




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     __lowerCAmelCase :

			"""simple docstring"""







			def __init__(      self			,						lowerCamelCase__			,						lowerCamelCase__=3			,						lowerCamelCase__=32			,						lowerCamelCase__=3			,						lowerCamelCase__=10			,						lowerCamelCase__=[8, 16, 32, 64]			,						lowerCamelCase__=[1, 1, 2, 1]			,						lowerCamelCase__=True			,						lowerCamelCase__=True			,						lowerCamelCase__="relu"			,						lowerCamelCase__=3			,						lowerCamelCase__=None			,						lowerCamelCase__=["stage2", "stage3", "stage4"]			,						lowerCamelCase__=[2, 3, 4]			,						lowerCamelCase__=1			,						)    ->      Tuple:



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


			def       lowercase_    (      self							)    ->      Any:



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

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

							__lowerCamelCase		       =    self.get_config()

							return config, pixel_values, labels


			def       lowercase_    (      self							)    ->      str:



							'''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       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      str:



							'''simple docstring'''
							__lowerCamelCase		       =    BitModel(config=lowerCamelCase__							)
							model.to(lowerCamelCase__							)
							model.eval()
							__lowerCamelCase		       =    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			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      Optional[Any]:



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


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      Tuple:



							'''simple docstring'''
							__lowerCamelCase		       =    BitBackbone(config=lowerCamelCase__							)
							model.to(lowerCamelCase__							)
							model.eval()
							__lowerCamelCase		       =    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
							__lowerCamelCase		       =    None
							__lowerCamelCase		       =    BitBackbone(config=lowerCamelCase__							)
							model.to(lowerCamelCase__							)
							model.eval()
							__lowerCamelCase		       =    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							)    ->      List[str]:



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







@require_torch
class     __lowerCAmelCase (       lowercase_			,				lowercase_			,				unittest.TestCase ):

			"""simple docstring"""







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

			snake_case_   		=       False
			snake_case_   		=       False
			snake_case_   		=       False
			snake_case_   		=       False
			snake_case_   		=       False


			def       lowercase_    (      self							)    ->      List[Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    BitModelTester(self							)
							__lowerCamelCase		       =    ConfigTester(self			,						config_class=lowerCamelCase__			,						has_text_modality=lowerCamelCase__							)


			def       lowercase_    (      self							)    ->      str:



							'''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       lowercase_    (      self							)    ->      Any:



							'''simple docstring'''
							return


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



							'''simple docstring'''
							pass


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



							'''simple docstring'''
							pass


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



							'''simple docstring'''
							pass


			def       lowercase_    (      self							)    ->      List[Any]:



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

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

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


			def       lowercase_    (      self							)    ->      Any:



							'''simple docstring'''
							__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCamelCase__							)


			def       lowercase_    (      self							)    ->      Tuple:



							'''simple docstring'''
							__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_backbone(*lowerCamelCase__							)


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



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

							for model_class in self.all_model_classes:
											__lowerCamelCase		       =    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							)    ->      Union[str, Any]:



							'''simple docstring'''







							def check_hidden_states_output(lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							):
											__lowerCamelCase		       =    model_class(lowerCamelCase__							)
											model.to(lowerCamelCase__							)
											model.eval()

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

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

											__lowerCamelCase		       =    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]			,						)

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

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

															check_hidden_states_output(lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)


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



							'''simple docstring'''
							pass


			def       lowercase_    (      self							)    ->      List[Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__							)






			@slow
			def       lowercase_    (      self							)    ->      Optional[Any]:



							'''simple docstring'''







							for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__lowerCamelCase		       =    BitModel.from_pretrained(lowerCamelCase__							)
											self.assertIsNotNone(lowerCamelCase__							)







def 			lowerCamelCase_		(       )     ->       List[str]:





				"""simple docstring"""


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







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

			"""simple docstring"""







			@cached_property
			def       lowercase_    (      self							)    ->      List[str]:



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






			@slow
			def       lowercase_    (      self							)    ->      Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(lowerCamelCase__							)

