import argparse
import os

import align
import numpy as np
import requests
import tensorflow as tf
import torch
from PIL import Image
from tokenizer import Tokenizer

from transformers import (
    AlignConfig,
    AlignModel,
    AlignProcessor,
    BertConfig,
    BertTokenizer,
    EfficientNetConfig,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase    :      str	 =						logging.get_logger(__name__)





def 		__lowerCamelCase							(UpperCAmelCase__   :							int     ):
					SCREAMING_SNAKE_CASE				=				tf.image.resize(UpperCAmelCase__					,       (3_4_6, 3_4_6)     )
					SCREAMING_SNAKE_CASE				=				tf.image.crop_to_bounding_box(UpperCAmelCase__					,       (3_4_6 - 2_8_9) // 2					,       (3_4_6 - 2_8_9) // 2					,       2_8_9					,       2_8_9     )
					return image





def 		__lowerCamelCase							():
					SCREAMING_SNAKE_CASE				=				EfficientNetConfig.from_pretrained("google/efficientnet-b7"     )
					SCREAMING_SNAKE_CASE				=				2_8_9
					SCREAMING_SNAKE_CASE				=				6_4_0
					SCREAMING_SNAKE_CASE				=				{"0": "LABEL_0", "1": "LABEL_1"}
					SCREAMING_SNAKE_CASE				=				{"LABEL_0": 0, "LABEL_1": 1}
					SCREAMING_SNAKE_CASE				=				[]

					SCREAMING_SNAKE_CASE				=				BertConfig()
					SCREAMING_SNAKE_CASE				=				AlignConfig.from_text_vision_configs(
					    text_config=UpperCAmelCase__					,       vision_config=UpperCAmelCase__					,       projection_dim=6_4_0     )
					return config





def 		__lowerCamelCase							():
					SCREAMING_SNAKE_CASE				=				"http://images.cocodataset.org/val2017/000000039769.jpg"
					SCREAMING_SNAKE_CASE				=				Image.open(requests.get(UpperCAmelCase__					,       stream=UpperCAmelCase__     ).raw     )
					return im





def 		__lowerCamelCase							():
					SCREAMING_SNAKE_CASE				=				EfficientNetImageProcessor(
					    do_center_crop=UpperCAmelCase__					,       rescale_factor=1 / 127.5					,       rescale_offset=UpperCAmelCase__					,       do_normalize=UpperCAmelCase__					,       include_top=UpperCAmelCase__					,       resample=Image.BILINEAR					,       )
					SCREAMING_SNAKE_CASE				=				BertTokenizer.from_pretrained("bert-base-uncased"     )
					SCREAMING_SNAKE_CASE				=				6_4
					SCREAMING_SNAKE_CASE				=				AlignProcessor(image_processor=UpperCAmelCase__					,       tokenizer=UpperCAmelCase__     )
					return processor





def 		__lowerCamelCase							(UpperCAmelCase__   :							List[str]     ):
					# EfficientNet image encoder
					SCREAMING_SNAKE_CASE				=				[v.split("_"     )[0].split("block"     )[1] for v in original_param_names if v.startswith("block"     )]
					SCREAMING_SNAKE_CASE				=				list(set(UpperCAmelCase__     )     )
					SCREAMING_SNAKE_CASE				=				sorted(UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				len(UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				{b: str(UpperCAmelCase__     ) for b, i in zip(UpperCAmelCase__					,       range(UpperCAmelCase__     )     )}

					SCREAMING_SNAKE_CASE				=				[]
					rename_keys.append(("stem_conv/kernel:0", "embeddings.convolution.weight")     )
					rename_keys.append(("stem_bn/gamma:0", "embeddings.batchnorm.weight")     )
					rename_keys.append(("stem_bn/beta:0", "embeddings.batchnorm.bias")     )
					rename_keys.append(("stem_bn/moving_mean:0", "embeddings.batchnorm.running_mean")     )
					rename_keys.append(("stem_bn/moving_variance:0", "embeddings.batchnorm.running_var")     )

