






"""simple docstring"""







import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


lowerCAmelCase_	:    Dict            =      os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F'{bindir}/../../examples/pytorch/translation'):
 from run_translation import main  # noqa


set_seed(4_2)
lowerCAmelCase_	:    List[Any]            =      '''sshleifer/student_marian_en_ro_6_1'''
lowerCAmelCase_	:    List[Any]            =      '''sshleifer/tiny-mbart'''





@require_torch
class   UpperCamelCase_ (       a_  ):



  def 		UpperCamelCase_				(      self ,		snake_case__=False ,		snake_case__=None ,		snake_case__=True ,		snake_case__=True ,		snake_case__=True ,		snake_case__=True ,		)			->			List[str]:



         """simple docstring"""





         UpperCAmelCase	    =			self.run_trainer(
             eval_steps=1 ,		max_len=12 ,		model_name=snake_case__ ,		num_train_epochs=1 ,		distributed=snake_case__ ,		extra_args_str=snake_case__ ,		predict_with_generate=snake_case__ ,		do_train=snake_case__ ,		do_eval=snake_case__ ,		do_predict=snake_case__ ,		)
         UpperCAmelCase	    =			TrainerState.load_from_json(os.path.join(snake_case__ ,		"""trainer_state.json"""				)				).log_history

         if not do_eval:
                return

         UpperCAmelCase	    =			[log for log in logs if """eval_loss""" in log.keys()]

         UpperCAmelCase	    =			eval_metrics[0]
         if predict_with_generate:
                assert "eval_bleu" in first_step_stats

                UpperCAmelCase	    =			eval_metrics[-1]
                assert isinstance(last_step_stats["""eval_bleu"""] ,		snake_case__				)
                assert not math.isnan(float(last_step_stats["""eval_loss"""]				)				), "eval_loss must not be `nan`"



  @require_torch_non_multi_gpu
  def 		UpperCamelCase_				(      self				)			->			List[Any]:



         """simple docstring"""





         self.run_seqaseq_quick()



  @require_torch_multi_gpu
  def 		UpperCamelCase_				(      self				)			->			Optional[int]:



         """simple docstring"""





         self.run_seqaseq_quick(distributed=snake_case__				)



  @require_torch_multi_gpu
  def 		UpperCamelCase_				(      self				)			->			Union[str, Any]:



         """simple docstring"""





         self.run_seqaseq_quick(distributed=snake_case__				)



  @unittest.skip("""Requires an update of the env running those tests"""				)
  @require_torch_multi_gpu
  @require_fairscale
  def 		UpperCamelCase_				(      self				)			->			Dict:



         """simple docstring"""





         self.run_seqaseq_quick(distributed=snake_case__ ,		extra_args_str="""--sharded_ddp simple"""				)



  @unittest.skip("""Requires an update of the env running those tests"""				)
  @require_torch_multi_gpu
  @require_fairscale
  def 		UpperCamelCase_				(      self				)			->			Optional[Any]:



         """simple docstring"""





         self.run_seqaseq_quick(distributed=snake_case__ ,		extra_args_str="""--sharded_ddp simple --fp16"""				)



  @unittest.skip("""Requires an update of the env running those tests"""				)
  @require_torch_multi_gpu
  @require_fairscale
  def 		UpperCamelCase_				(      self				)			->			Any:



         """simple docstring"""





         self.run_seqaseq_quick(distributed=snake_case__ ,		extra_args_str="""--sharded_ddp zero_dp_2""" ,		predict_with_generate=snake_case__				)



  @unittest.skip("""Requires an update of the env running those tests"""				)
  @require_torch_multi_gpu
  @require_fairscale
  def 		UpperCamelCase_				(      self				)			->			Tuple:



         """simple docstring"""





         self.run_seqaseq_quick(
             distributed=snake_case__ ,		extra_args_str="""--sharded_ddp zero_dp_2 --fp16""" ,		predict_with_generate=snake_case__				)



  @require_apex
  @require_torch_gpu
  def 		UpperCamelCase_				(      self				)			->			Optional[Any]:



