

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 ,				__UpperCamelCase ,				__UpperCamelCase=3 ,				__UpperCamelCase=32 ,				__UpperCamelCase=3 ,				__UpperCamelCase=10 ,				__UpperCamelCase=[8, 16, 32, 64] ,				__UpperCamelCase=[1, 1, 2, 1] ,				__UpperCamelCase=True ,				__UpperCamelCase=True ,				__UpperCamelCase="relu" ,				__UpperCamelCase=3 ,				__UpperCamelCase=None ,				__UpperCamelCase=["stage2", "stage3", "stage4"] ,				__UpperCamelCase=[2, 3, 4] ,				__UpperCamelCase=1 ,				):

				"""simple docstring"""


				snake_case_      		= parent
				snake_case_      		= batch_size
				snake_case_      		= image_size
				snake_case_      		= num_channels
				snake_case_      		= embeddings_size
				snake_case_      		= hidden_sizes
				snake_case_      		= depths
				snake_case_      		= is_training
				snake_case_      		= use_labels
				snake_case_      		= hidden_act
				snake_case_      		= num_labels
				snake_case_      		= scope
				snake_case_      		= len(__UpperCamelCase							)
				snake_case_      		= out_features
				snake_case_      		= out_indices
				snake_case_      		= num_groups


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

				snake_case_      		= self.get_config()

				return config, pixel_values, labels


			def 			__lowerCAmelCase (    self							):

				"""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 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= BitModel(config=__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= model(__UpperCamelCase							)
				self.parent.assertEqual(
				    result.last_hidden_state.shape ,				(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) ,				)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= self.num_labels
				snake_case_      		= BitForImageClassification(__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= model(__UpperCamelCase ,				labels=__UpperCamelCase							)
				self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= BitBackbone(config=__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= model(__UpperCamelCase							)

				# 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_      		= None
				snake_case_      		= BitBackbone(config=__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= model(__UpperCamelCase							)

				# 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 			__lowerCAmelCase (    self							):

				"""simple docstring"""


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







@require_torch
class SCREAMING_SNAKE_CASE  (		__snake_case							,						__snake_case							,						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 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= BitModelTester(self							)
				snake_case_      		= ConfigTester(self ,				config_class=__UpperCamelCase ,				has_text_modality=__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""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 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return


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

				"""simple docstring"""


				pass


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

				"""simple docstring"""


				pass


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

				"""simple docstring"""


				pass


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs_for_common()

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

					snake_case_      		= ['pixel_values']
					self.assertListEqual(arg_names[:1] ,				__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_backbone(*__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					snake_case_      		= model_class(config=__UpperCamelCase							)
					for name, module in model.named_modules():
						if isinstance(__UpperCamelCase ,				(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 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				def check_hidden_states_output(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):
					snake_case_      		= model_class(__UpperCamelCase							)
					model.to(__UpperCamelCase							)
					model.eval()

					with torch.no_grad():
						snake_case_      		= model(**self._prepare_for_class(__UpperCamelCase ,				__UpperCamelCase							)							)

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

					snake_case_      		= self.model_tester.num_stages
					self.assertEqual(len(__UpperCamelCase							) ,				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_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs_for_common()
				snake_case_      		= ['preactivation', 'bottleneck']
				for model_class in self.all_model_classes:
					for layer_type in layers_type:
						snake_case_      		= layer_type
						snake_case_      		= True
						check_hidden_states_output(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							)

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

						check_hidden_states_output(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							)


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

				"""simple docstring"""


				pass


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase							)







			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


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







def a():





	'''simple docstring'''
	snake_case_      		= 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 			__lowerCAmelCase (    self							):

				"""simple docstring"""


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







			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(__UpperCamelCase							)

				snake_case_      		= self.default_image_processor
				snake_case_      		= prepare_img()
				snake_case_      		= image_processor(images=__UpperCamelCase ,				return_tensors='pt'							).to(__UpperCamelCase							)

				# forward pass
				with torch.no_grad():
					snake_case_      		= model(**__UpperCamelCase							)

				# verify the logits
				snake_case_      		= torch.Size((1, 10_00)							)
				self.assertEqual(outputs.logits.shape ,				__UpperCamelCase							)

				snake_case_      		= torch.tensor([[-0.6526, -0.5263, -1.4398]]							).to(__UpperCamelCase							)

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







@require_torch
class SCREAMING_SNAKE_CASE  (		__snake_case							,						unittest.TestCase							):






			"""simple docstring"""




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

			__A						  =  False


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= BitModelTester(self							)



def a(lowercase__			, lowercase__	):





	'''simple docstring'''
	if not isinstance(lowercase__			, lowercase__	):
		raise ValueError('iterations must be defined as integers'	)
	if not isinstance(lowercase__			, lowercase__	) or not number >= 1:
		raise ValueError(
		    'starting number must be\n                         and integer and be more than 0'	)
	if not iterations >= 1:
		raise ValueError('Iterations must be done more than 0 times to play FizzBuzz'	)

	snake_case_      		= ''
	while number <= iterations:
		if number % 3 == 0:
			out += "Fizz"
		if number % 5 == 0:
			out += "Buzz"
		if 0 not in (number % 3, number % 5):
			out += str(lowercase__	)

		# print(out)
		number += 1
		out += " "
	return out


if __name__ == "__main__":
		import doctest

		doctest.testmod()
