






'''simple docstring'''







import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()


def 			__UpperCamelCase				(			lowercase_					:     str      ,					lowercase_					:     List[str]      ):
  """simple docstring"""




  a_	    = CanineConfig()
  a_	    = CanineModel(_UpperCamelCase      )
  model.eval()

  print(F'Building PyTorch model from configuration: {config}'      )

  # Load weights from tf checkpoint
  load_tf_weights_in_canine(_UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase      )

  # Save pytorch-model (weights and configuration)
  print(F'Save PyTorch model to {pytorch_dump_path}'      )
  model.save_pretrained(_UpperCamelCase      )

  # Save tokenizer files
  a_	    = CanineTokenizer()
  print(F'Save tokenizer files to {pytorch_dump_path}'      )
  tokenizer.save_pretrained(_UpperCamelCase      )


if __name__ == "__main__":
  __lowerCAmelCase					   =       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--tf_checkpoint_path",
      default=None,
      type=str,
      required=True,
      help="Path to the TensorFlow checkpoint. Should end with model.ckpt",
  )
  parser.add_argument(
      "--pytorch_dump_path",
      default=None,
      type=str,
      required=True,
      help="Path to a folder where the PyTorch model will be placed.",
  )
  __lowerCAmelCase					   =       parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)












'''simple docstring'''







import itertools
import os
import subprocess
from os.path import dirname

from parameterized import parameterized

from tests.trainer.test_trainer import TrainerIntegrationCommon  # noqa
from transformers import is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_tests_dir,
    require_deepspeed,
    require_torch_gpu,
    slow,
)
from transformers.trainer_utils import set_seed


if is_torch_available():
  from tests.trainer.test_trainer import (  # noqa
      RegressionModelConfig,
      RegressionPreTrainedModel,
      get_regression_trainer,
  )


set_seed(42)

__lowerCAmelCase					   =       get_tests_dir("fixtures")
__lowerCAmelCase					   =       os.path.join(dirname(get_tests_dir()))
__lowerCAmelCase					   =       dirname(os.path.abspath(__file__))

# default torch.distributed port
__lowerCAmelCase					   =       "10999"

__lowerCAmelCase					   =       "t5-small"

# *** Working Models ***
__lowerCAmelCase					   =       "hf-internal-testing/tiny-albert"
__lowerCAmelCase					   =       "sshleifer/bart-tiny-random"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-bert"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-bigbird_pegasus"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-big_bird"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-blenderbot"
__lowerCAmelCase					   =       "bigscience/bigscience-small-testing"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-deberta"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-deberta-v2"
__lowerCAmelCase					   =       "sshleifer/tiny-distilbert-base-cased"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-electra"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-flaubert"
__lowerCAmelCase					   =       "stas/tiny-wmt19-en-de"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-funnel"
__lowerCAmelCase					   =       "sshleifer/tiny-gpt2"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-gptj"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-gpt_neo"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-layoutlm"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-led"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-longformer"
__lowerCAmelCase					   =       "stas/tiny-m2m_100"  # hf tiny model is unsuitable
__lowerCAmelCase					   =       "sshleifer/tiny-marian-en-de"
__lowerCAmelCase					   =       "sshleifer/tiny-mbart"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-mobilebert"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-mpnet"
__lowerCAmelCase					   =       "stas/pegasus-cnn_dailymail-tiny-random"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-prophetnet"
__lowerCAmelCase					   =       "sshleifer/tiny-distilroberta-base"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-squeezebert"
__lowerCAmelCase					   =       "patrickvonplaten/t5-tiny-random"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-t5-v1.1"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-vit"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-xlm-roberta"
__lowerCAmelCase					   =       "sshleifer/tiny-xlnet-base-cased"


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-mt5"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-camembert"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-openai-gpt"

# missing tokenizer files
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-convbert"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-layoutlmv2"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-hubert"

# issues with tokenizer
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-ctrl"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-transfo-xl"  # same as ctrl

