


'''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				:  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 _SCREAMING_SNAKE_CASE (A    ,					A    ,					A    ,					A    ,					A=False    ,					A=True			)			->			List[Any]:






       """simple docstring"""







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

       lowercase__     ,lowercase__     ,lowercase__     ,lowercase__      =					MODEL_CLASSES[model_type]

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

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

       if compare_with_pt_model:
              lowercase__      =					tf_model(tf_model.dummy_inputs    ,					training=A			)  # build the network

              lowercase__      =					torch.load(A    ,					map_location='''cpu'''			)
              lowercase__      =					pt_model_class.from_pretrained(
                  pretrained_model_name_or_path=A    ,					config=A    ,					state_dict=A			)

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

              lowercase__      =					pto[0].numpy()
              lowercase__      =					tfo[0].numpy()
              lowercase__      =					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 _SCREAMING_SNAKE_CASE (A    ,					A    ,					A=None    ,					A=None    ,					A=False    ,					A=False    ,					A=False    ,					A=False    ,					)			->			List[str]:






       """simple docstring"""







       if args_model_type is None:
              lowercase__      =					list(MODEL_CLASSES.keys()			)
       else:
              lowercase__      =					[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()			)}."			)

              lowercase__     ,lowercase__     ,lowercase__     ,lowercase__     ,lowercase__      =					MODEL_CLASSES[model_type]

              if model_shortcut_names_or_path is None:
                     lowercase__      =					list(aws_model_maps.keys()			)
              if config_shortcut_names_or_path is None:
                     lowercase__      =					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
                            lowercase__      =					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:
                            lowercase__      =					cached_file(A    ,					A    ,					force_download=not use_cached_models			)
                     else:
                            lowercase__      =					config_shortcut_name

                     if model_shortcut_name in aws_model_maps:
                            lowercase__      =					cached_file(A    ,					A    ,					force_download=not use_cached_models			)
                     else:
                            lowercase__      =					model_shortcut_name

                     if os.path.isfile(A			):
                            lowercase__      =					'''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__":
 lowerCamelCase				:  List[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.')
 lowerCamelCase				:  Optional[Any]    		=      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 EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
 from transformers.models.esm.modeling_esm import (
     ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
     EsmEmbeddings,
     create_position_ids_from_input_ids,
 )
class     __lowerCAmelCase		:





      '''simple docstring'''






      def __init__(self       :       Optional[int]  ,		UpperCamelCase       :       Tuple  ,		UpperCamelCase       :       str=13  ,		UpperCamelCase       :       Optional[int]=7  ,		UpperCamelCase       :       Union[str, Any]=False  ,		UpperCamelCase       :       Dict=True  ,		UpperCamelCase       :       Optional[int]=False  ,		UpperCamelCase       :       Optional[Any]=True  ,		UpperCamelCase       :       Union[str, Any]=33  ,		UpperCamelCase       :       List[Any]=32  ,		UpperCamelCase       :       Dict=5  ,		UpperCamelCase       :       str=4  ,		UpperCamelCase       :       str=37  ,		UpperCamelCase       :       List[Any]="gelu"  ,		UpperCamelCase       :       List[str]=0.1  ,		UpperCamelCase       :       Dict=0.1  ,		UpperCamelCase       :       Dict=512  ,		UpperCamelCase       :       Any=16  ,		UpperCamelCase       :       List[Any]=2  ,		UpperCamelCase       :       List[Any]=0.02  ,		UpperCamelCase       :       Tuple=3  ,		UpperCamelCase       :       Optional[Any]=4  ,		UpperCamelCase       :       int=None  ,		):

             '''simple docstring'''







             lowercase__      =					parent
             lowercase__      =					batch_size
             lowercase__      =					seq_length
             lowercase__      =					is_training
             lowercase__      =					use_input_mask
             lowercase__      =					use_token_type_ids
             lowercase__      =					use_labels
             lowercase__      =					vocab_size
             lowercase__      =					hidden_size
             lowercase__      =					num_hidden_layers
             lowercase__      =					num_attention_heads
             lowercase__      =					intermediate_size
             lowercase__      =					hidden_act
             lowercase__      =					hidden_dropout_prob
             lowercase__      =					attention_probs_dropout_prob
             lowercase__      =					max_position_embeddings
             lowercase__      =					type_vocab_size
             lowercase__      =					type_sequence_label_size
             lowercase__      =					initializer_range
             lowercase__      =					num_labels
             lowercase__      =					num_choices
             lowercase__      =					scope



      def  UpperCamelCase__						(self       :       str					):

             '''simple docstring'''







             lowercase__      =					ids_tensor([self.batch_size, self.seq_length]  ,		self.vocab_size					)

             lowercase__      =					None
             if self.use_input_mask:
                    lowercase__      =					random_attention_mask([self.batch_size, self.seq_length]					)

             lowercase__      =					None
             lowercase__      =					None
             lowercase__      =					None
             if self.use_labels:
                    lowercase__      =					ids_tensor([self.batch_size]  ,		self.type_sequence_label_size					)
                    lowercase__      =					ids_tensor([self.batch_size, self.seq_length]  ,		self.num_labels					)
                    lowercase__      =					ids_tensor([self.batch_size]  ,		self.num_choices					)

             lowercase__      =					self.get_config()

             return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels



      def  UpperCamelCase__						(self       :       Optional[Any]					):