         """simple docstring"""





         self.run_seqaseq_quick(distributed=snake_case__ ,		extra_args_str="""--fp16 --fp16_backend=apex"""				)
         # test 2nd time - was getting eval_loss': nan'
         # to reproduce the problem set distributed=False
         self.run_seqaseq_quick(distributed=snake_case__ ,		extra_args_str="""--fp16 --fp16_backend=apex"""				)



  @parameterized.expand(["""base""", """low""", """high""", """mixed"""]				)
  @require_torch_multi_gpu
  def 		UpperCamelCase_				(      self ,		snake_case__				)			->			Dict:



         """simple docstring"""





         UpperCAmelCase	    =			{
             # test with the default log_level - should be info and thus log info once
             """base""": {"""extra_args_str""": """""", """n_matches""": 1},
             # test with low log_level and log_level_replica - should be noisy on all processes
             # now the info string should appear twice on 2 processes
             """low""": {"""extra_args_str""": """--log_level debug --log_level_replica debug""", """n_matches""": 2},
             # test with high log_level and low log_level_replica
             # now the info string should appear once only on the replica
             """high""": {"""extra_args_str""": """--log_level error --log_level_replica debug""", """n_matches""": 1},
             # test with high log_level and log_level_replica - should be quiet on all processes
             """mixed""": {"""extra_args_str""": """--log_level error --log_level_replica error""", """n_matches""": 0},
         }

         UpperCAmelCase	    =			experiments[experiment_id]
         UpperCAmelCase	    =			{"""distributed""": True, """predict_with_generate""": False, """do_eval""": False, """do_predict""": False}
         UpperCAmelCase	    =			"""Running training"""
         with CaptureStderr() as cl:
                self.run_seqaseq_quick(**snake_case__ ,		extra_args_str=data["""extra_args_str"""]				)
         UpperCAmelCase	    =			len(re.findall(snake_case__ ,		cl.err				)				)
         self.assertEqual(snake_case__ ,		data["""n_matches"""]				)



  @slow
  def 		UpperCamelCase_				(      self				)			->			Optional[Any]:



         """simple docstring"""





         UpperCAmelCase	    =			self.run_trainer(
             eval_steps=2 ,		max_len=1_28 ,		model_name=snake_case__ ,		learning_rate=3e-4 ,		num_train_epochs=10 ,		distributed=snake_case__ ,		)

         # Check metrics
         UpperCAmelCase	    =			TrainerState.load_from_json(os.path.join(snake_case__ ,		"""trainer_state.json"""				)				).log_history
         UpperCAmelCase	    =			[log for log in logs if """eval_loss""" in log.keys()]
         UpperCAmelCase	    =			eval_metrics[0]
         UpperCAmelCase	    =			eval_metrics[-1]

         assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
         assert isinstance(last_step_stats["""eval_bleu"""] ,		snake_case__				)

         # test if do_predict saves generations and metrics
         UpperCAmelCase	    =			os.listdir(snake_case__				)
         UpperCAmelCase	    =			{os.path.basename(snake_case__				) for p in contents}
         assert "generated_predictions.txt" in contents
         assert "predict_results.json" in contents



  @slow
  @require_bitsandbytes
  def 		UpperCamelCase_				(      self				)			->			Dict:



         """simple docstring"""





         from transformers.training_args import OptimizerNames

         def train_and_return_metrics(snake_case__				) -> Tuple[int, float]:
                UpperCAmelCase	    =			"""--skip_memory_metrics 0"""

                UpperCAmelCase	    =			self.run_trainer(
                    max_len=1_28 ,		model_name=snake_case__ ,		learning_rate=3e-4 ,		num_train_epochs=1 ,		optim=snake_case__ ,		distributed=snake_case__ ,		extra_args_str=snake_case__ ,		do_eval=snake_case__ ,		do_predict=snake_case__ ,		n_gpus_to_use=1 ,		)

                # Check metrics
                UpperCAmelCase	    =			TrainerState.load_from_json(Path(snake_case__ ,		"""trainer_state.json"""				)				).log_history
                UpperCAmelCase	    =			int(logs[0]["""train_mem_gpu_peaked_delta"""] / 2**20				)
                UpperCAmelCase	    =			int(logs[0]["""train_mem_gpu_alloc_delta"""] / 2**20				)

