


'''simple docstring'''



from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig


__lowercase     :						int	=     logging.get_logger(__name__)

# General docstring
__lowercase     :						List[str]	=     'RegNetConfig'

# Base docstring
__lowercase     :						Optional[Any]	=     'facebook/regnet-y-040'
__lowercase     :						Optional[int]	=     [1, 10_88, 7, 7]

# Image classification docstring
__lowercase     :						Tuple	=     'facebook/regnet-y-040'
__lowercase     :						int	=     'tabby, tabby cat'

__lowercase     :						int	=     [
    'facebook/regnet-y-040',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]





class 							__UpperCamelCase    (		nn.Module ):




	def __init__(       self						,    __a						,    __a						,    __a = 3						,    __a = 1						,    __a = 1						,    __a = "relu"						,    ):

						'''simple docstring'''





						super().__init__()
						__a      :	Optional[Any]    =  nn.Convad(
						    lowercase_						,    lowercase_						,    kernel_size=lowercase_						,    stride=lowercase_						,    padding=kernel_size // 2						,    groups=lowercase_						,    bias=lowercase_						,    )
						__a      :	List[Any]    =  nn.BatchNormad(lowercase_							)
						__a      :	List[Any]    =  ACTaFN[activation] if activation is not None else nn.Identity()





	def 				__UpperCAmelCase							(       self						,    __a							):

						'''simple docstring'''





						__a      :	List[str]    =  self.convolution(lowercase_							)
						__a      :	Dict    =  self.normalization(lowercase_							)
						__a      :	Tuple    =  self.activation(lowercase_							)
						return hidden_state





class 							__UpperCamelCase    (		nn.Module ):




	def __init__(       self						,    __a							):

						'''simple docstring'''





						super().__init__()
						__a      :	str    =  RegNetConvLayer(
						    config.num_channels						,    config.embedding_size						,    kernel_size=3						,    stride=2						,    activation=config.hidden_act							)
						__a      :	Dict    =  config.num_channels





	def 				__UpperCAmelCase							(       self						,    __a							):

						'''simple docstring'''





						__a      :	List[Any]    =  pixel_values.shape[1]
						if num_channels != self.num_channels:
											raise ValueError(
											    'Make sure that the channel dimension of the pixel values match with the one set in the configuration.'							)
						__a      :	Optional[Any]    =  self.embedder(lowercase_							)
						return hidden_state





class 							__UpperCamelCase    (		nn.Module ):




	def __init__(       self						,    __a						,    __a						,    __a = 2							):

						'''simple docstring'''





						super().__init__()
						__a      :	Tuple    =  nn.Convad(lowercase_						,    lowercase_						,    kernel_size=1						,    stride=lowercase_						,    bias=lowercase_							)
						__a      :	Optional[int]    =  nn.BatchNormad(lowercase_							)





	def 				__UpperCAmelCase							(       self						,    __a							):

						'''simple docstring'''





						__a      :	Optional[int]    =  self.convolution(lowercase_							)
						__a      :	Optional[int]    =  self.normalization(lowercase_							)
						return hidden_state





class 							__UpperCamelCase    (		nn.Module ):




	def __init__(       self						,    __a						,    __a							):

						'''simple docstring'''





						super().__init__()

						__a      :	Optional[int]    =  nn.AdaptiveAvgPoolad((1, 1)							)
						__a      :	List[Any]    =  nn.Sequential(
						    nn.Convad(lowercase_						,    lowercase_						,    kernel_size=1							)						,    nn.ReLU()						,    nn.Convad(lowercase_						,    lowercase_						,    kernel_size=1							)						,    nn.Sigmoid()						,    )





	def 				__UpperCAmelCase							(       self						,    __a							):

						'''simple docstring'''





						__a      :	List[str]    =  self.pooler(lowercase_							)
						__a      :	Union[str, Any]    =  self.attention(lowercase_							)
						__a      :	Dict    =  hidden_state * attention
						return hidden_state





class 							__UpperCamelCase    (		nn.Module ):




	def __init__(       self						,    __a						,    __a						,    __a						,    __a = 1							):

