



"""simple docstring"""
import os
import tempfile
import unittest

import numpy as np
import torch

from diffusers.models import ModelMixin, UNetaDConditionModel
from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
from diffusers.utils import (
    floats_tensor,
    logging,
    skip_mps,
    torch_device,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

__a           =							logging.get_logger(__name__)
def   A_    (     _lowercase,   _lowercase = True      ):







						'''simple docstring'''


						snake_case_					:str		=     {}
						for name in model.attn_processors.keys():
												snake_case_					:Dict		=     name.endswith("""attn2.processor"""      ) and not (
												    name.startswith("""transformer_in"""      ) or """temp_attentions""" in name.split("""."""      )
												)
												snake_case_					:Union[str, Any]		=     model.config.cross_attention_dim if has_cross_attention else None
												if name.startswith("""mid_block"""      ):
																		snake_case_					:Optional[Any]		=     model.config.block_out_channels[-1]
												elif name.startswith("""up_blocks"""      ):
																		snake_case_					:int		=     int(name[len("""up_blocks."""      )]      )
																		snake_case_					:int		=     list(reversed(model.config.block_out_channels      )      )[block_id]
												elif name.startswith("""down_blocks"""      ):
																		snake_case_					:Tuple		=     int(name[len("""down_blocks."""      )]      )
																		snake_case_					:str		=     model.config.block_out_channels[block_id]
												elif name.startswith("""transformer_in"""      ):
																		# Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
																		snake_case_					:List[Any]		=     8 * model.config.attention_head_dim

												snake_case_					:Union[str, Any]		=     LoRAAttnProcessor(hidden_size=_lowercase,   cross_attention_dim=_lowercase      )
												snake_case_					:Union[str, Any]		=     lora_attn_procs[name].to(model.device      )

												if mock_weights:
																		# add 1 to weights to mock trained weights
																		with torch.no_grad():
																								lora_attn_procs[name].to_q_lora.up.weight += 1
																								lora_attn_procs[name].to_k_lora.up.weight += 1
																								lora_attn_procs[name].to_v_lora.up.weight += 1
																								lora_attn_procs[name].to_out_lora.up.weight += 1

						return lora_attn_procs

@skip_mps
class 							lowerCamelCase    (     _lowerCAmelCase       ,		_lowerCAmelCase       ,		unittest.TestCase							):






					'''simple docstring'''





					_A						:     Optional[Any]		=	UNetaDConditionModel
					_A						:     Any		=	"""sample"""






					@property
					def   lowerCAmelCase_							(			self:				List[Any]		)  ->      Optional[Any]:
											snake_case_					:Optional[int]		=     4
											snake_case_					:str		=     4
											snake_case_					:List[str]		=     4
											snake_case_					:Any		=     (32, 32)

											snake_case_					:Dict		=     floats_tensor((batch_size, num_channels, num_frames) + sizes		).to(snake_case		)
											snake_case_					:Union[str, Any]		=     torch.tensor([10]		).to(snake_case		)
											snake_case_					:List[Any]		=     floats_tensor((batch_size, 4, 32)		).to(snake_case		)

											return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}






					@property
					def   lowerCAmelCase_							(			self:				List[str]		)  ->      int:
											return (4, 4, 32, 32)






					@property
					def   lowerCAmelCase_							(			self:				int		)  ->      List[str]:
											return (4, 4, 32, 32)






					def   lowerCAmelCase_							(			self:				Optional[int]		)  ->      List[str]:
											snake_case_					:Union[str, Any]		=     {
											    """block_out_channels""": (32, 64),
											    """down_block_types""": (
											        """CrossAttnDownBlock3D""",
											        """DownBlock3D""",
											    ),
											    """up_block_types""": ("""UpBlock3D""", """CrossAttnUpBlock3D"""),
											    """cross_attention_dim""": 32,
											    """attention_head_dim""": 8,
											    """out_channels""": 4,
											    """in_channels""": 4,
											    """layers_per_block""": 1,
											    """sample_size""": 32,
											}
											snake_case_					:List[str]		=     self.dummy_input
											return init_dict, inputs_dict






