

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_      :




	def __init__(     self						:     Any					,      __A						:     Any					,      __A						:     Optional[Any]=3					,      __A						:     List[str]=32					,      __A						:     Tuple=3					,      __A						:     Optional[Any]=10					,      __A						:     Optional[Any]=[8, 16, 32, 64]					,      __A						:     int=[1, 1, 2, 1]					,      __A						:     Any=True					,      __A						:     int=True					,      __A						:     Tuple="relu"					,      __A						:     Tuple=3					,      __A						:     Dict=None					,      __A						:     Optional[int]=["stage2", "stage3", "stage4"]					,      __A						:     Dict=[2, 3, 4]					,      __A						:     int=1					,      ):
		__A :			Any		    =			parent
		__A :			str		    =			batch_size
		__A :			Any		    =			image_size
		__A :			Any		    =			num_channels
		__A :			List[str]		    =			embeddings_size
		__A :			Optional[Any]		    =			hidden_sizes
		__A :			List[Any]		    =			depths
		__A :			Dict		    =			is_training
		__A :			Dict		    =			use_labels
		__A :			Optional[Any]		    =			hidden_act
		__A :			Union[str, Any]		    =			num_labels
		__A :			Optional[int]		    =			scope
		__A :			Union[str, Any]		    =			len(__A							)
		__A :			Dict		    =			out_features
		__A :			str		    =			out_indices
		__A :			int		    =			num_groups




	def        lowerCAmelCase_       (     self						:     List[Any]							):
		__A :			Any		    =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

		__A :			int		    =			None
		if self.use_labels:
			__A :			Optional[int]		    =			ids_tensor([self.batch_size]					,      self.num_labels							)

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

		return config, pixel_values, labels




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




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






@require_torch
class      lowerCamelCase_      (	_lowercase						,      _lowercase						,      unittest.TestCase	):
	_lowercase		:   Tuple     					=				(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
	_lowercase		:   List[Any]     					=				(
	    {'''feature-extraction''': BitModel, '''image-classification''': BitForImageClassification}
	    if is_torch_available()
	    else {}
	)

	_lowercase		:   int     					=				False
	_lowercase		:   str     					=				False
	_lowercase		:   List[str]     					=				False
	_lowercase		:   List[str]     					=				False
	_lowercase		:   Any     					=				False




	def        lowerCAmelCase_       (     self						:     str							):
		__A :			Any		    =			BitModelTester(self							)
		__A :			List[Any]		    =			ConfigTester(self					,      config_class=__A					,      has_text_modality=__A							)




	def        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		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						:     Tuple							):
		return




	@unittest.skip(reason="""Bit does not output attentions"""							)
	def        lowerCAmelCase_       (     self						:     Tuple							):
		pass




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




	@unittest.skip(reason="""Bit does not support input and output embeddings"""							)
	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		pass




	def        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		__A    ,				__A :			Optional[Any]		    =			self.model_tester.prepare_config_and_inputs_for_common()

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

			__A :			int		    =			["""pixel_values"""]
			self.assertListEqual(arg_names[:1]					,      __A							)




	def        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		__A :			List[str]		    =			self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*__A							)




	def        lowerCAmelCase_       (     self						:     str							):
		__A :			Optional[Any]		    =			self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_backbone(*__A							)




	def        lowerCAmelCase_       (     self						:     int							):
		__A    ,				__A :			int		    =			self.model_tester.prepare_config_and_inputs_for_common()

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

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

			__A :			Dict		    =			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

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

				# check that output_hidden_states also work using config
				del inputs_dict["output_hidden_states"]
				__A :			List[Any]		    =			True

				check_hidden_states_output(__A					,      __A					,      __A							)




	@unittest.skip(reason="""Bit does not use feedforward chunking"""							)
	def        lowerCAmelCase_       (     self						:     Any							):
		pass




	def        lowerCAmelCase_       (     self						:     Tuple							):
		__A :			List[str]		    =			self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_image_classification(*__A							)




	@slow
	def        lowerCAmelCase_       (     self						:     List[Any]							):
		for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			__A :			List[Any]		    =			BitModel.from_pretrained(__A							)
			self.assertIsNotNone(__A							)






def 							__SCREAMING_SNAKE_CASE     (   )					->				int:
	__A :			Union[str, Any]		    =			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
	return image






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




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




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

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

		# forward pass
		with torch.no_grad():
			__A :			Union[str, Any]		    =			model(**__A							)

		# verify the logits
		__A :			Optional[int]		    =			torch.Size((1, 1000)							)
		self.assertEqual(outputs.logits.shape					,      __A							)

		__A :			Tuple		    =			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      lowerCamelCase_      (	_lowercase						,      unittest.TestCase	):
	_lowercase		:   Tuple     					=				(BitBackbone,) if is_torch_available() else ()
	_lowercase		:   Dict     					=				BitConfig

	_lowercase		:   Tuple     					=				False




	def        lowerCAmelCase_       (     self						:     Optional[Any]							):
		__A :			Dict		    =			BitModelTester(self							)








import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp
def 							__SCREAMING_SNAKE_CASE     (   )					->				Tuple:
	__A :			List[Any]		    =			ArgumentParser(
	    description=(
	        """PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"""
	    )       )

	# Optional arguments for the launch helper
	parser.add_argument("""--num_cores"""			,type=a__			,default=1			,help="""Number of TPU cores to use (1 or 8)."""       )

	# positional
	parser.add_argument(
	    """training_script"""			,type=a__			,help=(
	        """The full path to the single TPU training """
	        """program/script to be launched in parallel, """
	        """followed by all the arguments for the """
	        """training script"""
	    )			,)

	# rest from the training program
	parser.add_argument("""training_script_args"""			,nargs=a__       )

	return parser.parse_args()
def 							__SCREAMING_SNAKE_CASE     (   )					->				str:
	__A :			Union[str, Any]		    =			parse_args()

	# Import training_script as a module.
	__A :			List[Any]		    =			Path(args.training_script       )
	sys.path.append(str(script_fpath.parent.resolve()       )       )
	__A :			str		    =			script_fpath.stem
	__A :			int		    =			importlib.import_module(a__       )

	# Patch sys.argv
	__A :			List[str]		    =			[args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores       )]

	xmp.spawn(mod._mp_fn			,args=()			,nprocs=args.num_cores       )


if __name__ == "__main__":
			main()