						'''simple docstring'''





						super().__init__()
						__a      :	Optional[Any]    =  in_channels != out_channels or stride != 1
						__a      :	List[str]    =  max(1						,    out_channels // config.groups_width							)
						__a      :	Tuple    =  (
						    RegNetShortCut(lowercase_						,    lowercase_						,    stride=lowercase_							) if should_apply_shortcut else nn.Identity()
						)
						__a      :	Optional[int]    =  nn.Sequential(
						    RegNetConvLayer(lowercase_						,    lowercase_						,    kernel_size=1						,    activation=config.hidden_act							)						,    RegNetConvLayer(lowercase_						,    lowercase_						,    stride=lowercase_						,    groups=lowercase_						,    activation=config.hidden_act							)						,    RegNetConvLayer(lowercase_						,    lowercase_						,    kernel_size=1						,    activation=lowercase_							)						,    )
						__a      :	Optional[Any]    =  ACTaFN[config.hidden_act]





	def 				__UpperCAmelCase							(       self						,    __a							):

						'''simple docstring'''





						__a      :	Dict    =  hidden_state
						__a      :	int    =  self.layer(lowercase_							)
						__a      :	List[Any]    =  self.shortcut(lowercase_							)
						hidden_state += residual
						__a      :	Tuple    =  self.activation(lowercase_							)
						return hidden_state





class 							__UpperCamelCase    (		nn.Module ):




	def __init__(       self						,    __a						,    __a						,    __a						,    __a = 1							):

						'''simple docstring'''





						super().__init__()
						__a      :	int    =  in_channels != out_channels or stride != 1
						__a      :	Any    =  max(1						,    out_channels // config.groups_width							)
						__a      :	Dict    =  (
						    RegNetShortCut(lowercase_						,    lowercase_						,    stride=lowercase_							) if should_apply_shortcut else nn.Identity()
						)
						__a      :	Union[str, Any]    =  nn.Sequential(
						    RegNetConvLayer(lowercase_						,    lowercase_						,    kernel_size=1						,    activation=config.hidden_act							)						,    RegNetConvLayer(lowercase_						,    lowercase_						,    stride=lowercase_						,    groups=lowercase_						,    activation=config.hidden_act							)						,    RegNetSELayer(lowercase_						,    reduced_channels=int(round(in_channels / 4							)							)							)						,    RegNetConvLayer(lowercase_						,    lowercase_						,    kernel_size=1						,    activation=lowercase_							)						,    )
						__a      :	int    =  ACTaFN[config.hidden_act]





	def 				__UpperCAmelCase							(       self						,    __a							):

						'''simple docstring'''





						__a      :	int    =  hidden_state
						__a      :	str    =  self.layer(lowercase_							)
						__a      :	List[str]    =  self.shortcut(lowercase_							)
						hidden_state += residual
						__a      :	Optional[int]    =  self.activation(lowercase_							)
						return hidden_state





class 							__UpperCamelCase    (		nn.Module ):




	def __init__(       self						,    __a						,    __a						,    __a						,    __a = 2						,    __a = 2						,    ):

						'''simple docstring'''





						super().__init__()

						__a      :	Optional[Any]    =  RegNetXLayer if config.layer_type == "x" else RegNetYLayer

						__a      :	Tuple    =  nn.Sequential(
						    # downsampling is done in the first layer with stride of 2
						    layer(
						        lowercase_						,    lowercase_						,    lowercase_						,    stride=lowercase_						,    )						,    *[layer(lowercase_						,    lowercase_						,    lowercase_							) for _ in range(depth - 1							)]						,    )





	def 				__UpperCAmelCase							(       self						,    __a							):

						'''simple docstring'''





						__a      :	Tuple    =  self.layers(lowercase_							)
						return hidden_state





class 							__UpperCamelCase    (		nn.Module ):




	def __init__(       self						,    __a							):

						'''simple docstring'''





						super().__init__()
						__a      :	Dict    =  nn.ModuleList([]							)
						# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
						self.stages.append(
						    RegNetStage(
						        lowercase_						,    config.embedding_size						,    config.hidden_sizes[0]						,    stride=2 if config.downsample_in_first_stage else 1						,    depth=config.depths[0]						,    )							)
						__a      :	Tuple    =  zip(config.hidden_sizes						,    config.hidden_sizes[1:]							)
						for (in_channels, out_channels), depth in zip(lowercase_						,    config.depths[1:]							):
											self.stages.append(RegNetStage(lowercase_						,    lowercase_						,    lowercase_						,    depth=lowercase_							)							)





	def 				__UpperCAmelCase							(       self						,    __a						,    __a = False						,    __a = True							):

