






"""simple docstring"""



from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


_lowerCAmelCase     :     Dict            =	logging.get_logger(__name__)  # pylint: disable=invalid-name

_lowerCAmelCase     :     List[Any]            =	'''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)["depth"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline("depth-estimation")

        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")

        >>> img = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/cat.png"
        ... ).resize((768, 768))


        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")

        >>> prompt = "A robot, 4k photo"
        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"

        >>> generator = torch.Generator(device="cuda").manual_seed(43)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      List[Any]       , snake_case    :      int       , snake_case    :      Dict=8							)->						Tuple:






     '''simple docstring'''



     UpperCAmelCase__		:  Union[str, Any]				=				height // scale_factor**2
     if height % scale_factor**2 != 0:
          new_height += 1
     UpperCAmelCase__		:  Optional[int]				=				width // scale_factor**2
     if width % scale_factor**2 != 0:
          new_width += 1
     return new_height * scale_factor, new_width * scale_factor


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      List[Any]       , snake_case    :      Any=512       , snake_case    :      Union[str, Any]=512							)->						Dict:






     '''simple docstring'''



     UpperCAmelCase__		:  Tuple				=				pil_image.resize((w, h)       , resample=Image.BICUBIC       , reducing_gap=1							)
     UpperCAmelCase__		:  str				=				np.array(pil_image.convert("RGB"							)							)
     UpperCAmelCase__		:  int				=				arr.astype(np.floataa							) / 127.5 - 1
     UpperCAmelCase__		:  Optional[Any]				=				np.transpose(UpperCAmelCase__       , [2, 0, 1]							)
     UpperCAmelCase__		:  str				=				torch.from_numpy(UpperCAmelCase__							).unsqueeze(0							)
     return image




class        lowerCAmelCase__       (  __magic_name__					):
   def __init__(						self	:     str				,       snake_case__	:     UNetaDConditionModel				,       snake_case__	:     DDPMScheduler				,       snake_case__	:     VQModel				,       ):


        '''simple docstring'''
        super().__init__()

        self.register_modules(
            unet=_UpperCamelCase				,       scheduler=_UpperCamelCase				,       movq=_UpperCamelCase				,       )
        UpperCAmelCase__		:  Tuple				=				2 ** (len(self.movq.config.block_out_channels				) - 1)
   def 	__a				(						self	:     str				,       snake_case__	:     List[str]				,       snake_case__	:     Union[str, Any]				,       snake_case__	:     List[str]				):


        '''simple docstring'''
        UpperCAmelCase__		:  List[str]				=				min(int(num_inference_steps * strength				)				,       _UpperCamelCase				)

        UpperCAmelCase__		:  Dict				=				max(num_inference_steps - init_timestep				,       0				)
        UpperCAmelCase__		:  Any				=				self.scheduler.timesteps[t_start:]

        return timesteps, num_inference_steps - t_start
   def 	__a				(						self	:     Union[str, Any]				,       snake_case__	:     Optional[Any]				,       snake_case__	:     str				,       snake_case__	:     Any				,       snake_case__	:     int				,       snake_case__	:     Union[str, Any]				,       snake_case__	:     Optional[int]				,       snake_case__	:     Union[str, Any]=None				):


        '''simple docstring'''

        if not isinstance(_UpperCamelCase				,       (torch.Tensor, PIL.Image.Image, list)				):
             raise ValueError(
                 f'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_UpperCamelCase				)}'				)

        UpperCAmelCase__		:  int				=				image.to(device=_UpperCamelCase				,       dtype=_UpperCamelCase				)

