






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 							SCREAMING_SNAKE_CASE__						:





					"""simple docstring"""






					def __init__( self					:  str							,		__A					:  Optional[int]							,		__A					:  Union[str, Any]=3							,		__A					:  Dict=3_2							,		__A					:  List[str]=3							,		__A					:  Optional[int]=1_0							,		__A					:  Any=[8, 1_6, 3_2, 6_4]							,		__A					:  Optional[Any]=[1, 1, 2, 1]							,		__A					:  Dict=True							,		__A					:  Optional[Any]=True							,		__A					:  Any="relu"							,		__A					:  Dict=3							,		__A					:  Dict=None							,		__A					:  int=["stage2", "stage3", "stage4"]							,		__A					:  List[Any]=[2, 3, 4]							,		__A					:  Tuple=1							,		):
						snake_case__			:				List[Any]       =  parent
						snake_case__			:				Optional[Any]       =  batch_size
						snake_case__			:				List[str]       =  image_size
						snake_case__			:				Optional[int]       =  num_channels
						snake_case__			:				int       =  embeddings_size
						snake_case__			:				Union[str, Any]       =  hidden_sizes
						snake_case__			:				Optional[int]       =  depths
						snake_case__			:				Union[str, Any]       =  is_training
						snake_case__			:				List[str]       =  use_labels
						snake_case__			:				Tuple       =  hidden_act
						snake_case__			:				Dict       =  num_labels
						snake_case__			:				Any       =  scope
						snake_case__			:				Any       =  len(__A	)
						snake_case__			:				List[str]       =  out_features
						snake_case__			:				Optional[int]       =  out_indices
						snake_case__			:				str       =  num_groups



					def 	_lowercase					( self					:  List[str]	):
						snake_case__			:				Optional[int]       =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

						snake_case__			:				List[Any]       =  self.get_config()

						return config, pixel_values, labels



					def 	_lowercase					( self					:  str	):
						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					:  Dict							,		__A					:  Tuple							,		__A					:  int							,		__A					:  Tuple	):
						snake_case__			:				List[Any]       =  BitModel(config=__A	)
						model.to(__A	)
						model.eval()
						snake_case__			:				List[Any]       =  model(__A	)
						self.parent.assertEqual(
						    result.last_hidden_state.shape							,		(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)							,		)



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



					def 	_lowercase					( self					:  int							,		__A					:  int							,		__A					:  List[Any]							,		__A					:  Optional[Any]	):
						snake_case__			:				Optional[Any]       =  BitBackbone(config=__A	)
						model.to(__A	)
						model.eval()
						snake_case__			:				Optional[Any]       =  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__			:				Union[str, Any]       =  None
						snake_case__			:				List[Any]       =  BitBackbone(config=__A	)
						model.to(__A	)
						model.eval()
						snake_case__			:				Any       =  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 	_lowercase					( self					:  Tuple	):
						snake_case__			:				int       =  self.prepare_config_and_inputs()
						snake_case__			:				Dict       =  config_and_inputs
						snake_case__			:				List[Any]       =  {"pixel_values": pixel_values}
						return config, inputs_dict



@require_torch
class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_			,       UpperCamelCase_			,       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					:  int	):
						snake_case__			:				List[Any]       =  BitModelTester(self	)
						snake_case__			:				int       =  ConfigTester(self							,		config_class=__A							,		has_text_modality=__A	)



					def 	_lowercase					( self					:  int	):
						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					:  int	):
						return



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



					@unittest.skip(reason="Bit does not use inputs_embeds"	)
					def 	_lowercase					( self					:  Dict	):
						pass



					@unittest.skip(reason="Bit does not support input and output embeddings"	)
					def 	_lowercase					( self					:  str	):
						pass



					def 	_lowercase					( self					:  Optional[Any]	):
						snake_case__			:				Dict       =  self.model_tester.prepare_config_and_inputs_for_common()

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

							snake_case__			:				Union[str, Any]       =  ["pixel_values"]
							self.assertListEqual(arg_names[:1]							,		__A	)



					def 	_lowercase					( self					:  int	):
						snake_case__			:				List[str]       =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__A	)



					def 	_lowercase					( self					:  Optional[int]	):
						snake_case__			:				str       =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_backbone(*__A	)



					def 	_lowercase					( self					:  Any	):
						snake_case__			:				Any       =  self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							snake_case__			:				str       =  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 	_lowercase					( self					:  Any	):
						def check_hidden_states_output(__A					:  Dict							,		__A					:  List[Any]							,		__A					:  List[Any]	):
							snake_case__			:				Any       =  model_class(__A	)
							model.to(__A	)
							model.eval()

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

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

							snake_case__			:				int       =  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__			:				Optional[int]       =  self.model_tester.prepare_config_and_inputs_for_common()
						snake_case__			:				List[str]       =  ["preactivation", "bottleneck"]
						for model_class in self.all_model_classes:
							for layer_type in layers_type:
								snake_case__			:				int       =  layer_type
								snake_case__			:				Tuple       =  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__			:				Optional[int]       =  True

								check_hidden_states_output(__A							,		__A							,		__A	)



