
import argparse
import os

import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn

from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(nlp.__version__) != version.parse("""0.8.3"""):
   raise Exception("""requires gluonnlp == 0.8.3""")

if version.parse(mx.__version__) != version.parse("""1.5.0"""):
   raise Exception("""requires mxnet == 1.5.0""")

logging.set_verbosity_info()
__lowerCamelCase							=       logging.get_logger(__name__)

__lowerCamelCase							=       """The Nymphenburg Palace is a beautiful palace in Munich!"""


def 			UpperCamelCase					(    __lowerCamelCase     : Union[str, Any]				,					__lowerCamelCase     : Any    ):
       snake_case       :   Tuple							=      {
           '''attention_cell''': '''multi_head''',
           '''num_layers''': 4,
           '''units''': 1024,
           '''hidden_size''': 768,
           '''max_length''': 512,
           '''num_heads''': 8,
           '''scaled''': True,
           '''dropout''': 0.1,
           '''use_residual''': True,
           '''embed_size''': 1024,
           '''embed_dropout''': 0.1,
           '''word_embed''': None,
           '''layer_norm_eps''': 1E-5,
           '''token_type_vocab_size''': 2,
       }

       snake_case       :   Optional[Any]							=      bort_4_8_768_1024_hparams

       # Let's construct the original Bort model here
       # Taken from official BERT implementation, see:
       # https://github.com/alexa/bort/blob/master/bort/bort.py
       snake_case       :   Tuple							=      BERTEncoder(
           attention_cell=predefined_args["attention_cell"]				,					num_layers=predefined_args["num_layers"]				,					units=predefined_args["units"]				,					hidden_size=predefined_args["hidden_size"]				,					max_length=predefined_args["max_length"]				,					num_heads=predefined_args["num_heads"]				,					scaled=predefined_args["scaled"]				,					dropout=predefined_args["dropout"]				,					output_attention=__lowerCamelCase				,					output_all_encodings=__lowerCamelCase				,					use_residual=predefined_args["use_residual"]				,					activation=predefined_args.get("activation"				,					"gelu"    )				,					layer_norm_eps=predefined_args.get("layer_norm_eps"				,					__lowerCamelCase    )				,					)

       # Vocab information needs to be fetched first
       # It's the same as RoBERTa, so RobertaTokenizer can be used later
       snake_case       :   List[str]							=      '''openwebtext_ccnews_stories_books_cased'''

       # Specify download folder to Gluonnlp's vocab
       snake_case       :   Union[str, Any]							=      os.path.join(get_home_dir()				,					"models"    )
       snake_case       :   List[str]							=      _load_vocab(__lowerCamelCase				,					__lowerCamelCase				,					__lowerCamelCase				,					cls=__lowerCamelCase    )

       snake_case       :   str							=      nlp.model.BERTModel(
           __lowerCamelCase				,					len(__lowerCamelCase    )				,					units=predefined_args["units"]				,					embed_size=predefined_args["embed_size"]				,					embed_dropout=predefined_args["embed_dropout"]				,					word_embed=predefined_args["word_embed"]				,					use_pooler=__lowerCamelCase				,					use_token_type_embed=__lowerCamelCase				,					token_type_vocab_size=predefined_args["token_type_vocab_size"]				,					use_classifier=__lowerCamelCase				,					use_decoder=__lowerCamelCase				,					)

       original_bort.load_parameters(__lowerCamelCase				,					cast_dtype=__lowerCamelCase				,					ignore_extra=__lowerCamelCase    )
       snake_case       :   List[Any]							=      original_bort._collect_params_with_prefix()