					for b in block_names:
										SCREAMING_SNAKE_CASE				=				block_name_mapping[b]
										rename_keys.append((F"block{b}_expand_conv/kernel:0", F"encoder.blocks.{hf_b}.expansion.expand_conv.weight")     )
										rename_keys.append((F"block{b}_expand_bn/gamma:0", F"encoder.blocks.{hf_b}.expansion.expand_bn.weight")     )
										rename_keys.append((F"block{b}_expand_bn/beta:0", F"encoder.blocks.{hf_b}.expansion.expand_bn.bias")     )
										rename_keys.append(
										    (F"block{b}_expand_bn/moving_mean:0", F"encoder.blocks.{hf_b}.expansion.expand_bn.running_mean")     )
										rename_keys.append(
										    (F"block{b}_expand_bn/moving_variance:0", F"encoder.blocks.{hf_b}.expansion.expand_bn.running_var")     )
										rename_keys.append(
										    (F"block{b}_dwconv/depthwise_kernel:0", F"encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight")     )
										rename_keys.append((F"block{b}_bn/gamma:0", F"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight")     )
										rename_keys.append((F"block{b}_bn/beta:0", F"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias")     )
										rename_keys.append(
										    (F"block{b}_bn/moving_mean:0", F"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean")     )
										rename_keys.append(
										    (F"block{b}_bn/moving_variance:0", F"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var")     )

										rename_keys.append((F"block{b}_se_reduce/kernel:0", F"encoder.blocks.{hf_b}.squeeze_excite.reduce.weight")     )
										rename_keys.append((F"block{b}_se_reduce/bias:0", F"encoder.blocks.{hf_b}.squeeze_excite.reduce.bias")     )
										rename_keys.append((F"block{b}_se_expand/kernel:0", F"encoder.blocks.{hf_b}.squeeze_excite.expand.weight")     )
										rename_keys.append((F"block{b}_se_expand/bias:0", F"encoder.blocks.{hf_b}.squeeze_excite.expand.bias")     )
										rename_keys.append(
										    (F"block{b}_project_conv/kernel:0", F"encoder.blocks.{hf_b}.projection.project_conv.weight")     )
										rename_keys.append((F"block{b}_project_bn/gamma:0", F"encoder.blocks.{hf_b}.projection.project_bn.weight")     )
										rename_keys.append((F"block{b}_project_bn/beta:0", F"encoder.blocks.{hf_b}.projection.project_bn.bias")     )
										rename_keys.append(
										    (F"block{b}_project_bn/moving_mean:0", F"encoder.blocks.{hf_b}.projection.project_bn.running_mean")     )
										rename_keys.append(
										    (F"block{b}_project_bn/moving_variance:0", F"encoder.blocks.{hf_b}.projection.project_bn.running_var")     )

					SCREAMING_SNAKE_CASE				=				{}
					for item in rename_keys:
										if item[0] in original_param_names:
															SCREAMING_SNAKE_CASE				=				"vision_model." + item[1]

    # BERT text encoder
					SCREAMING_SNAKE_CASE				=				[]
					SCREAMING_SNAKE_CASE				=				"tf_bert_model/bert"
					SCREAMING_SNAKE_CASE				=				"text_model"
					for i in range(1_2     ):
										rename_keys.append(
										    (
										        F"{old}/encoder/layer_._{i}/attention/self/query/kernel:0",
										        F"{new}.encoder.layer.{i}.attention.self.query.weight",
										    )     )
										rename_keys.append(
										    (
										        F"{old}/encoder/layer_._{i}/attention/self/query/bias:0",
										        F"{new}.encoder.layer.{i}.attention.self.query.bias",
										    )     )
										rename_keys.append(
										    (
										        F"{old}/encoder/layer_._{i}/attention/self/key/kernel:0",
										        F"{new}.encoder.layer.{i}.attention.self.key.weight",
										    )     )
										rename_keys.append(
										    (
										        F"{old}/encoder/layer_._{i}/attention/self/key/bias:0",
										        F"{new}.encoder.layer.{i}.attention.self.key.bias",
										    )     )
										rename_keys.append(
										    (
										        F"{old}/encoder/layer_._{i}/attention/self/value/kernel:0",
										        F"{new}.encoder.layer.{i}.attention.self.value.weight",
										    )     )
										rename_keys.append(
										    (
										        F"{old}/encoder/layer_._{i}/attention/self/value/bias:0",
										        F"{new}.encoder.layer.{i}.attention.self.value.bias",
										    )     )
										rename_keys.append(
										    (
										        F"{old}/encoder/layer_._{i}/attention/output/dense/kernel:0",
										        F"{new}.encoder.layer.{i}.attention.output.dense.weight",
										    )     )
										rename_keys.append(
										    (
										        F"{old}/encoder/layer_._{i}/attention/output/dense/bias:0",
										        F"{new}.encoder.layer.{i}.attention.output.dense.bias",
										    )     )
										rename_keys.append(
										    (
										        F"{old}/encoder/layer_._{i}/attention/output/LayerNorm/gamma:0",
										        F"{new}.encoder.layer.{i}.attention.output.LayerNorm.weight",
										    )     )
										rename_keys.append(
										    (
										        F"{old}/encoder/layer_._{i}/attention/output/LayerNorm/beta:0",
										        F"{new}.encoder.layer.{i}.attention.output.LayerNorm.bias",
										    )     )
										rename_keys.append(
										    (
										        F"{old}/encoder/layer_._{i}/intermediate/dense/kernel:0",
										        F"{new}.encoder.layer.{i}.intermediate.dense.weight",
										    )     )
										rename_keys.append(
										    (
										        F"{old}/encoder/layer_._{i}/intermediate/dense/bias:0",
										        F"{new}.encoder.layer.{i}.intermediate.dense.bias",
										    )     )
										rename_keys.append(
										    (F"{old}/encoder/layer_._{i}/output/dense/kernel:0", F"{new}.encoder.layer.{i}.output.dense.weight")     )
										rename_keys.append(
										    (F"{old}/encoder/layer_._{i}/output/dense/bias:0", F"{new}.encoder.layer.{i}.output.dense.bias")     )
										rename_keys.append(
										    (F"{old}/encoder/layer_._{i}/output/LayerNorm/gamma:0", F"{new}.encoder.layer.{i}.output.LayerNorm.weight")     )
										rename_keys.append(
										    (F"{old}/encoder/layer_._{i}/output/LayerNorm/beta:0", F"{new}.encoder.layer.{i}.output.LayerNorm.bias")     )

