import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
  import numpy as np
  import torch

  from . import (
      AlbertForPreTraining,
      BartForConditionalGeneration,
      BertForPreTraining,
      BertForQuestionAnswering,
      BertForSequenceClassification,
      CamembertForMaskedLM,
      CTRLLMHeadModel,
      DistilBertForMaskedLM,
      DistilBertForQuestionAnswering,
      DPRContextEncoder,
      DPRQuestionEncoder,
      DPRReader,
      ElectraForPreTraining,
      FlaubertWithLMHeadModel,
      GPTaLMHeadModel,
      LayoutLMForMaskedLM,
      LxmertForPreTraining,
      LxmertVisualFeatureEncoder,
      OpenAIGPTLMHeadModel,
      RobertaForMaskedLM,
      RobertaForSequenceClassification,
      TaForConditionalGeneration,
      TransfoXLLMHeadModel,
      XLMRobertaForMaskedLM,
      XLMWithLMHeadModel,
      XLNetLMHeadModel,
  )


logging.set_verbosity_info()

SCREAMING_SNAKE_CASE			:						Any				  =		{
    """bart""": (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """bert""": (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-large-uncased-whole-word-masking-finetuned-squad""": (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-large-cased-whole-word-masking-finetuned-squad""": (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-base-cased-finetuned-mrpc""": (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """dpr""": (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """gpt2""": (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlnet""": (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlm""": (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlm-roberta""": (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """transfo-xl""": (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """openai-gpt""": (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """roberta""": (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """layoutlm""": (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """roberta-large-mnli""": (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """camembert""": (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """flaubert""": (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """distilbert""": (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """distilbert-base-distilled-squad""": (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """lxmert""": (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """lxmert-visual-feature-encoder""": (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """ctrl""": (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """albert""": (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """t5""": (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """electra""": (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """wav2vec2""": (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}



def 	__A		(      _A				,					_A				,					_A				,					_A				,					_A=False				,					_A=True       ):



 """simple docstring"""


 if model_type not in MODEL_CLASSES:
  raise ValueError(f"""Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()       )}."""       )

 __a							,  __a							,  __a							,  __a							=	MODEL_CLASSES[model_type]

 # Initialise TF model
 if config_file in aws_config_map:
  __a							=	cached_file(_A				,					_A				,					force_download=not use_cached_models       )
 __a							=	config_class.from_json_file(_A       )
 __a							=	True
 __a							=	True
 print(f"""Building TensorFlow model from configuration: {config}"""       )
 __a							=	model_class(_A       )

 # Load weights from tf checkpoint
 if pytorch_checkpoint_path in aws_config_map.keys():
  __a							=	cached_file(
      _A				,					_A				,					force_download=not use_cached_models       )
 # Load PyTorch checkpoint in tf2 model:
 __a							=	load_pytorch_checkpoint_in_tfa_model(_A				,					_A       )

 if compare_with_pt_model:
  __a							=	tf_model(tf_model.dummy_inputs				,					training=_A       )  # build the network

  __a							=	torch.load(_A				,					map_location="cpu"       )
  __a							=	pt_model_class.from_pretrained(
      pretrained_model_name_or_path=_A				,					config=_A				,					state_dict=_A       )

  with torch.no_grad():
   __a							=	pt_model(**pt_model.dummy_inputs       )

  __a							=	pto[0].numpy()
  __a							=	tfo[0].numpy()
  __a							=	np.amax(np.abs(np_pt - np_tf       )       )
  print(f"""Max absolute difference between models outputs {diff}"""       )
  assert diff <= 2E-2, f"""Error, model absolute difference is >2e-2: {diff}"""

 # Save pytorch-model
 print(f"""Save TensorFlow model to {tf_dump_path}"""       )
 tf_model.save_weights(_A				,					save_format="h5"       )



def 	__A		(      _A				,					_A				,					_A=None				,					_A=None				,					_A=False				,					_A=False				,					_A=False				,					_A=False				,					):