       # Build our config 🤗
       snake_case       :   Optional[int]							=      {
           '''architectures''': ['''BertForMaskedLM'''],
           '''attention_probs_dropout_prob''': predefined_args['''dropout'''],
           '''hidden_act''': '''gelu''',
           '''hidden_dropout_prob''': predefined_args['''dropout'''],
           '''hidden_size''': predefined_args['''embed_size'''],
           '''initializer_range''': 0.02,
           '''intermediate_size''': predefined_args['''hidden_size'''],
           '''layer_norm_eps''': predefined_args['''layer_norm_eps'''],
           '''max_position_embeddings''': predefined_args['''max_length'''],
           '''model_type''': '''bort''',
           '''num_attention_heads''': predefined_args['''num_heads'''],
           '''num_hidden_layers''': predefined_args['''num_layers'''],
           '''pad_token_id''': 1,  # 2 = BERT, 1 = RoBERTa
           '''type_vocab_size''': 1,  # 2 = BERT, 1 = RoBERTa
           '''vocab_size''': len(__lowerCamelCase    ),
       }

       snake_case       :   str							=      BertConfig.from_dict(__lowerCamelCase    )
       snake_case       :   str							=      BertForMaskedLM(__lowerCamelCase    )
       hf_bort_model.eval()

       # Parameter mapping table (Gluonnlp to Transformers)
       # * denotes layer index
       #
       # | Gluon Parameter                                                | Transformers Parameter
       # | -------------------------------------------------------------- | ----------------------
       # | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
       # | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
       # | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
       # | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
       # | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
       # | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
       # | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
       # | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
       # | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
       # | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
       # | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
       # | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
       # | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
       # | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
       # | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
       # | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
       # | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
       # | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
       # | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
       # | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`

       # Helper function to convert MXNET Arrays to PyTorch
       def to_torch(__lowerCamelCase     : str    ) -> nn.Parameter:
              return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy()    )    )

       # Check param shapes and map new HF param back
       def check_and_map_params(__lowerCamelCase     : str				,					__lowerCamelCase     : Tuple    ):
              snake_case       :   str							=      hf_param.shape

              snake_case       :   int							=      to_torch(params[gluon_param]    )
              snake_case       :   Optional[Any]							=      gluon_param.shape

              assert (
                  shape_hf == shape_gluon
              ), f"""The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers"""

              return gluon_param

       snake_case       :   str							=      check_and_map_params(
           hf_bort_model.bert.embeddings.word_embeddings.weight				,					"word_embed.0.weight"    )
       snake_case       :   int							=      check_and_map_params(
           hf_bort_model.bert.embeddings.position_embeddings.weight				,					"encoder.position_weight"    )
       snake_case       :   int							=      check_and_map_params(
           hf_bort_model.bert.embeddings.LayerNorm.bias				,					"encoder.layer_norm.beta"    )
       snake_case       :   Optional[int]							=      check_and_map_params(
           hf_bort_model.bert.embeddings.LayerNorm.weight				,					"encoder.layer_norm.gamma"    )

       # Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
       snake_case       :   int							=      torch.zeros_like(
           hf_bort_model.bert.embeddings.token_type_embeddings.weight.data    )

       for i in range(hf_bort_config.num_hidden_layers    ):
              snake_case       :   BertLayer							=      hf_bort_model.bert.encoder.layer[i]

              # self attention
              snake_case       :   BertSelfAttention							=      layer.attention.self

              snake_case       :   Any							=      check_and_map_params(
                  self_attn.key.bias.data				,					f"""encoder.transformer_cells.{i}.attention_cell.proj_key.bias"""    )

              snake_case       :   int							=      check_and_map_params(
                  self_attn.key.weight.data				,					f"""encoder.transformer_cells.{i}.attention_cell.proj_key.weight"""    )
              snake_case       :   Optional[int]							=      check_and_map_params(
                  self_attn.query.bias.data				,					f"""encoder.transformer_cells.{i}.attention_cell.proj_query.bias"""    )
              snake_case       :   int							=      check_and_map_params(
                  self_attn.query.weight.data				,					f"""encoder.transformer_cells.{i}.attention_cell.proj_query.weight"""    )
              snake_case       :   Optional[int]							=      check_and_map_params(
                  self_attn.value.bias.data				,					f"""encoder.transformer_cells.{i}.attention_cell.proj_value.bias"""    )
              snake_case       :   Optional[Any]							=      check_and_map_params(
                  self_attn.value.weight.data				,					f"""encoder.transformer_cells.{i}.attention_cell.proj_value.weight"""    )

