




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     lowercase		:




							def __init__(    self       ,A__       ,A__=3       ,A__=3_2       ,A__=3       ,A__=1_0       ,A__=[8, 1_6, 3_2, 6_4]       ,A__=[1, 1, 2, 1]       ,A__=True       ,A__=True       ,A__="relu"       ,A__=3       ,A__=None       ,A__=["stage2", "stage3", "stage4"]       ,A__=[2, 3, 4]       ,A__=1       ,):
												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(snake_case__)
												lowercase						     =						out_features
												lowercase						     =						out_indices
												lowercase						     =						num_groups




							def 			A__				(    self):
												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 			A__				(    self):
												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 			A__				(    self       ,A__       ,A__       ,A__):
												lowercase						     =						BitModel(config=snake_case__)
												model.to(snake_case__)
												model.eval()
												lowercase						     =						model(snake_case__)
												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 			A__				(    self       ,A__       ,A__       ,A__):
												lowercase						     =						self.num_labels
												lowercase						     =						BitForImageClassification(snake_case__)
												model.to(snake_case__)
												model.eval()
												lowercase						     =						model(snake_case__       ,labels=snake_case__)
												self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.num_labels))




							def 			A__				(    self       ,A__       ,A__       ,A__):
												lowercase						     =						BitBackbone(config=snake_case__)
												model.to(snake_case__)
												model.eval()
												lowercase						     =						model(snake_case__)

												# 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=snake_case__)
												model.to(snake_case__)
												model.eval()
												lowercase						     =						model(snake_case__)

												# 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 			A__				(    self):
												lowercase						     =						self.prepare_config_and_inputs()
												lowercase						     =						config_and_inputs
												lowercase						     =						{'''pixel_values''': pixel_values}
												return config, inputs_dict







@require_torch
class     lowercase		( lowerCamelCase__	,  lowerCamelCase__	,  unittest.TestCase		):
							lowercase_	:	Dict	 =(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
							lowercase_	:	int	 =(
							    {"""feature-extraction""": BitModel, """image-classification""": BitForImageClassification}
							    if is_torch_available()
							    else {}
							)

							lowercase_	:	Optional[Any]	 =False
							lowercase_	:	Dict	 =False
							lowercase_	:	str	 =False
							lowercase_	:	Optional[int]	 =False
							lowercase_	:	Optional[Any]	 =False




							def 			A__				(    self):
												lowercase						     =						BitModelTester(self)
												lowercase						     =						ConfigTester(self       ,config_class=snake_case__       ,has_text_modality=snake_case__)




							def 			A__				(    self):
												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 			A__				(    self):
												return




							@unittest.skip(reason='''Bit does not output attentions''')
							def 			A__				(    self):
												pass




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




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




							def 			A__				(    self):
												lowercase						     =						self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	lowercase						     =						model_class(snake_case__)
																	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]       ,snake_case__)




							def 			A__				(    self):
												lowercase						     =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*snake_case__)




							def 			A__				(    self):
												lowercase						     =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_backbone(*snake_case__)




							def 			A__				(    self):
												lowercase						     =						self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	lowercase						     =						model_class(config=snake_case__)
																	for name, module in model.named_modules():
																						if isinstance(snake_case__       ,(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 			A__				(    self):





												def check_hidden_states_output(A__       ,A__       ,A__):
																	lowercase						     =						model_class(snake_case__)
																	model.to(snake_case__)
																	model.eval()

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

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

																	lowercase						     =						self.model_tester.num_stages
																	self.assertEqual(len(snake_case__)       ,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						     =						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(snake_case__       ,snake_case__       ,snake_case__)

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

																						check_hidden_states_output(snake_case__       ,snake_case__       ,snake_case__)




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




							def 			A__				(    self):
												lowercase						     =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*snake_case__)





							@slow
							def 			A__				(    self):





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



def 	UpperCamelCase   (							):
					'''simple docstring'''
					lowercase						     =						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
					return image







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




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





							@slow
							def 			A__				(    self):
												lowercase						     =						BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(snake_case__)

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

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

												# verify the logits
												lowercase						     =						torch.Size((1, 1_0_0_0))
												self.assertEqual(outputs.logits.shape       ,snake_case__)

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

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







@require_torch
class     lowercase		( lowerCamelCase__	,  unittest.TestCase		):
							lowercase_	:	Dict	 =(BitBackbone,) if is_torch_available() else ()
							lowercase_	:	Optional[int]	 =BitConfig

							lowercase_	:	Union[str, Any]	 =False




							def 			A__				(    self):
												lowercase						     =						BitModelTester(self)






from collections import OrderedDict
from typing import Mapping

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


lowercase__		:Optional[int]			  =   logging.get_logger(__name__)

lowercase__		:List[Any]			  =   {
    "camembert-base": "https://huggingface.co/camembert-base/resolve/main/config.json",
    "umberto-commoncrawl-cased-v1": (
        "https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"
    ),
    "umberto-wikipedia-uncased-v1": (
        "https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"
    ),
}







class     lowercase		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	List[Any]	 ='''camembert'''




							def __init__(    self       ,A__=3_0_5_2_2       ,A__=7_6_8       ,A__=1_2       ,A__=1_2       ,A__=3_0_7_2       ,A__="gelu"       ,A__=0.1       ,A__=0.1       ,A__=5_1_2       ,A__=2       ,A__=0.02       ,A__=1E-12       ,A__=1       ,A__=0       ,A__=2       ,A__="absolute"       ,A__=True       ,A__=None       ,**A__       ,):
												super().__init__(pad_token_id=A__       ,bos_token_id=A__       ,eos_token_id=A__       ,**A__)

												lowercase						     =						vocab_size
												lowercase						     =						hidden_size
												lowercase						     =						num_hidden_layers
												lowercase						     =						num_attention_heads
												lowercase						     =						hidden_act
												lowercase						     =						intermediate_size
												lowercase						     =						hidden_dropout_prob
												lowercase						     =						attention_probs_dropout_prob
												lowercase						     =						max_position_embeddings
												lowercase						     =						type_vocab_size
												lowercase						     =						initializer_range
												lowercase						     =						layer_norm_eps
												lowercase						     =						position_embedding_type
												lowercase						     =						use_cache
												lowercase						     =						classifier_dropout







class     lowercase		( SCREAMING_SNAKE_CASE__		):




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