					@unittest.skipIf(
					    torch_device != """cuda""" or not is_xformers_available()		,	reason="""XFormers attention is only available with CUDA and `xformers` installed"""		,	)
					def   lowerCAmelCase_							(			self:				Union[str, Any]		)  ->      List[str]:
											snake_case_,			snake_case_					:Any		=     self.prepare_init_args_and_inputs_for_common()
											snake_case_					:Dict		=     self.model_class(**snake_case		)

											model.enable_xformers_memory_efficient_attention()

											assert (
											    model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
											    == "XFormersAttnProcessor"
											), "xformers is not enabled"






					def   lowerCAmelCase_							(			self:				Union[str, Any]		)  ->      Tuple:
											snake_case_,			snake_case_					:List[str]		=     self.prepare_init_args_and_inputs_for_common()

											snake_case_					:Dict		=     32

											snake_case_					:Tuple		=     self.model_class(**snake_case		)
											model.to(snake_case		)
											model.eval()

											with torch.no_grad():
																	snake_case_					:List[Any]		=     model(**snake_case		)

																	if isinstance(snake_case		,	snake_case		):
																							snake_case_					:Optional[int]		=     output.sample

											self.assertIsNotNone(snake_case		)
											snake_case_					:Tuple		=     inputs_dict["""sample"""].shape
											self.assertEqual(output.shape		,	snake_case		,	"""Input and output shapes do not match"""		)






					def   lowerCAmelCase_							(			self:				Dict		)  ->      Optional[Any]:
											snake_case_,			snake_case_					:str		=     self.prepare_init_args_and_inputs_for_common()
											snake_case_					:Any		=     self.model_class(**snake_case		)
											model.to(snake_case		)
											model.eval()

											with torch.no_grad():
																	# Warmup pass when using mps (see #372)
																	if torch_device == "mps" and isinstance(snake_case		,	snake_case		):
																							model(**self.dummy_input		)

																	snake_case_					:Dict		=     model(**snake_case		)
																	if isinstance(snake_case		,	snake_case		):
																							snake_case_					:Optional[Any]		=     first.sample

																	snake_case_					:int		=     model(**snake_case		)
																	if isinstance(snake_case		,	snake_case		):
																							snake_case_					:Optional[Any]		=     second.sample

											snake_case_					:str		=     first.cpu().numpy()
											snake_case_					:str		=     second.cpu().numpy()
											snake_case_					:Optional[int]		=     out_a[~np.isnan(snake_case		)]
											snake_case_					:int		=     out_a[~np.isnan(snake_case		)]
											snake_case_					:int		=     np.amax(np.abs(out_a - out_a		)		)
											self.assertLessEqual(snake_case		,	1E-5		)






					def   lowerCAmelCase_							(			self:				str		)  ->      int:
											snake_case_,			snake_case_					:str		=     self.prepare_init_args_and_inputs_for_common()

											snake_case_					:Union[str, Any]		=     8

											snake_case_					:Optional[int]		=     self.model_class(**snake_case		)
											model.to(snake_case		)
											model.eval()

											model.set_attention_slice("""auto"""		)
											with torch.no_grad():
																	snake_case_					:Optional[Any]		=     model(**snake_case		)
											assert output is not None

											model.set_attention_slice("""max"""		)
											with torch.no_grad():
																	snake_case_					:List[Any]		=     model(**snake_case		)
											assert output is not None

											model.set_attention_slice(2		)
											with torch.no_grad():
																	snake_case_					:List[Any]		=     model(**snake_case		)
											assert output is not None






					def   lowerCAmelCase_							(			self:				Optional[Any]		)  ->      Optional[Any]:
											snake_case_,			snake_case_					:Optional[Any]		=     self.prepare_init_args_and_inputs_for_common()

											snake_case_					:List[str]		=     8

											snake_case_					:List[Any]		=     self.model_class(**snake_case		)
											model.to(snake_case		)

											with torch.no_grad():
																	snake_case_					:Any		=     model(**snake_case		).sample

											snake_case_					:Optional[Any]		=     create_lora_layers(snake_case		)

											# make sure we can set a list of attention processors
											model.set_attn_processor(snake_case		)
											model.to(snake_case		)

											# test that attn processors can be set to itself
											model.set_attn_processor(model.attn_processors		)

											with torch.no_grad():
																	snake_case_					:List[str]		=     model(**snake_case		,	cross_attention_kwargs={"""scale""": 0.0}		).sample
																	snake_case_					:List[Any]		=     model(**snake_case		,	cross_attention_kwargs={"""scale""": 0.5}		).sample
																	snake_case_					:Union[str, Any]		=     model(**snake_case		,	cross_attention_kwargs={"""scale""": 0.5}		).sample

											assert (samplea - samplea).abs().max() < 3E-3
											assert (samplea - samplea).abs().max() < 3E-3