        UpperCAmelCase__		:  Union[str, Any]				=				batch_size * num_images_per_prompt

        if image.shape[1] == 4:
             UpperCAmelCase__		:  int				=				image

        else:
             if isinstance(_UpperCamelCase				,       _UpperCamelCase				) and len(_UpperCamelCase				) != batch_size:
                  raise ValueError(
                      f'You have passed a list of generators of length {len(_UpperCamelCase				)}, but requested an effective batch'
                      f' size of {batch_size}. Make sure the batch size matches the length of the generators.'				)

             elif isinstance(_UpperCamelCase				,       _UpperCamelCase				):
                  UpperCAmelCase__		:  List[Any]				=				[
                      self.movq.encode(image[i : i + 1]				).latent_dist.sample(generator[i]				) for i in range(_UpperCamelCase				)
                  ]
                  UpperCAmelCase__		:  Dict				=				torch.cat(_UpperCamelCase				,       dim=0				)
             else:
                  UpperCAmelCase__		:  Any				=				self.movq.encode(_UpperCamelCase				).latent_dist.sample(_UpperCamelCase				)

             UpperCAmelCase__		:  Tuple				=				self.movq.config.scaling_factor * init_latents

        UpperCAmelCase__		:  Optional[Any]				=				torch.cat([init_latents]				,       dim=0				)

        UpperCAmelCase__		:  Union[str, Any]				=				init_latents.shape
        UpperCAmelCase__		:  Optional[int]				=				randn_tensor(_UpperCamelCase				,       generator=_UpperCamelCase				,       device=_UpperCamelCase				,       dtype=_UpperCamelCase				)

        # get latents
        UpperCAmelCase__		:  List[Any]				=				self.scheduler.add_noise(_UpperCamelCase				,       _UpperCamelCase				,       _UpperCamelCase				)

        UpperCAmelCase__		:  Optional[Any]				=				init_latents

        return latents
   def 	__a				(						self	:     List[str]				,       snake_case__	:     str=0				):


        '''simple docstring'''

        if is_accelerate_available():
             from accelerate import cpu_offload
        else:
             raise ImportError("Please install accelerate via `pip install accelerate`"				)

        UpperCAmelCase__		:  Optional[int]				=				torch.device(f'cuda:{gpu_id}'				)

        UpperCAmelCase__		:  List[str]				=				[
            self.unet,
            self.movq,
        ]
        for cpu_offloaded_model in models:
             if cpu_offloaded_model is not None:
                  cpu_offload(_UpperCamelCase				,       _UpperCamelCase				)
   def 	__a				(						self	:     Tuple				,       snake_case__	:     Optional[Any]=0				):


        '''simple docstring'''

        if is_accelerate_available() and is_accelerate_version(">="				,       "0.17.0.dev0"				):
             from accelerate import cpu_offload_with_hook
        else:
             raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."				)

        UpperCAmelCase__		:  str				=				torch.device(f'cuda:{gpu_id}'				)

        if self.device.type != "cpu":
             self.to("cpu"				,       silence_dtype_warnings=_UpperCamelCase				)
             torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

        UpperCAmelCase__		:  Union[str, Any]				=				None
        for cpu_offloaded_model in [self.unet, self.movq]:
             UpperCAmelCase__				,							UpperCAmelCase__		:  Optional[int]				=				cpu_offload_with_hook(_UpperCamelCase				,       _UpperCamelCase				,       prev_module_hook=_UpperCamelCase				)

        # We'll offload the last model manually.
        UpperCAmelCase__		:  List[Any]				=				hook
   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def 	__a				(						self	:     Any				):


        '''simple docstring'''

        if not hasattr(self.unet				,       "_hf_hook"				):
             return self.device
        for module in self.unet.modules():
             if (
                 hasattr(_UpperCamelCase				,       "_hf_hook"				)
                 and hasattr(module._hf_hook				,       "execution_device"				)
                 and module._hf_hook.execution_device is not None
             ):
                  return torch.device(module._hf_hook.execution_device				)
        return self.device




