





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 				A_				:







       """simple docstring"""





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







       def    __UpperCAmelCase (    self	:	int  )						->					Dict:
         _lowercase							= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

         _lowercase							= self.get_config()

         return config, pixel_values, labels







       def    __UpperCAmelCase (    self	:	List[str]  )						->					Union[str, 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    __UpperCAmelCase (    self	:	Tuple						,__A	:	List[str]						,__A	:	Optional[Any]						,__A	:	int  )						->					Any:
         _lowercase							= BitModel(config=__A  )
         model.to(__A  )
         model.eval()
         _lowercase							= 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    __UpperCAmelCase (    self	:	int						,__A	:	Any						,__A	:	Optional[int]						,__A	:	Union[str, Any]  )						->					List[str]:
         _lowercase							= self.num_labels
         _lowercase							= BitForImageClassification(__A  )
         model.to(__A  )
         model.eval()
         _lowercase							= model(__A						,labels=__A  )
         self.parent.assertEqual(result.logits.shape						,(self.batch_size, self.num_labels)  )







       def    __UpperCAmelCase (    self	:	Tuple						,__A	:	Optional[int]						,__A	:	Optional[int]						,__A	:	str  )						->					str:
         _lowercase							= BitBackbone(config=__A  )
         model.to(__A  )
         model.eval()
         _lowercase							= 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
         _lowercase							= None
         _lowercase							= BitBackbone(config=__A  )
         model.to(__A  )
         model.eval()
         _lowercase							= 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    __UpperCAmelCase (    self	:	Any  )						->					List[str]:
         _lowercase							= self.prepare_config_and_inputs()
         _lowercase						,		_lowercase						,		_lowercase							= config_and_inputs
         _lowercase							= {'pixel_values': pixel_values}
         return config, inputs_dict





@require_torch
class 				A_				(      UpperCAmelCase					,	UpperCAmelCase					,	unittest.TestCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : Optional[int]      =  (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
       SCREAMING_SNAKE_CASE_      : int      =  (
           {'''feature-extraction''': BitModel, '''image-classification''': BitForImageClassification}
           if is_torch_available()
           else {}
       )

       SCREAMING_SNAKE_CASE_      : str      =  False
       SCREAMING_SNAKE_CASE_      : Optional[Any]      =  False
       SCREAMING_SNAKE_CASE_      : Any      =  False
       SCREAMING_SNAKE_CASE_      : Any      =  False
       SCREAMING_SNAKE_CASE_      : int      =  False







       def    __UpperCAmelCase (    self	:	List[Any]  )						->					int:
         _lowercase							= BitModelTester(self  )
         _lowercase							= ConfigTester(self						,config_class=__A						,has_text_modality=__A  )







       def    __UpperCAmelCase (    self	:	str  )						->					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    __UpperCAmelCase (    self	:	List[Any]  )						->					int:
         return







       @unittest.skip(reason='Bit does not output attentions'  )
       def    __UpperCAmelCase (    self	:	str  )						->					Union[str, Any]:
         pass







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







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







       def    __UpperCAmelCase (    self	:	int  )						->					List[str]:
         _lowercase						,		_lowercase							= self.model_tester.prepare_config_and_inputs_for_common()

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

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







       def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					Optional[int]:
         _lowercase							= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__A  )







       def    __UpperCAmelCase (    self	:	Any  )						->					Optional[Any]:
         _lowercase							= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_backbone(*__A  )







       def    __UpperCAmelCase (    self	:	str  )						->					Any:
         _lowercase						,		_lowercase							= self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           _lowercase							= 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    __UpperCAmelCase (    self	:	Any  )						->					Dict:
         def check_hidden_states_output(__A	:	Optional[int]						,__A	:	List[Any]						,__A	:	Tuple  ):
           _lowercase							= model_class(__A  )
           model.to(__A  )
           model.eval()

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

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

           _lowercase							= 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]						,)

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

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

             check_hidden_states_output(__A						,__A						,__A  )







       @unittest.skip(reason='Bit does not use feedforward chunking'  )
       def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					Tuple:
         pass







       def    __UpperCAmelCase (    self	:	List[Any]  )						->					Tuple:
         _lowercase							= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__A  )







       @slow
       def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					Union[str, Any]:
         for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _lowercase							= BitModel.from_pretrained(__A  )
           self.assertIsNotNone(__A  )

def 							SCREAMING_SNAKE_CASE__       (  )      ->						Any:
  _lowercase							= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
  return image





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







       """simple docstring"""





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







       @slow
       def    __UpperCAmelCase (    self	:	Tuple  )						->					Any:
         _lowercase							= BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]  ).to(__A  )

