





'''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()

lowercase__						:			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__				(				lowercase					:     str,					lowercase					:     Tuple,					lowercase					:     Any,					lowercase					:     Any,					lowercase					:     List[Any]=False,					lowercase					:     str=True      )		->		Optional[int]:





     """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(snake_case__,					snake_case__,					force_download=not use_cached_models      )
     _UpperCamelCase				    =       config_class.from_json_file(snake_case__      )
     _UpperCamelCase				    =       True
     _UpperCamelCase				    =       True
     print(F"""Building TensorFlow model from configuration: {config}"""      )
     _UpperCamelCase				    =       model_class(snake_case__      )

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

     if compare_with_pt_model:
          _UpperCamelCase				    =       tf_model(tf_model.dummy_inputs,					training=snake_case__      )  # build the network

          _UpperCamelCase				    =       torch.load(snake_case__,					map_location='''cpu'''      )
          _UpperCamelCase				    =       pt_model_class.from_pretrained(
              pretrained_model_name_or_path=snake_case__,					config=snake_case__,					state_dict=snake_case__      )

          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(snake_case__,					save_format='''h5'''      )







def 					a__				(				lowercase					:     Tuple,					lowercase					:     Union[str, Any],					lowercase					:     List[Any]=None,					lowercase					:     Any=None,					lowercase					:     str=False,					lowercase					:     List[Any]=False,					lowercase					:     Optional[Any]=False,					lowercase					:     Any=False,					)		->		List[str]:





     """simple docstring"""

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

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

               if config_shortcut_name in aws_config_map:
                    _UpperCamelCase				    =       cached_file(snake_case__,					snake_case__,					force_download=not use_cached_models      )
               else:
                    _UpperCamelCase				    =       config_shortcut_name

               if model_shortcut_name in aws_model_maps:
                    _UpperCamelCase				    =       cached_file(snake_case__,					snake_case__,					force_download=not use_cached_models      )
               else:
                    _UpperCamelCase				    =       model_shortcut_name

               if os.path.isfile(snake_case__      ):
                    _UpperCamelCase				    =       '''converted_model'''

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


if __name__ == "__main__":
   lowercase__						:			Optional[Any]       =				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.')
   lowercase__						:			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,
   )









"""simple docstring"""




import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
					import torch






@is_pipeline_test
class     __magic_name__							(       unittest.TestCase				):






				'''simple docstring'''
				__UpperCamelCase			  =						MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
				__UpperCamelCase			  =						TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					TextaTextGenerationPipeline(model=_a		, tokenizer=_a )
							return generator, ["Something to write", "Something else"]





				def    _lowerCAmelCase				(       self		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					generator("""Something there""" )
							self.assertEqual(_a		, [{"""generated_text""": ANY(_a )}] )
							# These are encoder decoder, they don't just append to incoming string
							self.assertFalse(outputs[0]["""generated_text"""].startswith("""Something there""" ) )

							lowerCamelCase			      =					generator(["""This is great !""", """Something else"""]		, num_return_sequences=2		, do_sample=_a )
							self.assertEqual(
							    _a		, [
							        [{"""generated_text""": ANY(_a )}, {"""generated_text""": ANY(_a )}],
							        [{"""generated_text""": ANY(_a )}, {"""generated_text""": ANY(_a )}],
							    ]		, )

							lowerCamelCase			      =					generator(
							    ["""This is great !""", """Something else"""]		, num_return_sequences=2		, batch_size=2		, do_sample=_a )
							self.assertEqual(
							    _a		, [
							        [{"""generated_text""": ANY(_a )}, {"""generated_text""": ANY(_a )}],
							        [{"""generated_text""": ANY(_a )}, {"""generated_text""": ANY(_a )}],
							    ]		, )

							with self.assertRaises(_a ):
										generator(4 )





				@require_torch
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					pipeline("""text2text-generation"""		, model="""patrickvonplaten/t5-tiny-random"""		, framework="""pt""" )
							# do_sample=False necessary for reproducibility
							lowerCamelCase			      =					generator("""Something there"""		, do_sample=_a )
							self.assertEqual(_a		, [{"""generated_text""": """"""}] )

							lowerCamelCase			      =					3
							lowerCamelCase			      =					generator(
							    """Something there"""		, num_return_sequences=_a		, num_beams=_a		, )
							lowerCamelCase			      =					[
							    {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide Beide"""},
							    {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide"""},
							    {"""generated_text""": """"""},
							]
							self.assertEqual(_a		, _a )

							lowerCamelCase			      =					generator("""This is a test"""		, do_sample=_a		, num_return_sequences=2		, return_tensors=_a )
							self.assertEqual(
							    _a		, [
							        {"""generated_token_ids""": ANY(torch.Tensor )},
							        {"""generated_token_ids""": ANY(torch.Tensor )},
							    ]		, )
							lowerCamelCase			      =					generator.model.config.eos_token_id
							lowerCamelCase			      =					"""<pad>"""
							lowerCamelCase			      =					generator(
							    ["""This is a test""", """This is a second test"""]		, do_sample=_a		, num_return_sequences=2		, batch_size=2		, return_tensors=_a		, )
							self.assertEqual(
							    _a		, [
							        [
							            {"""generated_token_ids""": ANY(torch.Tensor )},
							            {"""generated_token_ids""": ANY(torch.Tensor )},
							        ],
							        [
							            {"""generated_token_ids""": ANY(torch.Tensor )},
							            {"""generated_token_ids""": ANY(torch.Tensor )},
							        ],
							    ]		, )





				@require_tf
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					pipeline("""text2text-generation"""		, model="""patrickvonplaten/t5-tiny-random"""		, framework="""tf""" )
							# do_sample=False necessary for reproducibility
							lowerCamelCase			      =					generator("""Something there"""		, do_sample=_a )
							self.assertEqual(_a		, [{"""generated_text""": """"""}] )



