


'''simple docstring'''



import importlib.metadata
import warnings
from copy import deepcopy

from packaging import version

from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available


if is_bitsandbytes_available():
 import bitsandbytes as bnb
 import torch
 import torch.nn as nn

 from ..pytorch_utils import ConvaD

if is_accelerate_available():
 from accelerate import init_empty_weights
 from accelerate.utils import find_tied_parameters

_lowercase    :    List[Any]			     =  logging.get_logger(__name__)


def    lowerCamelCase__   ( A     :    Union[str, Any]   ,     A     :    Any   ,     A     :    str   ,     A     :    List[str]=None   ,     A     :    int=None  ):




      '''simple docstring'''






      if "." in tensor_name:
            UpperCAmelCase         =			tensor_name.split('''.'''  )
            for split in splits[:-1]:
                  UpperCAmelCase         =			getattr(A   ,     A  )
                  if new_module is None:
                        raise ValueError(f"""{module} has no attribute {split}."""  )
                  UpperCAmelCase         =			new_module
            UpperCAmelCase         =			splits[-1]

      if tensor_name not in module._parameters and tensor_name not in module._buffers:
            raise ValueError(f"""{module} does not have a parameter or a buffer named {tensor_name}."""  )
      UpperCAmelCase         =			tensor_name in module._buffers
      UpperCAmelCase         =			getattr(A   ,     A  )

      if old_value.device == torch.device('''meta'''  ) and device not in ["meta", torch.device('''meta'''  )] and value is None:
            raise ValueError(f"""{tensor_name} is on the meta device, we need a `value` to put in on {device}."""  )

      UpperCAmelCase         =			False
      UpperCAmelCase         =			False
      if is_buffer or not is_bitsandbytes_available():
            UpperCAmelCase         =			False
            UpperCAmelCase         =			False
      else:
            UpperCAmelCase         =			hasattr(bnb.nn   ,     '''Params4bit'''  ) and isinstance(module._parameters[tensor_name]   ,     bnb.nn.Paramsabit  )
            UpperCAmelCase         =			isinstance(module._parameters[tensor_name]   ,     bnb.nn.IntaParams  )

      if is_abit or is_abit:
            UpperCAmelCase         =			module._parameters[tensor_name]
            if param.device.type != "cuda":
                  if value is None:
                        UpperCAmelCase         =			old_value.to(A  )
                  elif isinstance(A   ,     torch.Tensor  ):
                        UpperCAmelCase         =			value.to('''cpu'''  )
                        if value.dtype == torch.inta:
                              UpperCAmelCase         =			version.parse(importlib.metadata.version('''bitsandbytes'''  )  ) > version.parse(
                                  '''0.37.2'''  )
                              if not is_abit_serializable:
                                    raise ValueError(
                                        '''Detected int8 weights but the version of bitsandbytes is not compatible with int8 serialization. '''
                                        '''Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`.'''  )
                  else:
                        UpperCAmelCase         =			torch.tensor(A   ,     device='''cpu'''  )

                  # Support models using `Conv1D` in place of `nn.Linear` (e.g. gpt2) by transposing the weight matrix prior to quantization.
                  # Since weights are saved in the correct "orientation", we skip transposing when loading.
                  if issubclass(module.source_cls   ,     A  ) and fpaa_statistics is None:
                        UpperCAmelCase         =			new_value.T

                  UpperCAmelCase         =			old_value.__dict__
                  if is_abit:
                        UpperCAmelCase         =			bnb.nn.IntaParams(A   ,     requires_grad=A   ,     **A  ).to(A  )
                  elif is_abit:
                        UpperCAmelCase         =			bnb.nn.Paramsabit(A   ,     requires_grad=A   ,     **A  ).to(A  )

                  UpperCAmelCase         =			new_value
                  if fpaa_statistics is not None:
                        setattr(module.weight   ,     '''SCB'''   ,     fpaa_statistics.to(A  )  )

      else:
            if value is None:
                  UpperCAmelCase         =			old_value.to(A  )
            elif isinstance(A   ,     torch.Tensor  ):
                  UpperCAmelCase         =			value.to(A  )
            else:
                  UpperCAmelCase         =			torch.tensor(A   ,     device=A  )

            if is_buffer:
                  UpperCAmelCase         =			new_value
            else:
                  UpperCAmelCase         =			nn.Parameter(A   ,     requires_grad=old_value.requires_grad  )
                  UpperCAmelCase         =			new_value


def    lowerCamelCase__   ( A     :    List[str]   ,     A     :    Union[str, Any]=None   ,     A     :    Tuple=None   ,     A     :    str=None   ,     A     :    Union[str, Any]=False  ):




