



"""simple docstring"""




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       :      Dict							=							logging.get_logger(__name__)

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Optional[Any]       )						->     List[str]:





     '''simple docstring'''

     _UpperCAmelCase    :			Tuple              =					tf.image.resize(SCREAMING_SNAKE_CASE__		,						(346, 346)       )
     _UpperCAmelCase    :			Union[str, Any]              =					tf.image.crop_to_bounding_box(SCREAMING_SNAKE_CASE__		,						(346 - 289) // 2		,						(346 - 289) // 2		,						289		,						289       )
     return image

def 		__snake_case		(   )						->     List[Any]:





     '''simple docstring'''

     _UpperCAmelCase    :			Dict              =					EfficientNetConfig.from_pretrained("google/efficientnet-b7"       )
     _UpperCAmelCase    :			Dict              =					289
     _UpperCAmelCase    :			Optional[int]              =					640
     _UpperCAmelCase    :			Union[str, Any]              =					{"0": "LABEL_0", "1": "LABEL_1"}
     _UpperCAmelCase    :			Optional[Any]              =					{"LABEL_0": 0, "LABEL_1": 1}
     _UpperCAmelCase    :			Union[str, Any]              =					[]

     _UpperCAmelCase    :			Dict              =					BertConfig()
     _UpperCAmelCase    :			int              =					AlignConfig.from_text_vision_configs(
         text_config=SCREAMING_SNAKE_CASE__		,						vision_config=SCREAMING_SNAKE_CASE__		,						projection_dim=640       )
     return config

def 		__snake_case		(   )						->     Tuple:





     '''simple docstring'''

     _UpperCAmelCase    :			int              =					"http://images.cocodataset.org/val2017/000000039769.jpg"
     _UpperCAmelCase    :			Union[str, Any]              =					Image.open(requests.get(SCREAMING_SNAKE_CASE__		,						stream=SCREAMING_SNAKE_CASE__       ).raw       )
     return im

def 		__snake_case		(   )						->     Union[str, Any]:





     '''simple docstring'''

     _UpperCAmelCase    :			Any              =					EfficientNetImageProcessor(
         do_center_crop=SCREAMING_SNAKE_CASE__		,						rescale_factor=1 / 127.5		,						rescale_offset=SCREAMING_SNAKE_CASE__		,						do_normalize=SCREAMING_SNAKE_CASE__		,						include_top=SCREAMING_SNAKE_CASE__		,						resample=Image.BILINEAR		,						)
     _UpperCAmelCase    :			Optional[int]              =					BertTokenizer.from_pretrained("bert-base-uncased"       )
     _UpperCAmelCase    :			Union[str, Any]              =					64
     _UpperCAmelCase    :			str              =					AlignProcessor(image_processor=SCREAMING_SNAKE_CASE__		,						tokenizer=SCREAMING_SNAKE_CASE__       )
     return processor

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		List[str]       )						->     Dict:





     '''simple docstring'''

     _UpperCAmelCase    :			str              =					[v.split("_"       )[0].split("block"       )[1] for v in original_param_names if v.startswith("block"       )]
     _UpperCAmelCase    :			Dict              =					list(set(SCREAMING_SNAKE_CASE__       )       )
     _UpperCAmelCase    :			List[str]              =					sorted(SCREAMING_SNAKE_CASE__       )
     _UpperCAmelCase    :			List[str]              =					len(SCREAMING_SNAKE_CASE__       )
     _UpperCAmelCase    :			Dict              =					{b: str(SCREAMING_SNAKE_CASE__       ) for b, i in zip(SCREAMING_SNAKE_CASE__		,						range(SCREAMING_SNAKE_CASE__       )       )}

     _UpperCAmelCase    :			List[Any]              =					[]
     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:
          _UpperCAmelCase    :			List[Any]              =					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')       )

     _UpperCAmelCase    :			List[str]              =					{}
     for item in rename_keys:
          if item[0] in original_param_names:
               _UpperCAmelCase    :			Optional[int]              =					"vision_model." + item[1]

    # BERT text encoder
     _UpperCAmelCase    :			Any              =					[]
     _UpperCAmelCase    :			Optional[int]              =					"tf_bert_model/bert"
     _UpperCAmelCase    :			str              =					"text_model"
     for i in range(12       ):
          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:
               _UpperCAmelCase    :			Union[str, Any]              =					item[1]
     return key_mapping

