"""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

lowerCAmelCase__  :  Union[str, Any]						     =	logging.get_logger(__name__)



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase=None					,			lowerCamelCase=None       ):
				# Recurse if needed
				if "." in tensor_name:
								UpperCAmelCase__             =							tensor_name.split('.'       )
								for split in splits[:-1]:
												UpperCAmelCase__             =							getattr(lowerCamelCase					,			lowerCamelCase       )
												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(lowerCamelCase					,			lowerCamelCase       )

				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(lowerCamelCase       )
												elif isinstance(lowerCamelCase					,			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(lowerCamelCase					,			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					,			lowerCamelCase       ) and fpaa_statistics is None:
																UpperCAmelCase__             =							new_value.T

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

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

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

								if is_buffer:
												UpperCAmelCase__             =							new_value
								else:
												UpperCAmelCase__             =							nn.Parameter(lowerCamelCase					,			requires_grad=old_value.requires_grad       )
												UpperCAmelCase__             =							new_value



def   a_						(					lowerCamelCase					,			lowerCamelCase=None					,			lowerCamelCase=None					,			lowerCamelCase=None					,			lowerCamelCase=False       ):
				for name, module in model.named_children():
								if current_key_name is None:
												UpperCAmelCase__             =							[]
								current_key_name.append(lowerCamelCase       )

								if (isinstance(lowerCamelCase					,			nn.Linear       ) or isinstance(lowerCamelCase					,			lowerCamelCase       )) 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(lowerCamelCase       ) for key in modules_to_not_convert       ):
																with init_empty_weights():
																				if isinstance(lowerCamelCase					,			lowerCamelCase       ):
																								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(
																								    lowerCamelCase					,			lowerCamelCase					,			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(
																												    lowerCamelCase					,			lowerCamelCase					,			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(lowerCamelCase       )
																				# Force requires grad to False to avoid unexpected errors
																				model._modules[name].requires_grad_(lowerCamelCase       )
								if len(list(module.children()       )       ) > 0:
												UpperCAmelCase__      ,	UpperCAmelCase__             =							_replace_with_bnb_linear(
												    lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			has_been_replaced=lowerCamelCase					,			)
								# Remove the last key for recursion
								current_key_name.pop(-1       )
				return model, has_been_replaced



def   a_						(					lowerCamelCase					,			lowerCamelCase=None					,			lowerCamelCase=None					,			lowerCamelCase=None       ):
				UpperCAmelCase__             =							['lm_head'] if modules_to_not_convert is None else modules_to_not_convert
				UpperCAmelCase__      ,	UpperCAmelCase__             =							_replace_with_bnb_linear(
				    lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )

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



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







def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							deepcopy(lowerCamelCase       )  # this has 0 cost since it is done inside `init_empty_weights` context manager`
				tied_model.tie_weights()

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

				# Check if it is a base model
				UpperCAmelCase__             =							not hasattr(lowerCamelCase					,			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(lowerCamelCase       ) - set(lowerCamelCase       )
				UpperCAmelCase__             =							list(set(lowerCamelCase       )       ) + list(lowerCamelCase       )

				# 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(lowerCamelCase					,			''       )
								filtered_module_names.append(lowerCamelCase       )

				return filtered_module_names










import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
				from PIL import Image
else:





				class 						_A:


								"""simple docstring"""


								@staticmethod
								def 					UpperCAmelCase_		(			*_A     ,					**_A     ):
													pass







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      str:
					__A	:						str       =				hashlib.mda(image.tobytes()  )
					return m.hexdigest()[:10]







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      Dict:
					__A	:						Dict       =				np.array(a  )
					__A	:						List[Any]       =				npimg.shape
					return {"hash": hashimage(a  ), "shape": shape}





@is_pipeline_test
@require_vision
@require_torch
class 						_A(					unittest.TestCase   ):


				"""simple docstring"""


				UpperCamelCase    :  str					    =	dict(
				    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()   ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])   )
				UpperCamelCase    :  int					    =	dict(
				    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()   ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])   )



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ):
									__A	:						Dict       =				MaskGenerationPipeline(model=_A     ,					image_processor=_A     )
									return image_segmenter, [
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									]



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ):
									pass



				@require_tf
				@unittest.skip('Image segmentation not implemented in TF'     )
				def 					UpperCAmelCase_		(			self     ):
									pass



				@slow
				@require_torch
				def 					UpperCAmelCase_		(			self     ):
									__A	:						Union[str, Any]       =				pipeline('mask-generation'     ,					model='facebook/sam-vit-huge'     )

									__A	:						List[str]       =				image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg'     ,					points_per_batch=256     )