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

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

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

         _lowercase							= 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 				A_				(      UpperCAmelCase					,	unittest.TestCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : List[Any]      =  (BitBackbone,) if is_torch_available() else ()
       SCREAMING_SNAKE_CASE_      : List[Any]      =  BitConfig

       SCREAMING_SNAKE_CASE_      : List[str]      =  False







       def    __UpperCAmelCase (    self	:	int  )						->					Any:
         _lowercase							= BitModelTester(self  )






from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


snake_case 				=       logging.get_logger(__name__)

snake_case 				=       {
    """EleutherAI/gpt-j-6B""": """https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json""",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : str      =  '''gptj'''
       SCREAMING_SNAKE_CASE_      : int      =  {
           '''max_position_embeddings''': '''n_positions''',
           '''hidden_size''': '''n_embd''',
           '''num_attention_heads''': '''n_head''',
           '''num_hidden_layers''': '''n_layer''',
       }







       def __init__(    self	:	str						,__A	:	List[str]=5_0400						,__A	:	Dict=2048						,__A	:	List[str]=4096						,__A	:	Dict=28						,__A	:	Optional[int]=16						,__A	:	Tuple=64						,__A	:	Optional[int]=None						,__A	:	Optional[int]="gelu_new"						,__A	:	Dict=0.0						,__A	:	List[str]=0.0						,__A	:	Optional[int]=0.0						,__A	:	str=1e-5						,__A	:	List[Any]=0.02						,__A	:	str=True						,__A	:	int=5_0256						,__A	:	Union[str, Any]=5_0256						,__A	:	int=False						,**__A	:	List[Any]						,)						->					str:
         _lowercase							= vocab_size
         _lowercase							= n_positions
         _lowercase							= n_embd
         _lowercase							= n_layer
         _lowercase							= n_head
         _lowercase							= n_inner
         _lowercase							= rotary_dim
         _lowercase							= activation_function
         _lowercase							= resid_pdrop
         _lowercase							= embd_pdrop
         _lowercase							= attn_pdrop
         _lowercase							= layer_norm_epsilon
         _lowercase							= initializer_range
         _lowercase							= use_cache

         _lowercase							= bos_token_id
         _lowercase							= eos_token_id

         super().__init__(
             bos_token_id=__A						,eos_token_id=__A						,tie_word_embeddings=__A						,**__A  )





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       def __init__(    self	:	Tuple						,__A	:	PretrainedConfig						,__A	:	str = "default"						,__A	:	List[PatchingSpec] = None						,__A	:	bool = False						,)						->					Any:
         super().__init__(__A						,task=__A						,patching_specs=__A						,use_past=__A  )
         if not getattr(self._config						,'pad_token_id'						,__A  ):
           # TODO: how to do that better?
           _lowercase							= 0







       @property
       def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					Mapping[str, Mapping[int, str]]:
         _lowercase							= OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}  )
         if self.use_past:
           self.fill_with_past_key_values_(__A						,direction='inputs'  )
           _lowercase							= {0: 'batch', 1: 'past_sequence + sequence'}
         else:
           _lowercase							= {0: 'batch', 1: 'sequence'}

         return common_inputs







       @property
       def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					int:
         return self._config.n_layer







       @property
       def    __UpperCAmelCase (    self	:	Tuple  )						->					int:
         return self._config.n_head







       def    __UpperCAmelCase (    self	:	Any						,__A	:	PreTrainedTokenizer						,__A	:	int = -1						,__A	:	int = -1						,__A	:	bool = False						,__A	:	Optional[TensorType] = None						,)						->					Mapping[str, Any]:
         _lowercase							= super(__A						,self  ).generate_dummy_inputs(
             __A						,batch_size=__A						,seq_length=__A						,is_pair=__A						,framework=__A  )

         # We need to order the input in the way they appears in the forward()
         _lowercase							= OrderedDict({'input_ids': common_inputs['input_ids']}  )

         # Need to add the past_keys
         if self.use_past:
           if not is_torch_available():
             raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'  )
           else:
             import torch

             _lowercase						,		_lowercase							= common_inputs['input_ids'].shape
             # Not using the same length for past_key_values
             _lowercase							= seqlen + 2
             _lowercase							= (
                 batch,
                 self.num_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // self.num_attention_heads,
             )
             _lowercase							= [
                 (torch.zeros(__A  ), torch.zeros(__A  )) for _ in range(self.num_layers  )
             ]

         _lowercase							= common_inputs['attention_mask']
         if self.use_past:
           _lowercase							= ordered_inputs['attention_mask'].dtype
           _lowercase							= torch.cat(
               [ordered_inputs['attention_mask'], torch.ones(__A						,__A						,dtype=__A  )]						,dim=1  )

         return ordered_inputs







       @property
       def    __UpperCAmelCase (    self	:	str  )						->					int:
         return 13