code: 
  
  
  
  """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)
  
style_context: 
  
  
  
  """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		)
  