              # self attention output
              snake_case       :   BertSelfOutput							=      layer.attention.output

              snake_case       :   Union[str, Any]							=      check_and_map_params(
                  self_output.dense.bias				,					f"""encoder.transformer_cells.{i}.proj.bias"""    )
              snake_case       :   List[str]							=      check_and_map_params(
                  self_output.dense.weight				,					f"""encoder.transformer_cells.{i}.proj.weight"""    )
              snake_case       :   List[str]							=      check_and_map_params(
                  self_output.LayerNorm.bias				,					f"""encoder.transformer_cells.{i}.layer_norm.beta"""    )
              snake_case       :   Any							=      check_and_map_params(
                  self_output.LayerNorm.weight				,					f"""encoder.transformer_cells.{i}.layer_norm.gamma"""    )

              # intermediate
              snake_case       :   BertIntermediate							=      layer.intermediate

              snake_case       :   Dict							=      check_and_map_params(
                  intermediate.dense.bias				,					f"""encoder.transformer_cells.{i}.ffn.ffn_1.bias"""    )
              snake_case       :   Union[str, Any]							=      check_and_map_params(
                  intermediate.dense.weight				,					f"""encoder.transformer_cells.{i}.ffn.ffn_1.weight"""    )

              # output
              snake_case       :   BertOutput							=      layer.output

              snake_case       :   str							=      check_and_map_params(
                  bert_output.dense.bias				,					f"""encoder.transformer_cells.{i}.ffn.ffn_2.bias"""    )
              snake_case       :   Any							=      check_and_map_params(
                  bert_output.dense.weight				,					f"""encoder.transformer_cells.{i}.ffn.ffn_2.weight"""    )
              snake_case       :   int							=      check_and_map_params(
                  bert_output.LayerNorm.bias				,					f"""encoder.transformer_cells.{i}.ffn.layer_norm.beta"""    )
              snake_case       :   Any							=      check_and_map_params(
                  bert_output.LayerNorm.weight				,					f"""encoder.transformer_cells.{i}.ffn.layer_norm.gamma"""    )

       # Save space and energy 🎄
       hf_bort_model.half()

       # Compare output of both models
       snake_case       :   str							=      RobertaTokenizer.from_pretrained("roberta-base"    )

       snake_case       :   Tuple							=      tokenizer.encode_plus(__lowerCamelCase    )['''input_ids''']

       # Get gluon output
       snake_case       :   List[str]							=      mx.nd.array([input_ids]    )
       snake_case       :   Union[str, Any]							=      original_bort(inputs=__lowerCamelCase				,					token_types=[]    )

       # Get Transformer output (save and reload model again)
       hf_bort_model.save_pretrained(__lowerCamelCase    )
       snake_case       :   List[str]							=      BertModel.from_pretrained(__lowerCamelCase    )
       hf_bort_model.eval()

       snake_case       :   Tuple							=      tokenizer.encode_plus(__lowerCamelCase				,					return_tensors="pt"    )
       snake_case       :   Dict							=      hf_bort_model(**__lowerCamelCase    )[0]

       snake_case       :   List[str]							=      output_gluon[0].asnumpy()
       snake_case       :   Optional[Any]							=      output_hf[0].detach().numpy()

       snake_case       :   List[str]							=      np.max(np.abs(hf_layer - gluon_layer    )    ).item()
       snake_case       :   List[str]							=      np.allclose(__lowerCamelCase				,					__lowerCamelCase				,					atol=1E-3    )

       if success:
              print("✔️ Both model do output the same tensors"    )
       else:
              print("❌ Both model do **NOT** output the same tensors"    )
              print("Absolute difference is:"				,					__lowerCamelCase    )


if __name__ == "__main__":
   __lowerCamelCase							=       argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--bort_checkpoint_path""", default=None, type=str, required=True, help="""Path the official Bort params file."""
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
   )
   __lowerCamelCase							=       parser.parse_args()
   convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)