											# sample 2 and sample 3 should be different
											assert (samplea - samplea).abs().max() > 3E-3






					def   lowerCAmelCase_							(			self:				List[Any]		)  ->      Optional[Any]:
											snake_case_,			snake_case_					:Optional[int]		=     self.prepare_init_args_and_inputs_for_common()

											snake_case_					:Union[str, Any]		=     8

											torch.manual_seed(0		)
											snake_case_					:Optional[int]		=     self.model_class(**snake_case		)
											model.to(snake_case		)

											with torch.no_grad():
																	snake_case_					:Optional[Any]		=     model(**snake_case		).sample

											snake_case_					:List[str]		=     create_lora_layers(snake_case		)
											model.set_attn_processor(snake_case		)

											with torch.no_grad():
																	snake_case_					:List[Any]		=     model(**snake_case		,	cross_attention_kwargs={"""scale""": 0.5}		).sample

											with tempfile.TemporaryDirectory() as tmpdirname:
																	model.save_attn_procs(snake_case		)
																	self.assertTrue(os.path.isfile(os.path.join(snake_case		,	"""pytorch_lora_weights.bin"""		)		)		)
																	torch.manual_seed(0		)
																	snake_case_					:List[Any]		=     self.model_class(**snake_case		)
																	new_model.to(snake_case		)
																	new_model.load_attn_procs(snake_case		)

											with torch.no_grad():
																	snake_case_					:Optional[Any]		=     new_model(**snake_case		,	cross_attention_kwargs={"""scale""": 0.5}		).sample

											assert (sample - new_sample).abs().max() < 1E-3

											# LoRA and no LoRA should NOT be the same
											assert (sample - old_sample).abs().max() > 1E-4






					def   lowerCAmelCase_							(			self:				str		)  ->      Union[str, Any]:
											snake_case_,			snake_case_					:List[str]		=     self.prepare_init_args_and_inputs_for_common()

											snake_case_					:Tuple		=     8

											torch.manual_seed(0		)
											snake_case_					:List[str]		=     self.model_class(**snake_case		)
											model.to(snake_case		)

											with torch.no_grad():
																	snake_case_					:Optional[Any]		=     model(**snake_case		).sample

											snake_case_					:str		=     create_lora_layers(snake_case		)
											model.set_attn_processor(snake_case		)

											with torch.no_grad():
																	snake_case_					:Optional[int]		=     model(**snake_case		,	cross_attention_kwargs={"""scale""": 0.5}		).sample

											with tempfile.TemporaryDirectory() as tmpdirname:
																	model.save_attn_procs(snake_case		,	safe_serialization=snake_case		)
																	self.assertTrue(os.path.isfile(os.path.join(snake_case		,	"""pytorch_lora_weights.safetensors"""		)		)		)
																	torch.manual_seed(0		)
																	snake_case_					:Any		=     self.model_class(**snake_case		)
																	new_model.to(snake_case		)
																	new_model.load_attn_procs(snake_case		)

											with torch.no_grad():
																	snake_case_					:Optional[int]		=     new_model(**snake_case		,	cross_attention_kwargs={"""scale""": 0.5}		).sample

											assert (sample - new_sample).abs().max() < 3E-3

											# LoRA and no LoRA should NOT be the same
											assert (sample - old_sample).abs().max() > 1E-4






					def   lowerCAmelCase_							(			self:				Dict		)  ->      Optional[Any]:
											# enable deterministic behavior for gradient checkpointing
											snake_case_,			snake_case_					:Union[str, Any]		=     self.prepare_init_args_and_inputs_for_common()

											snake_case_					:Any		=     8

											torch.manual_seed(0		)
											snake_case_					:int		=     self.model_class(**snake_case		)
											model.to(snake_case		)

											snake_case_					:List[Any]		=     create_lora_layers(snake_case		,	mock_weights=snake_case		)
											model.set_attn_processor(snake_case		)
											# Saving as torch, properly reloads with directly filename
											with tempfile.TemporaryDirectory() as tmpdirname:
																	model.save_attn_procs(snake_case		)
																	self.assertTrue(os.path.isfile(os.path.join(snake_case		,	"""pytorch_lora_weights.bin"""		)		)		)
																	torch.manual_seed(0		)
																	snake_case_					:Union[str, Any]		=     self.model_class(**snake_case		)
																	new_model.to(snake_case		)
																	new_model.load_attn_procs(snake_case		,	weight_name="""pytorch_lora_weights.bin"""		)






