
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_=32       ,   A_=3       ,   A_=10       ,   A_=[8, 16, 32, 64]       ,   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       ,   )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  parent
				UpperCamelCase						=  batch_size
				UpperCamelCase						=  image_size
				UpperCamelCase						=  num_channels
				UpperCamelCase						=  embeddings_size
				UpperCamelCase						=  hidden_sizes
				UpperCamelCase						=  depths
				UpperCamelCase						=  is_training
				UpperCamelCase						=  use_labels
				UpperCamelCase						=  hidden_act
				UpperCamelCase						=  num_labels
				UpperCamelCase						=  scope
				UpperCamelCase						=  len(A_ )
				UpperCamelCase						=  out_features
				UpperCamelCase						=  out_indices
				UpperCamelCase						=  num_groups



		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

				UpperCamelCase						=  self.get_config()

				return config, pixel_values, labels



		def __UpperCamelCase				(				self )						->      List[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 __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_ )						->      Any:
				"""simple docstring"""
				UpperCamelCase						=  BitModel(config=A_ )
				model.to(A_ )
				model.eval()
				UpperCamelCase						=  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       ,   A_       ,   A_       ,   A_ )						->      Optional[int]:
				"""simple docstring"""
				UpperCamelCase						=  self.num_labels
				UpperCamelCase						=  BitForImageClassification(A_ )
				model.to(A_ )
				model.eval()
				UpperCamelCase						=  model(A_       ,   labels=A_ )
				self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, self.num_labels) )



		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_ )						->      Dict:
				"""simple docstring"""
				UpperCamelCase						=  BitBackbone(config=A_ )
				model.to(A_ )
				model.eval()
				UpperCamelCase						=  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
				UpperCamelCase						=  None
				UpperCamelCase						=  BitBackbone(config=A_ )
				model.to(A_ )
				model.eval()
				UpperCamelCase						=  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:
				"""simple docstring"""
				UpperCamelCase						=  self.prepare_config_and_inputs()
				UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase						=  config_and_inputs
				UpperCamelCase						=  {'pixel_values': pixel_values}
				return config, inputs_dict






@require_torch
class        lowercase      (	_SCREAMING_SNAKE_CASE						,     _SCREAMING_SNAKE_CASE						,     unittest.TestCase     ):
		__lowercase : List[Any]			  =       (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
		__lowercase : Any			  =       (
		    {"feature-extraction": BitModel, "image-classification": BitForImageClassification}
		    if is_torch_available()
		    else {}
		)

		__lowercase : Any			  =       False
		__lowercase : Any			  =       False
		__lowercase : int			  =       False
		__lowercase : Dict			  =       False
		__lowercase : Any			  =       False



		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				UpperCamelCase						=  BitModelTester(self )
				UpperCamelCase						=  ConfigTester(self       ,   config_class=A_       ,   has_text_modality=A_ )



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""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 __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				return



		@unittest.skip(reason='Bit does not output attentions' )
		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				pass



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



		@unittest.skip(reason='Bit does not support input and output embeddings' )
		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				pass



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				UpperCamelCase			,						UpperCamelCase						=  self.model_tester.prepare_config_and_inputs_for_common()

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

						UpperCamelCase						=  ['pixel_values']
						self.assertListEqual(arg_names[:1]       ,   A_ )



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*A_ )



		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				UpperCamelCase						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_backbone(*A_ )



		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				UpperCamelCase			,						UpperCamelCase						=  self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						UpperCamelCase						=  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 )						->      Optional[Any]:
				"""simple docstring"""
				def check_hidden_states_output(A_       ,   A_       ,   A_ ):
						UpperCamelCase						=  model_class(A_ )
						model.to(A_ )
						model.eval()

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

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

						UpperCamelCase						=  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]       ,   )

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

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

								check_hidden_states_output(A_       ,   A_       ,   A_ )



		@unittest.skip(reason='Bit does not use feedforward chunking' )
		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				pass



		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				UpperCamelCase						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*A_ )



		@slow
		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						UpperCamelCase						=  BitModel.from_pretrained(A_ )
						self.assertIsNotNone(A_ )






def    A (      )     ->						Tuple:
		'''simple docstring'''

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


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



		@cached_property
		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				return (
				    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ) if is_vision_available() else None
				)



		@slow
		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				UpperCamelCase						=  BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(A_ )

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

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

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

				UpperCamelCase						=  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        lowercase      (	_SCREAMING_SNAKE_CASE						,     unittest.TestCase     ):
		__lowercase : List[Any]			  =       (BitBackbone,) if is_torch_available() else ()
		__lowercase : List[Any]			  =       BitConfig

		__lowercase : int			  =       False



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  BitModelTester(self )


from collections import OrderedDict
from typing import Mapping

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


_UpperCAmelCase      :				Tuple          = logging.get_logger(__name__)

_UpperCAmelCase      :				Union[str, Any]          = {
    "facebook/data2vec-text-base": "https://huggingface.co/data2vec/resolve/main/config.json",
}


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : Dict			  =       "data2vec-text"



		def __init__(				self       ,   A_=30_522       ,   A_=768       ,   A_=12       ,   A_=12       ,   A_=3_072       ,   A_="gelu"       ,   A_=0.1       ,   A_=0.1       ,   A_=512       ,   A_=2       ,   A_=0.02       ,   A_=1e-12       ,   A_=1       ,   A_=0       ,   A_=2       ,   A_="absolute"       ,   A_=True       ,   A_=None       ,   **A_       ,   )						->      Any:
				"""simple docstring"""
				super().__init__(pad_token_id=A_       ,   bos_token_id=A_       ,   eos_token_id=A_       ,   **A_ )

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






class        lowercase      (	_SCREAMING_SNAKE_CASE     ):



		@property
		def __UpperCamelCase				(				self )						->      Mapping[str, Mapping[int, str]]:
				"""simple docstring"""
				if self.task == "multiple-choice":
						UpperCamelCase						=  {0: 'batch', 1: 'choice', 2: 'sequence'}
				else:
						UpperCamelCase						=  {0: 'batch', 1: 'sequence'}
				return OrderedDict(
				    [
				        ('input_ids', dynamic_axis),
				        ('attention_mask', dynamic_axis),
				    ] )