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

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

							# verify the logits
							__lowerCamelCase		       =    torch.Size((1, 1_000)							)
							self.assertEqual(outputs.logits.shape			,						lowerCamelCase__							)

							__lowerCamelCase		       =    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     __lowerCAmelCase (       lowercase_			,				unittest.TestCase ):

			"""simple docstring"""







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

			snake_case_   		=       False


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



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






'''simple docstring'''

import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase							:   str  						=	logging.get_logger(__name__)

lowerCamelCase							:   int  						=	{
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}
class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      lowerCAmelCase__					:     Union[str, Any]  					=							"""mvp"""
      lowerCAmelCase__					:     Optional[Any]  					=							["""past_key_values"""]
      lowerCAmelCase__					:     List[str]  					=							{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



      def __init__(self    :    Any   ,						UpperCamelCase    :    Optional[int]=50267   ,						UpperCamelCase    :    Tuple=1024   ,						UpperCamelCase    :    int=12   ,						UpperCamelCase    :    Tuple=4096   ,						UpperCamelCase    :    Dict=16   ,						UpperCamelCase    :    int=12   ,						UpperCamelCase    :    Optional[int]=4096   ,						UpperCamelCase    :    Optional[int]=16   ,						UpperCamelCase    :    Tuple=0.0   ,						UpperCamelCase    :    Tuple=0.0   ,						UpperCamelCase    :    List[Any]="gelu"   ,						UpperCamelCase    :    Union[str, Any]=1024   ,						UpperCamelCase    :    Optional[Any]=0.1   ,						UpperCamelCase    :    str=0.0   ,						UpperCamelCase    :    str=0.0   ,						UpperCamelCase    :    Optional[Any]=0.02   ,						UpperCamelCase    :    List[str]=0.0   ,						UpperCamelCase    :    List[str]=False   ,						UpperCamelCase    :    Optional[int]=True   ,						UpperCamelCase    :    Any=1   ,						UpperCamelCase    :    int=0   ,						UpperCamelCase    :    int=2   ,						UpperCamelCase    :    Any=True   ,						UpperCamelCase    :    Optional[Any]=2   ,						UpperCamelCase    :    Optional[Any]=2   ,						UpperCamelCase    :    Tuple=False   ,						UpperCamelCase    :    int=100   ,						UpperCamelCase    :    Optional[Any]=800   ,						**UpperCamelCase    :    str   ,						):

             '''simple docstring'''







             lowercase__      			= vocab_size
             lowercase__      			= max_position_embeddings
             lowercase__      			= d_model
             lowercase__      			= encoder_ffn_dim
             lowercase__      			= encoder_layers
             lowercase__      			= encoder_attention_heads
             lowercase__      			= decoder_ffn_dim
             lowercase__      			= decoder_layers
             lowercase__      			= decoder_attention_heads
             lowercase__      			= dropout
             lowercase__      			= attention_dropout
             lowercase__      			= activation_dropout
             lowercase__      			= activation_function
             lowercase__      			= init_std
             lowercase__      			= encoder_layerdrop
             lowercase__      			= decoder_layerdrop
             lowercase__      			= classifier_dropout
             lowercase__      			= use_cache
             lowercase__      			= encoder_layers
             lowercase__      			= scale_embedding  # scale factor will be sqrt(d_model) if True
             lowercase__      			= use_prompt
             lowercase__      			= prompt_length
             lowercase__      			= prompt_mid_dim

             super().__init__(
                 pad_token_id=UpperCamelCase   ,						bos_token_id=UpperCamelCase   ,						eos_token_id=UpperCamelCase   ,						is_encoder_decoder=UpperCamelCase   ,						decoder_start_token_id=UpperCamelCase   ,						forced_eos_token_id=UpperCamelCase   ,						**UpperCamelCase   ,						)

             if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''   ,						UpperCamelCase ):
                    lowercase__      			= self.bos_token_id
                    warnings.warn(
                        f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
                        '''The config can simply be saved and uploaded again to be fixed.''' )




