




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

UpperCamelCase__         =      logging.get_logger(__name__)







def      _a				(						SCREAMING_SNAKE_CASE_							:    Any				,				SCREAMING_SNAKE_CASE_							:    Optional[int]				,				SCREAMING_SNAKE_CASE_							:    Tuple				,				SCREAMING_SNAKE_CASE_							:    Dict=None				,				SCREAMING_SNAKE_CASE_							:    Tuple=None					):
					# Recurse if needed
					if "." in tensor_name:
										__lowerCAmelCase           =	tensor_name.split("."					)
										for split in splits[:-1]:
															__lowerCAmelCase           =	getattr(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)
															if new_module is None:
																				raise ValueError(F"""{module} has no attribute {split}."""					)
															__lowerCAmelCase           =	new_module
										__lowerCAmelCase           =	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}."""					)
					__lowerCAmelCase           =	tensor_name in module._buffers
					__lowerCAmelCase           =	getattr(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)

					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}."""					)

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

					if is_abit or is_abit:
										__lowerCAmelCase           =	module._parameters[tensor_name]
										if param.device.type != "cuda":
															if value is None:
																				__lowerCAmelCase           =	old_value.to(SCREAMING_SNAKE_CASE_					)
															elif isinstance(SCREAMING_SNAKE_CASE_				,				torch.Tensor					):
																				__lowerCAmelCase           =	value.to("cpu"					)
																				if value.dtype == torch.inta:
																									__lowerCAmelCase           =	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:
																				__lowerCAmelCase           =	torch.tensor(SCREAMING_SNAKE_CASE_				,				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				,				SCREAMING_SNAKE_CASE_					) and fpaa_statistics is None:
																				__lowerCAmelCase           =	new_value.T

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

															__lowerCAmelCase           =	new_value
															if fpaa_statistics is not None:
																				setattr(module.weight				,				"SCB"				,				fpaa_statistics.to(SCREAMING_SNAKE_CASE_					)					)

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

										if is_buffer:
															__lowerCAmelCase           =	new_value
										else:
															__lowerCAmelCase           =	nn.Parameter(SCREAMING_SNAKE_CASE_				,				requires_grad=old_value.requires_grad					)
															__lowerCAmelCase           =	new_value







def      _a				(						SCREAMING_SNAKE_CASE_							:    Optional[int]				,				SCREAMING_SNAKE_CASE_							:    Optional[Any]=None				,				SCREAMING_SNAKE_CASE_							:    Tuple=None				,				SCREAMING_SNAKE_CASE_							:    Optional[Any]=None				,				SCREAMING_SNAKE_CASE_							:    int=False					):
					for name, module in model.named_children():
										if current_key_name is None:
															__lowerCAmelCase           =	[]
										current_key_name.append(SCREAMING_SNAKE_CASE_					)

										if (isinstance(SCREAMING_SNAKE_CASE_				,				nn.Linear					) or isinstance(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)) 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(SCREAMING_SNAKE_CASE_					) for key in modules_to_not_convert					):
																				with init_empty_weights():
																									if isinstance(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					):
																														__lowerCAmelCase       ,							__lowerCAmelCase           =	module.weight.shape
																									else:
																														__lowerCAmelCase           =	module.in_features
																														__lowerCAmelCase           =	module.out_features

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







def      _a				(						SCREAMING_SNAKE_CASE_							:    Any				,				SCREAMING_SNAKE_CASE_							:    Dict=None				,				SCREAMING_SNAKE_CASE_							:    Tuple=None				,				SCREAMING_SNAKE_CASE_							:    Optional[Any]=None					):
					__lowerCAmelCase           =	["lm_head"] if modules_to_not_convert is None else modules_to_not_convert
					__lowerCAmelCase       ,							__lowerCAmelCase           =	_replace_with_bnb_linear(
					    SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)

