
"""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       lowerCAmelCase__				(						unittest.TestCase	):







     @slow
     def      lowercase	(							self			:			int			):
       # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       for model_name in ["bert-base-uncased"]:
         _snake_case					     =       AutoConfig.from_pretrained(_lowerCamelCase			)
         self.assertIsNotNone(_lowerCamelCase			)
         self.assertIsInstance(_lowerCamelCase    ,      _lowerCamelCase			)

         _snake_case					     =       TFAutoModel.from_pretrained(_lowerCamelCase    ,      from_pt=_lowerCamelCase			)
         self.assertIsNotNone(_lowerCamelCase			)
         self.assertIsInstance(_lowerCamelCase    ,      _lowerCamelCase			)

         _snake_case					     =       AutoModel.from_pretrained(_lowerCamelCase    ,      from_tf=_lowerCamelCase			)
         self.assertIsNotNone(_lowerCamelCase			)
         self.assertIsInstance(_lowerCamelCase    ,      _lowerCamelCase			)







     @slow
     def      lowercase	(							self			:			List[str]			):
       # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       for model_name in ["bert-base-uncased"]:
         _snake_case					     =       AutoConfig.from_pretrained(_lowerCamelCase			)
         self.assertIsNotNone(_lowerCamelCase			)
         self.assertIsInstance(_lowerCamelCase    ,      _lowerCamelCase			)

         _snake_case					     =       TFAutoModelForPreTraining.from_pretrained(_lowerCamelCase    ,      from_pt=_lowerCamelCase			)
         self.assertIsNotNone(_lowerCamelCase			)
         self.assertIsInstance(_lowerCamelCase    ,      _lowerCamelCase			)

         _snake_case					     =       AutoModelForPreTraining.from_pretrained(_lowerCamelCase    ,      from_tf=_lowerCamelCase			)
         self.assertIsNotNone(_lowerCamelCase			)
         self.assertIsInstance(_lowerCamelCase    ,      _lowerCamelCase			)







     @slow
     def      lowercase	(							self			:			int			):
       for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _snake_case					     =       AutoConfig.from_pretrained(_lowerCamelCase			)
         self.assertIsNotNone(_lowerCamelCase			)
         self.assertIsInstance(_lowerCamelCase    ,      _lowerCamelCase			)

         _snake_case					     =       TFAutoModelForCausalLM.from_pretrained(_lowerCamelCase    ,      from_pt=_lowerCamelCase			)
         _snake_case     ,					_snake_case					     =       TFAutoModelForCausalLM.from_pretrained(
             _lowerCamelCase    ,      output_loading_info=_lowerCamelCase    ,      from_pt=_lowerCamelCase			)
         self.assertIsNotNone(_lowerCamelCase			)
         self.assertIsInstance(_lowerCamelCase    ,      _lowerCamelCase			)

         _snake_case					     =       AutoModelForCausalLM.from_pretrained(_lowerCamelCase    ,      from_tf=_lowerCamelCase			)
         _snake_case     ,					_snake_case					     =       AutoModelForCausalLM.from_pretrained(
             _lowerCamelCase    ,      output_loading_info=_lowerCamelCase    ,      from_tf=_lowerCamelCase			)
         self.assertIsNotNone(_lowerCamelCase			)
         self.assertIsInstance(_lowerCamelCase    ,      _lowerCamelCase			)







     @slow
     def      lowercase	(							self			:			Optional[int]			):
       for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _snake_case					     =       AutoConfig.from_pretrained(_lowerCamelCase			)
         self.assertIsNotNone(_lowerCamelCase			)
         self.assertIsInstance(_lowerCamelCase    ,      _lowerCamelCase			)

         _snake_case					     =       TFAutoModelWithLMHead.from_pretrained(_lowerCamelCase    ,      from_pt=_lowerCamelCase			)
         self.assertIsNotNone(_lowerCamelCase			)
         self.assertIsInstance(_lowerCamelCase    ,      _lowerCamelCase			)

         _snake_case					     =       AutoModelWithLMHead.from_pretrained(_lowerCamelCase    ,      from_tf=_lowerCamelCase			)
         self.assertIsNotNone(_lowerCamelCase			)
         self.assertIsInstance(_lowerCamelCase    ,      _lowerCamelCase			)