					def   lowerCAmelCase_							(			self:				List[str]		)  ->      Tuple:
											snake_case_,			snake_case_					:Union[str, Any]		=     self.prepare_init_args_and_inputs_for_common()

											snake_case_					:Dict		=     8

											torch.manual_seed(0		)
											snake_case_					:Dict		=     self.model_class(**snake_case		)
											model.to(snake_case		)

											snake_case_					:Union[str, Any]		=     create_lora_layers(snake_case		,	mock_weights=snake_case		)
											model.set_attn_processor(snake_case		)
											# Saving as torch, properly reloads with directly filename
											with tempfile.TemporaryDirectory() as tmpdirname:
																	model.save_attn_procs(snake_case		)
																	self.assertTrue(os.path.isfile(os.path.join(snake_case		,	"""pytorch_lora_weights.bin"""		)		)		)
																	torch.manual_seed(0		)
																	snake_case_					:Optional[int]		=     self.model_class(**snake_case		)
																	new_model.to(snake_case		)
																	with self.assertRaises(snake_case		) as e:
																							new_model.load_attn_procs(snake_case		,	use_safetensors=snake_case		)
																	self.assertIn("""Error no file named pytorch_lora_weights.safetensors"""		,	str(e.exception		)		)






					def   lowerCAmelCase_							(			self:				Dict		)  ->      Dict:
											snake_case_,			snake_case_					:str		=     self.prepare_init_args_and_inputs_for_common()

											snake_case_					:int		=     8

											torch.manual_seed(0		)
											snake_case_					:List[Any]		=     self.model_class(**snake_case		)
											model.to(snake_case		)

											with torch.no_grad():
																	snake_case_					:List[str]		=     model(**snake_case		).sample

											snake_case_					:Optional[Any]		=     create_lora_layers(snake_case		)
											model.set_attn_processor(snake_case		)

											with torch.no_grad():
																	snake_case_					:List[Any]		=     model(**snake_case		,	cross_attention_kwargs={"""scale""": 0.0}		).sample

											model.set_attn_processor(AttnProcessor()		)

											with torch.no_grad():
																	snake_case_					:Dict		=     model(**snake_case		).sample

											assert (sample - new_sample).abs().max() < 1E-4
											assert (sample - old_sample).abs().max() < 3E-3






					@unittest.skipIf(
					    torch_device != """cuda""" or not is_xformers_available()		,	reason="""XFormers attention is only available with CUDA and `xformers` installed"""		,	)
					def   lowerCAmelCase_							(			self:				Any		)  ->      Union[str, Any]:
											# enable deterministic behavior for gradient checkpointing
											snake_case_,			snake_case_					:Optional[int]		=     self.prepare_init_args_and_inputs_for_common()

											snake_case_					:Optional[Any]		=     4

											torch.manual_seed(0		)
											snake_case_					:Dict		=     self.model_class(**snake_case		)
											model.to(snake_case		)
											snake_case_					:int		=     create_lora_layers(snake_case		)
											model.set_attn_processor(snake_case		)

											# default
											with torch.no_grad():
																	snake_case_					:Union[str, Any]		=     model(**snake_case		).sample

																	model.enable_xformers_memory_efficient_attention()
																	snake_case_					:Optional[int]		=     model(**snake_case		).sample

																	model.disable_xformers_memory_efficient_attention()
																	snake_case_					:int		=     model(**snake_case		).sample

											assert (sample - on_sample).abs().max() < 1E-4
											assert (sample - off_sample).abs().max() < 1E-4






					def   lowerCAmelCase_							(			self:				List[Any]		)  ->      str:
											snake_case_,			snake_case_					:List[Any]		=     self.prepare_init_args_and_inputs_for_common()
											snake_case_					:Dict		=     32

											snake_case_					:Any		=     self.model_class(**snake_case		)
											model.to(snake_case		)
											model.eval()

											with torch.no_grad():
																	snake_case_					:Dict		=     model(**snake_case		)[0]

											model.enable_forward_chunking()
											with torch.no_grad():
																	snake_case_					:Optional[Any]		=     model(**snake_case		)[0]

											self.assertEqual(output.shape		,	output_a.shape		,	"""Shape doesn't match"""		)
											assert np.abs(output.cpu() - output_a.cpu()		).max() < 1E-2


