
"""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 					lowerCAmelCase_					:



						'''simple docstring'''


						def __init__(			self  :   List[Any]	,A_  :   Dict	,A_  :   Optional[Any]=3	,A_  :   Optional[int]=32	,A_  :   Dict=3	,A_  :   Tuple=10	,A_  :   Tuple=[8, 16, 32, 64]	,A_  :   Optional[int]=[1, 1, 2, 1]	,A_  :   int=True	,A_  :   Dict=True	,A_  :   Union[str, Any]="relu"	,A_  :   Dict=3	,A_  :   Optional[int]=None	,A_  :   int=["stage2", "stage3", "stage4"]	,A_  :   Tuple=[2, 3, 4]	,A_  :   List[str]=1	,)  -> str:
							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(A_   )
							A     = out_features
							A     = out_indices
							A     = num_groups


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


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]	,A_  :   Union[str, Any]	,A_  :   str	,A_  :   Optional[Any]   )  -> str:
							A     = BitBackbone(config=A_   )
							model.to(A_   )
							model.eval()
							A     = 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
							A     = None
							A     = BitBackbone(config=A_   )
							model.to(A_   )
							model.eval()
							A     = 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     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> Union[str, Any]:
							A     = self.prepare_config_and_inputs()
							A ,   A ,   A     = config_and_inputs
							A     = {'pixel_values': pixel_values}
							return config, inputs_dict







@require_torch
class 					lowerCAmelCase_					(   _lowercase		,       _lowercase		,       unittest.TestCase     ):



						'''simple docstring'''


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

						_lowerCamelCase:     Union[str, Any]   =     False
						_lowerCamelCase:     Optional[int]   =     False
						_lowerCamelCase:     List[str]   =     False
						_lowerCamelCase:     int   =     False
						_lowerCamelCase:     Any   =     False


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> Optional[int]:
							A     = BitModelTester(self   )
							A     = ConfigTester(self	,config_class=A_	,has_text_modality=A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> Optional[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     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> Optional[Any]:
							return


						@unittest.skip(reason='Bit does not output attentions'   )
						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> List[str]:
							pass


						@unittest.skip(reason='Bit does not use inputs_embeds'   )
						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> Union[str, Any]:
							pass


						@unittest.skip(reason='Bit does not support input and output embeddings'   )
						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]   )  -> Dict:
							pass


						def     _SCREAMING_SNAKE_CASE   (			self  :   str   )  -> List[Any]:
							A ,   A     = self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								A     = model_class(A_   )
								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]	,A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> List[Any]:
							A     = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> Tuple:
							A     = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_backbone(*A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any   )  -> int:
							A ,   A     = self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								A     = 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     _SCREAMING_SNAKE_CASE   (			self  :   Tuple   )  -> int:
							def check_hidden_states_output(A_  :   Any	,A_  :   List[str]	,A_  :   int   ):
								A     = model_class(A_   )
								model.to(A_   )
								model.eval()

								with torch.no_grad():
									A     = model(**self._prepare_for_class(A_	,A_   )   )

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

								A     = 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]	,)

							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(A_	,A_	,A_   )

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

									check_hidden_states_output(A_	,A_	,A_   )


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


						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]   )  -> List[Any]:
							A     = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*A_   )




						@slow
						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Optional[Any]:
							for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								A     = BitModel.from_pretrained(A_   )
								self.assertIsNotNone(A_   )






def    _snake_case					(		):
	A     = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
	return image







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



						'''simple docstring'''


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




						@slow
						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Any:
							A     = BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ).to(A_   )

							A     = self.default_image_processor
							A     = prepare_img()
							A     = image_processor(images=A_	,return_tensors='pt'   ).to(A_   )

							# forward pass
							with torch.no_grad():
								A     = model(**A_   )

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

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

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







@require_torch
class 					lowerCAmelCase_					(   _lowercase		,       unittest.TestCase     ):



						'''simple docstring'''


						_lowerCamelCase:     Optional[Any]   =     (BitBackbone,) if is_torch_available() else ()
						_lowerCamelCase:     List[str]   =     BitConfig

						_lowerCamelCase:     Optional[Any]   =     False


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Union[str, Any]:
							A     = BitModelTester(self   )