     @slow
     def      lowercase	(							self			:			List[Any]			):
       for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _snake_case					     =       AutoConfig.from_pretrained(_lowerCamelCase			)
         self.assertIsNotNone(_lowerCamelCase			)
         self.assertIsInstance(_lowerCamelCase    ,      _lowerCamelCase			)

         _snake_case					     =       TFAutoModelForMaskedLM.from_pretrained(_lowerCamelCase    ,      from_pt=_lowerCamelCase			)
         _snake_case     ,					_snake_case					     =       TFAutoModelForMaskedLM.from_pretrained(
             _lowerCamelCase    ,      output_loading_info=_lowerCamelCase    ,      from_pt=_lowerCamelCase			)
         self.assertIsNotNone(_lowerCamelCase			)
         self.assertIsInstance(_lowerCamelCase    ,      _lowerCamelCase			)

         _snake_case					     =       AutoModelForMaskedLM.from_pretrained(_lowerCamelCase    ,      from_tf=_lowerCamelCase			)
         _snake_case     ,					_snake_case					     =       AutoModelForMaskedLM.from_pretrained(
             _lowerCamelCase    ,      output_loading_info=_lowerCamelCase    ,      from_tf=_lowerCamelCase			)
         self.assertIsNotNone(_lowerCamelCase			)
         self.assertIsInstance(_lowerCamelCase    ,      _lowerCamelCase			)







     @slow
     def      lowercase	(							self			:			int			):
       for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _snake_case					     =       AutoConfig.from_pretrained(_lowerCamelCase			)
         self.assertIsNotNone(_lowerCamelCase			)
         self.assertIsInstance(_lowerCamelCase    ,      _lowerCamelCase			)

         _snake_case					     =       TFAutoModelForSeqaSeqLM.from_pretrained(_lowerCamelCase    ,      from_pt=_lowerCamelCase			)
         _snake_case     ,					_snake_case					     =       TFAutoModelForSeqaSeqLM.from_pretrained(
             _lowerCamelCase    ,      output_loading_info=_lowerCamelCase    ,      from_pt=_lowerCamelCase			)
         self.assertIsNotNone(_lowerCamelCase			)
         self.assertIsInstance(_lowerCamelCase    ,      _lowerCamelCase			)

         _snake_case					     =       AutoModelForSeqaSeqLM.from_pretrained(_lowerCamelCase    ,      from_tf=_lowerCamelCase			)
         _snake_case     ,					_snake_case					     =       AutoModelForSeqaSeqLM.from_pretrained(
             _lowerCamelCase    ,      output_loading_info=_lowerCamelCase    ,      from_tf=_lowerCamelCase			)
         self.assertIsNotNone(_lowerCamelCase			)
         self.assertIsInstance(_lowerCamelCase    ,      _lowerCamelCase			)







     @slow
     def      lowercase	(							self			:			List[Any]			):
       # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       for model_name in ["bert-base-uncased"]:
         _snake_case					     =       AutoConfig.from_pretrained(_lowerCamelCase			)
         self.assertIsNotNone(_lowerCamelCase			)
         self.assertIsInstance(_lowerCamelCase    ,      _lowerCamelCase			)

         _snake_case					     =       TFAutoModelForSequenceClassification.from_pretrained(_lowerCamelCase    ,      from_pt=_lowerCamelCase			)
         self.assertIsNotNone(_lowerCamelCase			)
         self.assertIsInstance(_lowerCamelCase    ,      _lowerCamelCase			)

         _snake_case					     =       AutoModelForSequenceClassification.from_pretrained(_lowerCamelCase    ,      from_tf=_lowerCamelCase			)
         self.assertIsNotNone(_lowerCamelCase			)
         self.assertIsInstance(_lowerCamelCase    ,      _lowerCamelCase			)







     @slow
     def      lowercase	(							self			:			List[Any]			):
       # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       for model_name in ["bert-base-uncased"]:
         _snake_case					     =       AutoConfig.from_pretrained(_lowerCamelCase			)
         self.assertIsNotNone(_lowerCamelCase			)
         self.assertIsInstance(_lowerCamelCase    ,      _lowerCamelCase			)