						'''simple docstring'''





						__a      :	int    =  () if output_hidden_states else None

						for stage_module in self.stages:
											if output_hidden_states:
																__a      :	Optional[Any]    =  hidden_states + (hidden_state,)

											__a      :	Union[str, Any]    =  stage_module(lowercase_							)

						if output_hidden_states:
											__a      :	str    =  hidden_states + (hidden_state,)

						if not return_dict:
											return tuple(v for v in [hidden_state, hidden_states] if v is not None							)

						return BaseModelOutputWithNoAttention(last_hidden_state=lowercase_						,    hidden_states=lowercase_							)





class 							__UpperCamelCase    (		lowercase__ ):
	A_           =   RegNetConfig
	A_           =   """regnet"""
	A_           =   """pixel_values"""
	A_           =   True




	def 				__UpperCAmelCase							(       self						,    __a							):

						'''simple docstring'''





						if isinstance(lowercase_						,    nn.Convad							):
											nn.init.kaiming_normal_(module.weight						,    mode='fan_out'						,    nonlinearity='relu'							)
						elif isinstance(lowercase_						,    (nn.BatchNormad, nn.GroupNorm)							):
											nn.init.constant_(module.weight						,    1							)
											nn.init.constant_(module.bias						,    0							)





	def 				__UpperCAmelCase							(       self						,    __a						,    __a=False							):

						'''simple docstring'''





						if isinstance(lowercase_						,    lowercase_							):
											__a      :	Optional[Any]    =  value



__lowercase     :						Any	=     R'\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'

__lowercase     :						Dict	=     R'\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConvNextImageProcessor.__call__`] for details.\n\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.\n'





@add_start_docstrings(
    "The bare RegNet model outputting raw features without any specific head on top."		,				lowercase__		,				)
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class 							__UpperCamelCase    (		lowercase__ ):




	def __init__(       self						,    __a							):

						'''simple docstring'''





						super().__init__(lowercase_							)
						__a      :	int    =  config
						__a      :	List[str]    =  RegNetEmbeddings(lowercase_							)
						__a      :	Tuple    =  RegNetEncoder(lowercase_							)
						__a      :	Union[str, Any]    =  nn.AdaptiveAvgPoolad((1, 1)							)
						# Initialize weights and apply final processing
						self.post_init()





	@add_start_docstrings_to_model_forward(lowercase_							)
	@add_code_sample_docstrings(
	    checkpoint=_CHECKPOINT_FOR_DOC						,    output_type=lowercase_						,    config_class=_CONFIG_FOR_DOC						,    modality='vision'						,    expected_output=_EXPECTED_OUTPUT_SHAPE						,    )
	def 				__UpperCAmelCase							(       self						,    __a						,    __a = None						,    __a = None							):

						'''simple docstring'''





						__a      :	Any    =  (
						    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
						)
						__a      :	Dict    =  return_dict if return_dict is not None else self.config.use_return_dict

						__a      :	int    =  self.embedder(lowercase_							)

						__a      :	Dict    =  self.encoder(
						    lowercase_						,    output_hidden_states=lowercase_						,    return_dict=lowercase_							)

						__a      :	Optional[int]    =  encoder_outputs[0]

						__a      :	Optional[int]    =  self.pooler(lowercase_							)

						if not return_dict:
											return (last_hidden_state, pooled_output) + encoder_outputs[1:]

						return BaseModelOutputWithPoolingAndNoAttention(
						    last_hidden_state=lowercase_						,    pooler_output=lowercase_						,    hidden_states=encoder_outputs.hidden_states						,    )





@add_start_docstrings(
    "\n    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "		,				lowercase__		,				)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class 							__UpperCamelCase    (		lowercase__ ):




	def __init__(       self						,    __a							):