# (todo: sayakpaul) implement SLOW tests.




'''simple docstring'''






import logging
import os
import sys
from dataclasses import dataclass, field
from typing import List, Optional

import torch
from datasets import Dataset
from torch import nn
from tqdm.auto import tqdm

from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
    utils,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


a_       :     List[str] 							=     "\nDistills an NLI-based zero-shot classifier to a smaller, more efficient model with a fixed set of candidate class\nnames. Useful for speeding up zero-shot classification in cases where labeled training data is not available, but\nwhen only a single fixed set of classes is needed. Takes a teacher NLI model, student classifier model, unlabeled\ndataset, and set of K possible class names. Yields a single classifier with K outputs corresponding to the provided\nclass names.\n"

a_       :     Dict 							=     logging.getLogger(__name__)




@dataclass
class 			a  :
 _lowerCAmelCase			       =						field(
     default="""roberta-large-mnli"""	,					metadata={"""help""": """The NLI/zero-shot teacher model to be distilled."""}							)
 _lowerCAmelCase			       =						field(
     default="""This example is {}."""	,					metadata={
         """help""": (
             """Template used to turn class names into mock hypotheses for teacher NLI model. Must include {{}}"""
             """where class name is inserted."""
         )
     }	,					)
 _lowerCAmelCase			       =						field(
     default=3_2	,					metadata={"""help""": """Batch size for generating teacher predictions."""}							)
 _lowerCAmelCase			       =						field(
     default=_SCREAMING_SNAKE_CASE	,					metadata={
         """help""": (
             """Allow multiple classes to be true rather than forcing them to sum to 1 (sometimes called"""
             """multi-class multi-label classification)."""
         )
     }	,					)
 _lowerCAmelCase			       =						field(
     default=1.0	,					metadata={"""help""": """Temperature applied to teacher softmax for distillation."""}							)





@dataclass
class 			a  :
 _lowerCAmelCase			       =						field(
     default="""distilbert-base-uncased"""	,					metadata={"""help""": """The NLI/zero-shot teacher model to be distilled."""}							)





@dataclass
class 			a  :
 _lowerCAmelCase			       =						field(metadata={"""help""": """Text file with one unlabeled instance per line."""}							)
 _lowerCAmelCase			       =						field(metadata={"""help""": """Text file with one class name per line."""}							)
 _lowerCAmelCase			       =						field(
     default=_SCREAMING_SNAKE_CASE	,					metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the Rust tokenizers library) or not."""}	,					)





@dataclass
class 			a  (  _SCREAMING_SNAKE_CASE							):
 _lowerCAmelCase			       =						field(
     default=_SCREAMING_SNAKE_CASE	,					metadata={"""help""": """The output directory where the model predictions and checkpoints will be written."""}	,					)
 _lowerCAmelCase			       =						field(
     default=3_2	,					metadata={"""help""": """Batch size per GPU/TPU core/CPU for training."""}							)
 _lowerCAmelCase			       =						field(
     default=1_2_8	,					metadata={"""help""": """Batch size per GPU/TPU core/CPU for evaluation."""}							)
 _lowerCAmelCase			       =						field(default=1.0	,					metadata={"""help""": """Total number of training epochs to perform."""}							)
 _lowerCAmelCase			       =						field(default=_SCREAMING_SNAKE_CASE	,					metadata={"""help""": """Whether to run training of student model."""}							)
 _lowerCAmelCase			       =						field(
     default=_SCREAMING_SNAKE_CASE	,					metadata={
         """help""": (
             """Whether to evaluate the agreement of the final student predictions and the teacher predictions"""
             """after training."""
         )
     }	,					)
 _lowerCAmelCase			       =						field(
     default=0	,					metadata={
         """help""": (
             """Limit the total amount of checkpoints."""
             """Deletes the older checkpoints in the output_dir. Default is 0 (no checkpoints)."""
         )
     }	,					)





class 			a  (  _SCREAMING_SNAKE_CASE							):
 def 				__UpperCAmelCase		(			self   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__=False    )       ->				List[Any]:
      _a         =				inputs['labels']
      _a         =				model(inputs['input_ids']   ,					attention_mask=inputs['attention_mask']    )
      _a         =				outputs[0]