      '''simple docstring'''






      for name, module in model.named_children():
            if current_key_name is None:
                  UpperCAmelCase         =			[]
            current_key_name.append(A  )

            if (isinstance(A   ,     nn.Linear  ) or isinstance(A   ,     A  )) and name not in modules_to_not_convert:
                  # Check if the current key is not in the `modules_to_not_convert`
                  if not any(key in '''.'''.join(A  ) for key in modules_to_not_convert  ):
                        with init_empty_weights():
                              if isinstance(A   ,     A  ):
                                    UpperCAmelCase       ,       UpperCAmelCase         =			module.weight.shape
                              else:
                                    UpperCAmelCase         =			module.in_features
                                    UpperCAmelCase         =			module.out_features

                              if quantization_config.quantization_method() == "llm_int8":
                                    UpperCAmelCase         =			bnb.nn.LinearabitLt(
                                        A   ,     A   ,     module.bias is not None   ,     has_fpaa_weights=quantization_config.llm_inta_has_fpaa_weight   ,     threshold=quantization_config.llm_inta_threshold   ,     )
                                    UpperCAmelCase         =			True
                              else:
                                    if (
                                        quantization_config.llm_inta_skip_modules is not None
                                        and name in quantization_config.llm_inta_skip_modules
                                    ):
                                          pass
                                    else:
                                          UpperCAmelCase         =			bnb.nn.Linearabit(
                                              A   ,     A   ,     module.bias is not None   ,     quantization_config.bnb_abit_compute_dtype   ,     compress_statistics=quantization_config.bnb_abit_use_double_quant   ,     quant_type=quantization_config.bnb_abit_quant_type   ,     )
                                          UpperCAmelCase         =			True
                    # Store the module class in case we need to transpose the weight later
                              UpperCAmelCase         =			type(A  )
                              # Force requires grad to False to avoid unexpected errors
                              model._modules[name].requires_grad_(A  )
            if len(list(module.children()  )  ) > 0:
                  UpperCAmelCase       ,       UpperCAmelCase         =			_replace_with_bnb_linear(
                      A   ,     A   ,     A   ,     A   ,     has_been_replaced=A   ,     )
            # Remove the last key for recursion
            current_key_name.pop(-1  )
      return model, has_been_replaced


def    lowerCamelCase__   ( A     :    Any   ,     A     :    List[str]=None   ,     A     :    int=None   ,     A     :    Any=None  ):




      '''simple docstring'''






      UpperCAmelCase         =			['''lm_head'''] if modules_to_not_convert is None else modules_to_not_convert
      UpperCAmelCase       ,       UpperCAmelCase         =			_replace_with_bnb_linear(
          A   ,     A   ,     A   ,     A  )

      if not has_been_replaced:
            logger.warning(
                '''You are loading your model in 8bit or 4bit but no linear modules were found in your model.'''
                ''' Please double check your model architecture, or submit an issue on github if you think this is'''
                ''' a bug.'''  )

      return model


def    lowerCamelCase__   ( *A     :    Any   ,     **A     :    Optional[int]  ):




      '''simple docstring'''






      warnings.warn(
          '''`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead'''   ,     A   ,     )
      return replace_with_bnb_linear(*A   ,     **A  )


def    lowerCamelCase__   ( *A     :    str   ,     **A     :    Optional[int]  ):




      '''simple docstring'''






      warnings.warn(
          '''`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead'''   ,     A   ,     )
      return set_module_quantized_tensor_to_device(*A   ,     **A  )




def    lowerCamelCase__   ( A     :    List[Any]  ):




      '''simple docstring'''






      UpperCAmelCase         =			deepcopy(A  )  # this has 0 cost since it is done inside `init_empty_weights` context manager`
      tied_model.tie_weights()