# other issues with tiny models
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-ibert"  # multiple issues with either mlm/qa/clas
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-reformer"  # multiple issues with either mlm/qa/clas

# *** Lacking official examples to test with ***
# or not working with examples
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-dpr"
# - "dpr"  examples/research_projects/rag-end2end-retriever/
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-rag"
# - "rag" research_projects
__lowerCAmelCase					   =       ""
# - "luke" Entities classes - no plan to make such example
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-lxmert"
# - "lxmert" doesn't work with run_qa.py
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-clip"
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-speech_to_text"
# - "speech_to_text", nothing under pytorch examples


# *** Reactive mode ***
# models with low usage, unstable API, things about to change - do nothing about the following until someone runs into a problem
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-tapas"


def 			__UpperCamelCase				(			lowercase_					:     Optional[int]=False      ):
       """simple docstring"""




       a_	    = min(2      ,					get_gpu_count()      ) if distributed else 1
       a_	    = os.environ.get('DS_TEST_PORT'      ,					lowercase_      )
       return F'deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'.split()


def 			__UpperCamelCase				(			):
       """simple docstring"""




       a_	    = F'{FIXTURE_DIRECTORY}/tests_samples'
       a_	    = F'{data_dir_samples}/wmt_en_ro'
       a_	    = F'{data_dir_samples}/xsum'
       a_	    = '\n        --do_train\n        --max_train_samples 4\n        --per_device_train_batch_size 2\n        --num_train_epochs 1\n        --fp16\n        --report_to none\n        --overwrite_output_dir\n        '.split()

       # try to cover as many models as possible once (it's enough to run on one task per model)
       # but need a tiny model for each
       #
       # should have "{model_type.upper()}_TINY" corresponding vars defined, e.g., T5_TINY, etc.
       a_	    = {
           'trans': [
               'bart',
               'fsmt',
               'm2m_100',
               'marian',
               'mbart',
               't5',
               't5_v1',
               # "mt5", missing model files
           ],
           'sum': [
               'pegasus',
           ],
           'clm': [
               'big_bird',
               'bigbird_pegasus',
               'blenderbot',
               'bloom',
               'gpt2',
               'gpt_neo',
               'gptj',
               'xlm-roberta',
               'prophetnet',
               # "camembert", missing model files
           ],
           'mlm': [
               'albert',
               'deberta',
               'deberta-v2',
               'distilbert',
               'electra',
               'flaubert',
               'funnel',
               'layoutlm',
               # "reformer", # multiple issues with either mlm/qa/clas
           ],
           'qa': [
               'led',
               'longformer',
               'mobilebert',
               'mpnet',
               'roberta',
               'squeezebert',
               # "convbert", # missing tokenizer files
               # "layoutlmv2", missing model files
           ],
           'clas': [
               'bert',
               'xlnet',
               # "hubert", # missing tokenizer files
               # "ibert", # multiple issues with either mlm/qa/clas
               # "transfo-xl", # tokenizer issues as ctrl
               # "ctrl", # tokenizer issues
               # "openai-gpt", missing model files
               # "tapas", multiple issues
           ],
           'img_clas': [
               'vit',
           ],
       }

       a_	    = F'{ROOT_DIRECTORY}/examples/pytorch'