   @torch.no_grad()
   @replace_example_docstring(_UpperCamelCase				)
   def __call__(						self	:     Union[str, Any]				,       snake_case__	:     Union[torch.FloatTensor, List[torch.FloatTensor]]				,       snake_case__	:     Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]				,       snake_case__	:     Union[torch.FloatTensor, List[torch.FloatTensor]]				,       snake_case__	:     torch.FloatTensor				,       snake_case__	:     int = 5_1_2				,       snake_case__	:     int = 5_1_2				,       snake_case__	:     int = 1_0_0				,       snake_case__	:     float = 4.0				,       snake_case__	:     float = 0.3				,       snake_case__	:     int = 1				,       snake_case__	:     Optional[Union[torch.Generator, List[torch.Generator]]] = None				,       snake_case__	:     Optional[str] = "pil"				,       snake_case__	:     bool = True				,       ):


        '''simple docstring'''
        UpperCAmelCase__		:  Optional[int]				=				self._execution_device

        UpperCAmelCase__		:  List[Any]				=				guidance_scale > 1.0

        if isinstance(_UpperCamelCase				,       _UpperCamelCase				):
             UpperCAmelCase__		:  Optional[Any]				=				torch.cat(_UpperCamelCase				,       dim=0				)
        if isinstance(_UpperCamelCase				,       _UpperCamelCase				):
             UpperCAmelCase__		:  str				=				torch.cat(_UpperCamelCase				,       dim=0				)
        if isinstance(_UpperCamelCase				,       _UpperCamelCase				):
             UpperCAmelCase__		:  Optional[int]				=				torch.cat(_UpperCamelCase				,       dim=0				)

        UpperCAmelCase__		:  str				=				image_embeds.shape[0]

        if do_classifier_free_guidance:
             UpperCAmelCase__		:  int				=				image_embeds.repeat_interleave(_UpperCamelCase				,       dim=0				)
             UpperCAmelCase__		:  Any				=				negative_image_embeds.repeat_interleave(_UpperCamelCase				,       dim=0				)
             UpperCAmelCase__		:  Tuple				=				hint.repeat_interleave(_UpperCamelCase				,       dim=0				)

        UpperCAmelCase__		:  List[Any]				=				torch.cat([negative_image_embeds, image_embeds]				,       dim=0				).to(dtype=self.unet.dtype				,       device=_UpperCamelCase				)
        UpperCAmelCase__		:  Any				=				torch.cat([hint, hint]				,       dim=0				).to(dtype=self.unet.dtype				,       device=_UpperCamelCase				)

        if not isinstance(_UpperCamelCase				,       _UpperCamelCase				):
             UpperCAmelCase__		:  Optional[int]				=				[image]
        if not all(isinstance(_UpperCamelCase				,       (PIL.Image.Image, torch.Tensor)				) for i in image				):
             raise ValueError(
                 f'Input is in incorrect format: {[type(_UpperCamelCase				) for i in image]}. Currently, we only support  PIL image and pytorch tensor'				)

        UpperCAmelCase__		:  Tuple				=				torch.cat([prepare_image(_UpperCamelCase				,       _UpperCamelCase				,       _UpperCamelCase				) for i in image]				,       dim=0				)
        UpperCAmelCase__		:  List[Any]				=				image.to(dtype=image_embeds.dtype				,       device=_UpperCamelCase				)