      UpperCAmelCase         =			find_tied_parameters(A  )
      # For compatibility with Accelerate < 0.18
      if isinstance(A   ,     A  ):
            UpperCAmelCase         =			sum(list(tied_params.values()  )   ,     []  ) + list(tied_params.keys()  )
      else:
            UpperCAmelCase         =			sum(A   ,     []  )
      UpperCAmelCase         =			len(A  ) > 0

      # Check if it is a base model
      UpperCAmelCase         =			not hasattr(A   ,     model.base_model_prefix  )

      # Ignore this for base models (BertModel, GPT2Model, etc.)
      if (not has_tied_params) and is_base_model:
            return []

      # otherwise they have an attached head
      UpperCAmelCase         =			list(model.named_children()  )
      UpperCAmelCase         =			[list_modules[-1][0]]

      # add last module together with tied weights
      UpperCAmelCase         =			set(A  ) - set(A  )
      UpperCAmelCase         =			list(set(A  )  ) + list(A  )

      # remove ".weight" from the keys
      UpperCAmelCase         =			['''.weight''', '''.bias''']
      UpperCAmelCase         =			[]
      for name in list_untouched:
            for name_to_remove in names_to_remove:
                  if name_to_remove in name:
                        UpperCAmelCase         =			name.replace(A   ,     ''''''  )
            filtered_module_names.append(A  )

      return filtered_module_names









'''simple docstring'''



import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
	import jax
	import jax.numpy as jnp

	from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor






class  UpperCamelCase__(     unittest.TestCase ):




				def __init__(	self    :				Dict   ,					lowerCAmelCase    :				List[str]   ,					lowerCAmelCase    :				List[Any]=3   ,					lowerCAmelCase    :				Tuple=32   ,					lowerCAmelCase    :				Optional[int]=3   ,					lowerCAmelCase    :				Optional[Any]=10   ,					lowerCAmelCase    :				Optional[Any]=[10, 20, 30, 40]   ,					lowerCAmelCase    :				Optional[int]=[1, 1, 2, 1]   ,					lowerCAmelCase    :				Union[str, Any]=True   ,					lowerCAmelCase    :				Tuple=True   ,					lowerCAmelCase    :				List[Any]="relu"   ,					lowerCAmelCase    :				List[Any]=3   ,					lowerCAmelCase    :				Union[str, Any]=None   ,					)->  Optional[int]:

										"""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(lowerCAmelCase   )




				def   a__(	self    :				Optional[Any]   )->  Tuple:

										"""simple docstring"""




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

										UpperCAmelCase         =			self.get_config()

										return config, pixel_values




				def   a__(	self    :				str   )->  Optional[Any]:

										"""simple docstring"""




										return RegNetConfig(
										    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   ,					image_size=self.image_size   ,					)




				def   a__(	self    :				Union[str, Any]   ,					lowerCAmelCase    :				List[Any]   ,					lowerCAmelCase    :				Any   )->  Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase         =			FlaxRegNetModel(config=lowerCAmelCase   )
										UpperCAmelCase         =			model(lowerCAmelCase   )

										# Output shape (b, c, h, w)
										self.parent.assertEqual(
										    result.last_hidden_state.shape   ,					(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)   ,					)




				def   a__(	self    :				List[Any]   ,					lowerCAmelCase    :				List[str]   ,					lowerCAmelCase    :				Optional[Any]   )->  List[str]:

										"""simple docstring"""




										UpperCAmelCase         =			self.num_labels
										UpperCAmelCase         =			FlaxRegNetForImageClassification(config=lowerCAmelCase   )
										UpperCAmelCase         =			model(lowerCAmelCase   )
										self.parent.assertEqual(result.logits.shape   ,					(self.batch_size, self.num_labels)   )





				def   a__(	self    :				List[str]   )->  Any:

										"""simple docstring"""




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







@require_flax
class  UpperCamelCase__(     lowerCAmelCase		,       unittest.TestCase ):
				__magic_name__       :   Optional[int]							= (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

				__magic_name__       :   Optional[int]							= False
				__magic_name__       :   List[str]							= False
				__magic_name__       :   Dict							= False




				def   a__(	self    :				Union[str, Any]   )->  None:

										"""simple docstring"""




										UpperCAmelCase         =			FlaxRegNetModelTester(self   )
										UpperCAmelCase         =			ConfigTester(self   ,					config_class=lowerCAmelCase   ,					has_text_modality=lowerCAmelCase   )




				def   a__(	self    :				List[str]   )->  List[str]:

										"""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   a__(	self    :				Tuple   )->  Tuple:

										"""simple docstring"""




										return




				def   a__(	self    :				Optional[Any]   )->  Tuple:

										"""simple docstring"""




										UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase   )




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

										"""simple docstring"""




										UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase   )




				@unittest.skip(reason='''RegNet does not use inputs_embeds'''   )
				def   a__(	self    :				str   )->  Optional[Any]:

										"""simple docstring"""




										pass




				@unittest.skip(reason='''RegNet does not support input and output embeddings'''   )
				def   a__(	self    :				Any   )->  List[str]:

										"""simple docstring"""




										pass




				def   a__(	self    :				Any   )->  Optional[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(lowerCAmelCase   )
																UpperCAmelCase         =			inspect.signature(model.__call__   )
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																UpperCAmelCase         =			[*signature.parameters.keys()]

																UpperCAmelCase         =			['''pixel_values''']
																self.assertListEqual(arg_names[:1]   ,					lowerCAmelCase   )




				def   a__(	self    :				Tuple   )->  int:

										"""simple docstring"""




										def check_hidden_states_output(lowerCAmelCase    :				Optional[Any]   ,					lowerCAmelCase    :				Union[str, Any]   ,					lowerCAmelCase    :				int   ):
																UpperCAmelCase         =			model_class(lowerCAmelCase   )

																UpperCAmelCase         =			model(**self._prepare_for_class(lowerCAmelCase   ,					lowerCAmelCase   )   )

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

																UpperCAmelCase         =			self.model_tester.num_stages
																self.assertEqual(len(lowerCAmelCase   )   ,					expected_num_stages + 1   )

										UpperCAmelCase       ,       UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																UpperCAmelCase         =			True
																check_hidden_states_output(lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   )

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

																check_hidden_states_output(lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   )





				def   a__(	self    :				Union[str, Any]   )->  List[str]:

										"""simple docstring"""




										UpperCAmelCase       ,       UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__   ):
																						UpperCAmelCase         =			self._prepare_for_class(lowerCAmelCase   ,					lowerCAmelCase   )
																						UpperCAmelCase         =			model_class(lowerCAmelCase   )

																						@jax.jit
																						def model_jitted(lowerCAmelCase    :				Tuple   ,					**lowerCAmelCase    :				Tuple   ):
																												return model(pixel_values=lowerCAmelCase   ,					**lowerCAmelCase   )

																						with self.subTest('''JIT Enabled'''   ):
																												UpperCAmelCase         =			model_jitted(**lowerCAmelCase   ).to_tuple()

																						with self.subTest('''JIT Disabled'''   ):
																												with jax.disable_jit():
																																		UpperCAmelCase         =			model_jitted(**lowerCAmelCase   ).to_tuple()

																						self.assertEqual(len(lowerCAmelCase   )   ,					len(lowerCAmelCase   )   )
																						for jitted_output, output in zip(lowerCAmelCase   ,					lowerCAmelCase   ):
																												self.assertEqual(jitted_output.shape   ,					output.shape   )







def    lowerCamelCase__   ( ):




						'''simple docstring'''






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






@require_flax
class  UpperCamelCase__(     unittest.TestCase ):




				@cached_property
				def   a__(	self    :				Dict   )->  int:

										"""simple docstring"""




										return AutoImageProcessor.from_pretrained('''facebook/regnet-y-040'''   ) if is_vision_available() else None





				@slow
				def   a__(	self    :				Union[str, Any]   )->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			FlaxRegNetForImageClassification.from_pretrained('''facebook/regnet-y-040'''   )

										UpperCAmelCase         =			self.default_image_processor
										UpperCAmelCase         =			prepare_img()
										UpperCAmelCase         =			image_processor(images=lowerCAmelCase   ,					return_tensors='''np'''   )

										UpperCAmelCase         =			model(**lowerCAmelCase   )

										# verify the logits
										UpperCAmelCase         =			(1, 1000)
										self.assertEqual(outputs.logits.shape   ,					lowerCAmelCase   )

										UpperCAmelCase         =			jnp.array([-0.4180, -1.5051, -3.4836]   )

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