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



					def 	_lowercase					( self					:  Union[str, Any]	):
						snake_case__			:				Optional[Any]       =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*__A	)






					@slow
					def 	_lowercase					( self					:  int	):
						for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							snake_case__			:				str       =  BitModel.from_pretrained(__A	)
							self.assertIsNotNone(__A	)







def 					SCREAMING_SNAKE_CASE    (			):
	snake_case__			:				int       =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
	return image



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





					"""simple docstring"""






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






					@slow
					def 	_lowercase					( self					:  List[str]	):
						snake_case__			:				Any       =  BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]	).to(__A	)

						snake_case__			:				int       =  self.default_image_processor
						snake_case__			:				int       =  prepare_img()
						snake_case__			:				Optional[int]       =  image_processor(images=__A							,		return_tensors="pt"	).to(__A	)

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

						# verify the logits
						snake_case__			:				Optional[int]       =  torch.Size((1, 1_0_0_0)	)
						self.assertEqual(outputs.logits.shape							,		__A	)

						snake_case__			:				Dict       =  torch.tensor([[-0.6_5_2_6, -0.5_2_6_3, -1.4_3_9_8]]	).to(__A	)

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



@require_torch
class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_			,       unittest.TestCase						):





					"""simple docstring"""






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

					a_     =		False



					def 	_lowercase					( self					:  Optional[Any]	):
						snake_case__			:				int       =  BitModelTester(self	)











from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCamelCase :      List[str]										=				logging.get_logger(__name__)

__lowerCamelCase :      List[Any]										=				{
    """facebook/xmod-base""": """https://huggingface.co/facebook/xmod-base/resolve/main/config.json""",
    """facebook/xmod-large-prenorm""": """https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json""",
    """facebook/xmod-base-13-125k""": """https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-125k""": """https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-195k""": """https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json""",
    """facebook/xmod-base-60-125k""": """https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json""",
    """facebook/xmod-base-60-265k""": """https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json""",
    """facebook/xmod-base-75-125k""": """https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json""",
    """facebook/xmod-base-75-269k""": """https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json""",
}



class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					a_     =		"xmod"



					def __init__( self					:  List[str]							,		__A					:  List[str]=3_0_5_2_2							,		__A					:  Tuple=7_6_8							,		__A					:  str=1_2							,		__A					:  List[Any]=1_2							,		__A					:  List[str]=3_0_7_2							,		__A					:  List[str]="gelu"							,		__A					:  List[Any]=0.1							,		__A					:  Tuple=0.1							,		__A					:  str=5_1_2							,		__A					:  Union[str, Any]=2							,		__A					:  List[Any]=0.0_2							,		__A					:  List[str]=1e-1_2							,		__A					:  Tuple=1							,		__A					:  List[Any]=0							,		__A					:  Optional[Any]=2							,		__A					:  Optional[int]="absolute"							,		__A					:  Optional[int]=True							,		__A					:  Dict=None							,		__A					:  Optional[int]=False							,		__A					:  Dict=2							,		__A					:  List[str]=False							,		__A					:  Dict=True							,		__A					:  Union[str, Any]=True							,		__A					:  Tuple=("en_XX",)							,		__A					:  Optional[Any]=None							,		**__A					:  Tuple							,		):
						super().__init__(pad_token_id=__A							,		bos_token_id=__A							,		eos_token_id=__A							,		**__A	)

						snake_case__			:				Tuple       =  vocab_size
						snake_case__			:				Union[str, Any]       =  hidden_size
						snake_case__			:				int       =  num_hidden_layers
						snake_case__			:				str       =  num_attention_heads
						snake_case__			:				Tuple       =  hidden_act
						snake_case__			:				Optional[Any]       =  intermediate_size
						snake_case__			:				List[str]       =  hidden_dropout_prob
						snake_case__			:				Optional[int]       =  attention_probs_dropout_prob
						snake_case__			:				Union[str, Any]       =  max_position_embeddings
						snake_case__			:				str       =  type_vocab_size
						snake_case__			:				List[str]       =  initializer_range
						snake_case__			:				Dict       =  layer_norm_eps
						snake_case__			:				str       =  position_embedding_type
						snake_case__			:				List[str]       =  use_cache
						snake_case__			:				Tuple       =  classifier_dropout
						snake_case__			:				Any       =  pre_norm
						snake_case__			:				List[str]       =  adapter_reduction_factor
						snake_case__			:				List[Any]       =  adapter_layer_norm
						snake_case__			:				str       =  adapter_reuse_layer_norm
						snake_case__			:				Union[str, Any]       =  ln_before_adapter
						snake_case__			:				Tuple       =  list(__A	)
						snake_case__			:				int       =  default_language



class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					@property
					def 	_lowercase					( self					:  int	):
						if self.task == "multiple-choice":
							snake_case__			:				Any       =  {0: "batch", 1: "choice", 2: "sequence"}
						else:
							snake_case__			:				Any       =  {0: "batch", 1: "sequence"}
						return OrderedDict(
						    [
						        ("input_ids", dynamic_axis),
						        ("attention_mask", dynamic_axis),
						    ]	)