             '''simple docstring'''







             return EsmConfig(
                 vocab_size=self.vocab_size  ,		hidden_size=self.hidden_size  ,		pad_token_id=1  ,		num_hidden_layers=self.num_hidden_layers  ,		num_attention_heads=self.num_attention_heads  ,		intermediate_size=self.intermediate_size  ,		hidden_act=self.hidden_act  ,		hidden_dropout_prob=self.hidden_dropout_prob  ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,		max_position_embeddings=self.max_position_embeddings  ,		type_vocab_size=self.type_vocab_size  ,		initializer_range=self.initializer_range  ,		)



      def  UpperCamelCase__						(self       :       Optional[int]  ,		UpperCamelCase       :       Union[str, Any]  ,		UpperCamelCase       :       int  ,		UpperCamelCase       :       List[Any]  ,		UpperCamelCase       :       str  ,		UpperCamelCase       :       str  ,		UpperCamelCase       :       Dict					):

             '''simple docstring'''







             lowercase__      =					EsmModel(config=UpperCamelCase					)
             model.to(UpperCamelCase					)
             model.eval()
             lowercase__      =					model(UpperCamelCase  ,		attention_mask=UpperCamelCase					)
             lowercase__      =					model(UpperCamelCase					)
             lowercase__      =					model(UpperCamelCase					)

             self.parent.assertEqual(result.last_hidden_state.shape  ,		(self.batch_size, self.seq_length, self.hidden_size)					)
             self.parent.assertEqual(result.pooler_output.shape  ,		(self.batch_size, self.hidden_size)					)



      def  UpperCamelCase__						(self       :       str  ,		UpperCamelCase       :       Any  ,		UpperCamelCase       :       Tuple  ,		UpperCamelCase       :       Any  ,		UpperCamelCase       :       List[Any]  ,		UpperCamelCase       :       Optional[Any]  ,		UpperCamelCase       :       Optional[int]					):

             '''simple docstring'''







             lowercase__      =					EsmForMaskedLM(config=UpperCamelCase					)
             model.to(UpperCamelCase					)
             model.eval()
             lowercase__      =					model(UpperCamelCase  ,		attention_mask=UpperCamelCase  ,		labels=UpperCamelCase					)
             self.parent.assertEqual(result.logits.shape  ,		(self.batch_size, self.seq_length, self.vocab_size)					)



      def  UpperCamelCase__						(self       :       Any  ,		UpperCamelCase       :       str  ,		UpperCamelCase       :       List[Any]  ,		UpperCamelCase       :       Tuple  ,		UpperCamelCase       :       int  ,		UpperCamelCase       :       Union[str, Any]  ,		UpperCamelCase       :       List[Any]					):

             '''simple docstring'''







             lowercase__      =					self.num_labels
             lowercase__      =					EsmForTokenClassification(config=UpperCamelCase					)
             model.to(UpperCamelCase					)
             model.eval()
             lowercase__      =					model(UpperCamelCase  ,		attention_mask=UpperCamelCase  ,		labels=UpperCamelCase					)
             self.parent.assertEqual(result.logits.shape  ,		(self.batch_size, self.seq_length, self.num_labels)					)






      def  UpperCamelCase__						(self       :       Optional[Any]					):

             '''simple docstring'''







             lowercase__      =					self.prepare_config_and_inputs()
             (
                 (
                 lowercase__
             )     ,(
                 lowercase__
             )     ,(
                 lowercase__
             )     ,(
                 lowercase__
             )     ,(
                 lowercase__
             )     ,(
                 lowercase__
             )     ,
             )      =					config_and_inputs
             lowercase__      =					{'''input_ids''': input_ids, '''attention_mask''': input_mask}
             return config, inputs_dict






@require_torch
class     __lowerCAmelCase		(lowercase_    ,	lowercase_    ,	unittest.TestCase	):





      '''simple docstring'''






      lowerCAmelCase__   :	Dict       =				False

      lowerCAmelCase__   :	Optional[Any]       =				(
          (
              EsmForMaskedLM,
              EsmModel,
              EsmForSequenceClassification,
              EsmForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      lowerCAmelCase__   :	Optional[int]       =				()
      lowerCAmelCase__   :	Any       =				(
          {
              """feature-extraction""": EsmModel,
              """fill-mask""": EsmForMaskedLM,
              """text-classification""": EsmForSequenceClassification,
              """token-classification""": EsmForTokenClassification,
              """zero-shot""": EsmForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      lowerCAmelCase__   :	Any       =				True



      def  UpperCamelCase__						(self       :       List[Any]					):

             '''simple docstring'''







             lowercase__      =					EsmModelTester(self					)
             lowercase__      =					ConfigTester(self  ,		config_class=UpperCamelCase  ,		hidden_size=37					)