 """simple docstring"""


 if args_model_type is None:
  __a							=	list(MODEL_CLASSES.keys()       )
 else:
  __a							=	[args_model_type]

 for j, model_type in enumerate(_A				,					start=1       ):
  print("=" * 100       )
  print(f""" Converting model type {j}/{len(_A       )}: {model_type}"""       )
  print("=" * 100       )
  if model_type not in MODEL_CLASSES:
   raise ValueError(f"""Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()       )}."""       )

  __a							,  __a							,  __a							,  __a							,  __a							=	MODEL_CLASSES[model_type]

  if model_shortcut_names_or_path is None:
   __a							=	list(aws_model_maps.keys()       )
  if config_shortcut_names_or_path is None:
   __a							=	model_shortcut_names_or_path

  for i, (model_shortcut_name, config_shortcut_name) in enumerate(
      zip(_A				,					_A       )				,					start=1       ):
   print("-" * 100       )
   if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
    if not only_convert_finetuned_models:
     print(f"""    Skipping finetuned checkpoint {model_shortcut_name}"""       )
     continue
    __a							=	model_shortcut_name
   elif only_convert_finetuned_models:
    print(f"""    Skipping not finetuned checkpoint {model_shortcut_name}"""       )
    continue
   print(
       f"""    Converting checkpoint {i}/{len(_A       )}: {model_shortcut_name} - model_type {model_type}"""       )
   print("-" * 100       )

   if config_shortcut_name in aws_config_map:
    __a							=	cached_file(_A				,					_A				,					force_download=not use_cached_models       )
   else:
    __a							=	config_shortcut_name

   if model_shortcut_name in aws_model_maps:
    __a							=	cached_file(_A				,					_A				,					force_download=not use_cached_models       )
   else:
    __a							=	model_shortcut_name

   if os.path.isfile(_A       ):
    __a							=	"converted_model"

   convert_pt_checkpoint_to_tf(
       model_type=_A				,					pytorch_checkpoint_path=_A				,					config_file=_A				,					tf_dump_path=os.path.join(_A				,					model_shortcut_name + "-tf_model.h5"       )				,					compare_with_pt_model=_A				,					)
   if remove_cached_files:
    os.remove(_A       )
    os.remove(_A       )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE			:						Dict				  =		argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--tf_dump_path""", default=None, type=str, required=True, help="""Path to the output Tensorflow dump file."""
  )
  parser.add_argument(
      """--model_type""",
      default=None,
      type=str,
      help=(
          f'''Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and '''
          """convert all the models from AWS."""
      ),
  )
  parser.add_argument(
      """--pytorch_checkpoint_path""",
      default=None,
      type=str,
      help=(
          """Path to the PyTorch checkpoint path or shortcut name to download from AWS. """
          """If not given, will download and convert all the checkpoints from AWS."""
      ),
  )
  parser.add_argument(
      """--config_file""",
      default=None,
      type=str,
      help=(
          """The config json file corresponding to the pre-trained model. \n"""
          """This specifies the model architecture. If not given and """
          """--pytorch_checkpoint_path is not given or is a shortcut name """
          """use the configuration associated to the shortcut name on the AWS"""
      ),
  )
  parser.add_argument(
      """--compare_with_pt_model""", action="""store_true""", help="""Compare Tensorflow and PyTorch model predictions."""
  )
  parser.add_argument(
      """--use_cached_models""",
      action="""store_true""",
      help="""Use cached models if possible instead of updating to latest checkpoint versions.""",
  )
  parser.add_argument(
      """--remove_cached_files""",
      action="""store_true""",
      help="""Remove pytorch models after conversion (save memory when converting in batches).""",
  )
  parser.add_argument("""--only_convert_finetuned_models""", action="""store_true""", help="""Only convert finetuned models.""")
  SCREAMING_SNAKE_CASE			:						List[str]				  =		parser.parse_args()