      _a         =				-torch.sum(target_p * logits.log_softmax(dim=-1    )   ,					axis=-1    ).mean()

      if return_outputs:
           return loss, outputs

      return loss






def 						_A					(lowerCAmelCase__	:Optional[int]		) ->	Dict:




     '''simple docstring'''



     _a         =				[]
     with open(lowerCAmelCase__					,    'r'		) as f:
          for line in f:
               _a         =				line.strip()
               if len(lowerCAmelCase__		) > 0:
                    lines.append(lowerCAmelCase__		)
     return lines






def 						_A					(lowerCAmelCase__	:Union[str, Any]					,    lowerCAmelCase__	:List[str]					,    lowerCAmelCase__	:List[Any]		) ->	Union[str, Any]:




     '''simple docstring'''



     _a         =				[]
     _a         =				[]
     for example in examples:
          for name in class_names:
               premises.append(lowerCAmelCase__		)
               hypotheses.append(hypothesis_template.format(lowerCAmelCase__		)		)
     return premises, hypotheses






def 						_A					(lowerCAmelCase__	:Any		) ->	List[str]:




     '''simple docstring'''



     for label, ind in config.labelaid.items():
          if label.lower().startswith('entail'		):
               return ind
     logger.warning('Could not identify entailment dimension from teacher config label2id. Setting to -1.'		)
     return -1






def 						_A					(lowerCAmelCase__	:str					,    lowerCAmelCase__	:List[str]					,    lowerCAmelCase__	:List[str]					,    lowerCAmelCase__	:str					,    lowerCAmelCase__	:int					,    lowerCAmelCase__	:float					,    lowerCAmelCase__	:bool					,    lowerCAmelCase__	:bool					,    lowerCAmelCase__	:bool					,    lowerCAmelCase__	:bool					,    ) ->	Dict:




     '''simple docstring'''



     _a         =				AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase__		)
     _a         =				model.config
     if not no_cuda and torch.cuda.is_available():
          _a         =				nn.DataParallel(model.cuda()		)
          batch_size *= len(model.device_ids		)
     _a         =				AutoTokenizer.from_pretrained(lowerCAmelCase__					,    use_fast=lowerCAmelCase__		)

     _a   , _a         =				get_premise_hypothesis_pairs(lowerCAmelCase__					,    lowerCAmelCase__					,    lowerCAmelCase__		)
     _a         =				[]

     for i in tqdm(range(0					,    len(lowerCAmelCase__		)					,    lowerCAmelCase__		)		):
          _a         =				premises[i : i + batch_size]
          _a         =				hypotheses[i : i + batch_size]

          _a         =				tokenizer(
              lowerCAmelCase__					,    lowerCAmelCase__					,    padding=lowerCAmelCase__					,    truncation='only_first'					,    return_tensors='pt'					,    )

          with torch.cuda.amp.autocast(enabled=lowerCAmelCase__		):
               with torch.no_grad():
                    _a         =				model(**lowerCAmelCase__		)
          logits.append(outputs.logits.detach().cpu().float()		)

     _a         =				get_entailment_id(lowerCAmelCase__		)
     _a         =				-1 if entail_id == 0 else 0
     _a         =				torch.cat(lowerCAmelCase__					,    dim=0		)  # N*K x 3
     _a         =				logits.reshape(len(lowerCAmelCase__		)					,    len(lowerCAmelCase__		)					,    -1		)[..., [contr_id, entail_id]]  # N x K x 2

     if multi_label:
          # softmax over (contr, entail) logits for each class independently
          _a         =				(nli_logits / temperature).softmax(-1		)
     else:
          # softmax over entail logits across classes s.t. class probabilities sum to 1.
          _a         =				(nli_logits / temperature).softmax(1		)

     return nli_prob[..., 1]  # N x K






def 						_A					() ->	str:




     '''simple docstring'''



     _a         =				HfArgumentParser(
         (DataTrainingArguments, TeacherModelArguments, StudentModelArguments, DistillTrainingArguments)					,    description=lowerCAmelCase__					,    )

     if len(sys.argv		) == 2 and sys.argv[1].endswith('.json'		):
          # If we pass only one argument to the script and it's the path to a json file,
          # let's parse it to get our arguments.
          _a   , _a   , _a   , _a         =				parser.parse_json_file(
              json_file=os.path.abspath(sys.argv[1]		)		)
     else:
          _a   , _a   , _a   , _a         =				parser.parse_args_into_dataclasses()