						'''simple docstring'''





						super().__init__(lowercase_							)
						__a      :	Optional[int]    =  config.num_labels
						__a      :	Optional[int]    =  RegNetModel(lowercase_							)
						# classification head
						__a      :	Union[str, Any]    =  nn.Sequential(
						    nn.Flatten()						,    nn.Linear(config.hidden_sizes[-1]						,    config.num_labels							) if config.num_labels > 0 else nn.Identity()						,    )
						# initialize weights and apply final processing
						self.post_init()





	@add_start_docstrings_to_model_forward(lowercase_							)
	@add_code_sample_docstrings(
	    checkpoint=_IMAGE_CLASS_CHECKPOINT						,    output_type=lowercase_						,    config_class=_CONFIG_FOR_DOC						,    expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT						,    )
	def 				__UpperCAmelCase							(       self						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    ):

						'''simple docstring'''





						__a      :	int    =  return_dict if return_dict is not None else self.config.use_return_dict

						__a      :	List[str]    =  self.regnet(lowercase_						,    output_hidden_states=lowercase_						,    return_dict=lowercase_							)

						__a      :	List[str]    =  outputs.pooler_output if return_dict else outputs[1]

						__a      :	Any    =  self.classifier(lowercase_							)

						__a      :	Optional[Any]    =  None

						if labels is not None:
											if self.config.problem_type is None:
																if self.num_labels == 1:
																					__a      :	str    =  "regression"
																elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																					__a      :	int    =  "single_label_classification"
																else:
																					__a      :	int    =  "multi_label_classification"
											if self.config.problem_type == "regression":
																__a      :	Tuple    =  MSELoss()
																if self.num_labels == 1:
																					__a      :	Optional[Any]    =  loss_fct(logits.squeeze()						,    labels.squeeze()							)
																else:
																					__a      :	Any    =  loss_fct(lowercase_						,    lowercase_							)
											elif self.config.problem_type == "single_label_classification":
																__a      :	str    =  CrossEntropyLoss()
																__a      :	Any    =  loss_fct(logits.view(-1						,    self.num_labels							)						,    labels.view(-1							)							)
											elif self.config.problem_type == "multi_label_classification":
																__a      :	List[str]    =  BCEWithLogitsLoss()
																__a      :	List[Any]    =  loss_fct(lowercase_						,    lowercase_							)

						if not return_dict:
											__a      :	Optional[int]    =  (logits,) + outputs[2:]
											return (loss,) + output if loss is not None else output

						return ImageClassifierOutputWithNoAttention(loss=lowercase_						,    logits=lowercase_						,    hidden_states=outputs.hidden_states							)







'''simple docstring'''



import gc
import importlib.metadata
import tempfile
import unittest

from packaging import version

from transformers import (
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    BitsAndBytesConfig,
    pipeline,
)
from transformers.testing_utils import (
    is_torch_available,
    require_accelerate,
    require_bitsandbytes,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       str       ):
					if model.config.model_type == "gpt2":
										return model.transformer.h[0].mlp.c_fc
					return model.transformer.h[0].mlp.dense_ah_to_h


if is_torch_available():
	import torch
	import torch.nn as nn





	class 							__UpperCamelCase    (		nn.Module ):




		def __init__(       self						,    __a						,    __a							):

							'''simple docstring'''





							super().__init__()
							__a      :	int    =  module
							__a      :	List[Any]    =  nn.Sequential(
							    nn.Linear(module.in_features						,    __a						,    bias=__a							)						,    nn.Linear(__a						,    module.out_features						,    bias=__a							)						,    )
							__a      :	int    =  (2.0 / (5 * min(module.in_features						,    module.out_features							))) ** 0.5
							nn.init.normal_(self.adapter[0].weight						,    std=__a							)
							nn.init.zeros_(self.adapter[1].weight							)
							self.adapter.to(module.weight.device							)





		def 				__UpperCAmelCase							(       self						,    __a						,    *__a						,    **__a							):

							'''simple docstring'''





							return self.module(__a						,    *__a						,    **__a							) + self.adapter(__a							)





@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 							__UpperCamelCase    (		unittest.TestCase ):
	# We keep the constants inside the init function and model loading inside setUp function

	# We need to test on relatively large models (aka >1b parameters otherwise the quantiztion may not work as expected)
	# Therefore here we use only bloom-1b3 to test our module
	A_           =   "bigscience/bloom-1b7"

