





'''simple docstring'''

import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


a   	=    "true"

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase=82				,						__UpperCAmelCase=16   )    ->       Union[str, Any]:

   '''simple docstring'''







   set_seed(42   )
   __SCREAMING_SNAKE_CASE   				=							RegressionModel()
   __SCREAMING_SNAKE_CASE   				=							deepcopy(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							RegressionDataset(length=__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							DataLoader(__UpperCAmelCase				,						batch_size=__UpperCAmelCase   )
   model.to(accelerator.device   )
   __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							accelerator.prepare(__UpperCAmelCase				,						__UpperCAmelCase   )
   return model, ddp_model, dataloader

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase=False   )    ->       Optional[int]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							AutoTokenizer.from_pretrained("""hf-internal-testing/mrpc-bert-base-cased"""   )
   __SCREAMING_SNAKE_CASE   				=							load_dataset("""glue"""				,						"""mrpc"""				,						split="""validation"""   )

   def tokenize_function(__UpperCAmelCase   ):
      __SCREAMING_SNAKE_CASE   				=							tokenizer(examples["""sentence1"""]				,						examples["""sentence2"""]				,						truncation=__UpperCAmelCase				,						max_length=__UpperCAmelCase   )
      return outputs

   with accelerator.main_process_first():
      __SCREAMING_SNAKE_CASE   				=							dataset.map(
          __UpperCAmelCase				,						batched=__UpperCAmelCase				,						remove_columns=["""idx""", """sentence1""", """sentence2"""]				,						)

   __SCREAMING_SNAKE_CASE   				=							tokenized_datasets.rename_column("""label"""				,						"""labels"""   )

   def collate_fn(__UpperCAmelCase   ):
      if use_longest:
         return tokenizer.pad(__UpperCAmelCase				,						padding="""longest"""				,						return_tensors="""pt"""   )
      return tokenizer.pad(__UpperCAmelCase				,						padding="""max_length"""				,						max_length=128				,						return_tensors="""pt"""   )

   return DataLoader(__UpperCAmelCase				,						shuffle=__UpperCAmelCase				,						collate_fn=__UpperCAmelCase				,						batch_size=16   )

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       int:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							Accelerator(dispatch_batches=__UpperCAmelCase				,						split_batches=__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							get_dataloader(__UpperCAmelCase				,						not dispatch_batches   )
   __SCREAMING_SNAKE_CASE   				=							AutoModelForSequenceClassification.from_pretrained(
       """hf-internal-testing/mrpc-bert-base-cased"""				,						return_dict=__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							accelerator.prepare(__UpperCAmelCase				,						__UpperCAmelCase   )
   return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       int:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							[]
   for batch in dataloader:
      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							batch.values()
      with torch.no_grad():
         __SCREAMING_SNAKE_CASE   				=							model(__UpperCAmelCase   )
         __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							accelerator.gather_for_metrics((logit, target)   )
         logits_and_targets.append((logit, target)   )
   __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							[], []
   for logit, targ in logits_and_targets:
      logits.append(__UpperCAmelCase   )
      targs.append(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							torch.cat(__UpperCAmelCase   ), torch.cat(__UpperCAmelCase   )
   return logits, targs

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase=82				,						__UpperCAmelCase=False				,						__UpperCAmelCase=False				,						__UpperCAmelCase=16   )    ->       str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							get_basic_setup(__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							generate_predictions(__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )
   assert (
       len(__UpperCAmelCase   ) == num_samples
   ), f"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(__UpperCAmelCase   )}"""

def     __magic_name__							(				__UpperCAmelCase = False				,						__UpperCAmelCase = False   )    ->       Dict:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							evaluate.load("""glue"""				,						"""mrpc"""   )
   __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							get_mrpc_setup(__UpperCAmelCase				,						__UpperCAmelCase   )
   # First do baseline
   __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							setup["""no"""]
   model.to(__UpperCAmelCase   )
   model.eval()
   for batch in dataloader:
      batch.to(__UpperCAmelCase   )
      with torch.inference_mode():
         __SCREAMING_SNAKE_CASE   				=							model(**__UpperCAmelCase   )
      __SCREAMING_SNAKE_CASE   				=							outputs.logits.argmax(dim=-1   )
      metric.add_batch(predictions=__UpperCAmelCase				,						references=batch["""labels"""]   )
   __SCREAMING_SNAKE_CASE   				=							metric.compute()