        UpperCAmelCase__		:  str				=				self.movq.encode(_UpperCamelCase				)["latents"]
        UpperCAmelCase__		:  List[str]				=				latents.repeat_interleave(_UpperCamelCase				,       dim=0				)
        self.scheduler.set_timesteps(_UpperCamelCase				,       device=_UpperCamelCase				)
        UpperCAmelCase__				,							UpperCAmelCase__		:  List[Any]				=				self.get_timesteps(_UpperCamelCase				,       _UpperCamelCase				,       _UpperCamelCase				)
        UpperCAmelCase__		:  str				=				timesteps[:1].repeat(batch_size * num_images_per_prompt				)
        UpperCAmelCase__				,							UpperCAmelCase__		:  Optional[Any]				=				downscale_height_and_width(_UpperCamelCase				,       _UpperCamelCase				,       self.movq_scale_factor				)
        UpperCAmelCase__		:  Union[str, Any]				=				self.prepare_latents(
            _UpperCamelCase				,       _UpperCamelCase				,       _UpperCamelCase				,       _UpperCamelCase				,       image_embeds.dtype				,       _UpperCamelCase				,       _UpperCamelCase				)
        for i, t in enumerate(self.progress_bar(_UpperCamelCase				)				):
             # expand the latents if we are doing classifier free guidance
             UpperCAmelCase__		:  Tuple				=				torch.cat([latents] * 2				) if do_classifier_free_guidance else latents

             UpperCAmelCase__		:  Tuple				=				{"image_embeds": image_embeds, "hint": hint}
             UpperCAmelCase__		:  Tuple				=				self.unet(
                 sample=_UpperCamelCase				,       timestep=_UpperCamelCase				,       encoder_hidden_states=_UpperCamelCase				,       added_cond_kwargs=_UpperCamelCase				,       return_dict=_UpperCamelCase				,       )[0]

             if do_classifier_free_guidance:
                  UpperCAmelCase__				,							UpperCAmelCase__		:  Any				=				noise_pred.split(latents.shape[1]				,       dim=1				)
                  UpperCAmelCase__				,							UpperCAmelCase__		:  List[Any]				=				noise_pred.chunk(2				)
                  UpperCAmelCase__				,							UpperCAmelCase__		:  Tuple				=				variance_pred.chunk(2				)
                  UpperCAmelCase__		:  Union[str, Any]				=				noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                  UpperCAmelCase__		:  str				=				torch.cat([noise_pred, variance_pred_text]				,       dim=1				)

             if not (
                 hasattr(self.scheduler.config				,       "variance_type"				)
                 and self.scheduler.config.variance_type in ["learned", "learned_range"]
             ):
                  UpperCAmelCase__				,							UpperCAmelCase__		:  Optional[int]				=				noise_pred.split(latents.shape[1]				,       dim=1				)

             # compute the previous noisy sample x_t -> x_t-1

             UpperCAmelCase__		:  List[Any]				=				self.scheduler.step(
                 _UpperCamelCase				,       _UpperCamelCase				,       _UpperCamelCase				,       generator=_UpperCamelCase				,       )[0]

        # post-processing
        UpperCAmelCase__		:  Optional[Any]				=				self.movq.decode(_UpperCamelCase				,       force_not_quantize=_UpperCamelCase				)["sample"]

        if output_type not in ["pt", "np", "pil"]:
             raise ValueError(f'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'				)

        if output_type in ["np", "pil"]:
             UpperCAmelCase__		:  Optional[int]				=				image * 0.5 + 0.5
             UpperCAmelCase__		:  Optional[Any]				=				image.clamp(0				,       1				)
             UpperCAmelCase__		:  Union[str, Any]				=				image.cpu().permute(0				,       2				,       3				,       1				).float().numpy()

        if output_type == "pil":
             UpperCAmelCase__		:  Optional[Any]				=				self.numpy_to_pil(_UpperCamelCase				)

        if not return_dict:
             return (image,)

        return ImagePipelineOutput(images=_UpperCamelCase				)



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


_lowerCamelCase    :      Tuple	 =						'''
Distills an NLI-based zero-shot classifier to a smaller, more efficient model with a fixed set of candidate class
names. Useful for speeding up zero-shot classification in cases where labeled training data is not available, but
when only a single fixed set of classes is needed. Takes a teacher NLI model, student classifier model, unlabeled
dataset, and set of K possible class names. Yields a single classifier with K outputs corresponding to the provided
class names.
'''

_lowerCamelCase    :      Optional[Any]	 =						logging.getLogger(__name__)