									# Shortening by hashing
									__A	:						List[Any]       =				[]
									for i, o in enumerate(outputs['masks']     ):
														new_outupt += [{"mask": mask_to_test_readable(_A     ), "scores": outputs["scores"][i]}]

									# fmt: off
									self.assertEqual(
									    nested_simplify(_A     ,					decimals=4     )     ,					[
									        {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0_4_4_4},
									        {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0_2_1},
									        {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0_1_6_7},
									        {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0_1_3_2},
									        {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0_0_5_3},
									        {'mask': {'hash': 'e2d0b7a0b7', 'shape': (480, 640)}, 'scores': 0.9_9_6_7},
									        {'mask': {'hash': '453c7844bd', 'shape': (480, 640)}, 'scores': 0.9_9_3},
									        {'mask': {'hash': '3d44f2926d', 'shape': (480, 640)}, 'scores': 0.9_9_0_9},
									        {'mask': {'hash': '64033ddc3f', 'shape': (480, 640)}, 'scores': 0.9_8_7_9},
									        {'mask': {'hash': '801064ff79', 'shape': (480, 640)}, 'scores': 0.9_8_3_4},
									        {'mask': {'hash': '6172f276ef', 'shape': (480, 640)}, 'scores': 0.9_7_1_6},
									        {'mask': {'hash': 'b49e60e084', 'shape': (480, 640)}, 'scores': 0.9_6_1_2},
									        {'mask': {'hash': 'a811e775fd', 'shape': (480, 640)}, 'scores': 0.9_5_9_9},
									        {'mask': {'hash': 'a6a8ebcf4b', 'shape': (480, 640)}, 'scores': 0.9_5_5_2},
									        {'mask': {'hash': '9d8257e080', 'shape': (480, 640)}, 'scores': 0.9_5_3_2},
									        {'mask': {'hash': '32de6454a8', 'shape': (480, 640)}, 'scores': 0.9_5_1_6},
									        {'mask': {'hash': 'af3d4af2c8', 'shape': (480, 640)}, 'scores': 0.9_4_9_9},
									        {'mask': {'hash': '3c6db475fb', 'shape': (480, 640)}, 'scores': 0.9_4_8_3},
									        {'mask': {'hash': 'c290813fb9', 'shape': (480, 640)}, 'scores': 0.9_4_6_4},
									        {'mask': {'hash': 'b6f0b8f606', 'shape': (480, 640)}, 'scores': 0.9_4_3},
									        {'mask': {'hash': '92ce16bfdf', 'shape': (480, 640)}, 'scores': 0.9_4_3},
									        {'mask': {'hash': 'c749b25868', 'shape': (480, 640)}, 'scores': 0.9_4_0_8},
									        {'mask': {'hash': 'efb6cab859', 'shape': (480, 640)}, 'scores': 0.9_3_3_5},
									        {'mask': {'hash': '1ff2eafb30', 'shape': (480, 640)}, 'scores': 0.9_3_2_6},
									        {'mask': {'hash': '788b798e24', 'shape': (480, 640)}, 'scores': 0.9_2_6_2},
									        {'mask': {'hash': 'abea804f0e', 'shape': (480, 640)}, 'scores': 0.8_9_9_9},
									        {'mask': {'hash': '7b9e8ddb73', 'shape': (480, 640)}, 'scores': 0.8_9_8_6},
									        {'mask': {'hash': 'cd24047c8a', 'shape': (480, 640)}, 'scores': 0.8_9_8_4},
									        {'mask': {'hash': '6943e6bcbd', 'shape': (480, 640)}, 'scores': 0.8_8_7_3},
									        {'mask': {'hash': 'b5f47c9191', 'shape': (480, 640)}, 'scores': 0.8_8_7_1}
									    ]     ,					)
									# fmt: on





				@require_torch
				@slow
				def 					UpperCAmelCase_		(			self     ):
									__A	:						Optional[Any]       =				'facebook/sam-vit-huge'
									__A	:						List[str]       =				pipeline('mask-generation'     ,					model=_A     )

									__A	:						Tuple       =				image_segmenter(
									    'http://images.cocodataset.org/val2017/000000039769.jpg'     ,					pred_iou_thresh=1     ,					points_per_batch=256     )

									# Shortening by hashing
									__A	:						List[str]       =				[]
									for i, o in enumerate(outputs['masks']     ):
														new_outupt += [{"mask": mask_to_test_readable(_A     ), "scores": outputs["scores"][i]}]

									self.assertEqual(
									    nested_simplify(_A     ,					decimals=4     )     ,					[
									        {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0_4_4_4},
									        {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0_2_1_0},
									        {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0_1_6_7},
									        {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0_1_3_2},
									        {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0_0_5_3},
									    ]     ,					)