  # if args.pytorch_checkpoint_path is not None:
  #     convert_pt_checkpoint_to_tf(args.model_type.lower(),
  #                                 args.pytorch_checkpoint_path,
  #                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
  #                                 args.tf_dump_path,
  #                                 compare_with_pt_model=args.compare_with_pt_model,
  #                                 use_cached_models=args.use_cached_models)
  # else:
  convert_all_pt_checkpoints_to_tf(
      args.model_type.lower() if args.model_type is not None else None,
      args.tf_dump_path,
      model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
      if args.pytorch_checkpoint_path is not None
      else None,
      config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
      compare_with_pt_model=args.compare_with_pt_model,
      use_cached_models=args.use_cached_models,
      remove_cached_files=args.remove_cached_files,
      only_convert_finetuned_models=args.only_convert_finetuned_models,
  )



import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
SCREAMING_SNAKE_CASE			:						List[Any]				  =		logging.getLogger()

SCREAMING_SNAKE_CASE			:						List[str]				  =		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class  A_			(							a_			):

  def 		_UpperCAmelCase (		self			:   str  ,				__SCREAMING_SNAKE_CASE			:   int							):
   os.makedirs(__SCREAMING_SNAKE_CASE  ,				exist_ok=__SCREAMING_SNAKE_CASE							)
   __a							=	{"source": "What is love ?", "target": "life"}
   __a							=	{"train": 12, "val": 2, "test": 2}
   for split in ["train", "test", "val"]:
    for field in ["source", "target"]:
     __a							=	"\n".join([contents[field]] * n_lines[split]							)
     with open(os.path.join(__SCREAMING_SNAKE_CASE  ,				f"""{split}.{field}"""							)  ,				"w"							) as f:
      f.write(__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   str  ,				__SCREAMING_SNAKE_CASE			:   int  ,				__SCREAMING_SNAKE_CASE			:   str = "pytorch"							):
   __a							=	self.get_auto_remove_tmp_dir()
   __a							=	os.path.join(__SCREAMING_SNAKE_CASE  ,				"output"							)
   __a							=	os.path.join(__SCREAMING_SNAKE_CASE  ,				"data"							)
   self._create_dummy_data(data_dir=__SCREAMING_SNAKE_CASE							)

   __a							=	f"""
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            """.split()

   if gpus > 0:
    testargs.append(f"""--gpus={gpus}"""							)
    if is_apex_available():
     testargs.append("--fp16"							)
   else:
    testargs.append("--gpus=0"							)
    testargs.append("--distributed_backend=ddp_cpu"							)
    testargs.append("--num_processes=2"							)

   __a							=	[sys.executable, str(Path(finetune_rag.__file__							).resolve()							)] + testargs
   execute_subprocess_async(__SCREAMING_SNAKE_CASE  ,				env=self.get_env()							)

   __a							=	os.path.join(__SCREAMING_SNAKE_CASE  ,				"metrics.json"							)
   with open(__SCREAMING_SNAKE_CASE							) as f:
    __a							=	json.load(__SCREAMING_SNAKE_CASE							)
   return result

  @require_torch_gpu
  def 		_UpperCAmelCase (		self			:   Dict							):
   __a							=	self._run_finetune(gpus=1							)
   self.assertGreaterEqual(result["test"][0]["test_avg_em"]  ,				0.2							)

  @require_torch_multi_gpu
  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   __a							=	self._run_finetune(gpus=2							)
   self.assertGreaterEqual(result["test"][0]["test_avg_em"]  ,				0.2							)

  @require_torch_gpu
  @require_ray
  def 		_UpperCAmelCase (		self			:   Optional[Any]							):
   __a							=	self._run_finetune(gpus=1  ,				distributed_retriever="ray"							)
   self.assertGreaterEqual(result["test"][0]["test_avg_em"]  ,				0.2							)

  @require_torch_multi_gpu
  @require_ray
  def 		_UpperCAmelCase (		self			:   Any							):
   __a							=	self._run_finetune(gpus=1  ,				distributed_retriever="ray"							)
   self.assertGreaterEqual(result["test"][0]["test_avg_em"]  ,				0.2							)