         _snake_case					     =       TFAutoModelForQuestionAnswering.from_pretrained(_lowerCamelCase    ,      from_pt=_lowerCamelCase			)
         self.assertIsNotNone(_lowerCamelCase			)
         self.assertIsInstance(_lowerCamelCase    ,      _lowerCamelCase			)

         _snake_case					     =       AutoModelForQuestionAnswering.from_pretrained(_lowerCamelCase    ,      from_tf=_lowerCamelCase			)
         self.assertIsNotNone(_lowerCamelCase			)
         self.assertIsInstance(_lowerCamelCase    ,      _lowerCamelCase			)







     def      lowercase	(							self			:			Dict			):
       _snake_case					     =       TFAutoModelWithLMHead.from_pretrained(_lowerCamelCase    ,      from_pt=_lowerCamelCase			)
       self.assertIsInstance(_lowerCamelCase    ,      _lowerCamelCase			)
       self.assertEqual(model.num_parameters()    ,      14410			)
       self.assertEqual(model.num_parameters(only_trainable=_lowerCamelCase			)    ,      14410			)

       _snake_case					     =       AutoModelWithLMHead.from_pretrained(_lowerCamelCase    ,      from_tf=_lowerCamelCase			)
       self.assertIsInstance(_lowerCamelCase    ,      _lowerCamelCase			)
       self.assertEqual(model.num_parameters()    ,      14410			)
       self.assertEqual(model.num_parameters(only_trainable=_lowerCamelCase			)    ,      14410			)







     def      lowercase	(							self			:			Dict			):
       _snake_case					     =       TFAutoModelWithLMHead.from_pretrained(_lowerCamelCase    ,      from_pt=_lowerCamelCase			)
       self.assertIsInstance(_lowerCamelCase    ,      _lowerCamelCase			)
       self.assertEqual(model.num_parameters()    ,      14410			)
       self.assertEqual(model.num_parameters(only_trainable=_lowerCamelCase			)    ,      14410			)

       _snake_case					     =       AutoModelWithLMHead.from_pretrained(_lowerCamelCase    ,      from_tf=_lowerCamelCase			)
       self.assertIsInstance(_lowerCamelCase    ,      _lowerCamelCase			)
       self.assertEqual(model.num_parameters()    ,      14410			)
       self.assertEqual(model.num_parameters(only_trainable=_lowerCamelCase			)    ,      14410			)





"""simple docstring"""



import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


UpperCAmelCase__          = ['bart.large', 'bart.large.mnli', 'bart.large.cnn', 'bart_xsum/model.pt']
UpperCAmelCase__          = {'bart.large': BartModel, 'bart.large.mnli': BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse('0.9.0'):
 raise Exception('requires fairseq >= 0.9.0')


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

UpperCAmelCase__          = ' Hello world! cécé herlolip'

UpperCAmelCase__          = [
    ('model.classification_heads.mnli.dense.weight', 'classification_head.dense.weight'),
    ('model.classification_heads.mnli.dense.bias', 'classification_head.dense.bias'),
    ('model.classification_heads.mnli.out_proj.weight', 'classification_head.out_proj.weight'),
    ('model.classification_heads.mnli.out_proj.bias', 'classification_head.out_proj.bias'),
]
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[Any]		)     ->					Optional[int]:
  _snake_case					     =       [
      '''encoder.version''',
      '''decoder.version''',
      '''model.encoder.version''',
      '''model.decoder.version''',
      '''_float_tensor''',
  ]
  for k in ignore_keys:
    state_dict.pop(__lowerCamelCase ,					__lowerCamelCase		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[Any] ,					__lowerCamelCase   :       int ,					__lowerCamelCase   :       int		)     ->					int:
  _snake_case					     =       dct.pop(__lowerCamelCase		)
  _snake_case					     =       val
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Dict		)     ->					str:
  _snake_case					     =       torch.load(__lowerCamelCase ,					map_location='''cpu'''		)
  _snake_case					     =       torch.hub.load('''pytorch/fairseq''' ,					'''bart.large.cnn'''		).eval()
  hub_interface.model.load_state_dict(sd['''model''']		)
  return hub_interface
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[int]		)     ->					Union[str, Any]:
  _snake_case     ,					_snake_case					     =       emb.weight.shape
  _snake_case					     =       nn.Linear(__lowerCamelCase ,					__lowerCamelCase ,					bias=__lowerCamelCase		)
  _snake_case					     =       emb.weight.data
  return lin_layer
@torch.no_grad()
def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[Any] ,					__lowerCamelCase   :       Tuple ,					__lowerCamelCase   :       Union[str, Any]=None		)     ->					List[Any]:
  if not os.path.exists(__lowerCamelCase		):
    _snake_case					     =       torch.hub.load('''pytorch/fairseq''' ,					__lowerCamelCase		).eval()
  else:
    _snake_case					     =       load_xsum_checkpoint(__lowerCamelCase		)