					rename_keys.append((F"{old}/embeddings/word_embeddings/weight:0", F"{new}.embeddings.word_embeddings.weight")     )
					rename_keys.append(
					    (F"{old}/embeddings/position_embeddings/embeddings:0", F"{new}.embeddings.position_embeddings.weight")     )
					rename_keys.append(
					    (F"{old}/embeddings/token_type_embeddings/embeddings:0", F"{new}.embeddings.token_type_embeddings.weight")     )
					rename_keys.append((F"{old}/embeddings/LayerNorm/gamma:0", F"{new}.embeddings.LayerNorm.weight")     )
					rename_keys.append((F"{old}/embeddings/LayerNorm/beta:0", F"{new}.embeddings.LayerNorm.bias")     )

					rename_keys.append((F"{old}/pooler/dense/kernel:0", F"{new}.pooler.dense.weight")     )
					rename_keys.append((F"{old}/pooler/dense/bias:0", F"{new}.pooler.dense.bias")     )
					rename_keys.append(("dense/kernel:0", "text_projection.weight")     )
					rename_keys.append(("dense/bias:0", "text_projection.bias")     )
					rename_keys.append(("dense/bias:0", "text_projection.bias")     )
					rename_keys.append(("temperature:0", "temperature")     )

					for item in rename_keys:
										if item[0] in original_param_names:
															SCREAMING_SNAKE_CASE				=				item[1]
					return key_mapping





def 		__lowerCamelCase							(UpperCAmelCase__   :							int					,       UpperCAmelCase__   :							List[Any]					,       UpperCAmelCase__   :							Union[str, Any]     ):
					list(hf_params.keys()     )

					for key, value in tf_params.items():
										if key not in key_mapping:
															continue

										SCREAMING_SNAKE_CASE				=				key_mapping[key]
										if "_conv" in key and "kernel" in key:
															SCREAMING_SNAKE_CASE				=				torch.from_numpy(UpperCAmelCase__     ).permute(3					,       2					,       0					,       1     )
										elif "embeddings" in key:
															SCREAMING_SNAKE_CASE				=				torch.from_numpy(UpperCAmelCase__     )
										elif "depthwise_kernel" in key:
															SCREAMING_SNAKE_CASE				=				torch.from_numpy(UpperCAmelCase__     ).permute(2					,       3					,       0					,       1     )
										elif "kernel" in key:
															SCREAMING_SNAKE_CASE				=				torch.from_numpy(np.transpose(UpperCAmelCase__     )     )
										elif "temperature" in key:
															SCREAMING_SNAKE_CASE				=				value
										elif "bn/gamma" or "bn/beta" in key:
															SCREAMING_SNAKE_CASE				=				torch.from_numpy(np.transpose(UpperCAmelCase__     )     ).squeeze()
										else:
															SCREAMING_SNAKE_CASE				=				torch.from_numpy(UpperCAmelCase__     )

										# Replace HF parameters with original TF model parameters
										hf_params[hf_key].copy_(UpperCAmelCase__     )





@torch.no_grad()
def 		__lowerCamelCase							(UpperCAmelCase__   :							str					,       UpperCAmelCase__   :							int					,       UpperCAmelCase__   :							int					,       UpperCAmelCase__   :							List[str]     ):
					SCREAMING_SNAKE_CASE				=				6_4
					SCREAMING_SNAKE_CASE				=				Tokenizer(UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				align.Align("efficientnet-b7"					,       "bert-base"					,       6_4_0					,       UpperCAmelCase__					,       tok.get_vocab_size()     )
					original_model.compile()
					original_model.load_weights(UpperCAmelCase__     )

