor
     __A									= prepare_img()
     __A									= image_processor(images=_lowerCamelCase,       return_tensors='''pt'''  ).to(_lowerCamelCase  )

     # forward pass
     with torch.no_grad():
       __A									= model(**_lowerCamelCase  )

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

     __A									= torch.tensor([[-0.65_26, -0.52_63, -1.43_98]]  ).to(_lowerCamelCase  )

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





@require_torch
class 					snake_case		(							_lowerCAmelCase   ,	unittest.TestCase						):


   '''simple docstring'''

   A_						:	List[str]						 = (BitBackbone,) if is_torch_available() else ()
   A_						:	Optional[int]						 = BitConfig

   A_						:	Tuple						 = False


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     __A									= BitModelTester(self  )












"""simple docstring"""


import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
       import jax.numpy as jnp

       from transformers.models.roformer.modeling_flax_roformer import (
           FlaxRoFormerForMaskedLM,
           FlaxRoFormerForMultipleChoice,
           FlaxRoFormerForQuestionAnswering,
           FlaxRoFormerForSequenceClassification,
           FlaxRoFormerForTokenClassification,
           FlaxRoFormerModel,
       )


class 					snake_case		(							unittest.TestCase						):


   '''simple docstring'''

   def __init__(	self :     Optional[Any],       _lowerCamelCase :     Tuple,       _lowerCamelCase :     List[str]=13,       _lowerCamelCase :     Optional[Any]=7,       _lowerCamelCase :     Optional[int]=True,       _lowerCamelCase :     int=True,       _lowerCamelCase :     List[str]=True,       _lowerCamelCase :     Optional[Any]=True,       _lowerCamelCase :     int=99,       _lowerCamelCase :     Optional[int]=32,       _lowerCamelCase :     Tuple=5,       _lowerCamelCase :     Tuple=4,       _lowerCamelCase :     str=37,       _lowerCamelCase :     Union[str, Any]="gelu",       _lowerCamelCase :     int=0.1,       _lowerCamelCase :     List[Any]=0.1,       _lowerCamelCase :     Dict=5_12,       _lowerCamelCase :     List[Any]=16,       _lowerCamelCase :     Any=2,       _lowerCamelCase :     Any=0.02,       _lowerCamelCase :     Dict=4,       ):

     '''simple docstring'''
     __A									= parent
     __A									= batch_size
     __A									= seq_length
     __A									= is_training
     __A									= use_attention_mask
     __A									= use_token_type_ids
     __A									= use_labels
     __A									= vocab_size
     __A									= hidden_size
     __A									= num_hidden_layers
     __A									= num_attention_heads
     __A									= intermediate_size
     __A									= hidden_act
     __A									= hidden_dropout_prob
     __A									= attention_probs_dropout_prob
     __A									= max_position_embeddings
     __A									= type_vocab_size
     __A									= type_sequence_label_size
     __A									= initializer_range
     __A									= num_choices


   def 				_SCREAMING_SNAKE_CASE				(	self :     Any  ):

     '''simple docstring'''
     __A									= ids_tensor([self.batch_size, self.seq_length],       self.vocab_size  )

     __A									= None
     if self.use_attention_mask:
       __A									= random_attention_mask([self.batch_size, self.seq_length]  )

     __A									= None
     if self.use_token_type_ids:
       __A									= ids_tensor([self.batch_size, self.seq_length],       self.type_vocab_size  )

     __A									= RoFormerConfig(
         vocab_size=self.vocab_size,       hidden_size=self.hidden_size,       num_hidden_layers=self.num_hidden_layers,       num_attention_heads=self.num_attention_heads,       intermediate_size=self.intermediate_size,       hidden_act=self.hidden_act,       hidden_dropout_prob=self.hidden_dropout_prob,       attention_probs_dropout_prob=self.attention_probs_dropout_prob,       max_position_embeddings=self.max_position_embeddings,       type_vocab_size=self.type_vocab_size,       is_decoder=_lowerCamelCase,       initializer_range=self.initializer_range,       )

     return config, input_ids, token_type_ids, attention_mask






   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int]  ):

     '''simple docstring'''
     __A									= self.prepare_config_and_inputs()
     __A			,    __A			,    __A			,    __A									= config_and_inputs
     __A									= {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
     return config, inputs_dict





@require_flax
class 					snake_case		(							_lowerCAmelCase   ,	unittest.TestCase						):


   '''simple docstring'''

   A_						:	Dict						 = True

   A_						:	Tuple						 = (
       (
           FlaxRoFormerModel,
           FlaxRoFormerForMaskedLM,
           FlaxRoFormerForSequenceClassification,
           FlaxRoFormerForTokenClassification,
           FlaxRoFormerForMultipleChoice,
           FlaxRoFormerForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any]  ):

     '''simple docstring'''
     __A									= FlaxRoFormerModelTester(self  )






   @slow
   def 				_SCREAMING_SNAKE_CASE				(	self :     Any  ):

     '''simple docstring'''
     for model_class_name in self.all_model_classes:
       __A									= model_class_name.from_pretrained('''junnyu/roformer_chinese_small''',       from_pt=_lowerCamelCase  )
       __A									= model(np.ones((1, 1)  )  )
       self.assertIsNotNone(_lowerCamelCase  )





@require_flax
class 					snake_case		(							unittest.TestCase						):


   '''simple docstring'''

   @slow
   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     __A									= FlaxRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''  )
     __A									= jnp.array([[0, 1, 2, 3, 4, 5]]  )
     __A									= model(_lowerCamelCase  )[0]

     __A									= 5_00_00

     __A									= (1, 6, vocab_size)
     self.assertEqual(output.shape,       _lowerCamelCase  )

     __A									= jnp.array(
         [[[-0.12_05, -1.02_65, 0.29_22], [-1.51_34, 0.19_74, 0.15_19], [-5.01_35, -3.90_03, -0.84_04]]]  )

     self.assertTrue(jnp.allclose(output[:, :3, :3],       _lowerCamelCase,       atol=1e-4  )  )