'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import numpy
							import tensorflow as tf

							from transformers import (
							    TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
							    TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
							    TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
							    BertConfig,
							    DPRConfig,
							    TFDPRContextEncoder,
							    TFDPRQuestionEncoder,
							    TFDPRReader,
							)






class 		a							:


				def __init__(						self       :   Any			,							lowercase_       :   int			,							lowercase_       :   str=13			,							lowercase_       :   Union[str, Any]=7			,							lowercase_       :   Any=True			,							lowercase_       :   List[str]=True			,							lowercase_       :   Tuple=True			,							lowercase_       :   List[Any]=True			,							lowercase_       :   str=99			,							lowercase_       :   int=32			,							lowercase_       :   int=2			,							lowercase_       :   Any=4			,							lowercase_       :   Union[str, Any]=37			,							lowercase_       :   Optional[int]="gelu"			,							lowercase_       :   Dict=0.1			,							lowercase_       :   Optional[Any]=0.1			,							lowercase_       :   Union[str, Any]=512			,							lowercase_       :   Dict=16			,							lowercase_       :   int=2			,							lowercase_       :   Any=0.02			,							lowercase_       :   int=3			,							lowercase_       :   Any=4			,							lowercase_       :   List[Any]=None			,							lowercase_       :   List[str]=0			,							):
									snake_case_		     =							parent
									snake_case_		     =							batch_size
									snake_case_		     =							seq_length
									snake_case_		     =							is_training
									snake_case_		     =							use_input_mask
									snake_case_		     =							use_token_type_ids
									snake_case_		     =							use_labels
									snake_case_		     =							vocab_size
									snake_case_		     =							hidden_size
									snake_case_		     =							num_hidden_layers
									snake_case_		     =							num_attention_heads
									snake_case_		     =							intermediate_size
									snake_case_		     =							hidden_act
									snake_case_		     =							hidden_dropout_prob
									snake_case_		     =							attention_probs_dropout_prob
									snake_case_		     =							max_position_embeddings
									snake_case_		     =							type_vocab_size
									snake_case_		     =							type_sequence_label_size
									snake_case_		     =							initializer_range
									snake_case_		     =							num_labels
									snake_case_		     =							num_choices
									snake_case_		     =							scope
									snake_case_		     =							projection_dim


				def     A_					(						self       :   int   ):
									snake_case_		     =							ids_tensor([self.batch_size, self.seq_length]			,							self.vocab_size   )

									snake_case_		     =							None
									if self.use_input_mask:
														# follow test_modeling_tf_ctrl.py
														snake_case_		     =							random_attention_mask([self.batch_size, self.seq_length]   )

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

									snake_case_		     =							None
									snake_case_		     =							None
									snake_case_		     =							None
									if self.use_labels:
														snake_case_		     =							ids_tensor([self.batch_size]			,							self.type_sequence_label_size   )
														snake_case_		     =							ids_tensor([self.batch_size, self.seq_length]			,							self.num_labels   )
														snake_case_		     =							ids_tensor([self.batch_size]			,							self.num_choices   )

									snake_case_		     =							BertConfig(
									    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=lowercase_			,							initializer_range=self.initializer_range			,							)
									snake_case_		     =							DPRConfig(projection_dim=self.projection_dim			,							**config.to_dict()   )

									return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


				def     A_					(						self       :   Dict			,							lowercase_       :   Optional[Any]			,							lowercase_       :   int			,							lowercase_       :   Optional[int]			,							lowercase_       :   int			,							lowercase_       :   int			,							lowercase_       :   Union[str, Any]			,							lowercase_       :   str   ):
									snake_case_		     =							TFDPRContextEncoder(config=lowercase_   )
									snake_case_		     =							model(lowercase_			,							attention_mask=lowercase_			,							token_type_ids=lowercase_   )
									snake_case_		     =							model(lowercase_			,							token_type_ids=lowercase_   )
									snake_case_		     =							model(lowercase_   )
									self.parent.assertEqual(result.pooler_output.shape			,							(self.batch_size, self.projection_dim or self.hidden_size)   )


				def     A_					(						self       :   Any			,							lowercase_       :   Optional[Any]			,							lowercase_       :   List[str]			,							lowercase_       :   Union[str, Any]			,							lowercase_       :   Dict			,							lowercase_       :   Dict			,							lowercase_       :   Dict			,							lowercase_       :   Any   ):
									snake_case_		     =							TFDPRQuestionEncoder(config=lowercase_   )
									snake_case_		     =							model(lowercase_			,							attention_mask=lowercase_			,							token_type_ids=lowercase_   )
									snake_case_		     =							model(lowercase_			,							token_type_ids=lowercase_   )
									snake_case_		     =							model(lowercase_   )
									self.parent.assertEqual(result.pooler_output.shape			,							(self.batch_size, self.projection_dim or self.hidden_size)   )