					SCREAMING_SNAKE_CASE				=				original_model.trainable_variables
					SCREAMING_SNAKE_CASE				=				original_model.non_trainable_variables
					SCREAMING_SNAKE_CASE				=				{param.name: param.numpy() for param in tf_params}
					for param in tf_non_train_params:
										SCREAMING_SNAKE_CASE				=				param.numpy()
					SCREAMING_SNAKE_CASE				=				list(tf_params.keys()     )

					# Load HuggingFace model
					SCREAMING_SNAKE_CASE				=				get_align_config()
					SCREAMING_SNAKE_CASE				=				AlignModel(UpperCAmelCase__     ).eval()
					SCREAMING_SNAKE_CASE				=				hf_model.state_dict()

					# Create src-to-dst parameter name mapping dictionary
					print("Converting parameters..."     )
					SCREAMING_SNAKE_CASE				=				rename_keys(UpperCAmelCase__     )
					replace_params(UpperCAmelCase__					,       UpperCAmelCase__					,       UpperCAmelCase__     )

					# Initialize processor
					SCREAMING_SNAKE_CASE				=				get_processor()
					SCREAMING_SNAKE_CASE				=				processor(
					    images=prepare_img()					,       text="A picture of a cat"					,       padding="max_length"					,       max_length=6_4					,       return_tensors="pt"     )

					# HF model inference
					hf_model.eval()
					with torch.no_grad():
										SCREAMING_SNAKE_CASE				=				hf_model(**UpperCAmelCase__     )

					SCREAMING_SNAKE_CASE				=				outputs.image_embeds.detach().numpy()
					SCREAMING_SNAKE_CASE				=				outputs.text_embeds.detach().numpy()

					# Original model inference
					SCREAMING_SNAKE_CASE				=				False
					SCREAMING_SNAKE_CASE				=				EfficientNetImageProcessor(
					    do_center_crop=UpperCAmelCase__					,       do_rescale=UpperCAmelCase__					,       do_normalize=UpperCAmelCase__					,       include_top=UpperCAmelCase__					,       resample=Image.BILINEAR					,       )
					SCREAMING_SNAKE_CASE				=				tf_image_processor(images=prepare_img()					,       return_tensors="tf"					,       data_format="channels_last"     )["pixel_values"]
					SCREAMING_SNAKE_CASE				=				tok(tf.constant(["A picture of a cat"]     )     )

					SCREAMING_SNAKE_CASE				=				original_model.image_encoder(UpperCAmelCase__					,       training=UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				original_model.text_encoder(UpperCAmelCase__					,       training=UpperCAmelCase__     )

					SCREAMING_SNAKE_CASE				=				tf.nn.la_normalize(UpperCAmelCase__					,       axis=-1     )
					SCREAMING_SNAKE_CASE				=				tf.nn.la_normalize(UpperCAmelCase__					,       axis=-1     )

					# Check whether original and HF model outputs match  -> np.allclose
					if not np.allclose(UpperCAmelCase__					,       UpperCAmelCase__					,       atol=1e-3     ):
										raise ValueError("The predicted image features are not the same."     )
					if not np.allclose(UpperCAmelCase__					,       UpperCAmelCase__					,       atol=1e-3     ):
										raise ValueError("The predicted text features are not the same."     )
					print("Model outputs match!"     )

					if save_model:
										# Create folder to save model
										if not os.path.isdir(UpperCAmelCase__     ):
															os.mkdir(UpperCAmelCase__     )
										# Save converted model and image processor
										hf_model.save_pretrained(UpperCAmelCase__     )
										processor.save_pretrained(UpperCAmelCase__     )

					if push_to_hub:
										# Push model and image processor to hub
										print("Pushing converted ALIGN to the hub..."     )
										processor.push_to_hub("align-base"     )
										hf_model.push_to_hub("align-base"     )


if __name__ == "__main__":
							_lowerCamelCase    :      List[Any]	 =						argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--checkpoint_path''',
							    default='''./weights/model-weights''',
							    type=str,
							    help='''Path to the pretrained TF ALIGN checkpoint.''',
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''',
							    default='''hf_model''',
							    type=str,
							    help='''Path to the output PyTorch model directory.''',
							)
							parser.add_argument('''--save_model''', action='''store_true''', help='''Save model to local''')
							parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')

							_lowerCamelCase    :      Union[str, Any]	 =						parser.parse_args()
							convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)





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()