	# Constant values
	A_           =   2.109659552692574

	A_           =   "Hello my name is"
	A_           =   set()
	EXPECTED_OUTPUTS.add("Hello my name is John and I am a professional photographer. I" )
	EXPECTED_OUTPUTS.add("Hello my name is John.\nI am a friend of your father.\n" )
	EXPECTED_OUTPUTS.add("Hello my name is John Doe, I am a student at the University" )
	A_           =   10




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	List[Any]    =  AutoTokenizer.from_pretrained(self.model_name							)





class 							__UpperCamelCase    (		lowerCAmelCase_ ):




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						super().setUp()

						# Models and tokenizer
						__a      :	int    =  AutoModelForCausalLM.from_pretrained(
						    self.model_name						,    torch_dtype=torch.floataa						,    device_map='auto'							)
						__a      :	Union[str, Any]    =  AutoModelForCausalLM.from_pretrained(self.model_name						,    load_in_abit=__a						,    device_map='auto'							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						del self.model_fpaa
						del self.model_abit

						gc.collect()
						torch.cuda.empty_cache()




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	List[str]    =  self.model_abit.config

						self.assertTrue(hasattr(__a						,    'quantization_config'							)							)

						__a      :	Union[str, Any]    =  config.to_dict()
						__a      :	Tuple    =  config.to_diff_dict()

						__a      :	Tuple    =  config.to_json_string()




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						from bitsandbytes.nn import Paramsabit

						__a      :	List[Any]    =  self.model_fpaa.get_memory_footprint()
						__a      :	List[Any]    =  self.model_abit.get_memory_footprint()

						self.assertAlmostEqual(mem_fpaa / mem_abit						,    self.EXPECTED_RELATIVE_DIFFERENCE							)
						__a      :	Tuple    =  get_some_linear_layer(self.model_abit							)
						self.assertTrue(linear.weight.__class__ == Paramsabit							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						from transformers import TaPreTrainedModel

						self.model_fpaa.get_memory_footprint()
						self.model_abit.get_memory_footprint()

						for name, module in self.model_abit.named_modules():
											if isinstance(__a						,    torch.nn.Linear							):
																if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
																					# 4-bit parameters are packed in uint8 variables
																					self.assertTrue(module.weight.dtype == torch.uinta							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Union[str, Any]    =  self.tokenizer(self.input_text						,    return_tensors='pt'							)
						__a      :	Union[str, Any]    =  self.model_abit.generate(input_ids=encoded_input['input_ids'].to(0							)						,    max_new_tokens=10							)

						self.assertIn(self.tokenizer.decode(output_sequences[0]						,    skip_special_tokens=__a							)						,    self.EXPECTED_OUTPUTS							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Tuple    =  BitsAndBytesConfig()
						__a      :	Tuple    =  True

						__a      :	int    =  AutoModelForCausalLM.from_pretrained(
						    self.model_name						,    quantization_config=__a						,    device_map='auto'							)

						__a      :	str    =  self.tokenizer(self.input_text						,    return_tensors='pt'							)
						__a      :	List[Any]    =  model_abit_from_config.generate(
						    input_ids=encoded_input['input_ids'].to(0							)						,    max_new_tokens=10							)

						self.assertIn(self.tokenizer.decode(output_sequences[0]						,    skip_special_tokens=__a							)						,    self.EXPECTED_OUTPUTS							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						with self.assertRaises(__a							), tempfile.TemporaryDirectory() as tmpdirname:
											self.model_abit.save_pretrained(__a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	str    =  BitsAndBytesConfig()

						with self.assertRaises(__a							):
											__a      :	List[str]    =  AutoModelForCausalLM.from_pretrained(
											    self.model_name						,    quantization_config=__a						,    load_in_abit=__a						,    device_map='auto'						,    bnb_abit_quant_type='nf4'						,    )




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						with self.assertRaises(__a							):
											# Tries with `str`
											self.model_abit.to('cpu'							)

						with self.assertRaises(__a							):
											# Tries with a `dtype``
											self.model_abit.to(torch.floataa							)

						with self.assertRaises(__a							):
											# Tries with a `device`
											self.model_abit.to(torch.device('cuda:0'							)							)

						with self.assertRaises(__a							):
											# Tries with a `device`
											self.model_abit.float()

						with self.assertRaises(__a							):
											# Tries with a `device`
											self.model_abit.half()