				def     A_					(						self       :   str			,							lowercase_       :   Tuple			,							lowercase_       :   Tuple			,							lowercase_       :   Tuple			,							lowercase_       :   Tuple			,							lowercase_       :   Tuple			,							lowercase_       :   Dict			,							lowercase_       :   Dict   ):
									snake_case_		     =							TFDPRReader(config=lowercase_   )
									snake_case_		     =							model(lowercase_			,							attention_mask=lowercase_   )

									self.parent.assertEqual(result.start_logits.shape			,							(self.batch_size, self.seq_length)   )
									self.parent.assertEqual(result.end_logits.shape			,							(self.batch_size, self.seq_length)   )
									self.parent.assertEqual(result.relevance_logits.shape			,							(self.batch_size,)   )


				def     A_					(						self       :   Union[str, Any]   ):
									snake_case_		     =							self.prepare_config_and_inputs()
									(
									    (
									    snake_case_
									)     ,(
									    snake_case_
									)     ,(
									    snake_case_
									)     ,(
									    snake_case_
									)     ,(
									    snake_case_
									)     ,(
									    snake_case_
									)     ,(
									    snake_case_
									)     ,
									)		     =							config_and_inputs
									snake_case_		     =							{'''input_ids''': input_ids}
									return config, inputs_dict







@require_tf
class 		a							(  _lowerCamelCase  ,	_lowerCamelCase  ,	unittest.TestCase	):
				snake_case_           =						(
				    (
				        TFDPRContextEncoder,
				        TFDPRQuestionEncoder,
				        TFDPRReader,
				    )
				    if is_tf_available()
				    else ()
				)
				snake_case_           =						{"feature-extraction": TFDPRQuestionEncoder} if is_tf_available() else {}

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


				def     A_					(						self       :   List[Any]   ):
									snake_case_		     =							TFDPRModelTester(self   )
									snake_case_		     =							ConfigTester(self			,							config_class=lowercase_			,							hidden_size=37   )


				def     A_					(						self       :   Any   ):
									self.config_tester.run_common_tests()


				def     A_					(						self       :   int   ):
									snake_case_		     =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_dpr_context_encoder(*lowercase_   )


				def     A_					(						self       :   List[Any]   ):
									snake_case_		     =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_dpr_question_encoder(*lowercase_   )


				def     A_					(						self       :   Any   ):
									snake_case_		     =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_dpr_reader(*lowercase_   )


				@slow
				def     A_					(						self       :   int   ):
									for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														snake_case_		     =							TFDPRContextEncoder.from_pretrained(lowercase_   )
														self.assertIsNotNone(lowercase_   )

									for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														snake_case_		     =							TFDPRContextEncoder.from_pretrained(lowercase_   )
														self.assertIsNotNone(lowercase_   )

									for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														snake_case_		     =							TFDPRQuestionEncoder.from_pretrained(lowercase_   )
														self.assertIsNotNone(lowercase_   )

									for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														snake_case_		     =							TFDPRReader.from_pretrained(lowercase_   )
														self.assertIsNotNone(lowercase_   )







@require_tf
class 		a							(  unittest.TestCase	):


				@slow
				def     A_					(						self       :   Dict   ):
									snake_case_		     =							TFDPRQuestionEncoder.from_pretrained('''facebook/dpr-question_encoder-single-nq-base'''   )

									snake_case_		     =							tf.constant(
									    [[101, 7592, 1010, 2003, 2026, 3899, 1_0140, 1029, 102]]   )  # [CLS] hello, is my dog cute? [SEP]
									snake_case_		     =							model(lowercase_   )[0]  # embedding shape = (1, 768)
									# compare the actual values for a slice.
									snake_case_		     =							tf.constant(
									    [
									        [
									            0.0323_6253,
									            0.1275_3335,
									            0.1681_8509,
									            0.0027_9786,
									            0.389_6933,
									            0.2426_4945,
									            0.217_8971,
									            -0.0233_5227,
									            -0.0848_1959,
									            -0.1432_4117,
									        ]
									    ]   )
									self.assertTrue(numpy.allclose(output[:, :10].numpy()			,							expected_slice.numpy()			,							atol=1e-4   )   )