  bart.model.upgrade_state_dict(bart.model.state_dict()		)
  if hf_checkpoint_name is None:
    _snake_case					     =       checkpoint_path.replace('''.''' ,					'''-'''		)
  _snake_case					     =       BartConfig.from_pretrained(__lowerCamelCase		)
  _snake_case					     =       bart.encode(__lowerCamelCase		).unsqueeze(0		)
  _snake_case					     =       BartTokenizer.from_pretrained(__lowerCamelCase		).encode(__lowerCamelCase ,					return_tensors='''pt'''		).unsqueeze(0		)
  if not torch.eq(__lowerCamelCase ,					__lowerCamelCase		).all():
    raise ValueError(
        f'''converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}'''		)

  if checkpoint_path == "bart.large.mnli":
    _snake_case					     =       bart.state_dict()
    remove_ignore_keys_(__lowerCamelCase		)
    _snake_case					     =       state_dict['''model.decoder.embed_tokens.weight''']
    for src, dest in mnli_rename_keys:
      rename_key(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		)
    _snake_case					     =       BartForSequenceClassification(__lowerCamelCase		).eval()
    model.load_state_dict(__lowerCamelCase		)
    _snake_case					     =       bart.predict('''mnli''' ,					__lowerCamelCase ,					return_logits=__lowerCamelCase		)
    _snake_case					     =       model(__lowerCamelCase		)[0]  # logits
  else:  # no classification heads to worry about
    _snake_case					     =       bart.model.state_dict()
    remove_ignore_keys_(__lowerCamelCase		)
    _snake_case					     =       state_dict['''decoder.embed_tokens.weight''']
    _snake_case					     =       bart.extract_features(__lowerCamelCase		)
    if hf_checkpoint_name == "facebook/bart-large":
      _snake_case					     =       BartModel(__lowerCamelCase		).eval()
      model.load_state_dict(__lowerCamelCase		)
      _snake_case					     =       model(__lowerCamelCase		).model[0]
    else:
      _snake_case					     =       BartForConditionalGeneration(__lowerCamelCase		).eval()  # an existing summarization ckpt
      model.model.load_state_dict(__lowerCamelCase		)
      if hasattr(__lowerCamelCase ,					'''lm_head'''		):
        _snake_case					     =       make_linear_from_emb(model.model.shared		)
      _snake_case					     =       model.model(__lowerCamelCase		)[0]

    # Check results
  if fairseq_output.shape != new_model_outputs.shape:
    raise ValueError(
        f'''`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}'''		)
  if (fairseq_output != new_model_outputs).any().item():
    raise ValueError('''Some values in `fairseq_output` are different from `new_model_outputs`'''		)
  Path(__lowerCamelCase		).mkdir(exist_ok=__lowerCamelCase		)
  model.save_pretrained(__lowerCamelCase		)


if __name__ == "__main__":
 UpperCAmelCase__          = argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     'fairseq_path', type=str, help='bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'
 )
 parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
 parser.add_argument(
     '--hf_config', default=None, type=str, help='Which huggingface architecture to use: bart-large-xsum'
 )
 UpperCAmelCase__          = parser.parse_args()
 convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)