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		List[Any]		,						SCREAMING_SNAKE_CASE__			:		List[Any]		,						SCREAMING_SNAKE_CASE__			:		List[str]       )						->     str:





     '''simple docstring'''

     list(hf_params.keys()       )

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

          _UpperCAmelCase    :			Optional[int]              =					key_mapping[key]
          if "_conv" in key and "kernel" in key:
               _UpperCAmelCase    :			List[Any]              =					torch.from_numpy(SCREAMING_SNAKE_CASE__       ).permute(3		,						2		,						0		,						1       )
          elif "embeddings" in key:
               _UpperCAmelCase    :			str              =					torch.from_numpy(SCREAMING_SNAKE_CASE__       )
          elif "depthwise_kernel" in key:
               _UpperCAmelCase    :			Tuple              =					torch.from_numpy(SCREAMING_SNAKE_CASE__       ).permute(2		,						3		,						0		,						1       )
          elif "kernel" in key:
               _UpperCAmelCase    :			Optional[int]              =					torch.from_numpy(np.transpose(SCREAMING_SNAKE_CASE__       )       )
          elif "temperature" in key:
               _UpperCAmelCase    :			Union[str, Any]              =					value
          elif "bn/gamma" or "bn/beta" in key:
               _UpperCAmelCase    :			Optional[Any]              =					torch.from_numpy(np.transpose(SCREAMING_SNAKE_CASE__       )       ).squeeze()
          else:
               _UpperCAmelCase    :			str              =					torch.from_numpy(SCREAMING_SNAKE_CASE__       )

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

@torch.no_grad()
def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Tuple		,						SCREAMING_SNAKE_CASE__			:		str		,						SCREAMING_SNAKE_CASE__			:		str		,						SCREAMING_SNAKE_CASE__			:		Union[str, Any]       )						->     int:





     '''simple docstring'''

     _UpperCAmelCase    :			Dict              =					64
     _UpperCAmelCase    :			Dict              =					Tokenizer(SCREAMING_SNAKE_CASE__       )
     _UpperCAmelCase    :			int              =					align.Align("efficientnet-b7"		,						"bert-base"		,						640		,						SCREAMING_SNAKE_CASE__		,						tok.get_vocab_size()       )
     original_model.compile()
     original_model.load_weights(SCREAMING_SNAKE_CASE__       )

     _UpperCAmelCase    :			List[str]              =					original_model.trainable_variables
     _UpperCAmelCase    :			List[Any]              =					original_model.non_trainable_variables
     _UpperCAmelCase    :			int              =					{param.name: param.numpy() for param in tf_params}
     for param in tf_non_train_params:
          _UpperCAmelCase    :			List[Any]              =					param.numpy()
     _UpperCAmelCase    :			str              =					list(tf_params.keys()       )

     # Load HuggingFace model
     _UpperCAmelCase    :			Dict              =					get_align_config()
     _UpperCAmelCase    :			Dict              =					AlignModel(SCREAMING_SNAKE_CASE__       ).eval()
     _UpperCAmelCase    :			int              =					hf_model.state_dict()

     # Create src-to-dst parameter name mapping dictionary
     print("Converting parameters..."       )
     _UpperCAmelCase    :			str              =					rename_keys(SCREAMING_SNAKE_CASE__       )
     replace_params(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )

     # Initialize processor
     _UpperCAmelCase    :			List[str]              =					get_processor()
     _UpperCAmelCase    :			Union[str, Any]              =					processor(
         images=prepare_img()		,						text="A picture of a cat"		,						padding="max_length"		,						max_length=64		,						return_tensors="pt"       )

     # HF model inference
     hf_model.eval()
     with torch.no_grad():
          _UpperCAmelCase    :			List[str]              =					hf_model(**SCREAMING_SNAKE_CASE__       )

     _UpperCAmelCase    :			int              =					outputs.image_embeds.detach().numpy()
     _UpperCAmelCase    :			List[str]              =					outputs.text_embeds.detach().numpy()