                UpperCAmelCase	    =			logs[0]["""train_loss"""]
                return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

         UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase	    =			train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value				)
         UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase	    =			train_and_return_metrics(OptimizerNames.ADAMW_BNB.value				)

         UpperCAmelCase	    =			gpu_alloc_mem_orig - gpu_alloc_mem_bnb

         UpperCAmelCase	    =			gpu_peak_mem_orig + gpu_alloc_mem_orig
         UpperCAmelCase	    =			gpu_peak_mem_bnb + gpu_alloc_mem_bnb
         UpperCAmelCase	    =			gpu_total_mem_orig - gpu_total_mem_bnb

         # sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
         # doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
         # in 2 bytes and the diff in optim memory usage is derived as so:
         #
         # - normal 25*8=~200MB (8 bytes per param)
         # - bnb    25*2= ~50MB (2 bytes per param)
         #
         # Thus we should expect ~150MB total memory saved.
         #
         # Peak memory should be the same - the total should be different by about that same margin
         #
         # After leaving a small margin to accommodate for differences between gpus let's check
         # that we have at least 120MB in savings
         UpperCAmelCase	    =			1_20

         # uncomment the following if this test starts failing - requires py38 for a new print feature
         # gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
         # print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
         # print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
         # print(f"{gpu_alloc_mem_diff=}MB")
         # print(f"{gpu_peak_mem_diff=}MB")
         # print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
         # print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

         self.assertGreater(
             snake_case__ ,		snake_case__ ,		"""should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got"""
             f''' a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and'''
             f''' gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB''' ,		)

         self.assertGreater(
             snake_case__ ,		snake_case__ ,		"""should use ~150MB less total gpu memory with BNB, compared to without it for this model but got"""
             f''' a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and'''
             f''' gpu_total_mem_bnb={gpu_total_mem_bnb}MB''' ,		)

         self.assertEqual(
             snake_case__ ,		snake_case__ ,		f'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'''				)



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ = 3e-3 ,		snake_case__ = "adafactor" ,		snake_case__ = False ,		snake_case__ = None ,		snake_case__ = 0 ,		snake_case__ = True ,		snake_case__ = True ,		snake_case__ = True ,		snake_case__ = True ,		snake_case__ = None ,		)			->			Optional[Any]:



         """simple docstring"""





         UpperCAmelCase	    =			self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
         UpperCAmelCase	    =			self.get_auto_remove_tmp_dir()
         UpperCAmelCase	    =			f'''
            --model_name_or_path {model_name}
            --train_file {data_dir}/train.json
            --validation_file {data_dir}/val.json
            --test_file {data_dir}/test.json
            --output_dir {output_dir}
            --overwrite_output_dir
            --max_train_samples 8
            --max_source_length {max_len}
            --max_target_length {max_len}
            --do_train
            --num_train_epochs {str(snake_case__				)}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(snake_case__				)}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        '''.split()

         UpperCAmelCase	    =			f'''
            --do_eval
            --per_device_eval_batch_size 4
            --max_eval_samples 8
            --val_max_target_length {max_len}
            --evaluation_strategy steps
            --eval_steps {str(snake_case__				)}
        '''.split()

         UpperCAmelCase	    =			"""
            --do_predict
        """.split()

         UpperCAmelCase	    =			[]
         if do_train:
                args += args_train

         if do_eval:
                args += args_eval

         if do_predict:
                args += args_predict

         if predict_with_generate:
                args += "--predict_with_generate".split()

         if do_train:
                if optim == "adafactor":
                       args += "--adafactor".split()
                else:
                       args += f'''--optim {optim}'''.split()

         if extra_args_str is not None:
                args += extra_args_str.split()

         if distributed:
                if n_gpus_to_use is None:
                       UpperCAmelCase	    =			get_gpu_count()
                UpperCAmelCase	    =			get_torch_dist_unique_port()
                UpperCAmelCase	    =			f'''
                -m torch.distributed.run
                --nproc_per_node={n_gpus_to_use}
                --master_port={master_port}
                {self.examples_dir_str}/pytorch/translation/run_translation.py
            '''.split()
                UpperCAmelCase	    =			[sys.executable] + distributed_args + args
                # keep for quick debug
                # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
                execute_subprocess_async(snake_case__ ,		env=self.get_env()				)
         else:
                UpperCAmelCase	    =			["""run_translation.py"""] + args
                with patch.object(snake_case__ ,		"""argv""" ,		snake_case__				):
                       main()

         return output_dir








"""simple docstring"""