       a_	    = {
           'trans': F'\n        {scripts_dir}/translation/run_translation.py\n        --train_file {data_dir_wmt}/train.json\n        --source_lang en\n        --target_lang ro\n        ',
           'sum': F'\n        {scripts_dir}/summarization/run_summarization.py\n        --train_file {data_dir_xsum}/sample.json\n        --max_source_length 12\n        --max_target_length 12\n        --lang en\n        ',
           'clm': F'\n        {scripts_dir}/language-modeling/run_clm.py\n        --train_file {FIXTURE_DIRECTORY}/sample_text.txt\n        --block_size 8\n        ',
           'mlm': F'\n        {scripts_dir}/language-modeling/run_mlm.py\n        --train_file {FIXTURE_DIRECTORY}/sample_text.txt\n        ',
           'qa': F'\n        {scripts_dir}/question-answering/run_qa.py\n        --train_file {data_dir_samples}/SQUAD/sample.json\n        ',
           'clas': F'\n        {scripts_dir}/text-classification/run_glue.py\n        --train_file {data_dir_samples}/MRPC/train.csv\n        --max_seq_length 12\n        --task_name MRPC\n        ',
           'img_clas': F'\n        {scripts_dir}/image-classification/run_image_classification.py\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --remove_unused_columns False\n            --max_steps 10\n            --image_processor_name {DS_TESTS_DIRECTORY}/vit_feature_extractor.json\n        ',
       }

       a_	    = get_launcher(distributed=lowercase_      )

       a_	    = {}
       for task, args in tasks.items():
              a_	    = args.split()
              for model in tasksamodels[task]:
                     a_	    = globals()[F'{model.upper().replace("-"      ,					"_"      )}_TINY']
                     a_	    = F'--model_name_or_path {model_name}'.split()
                     a_	    = launcher + args + args_model + args_main

                     # # generation special case
                     # if task == "gen":
                     #     launcher = f"deepspeed --num_nodes 1 --num_gpus 1".split()
                     #     args_model += f"--model_type {model}".split()
                     #     cmds[f"{task}_{model}"] = launcher + args + args_model
                     # else:

       return cmds


__lowerCAmelCase					   =       make_task_cmds()

__lowerCAmelCase					   =       "zero2"
__lowerCAmelCase					   =       "zero3"

__lowerCAmelCase					   =       [ZEROa, ZEROa]


def 			__UpperCamelCase				(			lowercase_					:     str      ,					lowercase_					:     Optional[int]      ,					lowercase_					:     Dict      ):
       """simple docstring"""




       a_	    = parameterized.to_safe_name('_'.join(str(lowercase_      ) for x in param.args      )      )
       return F'{func.__name__}_{param_based_name}'


# Cartesian-product of zero stages with models to test
__lowerCAmelCase					   =       list(itertools.product(stages, task_cmds.keys()))




@slow
@require_deepspeed
@require_torch_gpu
class      __SCREAMING_SNAKE_CASE					(__A					):


  """simple docstring"""


  def       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__  ):






         """simple docstring"""


         if task not in task_cmds:
                raise ValueError(f'don\'t know of task {task}, have {task_cmds.keys()}'  )

         a_	    = task_cmds[task]
         a_	    = f'--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'.split()

         a_	    = self.get_auto_remove_tmp_dir()
         a_	    = f'--output_dir {output_dir}'.split()

         cmd += args_ds + args_out

         return cmd, output_dir







  @parameterized.expand(UpperCamelCase__      ,  name_func=UpperCamelCase__  )
  def       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__  ):






         """simple docstring"""


         a_    ,  a_	    = self.get_task_cmd(UpperCamelCase__      ,  UpperCamelCase__  )

         # 1. generate the checkpoint
         cmd += "--save_steps 1".split()
         # keep for quick debug
         # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] + cmd)); die
         execute_subprocess_async(UpperCamelCase__      ,  env=self.get_env()  )

         # 2. test that the fp32 weights get reconsolidated
         a_	    = f'{output_dir}/checkpoint-1'
         a_	    = f'{chkpt_dir}/out.bin'
         a_	    = f'{chkpt_dir}/zero_to_fp32.py {chkpt_dir} {recovered_model_path}'
         # keep for quick debug
         # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
         subprocess.check_call(UpperCamelCase__      ,  shell=UpperCamelCase__  )
         assert os.path.exists(UpperCamelCase__  ), f'{recovered_model_path} was not found'

         # possibly could also test that the resulting saved model is usable but given that we use
         # random models we won't know if it's any good