     # Original model inference
     _UpperCAmelCase    :			Tuple              =					False
     _UpperCAmelCase    :			Optional[Any]              =					EfficientNetImageProcessor(
         do_center_crop=SCREAMING_SNAKE_CASE__		,						do_rescale=SCREAMING_SNAKE_CASE__		,						do_normalize=SCREAMING_SNAKE_CASE__		,						include_top=SCREAMING_SNAKE_CASE__		,						resample=Image.BILINEAR		,						)
     _UpperCAmelCase    :			List[Any]              =					tf_image_processor(images=prepare_img()		,						return_tensors="tf"		,						data_format="channels_last"       )["pixel_values"]
     _UpperCAmelCase    :			str              =					tok(tf.constant(["A picture of a cat"]       )       )

     _UpperCAmelCase    :			Union[str, Any]              =					original_model.image_encoder(SCREAMING_SNAKE_CASE__		,						training=SCREAMING_SNAKE_CASE__       )
     _UpperCAmelCase    :			Tuple              =					original_model.text_encoder(SCREAMING_SNAKE_CASE__		,						training=SCREAMING_SNAKE_CASE__       )

     _UpperCAmelCase    :			str              =					tf.nn.la_normalize(SCREAMING_SNAKE_CASE__		,						axis=-1       )
     _UpperCAmelCase    :			Dict              =					tf.nn.la_normalize(SCREAMING_SNAKE_CASE__		,						axis=-1       )

     # Check whether original and HF model outputs match  -> np.allclose
     if not np.allclose(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						atol=1E-3       ):
          raise ValueError("The predicted image features are not the same."       )
     if not np.allclose(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						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(SCREAMING_SNAKE_CASE__       ):
               os.mkdir(SCREAMING_SNAKE_CASE__       )
          # Save converted model and image processor
          hf_model.save_pretrained(SCREAMING_SNAKE_CASE__       )
          processor.save_pretrained(SCREAMING_SNAKE_CASE__       )

     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       :      Optional[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)





"""simple docstring"""




import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


_lowerCAmelCase       :      Optional[Any]							=							torch.cuda.is_available()
_lowerCAmelCase       :      str							=							[" Sam ate lunch today.", "Sams lunch ingredients."]
_lowerCAmelCase       :      Dict							=							["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
_lowerCAmelCase       :      Union[str, Any]							=							{
    "max_tokens_per_batch": None,
    "supervise_forward": True,
    "normalize_hidden": True,
    "label_smoothing": 0.2,
    "eval_max_gen_length": None,
    "eval_beams": 1,
    "val_metric": "loss",
    "save_top_k": 1,
    "adafactor": True,
    "early_stopping_patience": 2,
    "logger_name": "default",
    "length_penalty": 0.5,
    "cache_dir": "",
    "task": "summarization",
    "num_workers": 2,
    "alpha_hid": 0,
    "freeze_embeds": True,
    "enc_only": False,
    "tgt_suffix": "",
    "resume_from_checkpoint": None,
    "sortish_sampler": True,
    "student_decoder_layers": 1,
    "val_check_interval": 1.0,
    "output_dir": "",
    "fp16": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    "no_teacher": False,
    "fp16_opt_level": "O1",
    "gpus": 1 if CUDA_AVAILABLE else 0,
    "n_tpu_cores": 0,
    "max_grad_norm": 1.0,
    "do_train": True,
    "do_predict": True,
    "accumulate_grad_batches": 1,
    "server_ip": "",
    "server_port": "",
    "seed": 42,
    "model_name_or_path": "sshleifer/bart-tiny-random",
    "config_name": "",
    "tokenizer_name": "facebook/bart-large",
    "do_lower_case": False,
    "learning_rate": 0.3,
    "lr_scheduler": "linear",
    "weight_decay": 0.0,
    "adam_epsilon": 1E-08,
    "warmup_steps": 0,
    "max_epochs": 1,
    "train_batch_size": 2,
    "eval_batch_size": 2,
    "max_source_length": 12,
    "max_target_length": 12,
    "val_max_target_length": 12,
    "test_max_target_length": 12,
    "fast_dev_run": False,
    "no_cache": False,
    "n_train": -1,
    "n_val": -1,
    "n_test": -1,
    "student_encoder_layers": 1,
    "freeze_encoder": False,
    "auto_scale_batch_size": False,
    "overwrite_output_dir": False,
    "student": None,
}

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Path		,						SCREAMING_SNAKE_CASE__			:		list       )						->     Dict:





     '''simple docstring'''

     _UpperCAmelCase    :			Tuple              =					"\n".join(SCREAMING_SNAKE_CASE__       )
     Path(SCREAMING_SNAKE_CASE__       ).open("w"       ).writelines(SCREAMING_SNAKE_CASE__       )

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		List[Any]       )						->     Union[str, Any]:





     '''simple docstring'''

     for split in ["train", "val", "test"]:
          _dump_articles(os.path.join(SCREAMING_SNAKE_CASE__		,						f'{split}.source'       )		,						SCREAMING_SNAKE_CASE__       )
          _dump_articles(os.path.join(SCREAMING_SNAKE_CASE__		,						f'{split}.target'       )		,						SCREAMING_SNAKE_CASE__       )
     return tmp_dir






class 			UpperCAmelCase_						(       _UpperCamelCase		):



      @classmethod
      def 		snake_case_      (  cls  :			Optional[int]		):
           return cls



      @require_torch_multi_gpu
      def 		snake_case_      (  self  :			Union[str, Any]		):
           _UpperCAmelCase    :			Dict              =					{
               "no_teacher": True,
               "freeze_encoder": True,
               "gpus": 2,
               "overwrite_output_dir": True,
               "sortish_sampler": True,
           }
           self._test_distiller_cli_fork(A  ,			check_contents=A		)



      def 		snake_case_      (  self  :			List[str]  ,			A  :			List[Any]  ,			A  :			Optional[Any]=True		):
           _UpperCAmelCase    :			int              =					{
               "label_smoothing": 0.0,
               "early_stopping_patience": -1,
               "train_batch_size": 1,
               "eval_batch_size": 2,
               "max_epochs": 2,
               "alpha_mlm": 0.2,
               "alpha_ce": 0.8,
               "do_predict": True,
               "model_name_or_path": "sshleifer/tinier_bart",
               "teacher": CHEAP_ARGS["model_name_or_path"],
               "val_check_interval": 0.5,
           }
           default_updates.update(A		)
           _UpperCAmelCase    :			dict              =					CHEAP_ARGS.copy()
           _UpperCAmelCase    :			Union[str, Any]              =					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
           _UpperCAmelCase    :			List[Any]              =					self.get_auto_remove_tmp_dir()
           args_d.update(data_dir=A  ,			output_dir=A  ,			**A		)

           def convert(A  :			List[Any]  ,			A  :			Optional[int]		):
                if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
                     return ""
                if v is False or v is None:
                     return ""
                if v is True:  # or len(str(v))==0:
                     return f'--{k}'
                return f'--{k}={v}'

           _UpperCAmelCase    :			Tuple              =					[x for x in (convert(A  ,			A		) for k, v in args_d.items()) if len(A		)]
           _UpperCAmelCase    :			Union[str, Any]              =					[sys.executable, f'{self.test_file_dir}/distillation.py'] + cli_args
           execute_subprocess_async(A  ,			env=self.get_env()		)

           _UpperCAmelCase    :			int              =					os.listdir(A		)
           _UpperCAmelCase    :			List[Any]              =					{os.path.basename(A		) for p in contents}
           _UpperCAmelCase    :			List[Any]              =					[p for p in contents if p.endswith("ckpt"		)]
           assert len(A		) > 0

           self.assertIn("test_generations.txt"  ,			A		)
           self.assertIn("test_results.txt"  ,			A		)

           # get the following from the module, (we don't have access to `model` here)
           _UpperCAmelCase    :			List[Any]              =					os.path.join(A  ,			"metrics.json"		)
           _UpperCAmelCase    :			str              =					"rouge2"

           _UpperCAmelCase    :			List[Any]              =					load_json(A		)
           # {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
           print(A		)
           _UpperCAmelCase    :			List[str]              =					metrics["val"][-1]
           self.assertGreaterEqual(last_step_stats["val_avg_gen_time"]  ,			0.01		)
           self.assertIsInstance(last_step_stats[f'val_avg_{val_metric}']  ,			A		)
           self.assertEqual(len(metrics["test"]		)  ,			1		)
           _UpperCAmelCase    :			Dict              =					int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) / 2 + 1		)
           self.assertEqual(len(metrics["val"]		)  ,			A		)