"""simple docstring"""







from __future__ import annotations

import unittest

from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow


if is_tf_available():
							from transformers import (
							    AutoConfig,
							    BertConfig,
							    GPTaConfig,
							    TaConfig,
							    TFAutoModel,
							    TFAutoModelForCausalLM,
							    TFAutoModelForMaskedLM,
							    TFAutoModelForPreTraining,
							    TFAutoModelForQuestionAnswering,
							    TFAutoModelForSeqaSeqLM,
							    TFAutoModelForSequenceClassification,
							    TFAutoModelWithLMHead,
							    TFBertForMaskedLM,
							    TFBertForPreTraining,
							    TFBertForQuestionAnswering,
							    TFBertForSequenceClassification,
							    TFBertModel,
							    TFGPTaLMHeadModel,
							    TFRobertaForMaskedLM,
							    TFTaForConditionalGeneration,
							)
							from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
							from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
							from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST

if is_torch_available():
							from transformers import (
							    AutoModel,
							    AutoModelForCausalLM,
							    AutoModelForMaskedLM,
							    AutoModelForPreTraining,
							    AutoModelForQuestionAnswering,
							    AutoModelForSeqaSeqLM,
							    AutoModelForSequenceClassification,
							    AutoModelWithLMHead,
							    BertForMaskedLM,
							    BertForPreTraining,
							    BertForQuestionAnswering,
							    BertForSequenceClassification,
							    BertModel,
							    GPTaLMHeadModel,
							    RobertaForMaskedLM,
							    TaForConditionalGeneration,
							)
@is_pt_tf_cross_test
class        __A       (	unittest.TestCase    ):







		'''simple docstring'''





		@slow
		def 					UpperCAmelCase    (	self						:  Optional[Any]						)					->		Union[str, Any]:






				"""simple docstring"""



				for model_name in ["bert-base-uncased"]:
						lowercase__   :    Tuple				=			AutoConfig.from_pretrained(_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    Dict				=			TFAutoModel.from_pretrained(_snake_case			,from_pt=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    List[str]				=			AutoModel.from_pretrained(_snake_case			,from_tf=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)







		@slow
		def 					UpperCAmelCase    (	self						:  Union[str, Any]						)					->		Tuple:






				"""simple docstring"""



				for model_name in ["bert-base-uncased"]:
						lowercase__   :    Dict				=			AutoConfig.from_pretrained(_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    str				=			TFAutoModelForPreTraining.from_pretrained(_snake_case			,from_pt=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    Optional[Any]				=			AutoModelForPreTraining.from_pretrained(_snake_case			,from_tf=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)







		@slow
		def 					UpperCAmelCase    (	self						:  Tuple						)					->		Dict:






				"""simple docstring"""



				for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						lowercase__   :    Any				=			AutoConfig.from_pretrained(_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    List[str]				=			TFAutoModelForCausalLM.from_pretrained(_snake_case			,from_pt=_snake_case						)
						lowercase__		,			lowercase__   :    Optional[Any]				=			TFAutoModelForCausalLM.from_pretrained(
						    _snake_case			,output_loading_info=_snake_case			,from_pt=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    Union[str, Any]				=			AutoModelForCausalLM.from_pretrained(_snake_case			,from_tf=_snake_case						)
						lowercase__		,			lowercase__   :    Optional[Any]				=			AutoModelForCausalLM.from_pretrained(
						    _snake_case			,output_loading_info=_snake_case			,from_tf=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)







		@slow
		def 					UpperCAmelCase    (	self						:  Any						)					->		Tuple:






				"""simple docstring"""



				for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						lowercase__   :    Any				=			AutoConfig.from_pretrained(_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    Optional[Any]				=			TFAutoModelWithLMHead.from_pretrained(_snake_case			,from_pt=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    Any				=			AutoModelWithLMHead.from_pretrained(_snake_case			,from_tf=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)







		@slow
		def 					UpperCAmelCase    (	self						:  List[str]						)					->		Optional[Any]:






				"""simple docstring"""



				for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						lowercase__   :    str				=			AutoConfig.from_pretrained(_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    Union[str, Any]				=			TFAutoModelForMaskedLM.from_pretrained(_snake_case			,from_pt=_snake_case						)
						lowercase__		,			lowercase__   :    str				=			TFAutoModelForMaskedLM.from_pretrained(
						    _snake_case			,output_loading_info=_snake_case			,from_pt=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    List[str]				=			AutoModelForMaskedLM.from_pretrained(_snake_case			,from_tf=_snake_case						)
						lowercase__		,			lowercase__   :    Any				=			AutoModelForMaskedLM.from_pretrained(
						    _snake_case			,output_loading_info=_snake_case			,from_tf=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)







		@slow
		def 					UpperCAmelCase    (	self						:  List[Any]						)					->		Dict:






				"""simple docstring"""



				for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						lowercase__   :    Union[str, Any]				=			AutoConfig.from_pretrained(_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    List[str]				=			TFAutoModelForSeqaSeqLM.from_pretrained(_snake_case			,from_pt=_snake_case						)
						lowercase__		,			lowercase__   :    List[str]				=			TFAutoModelForSeqaSeqLM.from_pretrained(
						    _snake_case			,output_loading_info=_snake_case			,from_pt=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    Any				=			AutoModelForSeqaSeqLM.from_pretrained(_snake_case			,from_tf=_snake_case						)
						lowercase__		,			lowercase__   :    Optional[int]				=			AutoModelForSeqaSeqLM.from_pretrained(
						    _snake_case			,output_loading_info=_snake_case			,from_tf=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)







		@slow
		def 					UpperCAmelCase    (	self						:  List[str]						)					->		Union[str, Any]:






				"""simple docstring"""



				for model_name in ["bert-base-uncased"]:
						lowercase__   :    Tuple				=			AutoConfig.from_pretrained(_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    Any				=			TFAutoModelForSequenceClassification.from_pretrained(_snake_case			,from_pt=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    Union[str, Any]				=			AutoModelForSequenceClassification.from_pretrained(_snake_case			,from_tf=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)







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






				"""simple docstring"""



				for model_name in ["bert-base-uncased"]:
						lowercase__   :    List[Any]				=			AutoConfig.from_pretrained(_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    str				=			TFAutoModelForQuestionAnswering.from_pretrained(_snake_case			,from_pt=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    Any				=			AutoModelForQuestionAnswering.from_pretrained(_snake_case			,from_tf=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)







		def 					UpperCAmelCase    (	self						:  Dict						)					->		Any:






				"""simple docstring"""



				lowercase__   :    Optional[Any]				=			TFAutoModelWithLMHead.from_pretrained(_snake_case			,from_pt=_snake_case						)
				self.assertIsInstance(_snake_case			,_snake_case						)
				self.assertEqual(model.num_parameters()			,14_410						)
				self.assertEqual(model.num_parameters(only_trainable=_snake_case						)			,14_410						)

				lowercase__   :    Union[str, Any]				=			AutoModelWithLMHead.from_pretrained(_snake_case			,from_tf=_snake_case						)
				self.assertIsInstance(_snake_case			,_snake_case						)
				self.assertEqual(model.num_parameters()			,14_410						)
				self.assertEqual(model.num_parameters(only_trainable=_snake_case						)			,14_410						)







		def 					UpperCAmelCase    (	self						:  int						)					->		List[Any]:






				"""simple docstring"""



				lowercase__   :    List[Any]				=			TFAutoModelWithLMHead.from_pretrained(_snake_case			,from_pt=_snake_case						)
				self.assertIsInstance(_snake_case			,_snake_case						)
				self.assertEqual(model.num_parameters()			,14_410						)
				self.assertEqual(model.num_parameters(only_trainable=_snake_case						)			,14_410						)

				lowercase__   :    int				=			AutoModelWithLMHead.from_pretrained(_snake_case			,from_tf=_snake_case						)
				self.assertIsInstance(_snake_case			,_snake_case						)
				self.assertEqual(model.num_parameters()			,14_410						)
				self.assertEqual(model.num_parameters(only_trainable=_snake_case						)			,14_410						)