						# Test if we did not break anything
						__a      :	List[str]    =  self.tokenizer(self.input_text						,    return_tensors='pt'							)

						__a      :	Optional[int]    =  self.model_fpaa.to(torch.floataa							)
						__a      :	Tuple    =  self.model_fpaa.generate(input_ids=encoded_input['input_ids'].to(0							)						,    max_new_tokens=10							)

						# Check this does not throw an error
						__a      :	List[Any]    =  self.model_fpaa.to('cpu'							)

						# Check this does not throw an error
						__a      :	Union[str, Any]    =  self.model_fpaa.half()

						# Check this does not throw an error
						__a      :	Union[str, Any]    =  self.model_fpaa.float()





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	str    =  AutoModelForSeqaSeqLM.from_pretrained('t5-small'						,    load_in_abit=__a						,    device_map='auto'							)
						self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa							)





@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 							__UpperCamelCase    (		unittest.TestCase ):




	@classmethod
	def 				__UpperCAmelCase							(       cls							):

						'''simple docstring'''





						__a      :	Any    =  't5-small'
						__a      :	Tuple    =  'google/flan-t5-small'  # flan-t5 uses dense-act instead of dense-relu-dense
						__a      :	int    =  AutoTokenizer.from_pretrained(cls.model_name							)
						__a      :	Union[str, Any]    =  'Translate in German: Hello, my dog is cute'




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						gc.collect()
						torch.cuda.empty_cache()




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						from transformers import TaForConditionalGeneration

						__a      :	Optional[int]    =  TaForConditionalGeneration._keep_in_fpaa_modules
						__a      :	List[str]    =  None

						# test with `t5-small`
						__a      :	List[str]    =  TaForConditionalGeneration.from_pretrained(self.model_name						,    load_in_abit=__a						,    device_map='auto'							)
						__a      :	Optional[int]    =  self.tokenizer(self.input_text						,    return_tensors='pt'							).to(0							)
						__a      :	Any    =  model.generate(**__a							)

						# test with `flan-t5-small`
						__a      :	List[str]    =  TaForConditionalGeneration.from_pretrained(
						    self.dense_act_model_name						,    load_in_abit=__a						,    device_map='auto'							)
						__a      :	str    =  self.tokenizer(self.input_text						,    return_tensors='pt'							).to(0							)
						__a      :	List[Any]    =  model.generate(**__a							)
						__a      :	Optional[int]    =  modules





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						import bitsandbytes as bnb

						from transformers import TaForConditionalGeneration

						# test with `t5-small`
						__a      :	List[Any]    =  TaForConditionalGeneration.from_pretrained(self.model_name						,    load_in_abit=__a						,    device_map='auto'							)

						# there was a bug with decoders - this test checks that it is fixed
						self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q						,    bnb.nn.Linearabit							)							)

						__a      :	str    =  self.tokenizer(self.input_text						,    return_tensors='pt'							).to(0							)
						__a      :	List[str]    =  model.generate(**__a							)

						# test with `flan-t5-small`
						__a      :	List[Any]    =  TaForConditionalGeneration.from_pretrained(
						    self.dense_act_model_name						,    load_in_abit=__a						,    device_map='auto'							)
						__a      :	Optional[Any]    =  self.tokenizer(self.input_text						,    return_tensors='pt'							).to(0							)
						__a      :	int    =  model.generate(**__a							)





class 							__UpperCamelCase    (		lowerCAmelCase_ ):




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						super().setUp()
						# model_name
						__a      :	List[Any]    =  'bigscience/bloom-560m'
						__a      :	Union[str, Any]    =  't5-small'