@dataclass
class   lowercase			:
							lowercase__  :				Optional[str]									=							field(
							    default="""roberta-large-mnli""" ,				metadata={"""help""": """The NLI/zero-shot teacher model to be distilled."""}   )
							lowercase__  :				Optional[str]									=							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."""
							        )
							    } ,				)
							lowercase__  :				Optional[int]									=							field(
							    default=32 ,				metadata={"""help""": """Batch size for generating teacher predictions."""}   )
							lowercase__  :				Optional[bool]									=							field(
							    default=a ,				metadata={
							        """help""": (
							            """Allow multiple classes to be true rather than forcing them to sum to 1 (sometimes called"""
							            """multi-class multi-label classification)."""
							        )
							    } ,				)
							lowercase__  :				Optional[float]									=							field(
							    default=1.0 ,				metadata={"""help""": """Temperature applied to teacher softmax for distillation."""}   )



@dataclass
class   lowercase			:
							lowercase__  :				Optional[str]									=							field(
							    default="""distilbert-base-uncased""" ,				metadata={"""help""": """The NLI/zero-shot teacher model to be distilled."""}   )



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



@dataclass
class   lowercase			(   a   ):
							lowercase__  :				Optional[str]									=							field(
							    default=a ,				metadata={"""help""": """The output directory where the model predictions and checkpoints will be written."""} ,				)
							lowercase__  :				int									=							field(
							    default=32 ,				metadata={"""help""": """Batch size per GPU/TPU core/CPU for training."""}   )
							lowercase__  :				int									=							field(
							    default=128 ,				metadata={"""help""": """Batch size per GPU/TPU core/CPU for evaluation."""}   )
							lowercase__  :				float									=							field(default=1.0 ,				metadata={"""help""": """Total number of training epochs to perform."""}   )
							lowercase__  :				bool									=							field(default=a ,				metadata={"""help""": """Whether to run training of student model."""}   )
							lowercase__  :				bool									=							field(
							    default=a ,				metadata={
							        """help""": (
							            """Whether to evaluate the agreement of the final student predictions and the teacher predictions"""
							            """after training."""
							        )
							    } ,				)
							lowercase__  :				Optional[int]									=							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   lowercase			(   a   ):




							def 		__snake_case(  self      :    Optional[Any]		,							_UpperCamelCase      :    str		,							_UpperCamelCase      :    Dict		,							_UpperCamelCase      :    List[str]=False					)    -> Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				inputs["labels"]
												SCREAMING_SNAKE_CASE				=				model(inputs["input_ids"]		,							attention_mask=inputs["attention_mask"]					)
												SCREAMING_SNAKE_CASE				=				outputs[0]

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

												if return_outputs:
																	return loss, outputs

												return loss





def 		__lowerCamelCase							(UpperCAmelCase__   :							List[Any]     ):
					SCREAMING_SNAKE_CASE				=				[]
					with open(UpperCAmelCase__					,       "r"     ) as f:
										for line in f:
															SCREAMING_SNAKE_CASE				=				line.strip()
															if len(UpperCAmelCase__     ) > 0:
																				lines.append(UpperCAmelCase__     )
					return lines





def 		__lowerCamelCase							(UpperCAmelCase__   :							str					,       UpperCAmelCase__   :							List[str]					,       UpperCAmelCase__   :							List[Any]     ):
					SCREAMING_SNAKE_CASE				=				[]
					SCREAMING_SNAKE_CASE				=				[]
					for example in examples:
										for name in class_names:
															premises.append(UpperCAmelCase__     )
															hypotheses.append(hypothesis_template.format(UpperCAmelCase__     )     )
					return premises, hypotheses