   # Then do distributed
   __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							setup["""ddp"""]
   model.eval()
   for batch in dataloader:
      with torch.inference_mode():
         __SCREAMING_SNAKE_CASE   				=							model(**__UpperCAmelCase   )
      __SCREAMING_SNAKE_CASE   				=							outputs.logits.argmax(dim=-1   )
      __SCREAMING_SNAKE_CASE   				=							batch["""labels"""]
      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							accelerator.gather_for_metrics((preds, references)   )
      metric.add_batch(predictions=__UpperCAmelCase				,						references=__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							metric.compute()

   for key in "accuracy f1".split():
      assert math.isclose(
          baseline[key]				,						distributed[key]   ), f"""Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"""

def     __magic_name__							(				)    ->       Optional[Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							Accelerator(split_batches=__UpperCAmelCase				,						dispatch_batches=__UpperCAmelCase   )
   if accelerator.is_local_main_process:
      datasets.utils.logging.set_verbosity_warning()
      transformers.utils.logging.set_verbosity_warning()
   else:
      datasets.utils.logging.set_verbosity_error()
      transformers.utils.logging.set_verbosity_error()
   # These are a bit slower so they should only be ran on the GPU or TPU
   if torch.cuda.is_available() or is_tpu_available():
      if accelerator.is_local_main_process:
         print("""**Testing gather_for_metrics**"""   )
      for split_batches in [True, False]:
         for dispatch_batches in [True, False]:
            if accelerator.is_local_main_process:
               print(f"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`"""   )
            test_mrpc(__UpperCAmelCase				,						__UpperCAmelCase   )
            accelerator.state._reset_state()
   if accelerator.is_local_main_process:
      print("""**Test torch metrics**"""   )
   for split_batches in [True, False]:
      for dispatch_batches in [True, False]:
         __SCREAMING_SNAKE_CASE   				=							Accelerator(split_batches=__UpperCAmelCase				,						dispatch_batches=__UpperCAmelCase   )
         if accelerator.is_local_main_process:
            print(f"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"""   )
         test_torch_metrics(__UpperCAmelCase				,						99   )
         accelerator.state._reset_state()
   if accelerator.is_local_main_process:
      print("""**Test last batch is not dropped when perfectly divisible**"""   )
   __SCREAMING_SNAKE_CASE   				=							Accelerator()
   test_torch_metrics(__UpperCAmelCase				,						512   )
   accelerator.state._reset_state()

def     __magic_name__							(				__UpperCAmelCase   )    ->       Dict:

   '''simple docstring'''







   main()


if __name__ == "__main__":
    main()









'''simple docstring'''







from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand






def       lowercase					(_A       ):







       """simple docstring"""







       return ConvertCommand(
           args.model_type ,    args.tf_checkpoint ,    args.pytorch_dump_output ,    args.config ,    args.finetuning_task_name       )


lowerCAmelCase     :  Optional[int]											=    """
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
"""
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     @staticmethod
     def a						(			snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							parser.add_parser(
                'convert'   ,						help='CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.'   ,						)
            train_parser.add_argument('--model_type'   ,						type=snake_case__   ,						required=snake_case__   ,						help='Model\'s type.' )
            train_parser.add_argument(
                '--tf_checkpoint'   ,						type=snake_case__   ,						required=snake_case__   ,						help='TensorFlow checkpoint path or folder.' )
            train_parser.add_argument(
                '--pytorch_dump_output'   ,						type=snake_case__   ,						required=snake_case__   ,						help='Path to the PyTorch saved model output.' )
            train_parser.add_argument('--config'   ,						type=snake_case__   ,						default=''   ,						help='Configuration file path or folder.' )
            train_parser.add_argument(
                '--finetuning_task_name'   ,						type=snake_case__   ,						default=snake_case__   ,						help='Optional fine-tuning task name if the TF model was a finetuned model.'   ,						)
            train_parser.set_defaults(func=snake_case__ )







     def __init__(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						*snake_case__   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							logging.get_logger('transformers-cli/converting' )

            self._logger.info(F'Loading model {model_type}' )
            _lowerCAmelCase     :				Any       =							model_type
            _lowerCAmelCase     :				Any       =							tf_checkpoint
            _lowerCAmelCase     :				List[str]       =							pytorch_dump_output
            _lowerCAmelCase     :				int       =							config
            _lowerCAmelCase     :				str       =							finetuning_task_name







     def a						(			self ):