						# Different types of model

						__a      :	Optional[Any]    =  AutoModel.from_pretrained(self.model_name						,    load_in_abit=__a						,    device_map='auto'							)
						# Sequence classification model
						__a      :	Dict    =  AutoModelForSequenceClassification.from_pretrained(
						    self.model_name						,    load_in_abit=__a						,    device_map='auto'							)
						# CausalLM model
						__a      :	Union[str, Any]    =  AutoModelForCausalLM.from_pretrained(self.model_name						,    load_in_abit=__a						,    device_map='auto'							)
						# Seq2seq model
						__a      :	Any    =  AutoModelForSeqaSeqLM.from_pretrained(
						    self.seq_to_seq_name						,    load_in_abit=__a						,    device_map='auto'							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						del self.base_model
						del self.sequence_model
						del self.model_abit
						del self.seq_to_seq_model

						gc.collect()
						torch.cuda.empty_cache()





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						from bitsandbytes.nn import Paramsabit

						self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit							)

						# Other heads should be nn.Parameter
						self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter							)
						self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter							)
						self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter							)





class 							__UpperCamelCase    (		lowerCAmelCase_ ):




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						super().setUp()




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						del self.pipe

						gc.collect()
						torch.cuda.empty_cache()





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Dict    =  pipeline(
						    'text-generation'						,    model=self.model_name						,    model_kwargs={'device_map': 'auto', 'load_in_4bit': True, 'torch_dtype': torch.floataa}						,    max_new_tokens=self.MAX_NEW_TOKENS						,    )

						# Real second forward pass
						__a      :	str    =  self.pipe(self.input_text							)
						self.assertIn(pipeline_output[0]['generated_text']						,    self.EXPECTED_OUTPUTS							)





@require_torch_multi_gpu
class 							__UpperCamelCase    (		lowerCAmelCase_ ):




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						super().setUp()





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Any    =  AutoModelForCausalLM.from_pretrained(
						    self.model_name						,    load_in_abit=__a						,    device_map='balanced'							)

						# Check correct device map
						self.assertEqual(set(model_parallel.hf_device_map.values()							)						,    {0, 1}							)

						# Check that inference pass works on the model
						__a      :	List[Any]    =  self.tokenizer(self.input_text						,    return_tensors='pt'							)

						# Second real batch
						__a      :	str    =  model_parallel.generate(input_ids=encoded_input['input_ids'].to(0							)						,    max_new_tokens=10							)
						self.assertIn(self.tokenizer.decode(output_parallel[0]						,    skip_special_tokens=__a							)						,    self.EXPECTED_OUTPUTS							)





class 							__UpperCamelCase    (		lowerCAmelCase_ ):




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Union[str, Any]    =  'facebook/opt-350m'
						super().setUp()





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						if version.parse(importlib.metadata.version('bitsandbytes'							)							) < version.parse('0.37.0'							):
											return

						# Step 1: freeze all parameters
						__a      :	Union[str, Any]    =  AutoModelForCausalLM.from_pretrained(self.model_name						,    load_in_abit=__a							)

						self.assertEqual(set(model.hf_device_map.values()							)						,    {torch.cuda.current_device()}							)

						for param in model.parameters():
											__a      :	Tuple    =  False  # freeze the model - train adapters later
											if param.ndim == 1:
																# cast the small parameters (e.g. layernorm) to fp32 for stability
																__a      :	Tuple    =  param.data.to(torch.floataa							)

        # Step 2: add adapters
						for _, module in model.named_modules():
											if "OPTAttention" in repr(type(__a							)							):
																__a      :	str    =  LoRALayer(module.q_proj						,    rank=16							)
																__a      :	str    =  LoRALayer(module.k_proj						,    rank=16							)
																__a      :	Optional[int]    =  LoRALayer(module.v_proj						,    rank=16							)

        # Step 3: dummy batch
						__a      :	List[str]    =  self.tokenizer('Test batch '						,    return_tensors='pt'							).to(0							)

						# Step 4: Check if the gradient is not None
						with torch.cuda.amp.autocast():
											__a      :	int    =  model.forward(**__a							)
											out.logits.norm().backward()

						for module in model.modules():
											if isinstance(__a						,    __a							):
																self.assertTrue(module.adapter[1].weight.grad is not None							)
																self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0							)
											elif isinstance(__a						,    nn.Embedding							):
																self.assertTrue(module.weight.grad is None							)





class 							__UpperCamelCase    (		lowerCAmelCase_ ):
	A_           =   "gpt2-xl"
	A_           =   3.3191854854152187