def 		__lowerCamelCase							(UpperCAmelCase__   :							Optional[int]     ):
					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 		__lowerCamelCase							(UpperCAmelCase__   :							str					,       UpperCAmelCase__   :							List[str]					,       UpperCAmelCase__   :							List[str]					,       UpperCAmelCase__   :							str					,       UpperCAmelCase__   :							int					,       UpperCAmelCase__   :							float					,       UpperCAmelCase__   :							bool					,       UpperCAmelCase__   :							bool					,       UpperCAmelCase__   :							bool					,       UpperCAmelCase__   :							bool					,       ):
					SCREAMING_SNAKE_CASE				=				AutoModelForSequenceClassification.from_pretrained(UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				model.config
					if not no_cuda and torch.cuda.is_available():
										SCREAMING_SNAKE_CASE				=				nn.DataParallel(model.cuda()     )
										batch_size *= len(model.device_ids     )
					SCREAMING_SNAKE_CASE				=				AutoTokenizer.from_pretrained(UpperCAmelCase__					,       use_fast=UpperCAmelCase__     )

					SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE				=				get_premise_hypothesis_pairs(UpperCAmelCase__					,       UpperCAmelCase__					,       UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				[]

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

										SCREAMING_SNAKE_CASE				=				tokenizer(
										    UpperCAmelCase__					,       UpperCAmelCase__					,       padding=UpperCAmelCase__					,       truncation="only_first"					,       return_tensors="pt"					,       )

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

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

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

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





def 		__lowerCamelCase							():
					SCREAMING_SNAKE_CASE				=				HfArgumentParser(
					    (DataTrainingArguments, TeacherModelArguments, StudentModelArguments, DistillTrainingArguments)					,       description=UpperCAmelCase__					,       )

					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.
										SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE				=				parser.parse_json_file(
										    json_file=os.path.abspath(sys.argv[1]     )     )
					else:
										SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE				=				parser.parse_args_into_dataclasses()

					# Detecting last checkpoint.
					SCREAMING_SNAKE_CASE				=				None
					if os.path.isdir(training_args.output_dir     ) and training_args.do_train and not training_args.overwrite_output_dir:
										SCREAMING_SNAKE_CASE				=				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
					SCREAMING_SNAKE_CASE				=				read_lines(data_args.data_file     )
					SCREAMING_SNAKE_CASE				=				read_lines(data_args.class_names_file     )

					# 2. get teacher predictions and load into dataset
					logger.info("Generating predictions from zero-shot teacher model"     )
					SCREAMING_SNAKE_CASE				=				get_teacher_predictions(
					    teacher_args.teacher_name_or_path					,       UpperCAmelCase__					,       UpperCAmelCase__					,       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					,       )
					SCREAMING_SNAKE_CASE				=				Dataset.from_dict(
					    {
					        "text": examples,
					        "labels": teacher_soft_preds,
					    }     )

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

					# 4. train student on teacher predictions
					SCREAMING_SNAKE_CASE				=				dataset.map(UpperCAmelCase__					,       input_columns="text"     )
					dataset.set_format("torch"     )

					def compute_metrics(UpperCAmelCase__   :							Dict					,       UpperCAmelCase__   :							Optional[int]=False     ):
										SCREAMING_SNAKE_CASE				=				p.predictions.argmax(-1     )
										SCREAMING_SNAKE_CASE				=				p.label_ids.argmax(-1     )  # "label_ids" are actually distributions
										return {"agreement": (preds == proxy_labels).mean().item()}

					SCREAMING_SNAKE_CASE				=				DistillationTrainer(
					    model=UpperCAmelCase__					,       tokenizer=UpperCAmelCase__					,       args=UpperCAmelCase__					,       train_dataset=UpperCAmelCase__					,       compute_metrics=UpperCAmelCase__					,       )

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

					if training_args.do_eval:
										SCREAMING_SNAKE_CASE				=				trainer.evaluate(eval_dataset=UpperCAmelCase__     )["eval_agreement"]
										logger.info(F"Agreement of student and teacher predictions: {agreement * 1_0_0:0.2f}%"     )

					trainer.save_model()


if __name__ == "__main__":
							main()