      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             self.config_tester.run_common_tests()



      def  UpperCamelCase__						(self       :       Optional[Any]					):

             '''simple docstring'''







             lowercase__      =					self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*UpperCamelCase					)



      def  UpperCamelCase__						(self       :       Any					):

             '''simple docstring'''







             lowercase__      =					self.model_tester.prepare_config_and_inputs()
             for type in ["absolute", "relative_key", "relative_key_query"]:
                    lowercase__      =					type
                    self.model_tester.create_and_check_model(*UpperCamelCase					)



      def  UpperCamelCase__						(self       :       Optional[int]					):

             '''simple docstring'''







             lowercase__      =					self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase					)



      def  UpperCamelCase__						(self       :       List[Any]					):

             '''simple docstring'''







             lowercase__      =					self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_token_classification(*UpperCamelCase					)



      @slow
      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    lowercase__      =					EsmModel.from_pretrained(UpperCamelCase					)
                    self.assertIsNotNone(UpperCamelCase					)



      def  UpperCamelCase__						(self       :       Optional[int]					):

             '''simple docstring'''







             lowercase__      =					self.model_tester.prepare_config_and_inputs()[0]
             lowercase__      =					EsmEmbeddings(config=UpperCamelCase					)

             lowercase__      =					torch.as_tensor([[12, 31, 13, model.padding_idx]]					)
             lowercase__      =					torch.as_tensor(
                 [
                     [
                         0 + model.padding_idx + 1,
                         1 + model.padding_idx + 1,
                         2 + model.padding_idx + 1,
                         model.padding_idx,
                     ]
                 ]					)
             lowercase__      =					create_position_ids_from_input_ids(UpperCamelCase  ,		model.padding_idx					)
             self.assertEqual(position_ids.shape  ,		expected_positions.shape					)
             self.assertTrue(torch.all(torch.eq(UpperCamelCase  ,		UpperCamelCase					)					)					)



      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             lowercase__      =					self.model_tester.prepare_config_and_inputs()[0]
             lowercase__      =					EsmEmbeddings(config=UpperCamelCase					)

             lowercase__      =					torch.empty(2  ,		4  ,		30					)
             lowercase__      =					[
                 0 + embeddings.padding_idx + 1,
                 1 + embeddings.padding_idx + 1,
                 2 + embeddings.padding_idx + 1,
                 3 + embeddings.padding_idx + 1,
             ]
             lowercase__      =					torch.as_tensor([expected_single_positions, expected_single_positions]					)
             lowercase__      =					embeddings.create_position_ids_from_inputs_embeds(UpperCamelCase					)
             self.assertEqual(position_ids.shape  ,		expected_positions.shape					)
             self.assertTrue(torch.all(torch.eq(UpperCamelCase  ,		UpperCamelCase					)					)					)



      @unittest.skip('''Esm does not support embedding resizing'''					)
      def  UpperCamelCase__						(self       :       Optional[int]					):

             '''simple docstring'''







             pass



      @unittest.skip('''Esm does not support embedding resizing'''					)
      def  UpperCamelCase__						(self       :       Optional[Any]					):

             '''simple docstring'''







             pass






      @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''					)
      def  UpperCamelCase__						(self       :       Optional[int]					):

             '''simple docstring'''







             pass






@require_torch
class     __lowerCAmelCase		(lowercase_	):





      '''simple docstring'''






      @slow
      def  UpperCamelCase__						(self       :       Optional[Any]					):

             '''simple docstring'''







             with torch.no_grad():
                    lowercase__      =					EsmForMaskedLM.from_pretrained('''facebook/esm2_t6_8M_UR50D'''					)
                    model.eval()
                    lowercase__      =					torch.tensor([[0, 1, 2, 3, 4, 5]]					)
                    lowercase__      =					model(UpperCamelCase					)[0]

                    lowercase__      =					33

                    lowercase__      =					torch.Size((1, 6, vocab_size)					)
                    self.assertEqual(output.shape  ,		UpperCamelCase					)

                    lowercase__      =					torch.tensor(
                        [[[8.92_15, -10.58_98, -6.46_71], [-6.39_67, -13.91_14, -1.12_12], [-7.78_12, -13.95_16, -3.74_06]]]					)
                    self.assertTrue(torch.allclose(output[:, :3, :3]  ,		UpperCamelCase  ,		atol=1E-4					)					)






      @slow
      def  UpperCamelCase__						(self       :       Optional[Any]					):

             '''simple docstring'''







             with torch.no_grad():
                    lowercase__      =					EsmModel.from_pretrained('''facebook/esm2_t6_8M_UR50D'''					)
                    model.eval()

                    lowercase__      =					torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]					)
                    lowercase__      =					model(UpperCamelCase					)[0]
                    # compare the actual values for a slice.
                    lowercase__      =					torch.tensor(
                        [[[0.14_44, 0.54_13, 0.32_48], [0.30_34, 0.00_53, 0.31_08], [0.32_28, -0.24_99, 0.34_15]]]					)
                    self.assertTrue(torch.allclose(output[:, :3, :3]  ,		UpperCamelCase  ,		atol=1E-4					)					)