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()

lowerCAmelCase_	:    List[str]            =      {
    '''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       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase=False	,      lowerCAmelCase=True		):







       '''simple docstring'''
       if model_type not in MODEL_CLASSES:
              raise ValueError(F'''Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()		)}.'''		)

       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase	    =			MODEL_CLASSES[model_type]

       # Initialise TF model
       if config_file in aws_config_map:
              UpperCAmelCase	    =			cached_file(lowerCAmelCase	,      lowerCAmelCase	,      force_download=not use_cached_models		)
       UpperCAmelCase	    =			config_class.from_json_file(lowerCAmelCase		)
       UpperCAmelCase	    =			True
       UpperCAmelCase	    =			True
       print(F'''Building TensorFlow model from configuration: {config}'''		)
       UpperCAmelCase	    =			model_class(lowerCAmelCase		)

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

       if compare_with_pt_model:
              UpperCAmelCase	    =			tf_model(tf_model.dummy_inputs	,      training=lowerCAmelCase		)  # build the network

              UpperCAmelCase	    =			torch.load(lowerCAmelCase	,      map_location="""cpu"""		)
              UpperCAmelCase	    =			pt_model_class.from_pretrained(
                  pretrained_model_name_or_path=lowerCAmelCase	,      config=lowerCAmelCase	,      state_dict=lowerCAmelCase		)

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

              UpperCAmelCase	    =			pto[0].numpy()
              UpperCAmelCase	    =			tfo[0].numpy()
              UpperCAmelCase	    =			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(lowerCAmelCase	,      save_format="""h5"""		)






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase=None	,      lowerCAmelCase=None	,      lowerCAmelCase=False	,      lowerCAmelCase=False	,      lowerCAmelCase=False	,      lowerCAmelCase=False	,      ):







       '''simple docstring'''
       if args_model_type is None:
              UpperCAmelCase	    =			list(MODEL_CLASSES.keys()		)
       else:
              UpperCAmelCase	    =			[args_model_type]

       for j, model_type in enumerate(lowerCAmelCase	,      start=1		):
              print("""=""" * 100		)
              print(F''' Converting model type {j}/{len(lowerCAmelCase		)}: {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()		)}.'''		)

              UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase	    =			MODEL_CLASSES[model_type]

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

              for i, (model_shortcut_name, config_shortcut_name) in enumerate(
                  zip(lowerCAmelCase	,      lowerCAmelCase		)	,      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
                            UpperCAmelCase	    =			model_shortcut_name
                     elif only_convert_finetuned_models:
                            print(F'''    Skipping not finetuned checkpoint {model_shortcut_name}'''		)
                            continue
                     print(
                         F'''    Converting checkpoint {i}/{len(lowerCAmelCase		)}: {model_shortcut_name} - model_type {model_type}'''		)
                     print("""-""" * 100		)

                     if config_shortcut_name in aws_config_map:
                            UpperCAmelCase	    =			cached_file(lowerCAmelCase	,      lowerCAmelCase	,      force_download=not use_cached_models		)
                     else:
                            UpperCAmelCase	    =			config_shortcut_name

                     if model_shortcut_name in aws_model_maps:
                            UpperCAmelCase	    =			cached_file(lowerCAmelCase	,      lowerCAmelCase	,      force_download=not use_cached_models		)
                     else:
                            UpperCAmelCase	    =			model_shortcut_name

                     if os.path.isfile(lowerCAmelCase		):
                            UpperCAmelCase	    =			"""converted_model"""

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


if __name__ == "__main__":
 lowerCAmelCase_	:    str            =      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.''')
 lowerCAmelCase_	:    Optional[int]            =      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,
 )