            '''simple docstring'''

            if self._model_type == "albert":
                   try:
                          from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
                              convert_tf_checkpoint_to_pytorch,
                          )
                   except ImportError:
                          raise ImportError(snake_case__ )

                   convert_tf_checkpoint_to_pytorch(self._tf_checkpoint   ,						self._config   ,						self._pytorch_dump_output )
            elif self._model_type == "bert":
                   try:
                          from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
                              convert_tf_checkpoint_to_pytorch,
                          )
                   except ImportError:
                          raise ImportError(snake_case__ )

                   convert_tf_checkpoint_to_pytorch(self._tf_checkpoint   ,						self._config   ,						self._pytorch_dump_output )
            elif self._model_type == "funnel":
                   try:
                          from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
                              convert_tf_checkpoint_to_pytorch,
                          )
                   except ImportError:
                          raise ImportError(snake_case__ )

                   convert_tf_checkpoint_to_pytorch(self._tf_checkpoint   ,						self._config   ,						self._pytorch_dump_output )
            elif self._model_type == "t5":
                   try:
                          from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
                   except ImportError:
                          raise ImportError(snake_case__ )

                   convert_tf_checkpoint_to_pytorch(self._tf_checkpoint   ,						self._config   ,						self._pytorch_dump_output )
            elif self._model_type == "gpt":
                   from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
                       convert_openai_checkpoint_to_pytorch,
                   )

                   convert_openai_checkpoint_to_pytorch(self._tf_checkpoint   ,						self._config   ,						self._pytorch_dump_output )
            elif self._model_type == "transfo_xl":
                   try:
                          from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
                              convert_transfo_xl_checkpoint_to_pytorch,
                          )
                   except ImportError:
                          raise ImportError(snake_case__ )

                   if "ckpt" in self._tf_checkpoint.lower():
                          _lowerCAmelCase     :				List[Any]       =							self._tf_checkpoint
                          _lowerCAmelCase     :				Optional[int]       =							''
                   else:
                          _lowerCAmelCase     :				int       =							self._tf_checkpoint
                          _lowerCAmelCase     :				Dict       =							''
                   convert_transfo_xl_checkpoint_to_pytorch(
                       snake_case__   ,						self._config   ,						self._pytorch_dump_output   ,						snake_case__ )
            elif self._model_type == "gpt2":
                   try:
                          from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
                              convert_gpta_checkpoint_to_pytorch,
                          )
                   except ImportError:
                          raise ImportError(snake_case__ )

                   convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint   ,						self._config   ,						self._pytorch_dump_output )
            elif self._model_type == "xlnet":
                   try:
                          from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
                              convert_xlnet_checkpoint_to_pytorch,
                          )
                   except ImportError:
                          raise ImportError(snake_case__ )

                   convert_xlnet_checkpoint_to_pytorch(
                       self._tf_checkpoint   ,						self._config   ,						self._pytorch_dump_output   ,						self._finetuning_task_name )
            elif self._model_type == "xlm":
                   from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
                       convert_xlm_checkpoint_to_pytorch,
                   )

                   convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint   ,						self._pytorch_dump_output )
            elif self._model_type == "lxmert":
                   from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
                       convert_lxmert_checkpoint_to_pytorch,
                   )

                   convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint   ,						self._pytorch_dump_output )
            elif self._model_type == "rembert":
                   from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
                       convert_rembert_tf_checkpoint_to_pytorch,
                   )

                   convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint   ,						self._config   ,						self._pytorch_dump_output )
            else:
                   raise ValueError(
                       '--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]' )