					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				(						*SCREAMING_SNAKE_CASE_							:    str				,				**SCREAMING_SNAKE_CASE_							:    Any					):
					warnings.warn(
					    "`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead"				,				SCREAMING_SNAKE_CASE_				,				)
					return replace_with_bnb_linear(*SCREAMING_SNAKE_CASE_				,				**SCREAMING_SNAKE_CASE_					)







def      _a				(						*SCREAMING_SNAKE_CASE_							:    Any				,				**SCREAMING_SNAKE_CASE_							:    Optional[int]					):
					warnings.warn(
					    "`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead"				,				SCREAMING_SNAKE_CASE_				,				)
					return set_module_quantized_tensor_to_device(*SCREAMING_SNAKE_CASE_				,				**SCREAMING_SNAKE_CASE_					)







def      _a				(						SCREAMING_SNAKE_CASE_							:    Any					):
					__lowerCAmelCase           =	deepcopy(SCREAMING_SNAKE_CASE_					)  # this has 0 cost since it is done inside `init_empty_weights` context manager`
					tied_model.tie_weights()

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

					# Check if it is a base model
					__lowerCAmelCase           =	not hasattr(SCREAMING_SNAKE_CASE_				,				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
					__lowerCAmelCase           =	list(model.named_children()					)
					__lowerCAmelCase           =	[list_modules[-1][0]]

					# add last module together with tied weights
					__lowerCAmelCase           =	set(SCREAMING_SNAKE_CASE_					) - set(SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase           =	list(set(SCREAMING_SNAKE_CASE_					)					) + list(SCREAMING_SNAKE_CASE_					)

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

					return filtered_module_names










import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image
else:
					class       a__   :






											@staticmethod
											def     __SCREAMING_SNAKE_CASE(       *_A		,      **_A      ):




																"""simple docstring"""



																pass







def      _a				(						SCREAMING_SNAKE_CASE_							:    Image					):
					__lowerCAmelCase           =	hashlib.mda(image.tobytes()					)
					return m.hexdigest()


@is_pipeline_test
@require_vision
@require_timm
@require_torch
class       a__   ( unittest.TestCase				):
						_a							:   Tuple =   MODEL_FOR_DEPTH_ESTIMATION_MAPPING






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	DepthEstimationPipeline(model=_A		,      image_processor=_A      )
											return depth_estimator, [
											    "./tests/fixtures/tests_samples/COCO/000000039769.png",
											    "./tests/fixtures/tests_samples/COCO/000000039769.png",
											]






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
											self.assertEqual({"predicted_depth": ANY(torch.Tensor      ), "depth": ANY(Image.Image      )}		,      _A      )
											import datasets

											__lowerCAmelCase           =	datasets.load_dataset("hf-internal-testing/fixtures_image_utils"		,      "image"		,      split="test"      )
											__lowerCAmelCase           =	depth_estimator(
											    [
											        Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      ),
											        "http://images.cocodataset.org/val2017/000000039769.jpg",
											        # RGBA
											        dataset[0]["file"],
											        # LA
											        dataset[1]["file"],
											        # L
											        dataset[2]["file"],
											    ]      )
											self.assertEqual(
											    [
											        {"predicted_depth": ANY(torch.Tensor      ), "depth": ANY(Image.Image      )},
											        {"predicted_depth": ANY(torch.Tensor      ), "depth": ANY(Image.Image      )},
											        {"predicted_depth": ANY(torch.Tensor      ), "depth": ANY(Image.Image      )},
											        {"predicted_depth": ANY(torch.Tensor      ), "depth": ANY(Image.Image      )},
											        {"predicted_depth": ANY(torch.Tensor      ), "depth": ANY(Image.Image      )},
											    ]		,      _A		,      )






						@require_tf
						@unittest.skip("Depth estimation is not implemented in TF"      )
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											pass






						@slow
						@require_torch
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	"Intel/dpt-large"
											__lowerCAmelCase           =	pipeline("depth-estimation"		,      model=_A      )
											__lowerCAmelCase           =	depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"      )
											__lowerCAmelCase           =	hashimage(outputs["depth"]      )

											# This seems flaky.
											# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
											self.assertEqual(nested_simplify(outputs["predicted_depth"].max().item()      )		,      29.3_04      )
											self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item()      )		,      2.6_62      )






						@require_torch
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"      )