     # Detecting last checkpoint.
     _a         =				None
     if os.path.isdir(training_args.output_dir		) and training_args.do_train and not training_args.overwrite_output_dir:
          _a         =				get_last_checkpoint(training_args.output_dir		)
          if last_checkpoint is None and len(os.listdir(training_args.output_dir		)		) > 0:
               raise ValueError(
                   f'Output directory ({training_args.output_dir}) already exists and is not empty. '
                   'Use --overwrite_output_dir to overcome.'		)
          elif last_checkpoint is not None:
               logger.info(
                   f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
                   'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'		)

    # Setup logging
     logging.basicConfig(
         format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'					,    datefmt='%m/%d/%Y %H:%M:%S'					,    handlers=[logging.StreamHandler(sys.stdout		)]					,    )
     logger.setLevel(logging.INFO if is_main_process(training_args.local_rank		) else logging.WARN		)

     # Log on each process the small summary:
     logger.warning(
         f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
         + f'distributed training: {bool(training_args.local_rank != -1		)}, 16-bits training: {training_args.fpaa}'		)
     # Set the verbosity to info of the Transformers logger (on main process only):
     if is_main_process(training_args.local_rank		):
          utils.logging.set_verbosity_info()
          utils.logging.enable_default_handler()
          utils.logging.enable_explicit_format()

     if training_args.local_rank != -1:
          raise ValueError('Distributed training is not currently supported.'		)
     if training_args.tpu_num_cores is not None:
          raise ValueError('TPU acceleration is not currently supported.'		)

     logger.info(f'Training/evaluation parameters {training_args}'		)

     # Set seed before initializing model.
     set_seed(training_args.seed		)

     # 1. read in data
     _a         =				read_lines(data_args.data_file		)
     _a         =				read_lines(data_args.class_names_file		)

     # 2. get teacher predictions and load into dataset
     logger.info('Generating predictions from zero-shot teacher model'		)
     _a         =				get_teacher_predictions(
         teacher_args.teacher_name_or_path					,    lowerCAmelCase__					,    lowerCAmelCase__					,    teacher_args.hypothesis_template					,    teacher_args.teacher_batch_size					,    teacher_args.temperature					,    teacher_args.multi_label					,    data_args.use_fast_tokenizer					,    training_args.no_cuda					,    training_args.fpaa					,    )
     _a         =				Dataset.from_dict(
         {
             'text': examples,
             'labels': teacher_soft_preds,
         }		)

     # 3. create student
     logger.info('Initializing student model'		)
     _a         =				AutoModelForSequenceClassification.from_pretrained(
         student_args.student_name_or_path					,    num_labels=len(lowerCAmelCase__		)		)
     _a         =				AutoTokenizer.from_pretrained(student_args.student_name_or_path					,    use_fast=data_args.use_fast_tokenizer		)
     _a         =				dict(enumerate(lowerCAmelCase__		)		)
     _a         =				{label: i for i, label in enumerate(lowerCAmelCase__		)}

     # 4. train student on teacher predictions
     _a         =				dataset.map(lowerCAmelCase__					,    input_columns='text'		)
     dataset.set_format('torch'		)

     def compute_metrics(lowerCAmelCase__	:Tuple					,    lowerCAmelCase__	:List[Any]=False		):
          _a         =				p.predictions.argmax(-1		)
          _a         =				p.label_ids.argmax(-1		)  # "label_ids" are actually distributions
          return {"agreement": (preds == proxy_labels).mean().item()}

     _a         =				DistillationTrainer(
         model=lowerCAmelCase__					,    tokenizer=lowerCAmelCase__					,    args=lowerCAmelCase__					,    train_dataset=lowerCAmelCase__					,    compute_metrics=lowerCAmelCase__					,    )

     if training_args.do_train:
          logger.info('Training student model on teacher predictions'		)
          trainer.train()

     if training_args.do_eval:
          _a         =				trainer.evaluate(eval_dataset=lowerCAmelCase__		)['eval_agreement']
          logger.info(f'Agreement of student and teacher predictions: {agreement * 1_00:0.2f}%'		)

     trainer.save_model()


if __name__ == "__main__":
 main()

