
"""simple docstring"""



from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
 from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


UpperCAmelCase__          = logging.get_logger(__name__)

UpperCAmelCase__          = {
    'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/resolve/main/config.json',
    'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/config.json',
    'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json',
    'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json',
    'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/config.json',
    'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json',
}

class       lowerCAmelCase__				(						A_	):
     __a						=					"""bloom"""
     __a						=					["""past_key_values"""]
     __a						=					{
         """num_hidden_layers""": """n_layer""",
         """num_attention_heads""": """n_head""",
     }







     def __init__(							self			:			Dict    ,      _lowerCamelCase			:			List[Any]=250880    ,      _lowerCamelCase			:			str=64    ,      _lowerCamelCase			:			int=2    ,      _lowerCamelCase			:			Union[str, Any]=8    ,      _lowerCamelCase			:			Any=1e-5    ,      _lowerCamelCase			:			List[str]=0.0_2    ,      _lowerCamelCase			:			Tuple=True    ,      _lowerCamelCase			:			Any=1    ,      _lowerCamelCase			:			Any=2    ,      _lowerCamelCase			:			List[str]=False    ,      _lowerCamelCase			:			Any=0.0    ,      _lowerCamelCase			:			Any=0.0    ,      _lowerCamelCase			:			List[Any]=1    ,      _lowerCamelCase			:			Union[str, Any]=False    ,      **_lowerCamelCase			:			Optional[int]    ,      ):
       _snake_case					     =       vocab_size
       # Backward compatibility with n_embed kwarg
       _snake_case					     =       kwargs.pop('''n_embed'''    ,      _lowerCamelCase			)
       _snake_case					     =       hidden_size if n_embed is None else n_embed
       _snake_case					     =       n_layer
       _snake_case					     =       n_head
       _snake_case					     =       layer_norm_epsilon
       _snake_case					     =       initializer_range
       _snake_case					     =       use_cache
       _snake_case					     =       pretraining_tp
       _snake_case					     =       apply_residual_connection_post_layernorm
       _snake_case					     =       hidden_dropout
       _snake_case					     =       attention_dropout

       _snake_case					     =       bos_token_id
       _snake_case					     =       eos_token_id
       _snake_case					     =       slow_but_exact

       super().__init__(bos_token_id=_lowerCamelCase    ,      eos_token_id=_lowerCamelCase    ,      **_lowerCamelCase			)






class       lowerCAmelCase__				(						A_	):
     __a						=					version.parse("""1.12"""	)







     def __init__(							self			:			List[str]    ,      _lowerCamelCase			:			PretrainedConfig    ,      _lowerCamelCase			:			str = "default"    ,      _lowerCamelCase			:			List[PatchingSpec] = None    ,      _lowerCamelCase			:			bool = False    ,      ):
       super().__init__(_lowerCamelCase    ,      task=_lowerCamelCase    ,      patching_specs=_lowerCamelCase    ,      use_past=_lowerCamelCase			)
       if not getattr(self._config    ,      '''pad_token_id'''    ,      _lowerCamelCase			):
         # TODO: how to do that better?
         _snake_case					     =       0







     @property
     def      lowercase	(							self			:			Optional[Any]			):
       _snake_case					     =       OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}			)
       if self.use_past:
         # BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
         self.fill_with_past_key_values_(_lowerCamelCase    ,      direction='''inputs'''    ,      inverted_values_shape=_lowerCamelCase			)
         _snake_case					     =       {0: '''batch''', 1: '''past_sequence + sequence'''}
       else:
         _snake_case					     =       {0: '''batch''', 1: '''sequence'''}

       return common_inputs







     @property
     def      lowercase	(							self			:			Optional[int]			):
       return self._config.n_layer







     @property
     def      lowercase	(							self			:			Any			):
       return self._config.n_head







     @property
     def      lowercase	(							self			:			List[str]			):
       return 1e-3







     def      lowercase	(							self			:			Optional[Any]    ,      _lowerCamelCase			:			"PreTrainedTokenizer"    ,      _lowerCamelCase			:			int = -1    ,      _lowerCamelCase			:			int = -1    ,      _lowerCamelCase			:			bool = False    ,      _lowerCamelCase			:			Optional["TensorType"] = None    ,      ):
       _snake_case					     =       super(_lowerCamelCase    ,      self			).generate_dummy_inputs(
           _lowerCamelCase    ,      batch_size=_lowerCamelCase    ,      seq_length=_lowerCamelCase    ,      is_pair=_lowerCamelCase    ,      framework=_lowerCamelCase			)

       # We need to order the input in the way they appears in the forward()
       _snake_case					     =       OrderedDict({'''input_ids''': common_inputs['''input_ids''']}			)

       # Need to add the past_keys
       if self.use_past:
         if not is_torch_available():
           raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''			)
         else:
           import torch

           _snake_case     ,					_snake_case					     =       common_inputs['''input_ids'''].shape
           # Not using the same length for past_key_values
           _snake_case					     =       seqlen + 2
           _snake_case					     =       self._config.hidden_size // self.num_attention_heads
           _snake_case					     =       (
               batch * self.num_attention_heads,
               head_dim,
               past_key_values_length,
           )
           _snake_case					     =       (
               batch * self.num_attention_heads,
               past_key_values_length,
               head_dim,
           )
           _snake_case					     =       [
               (torch.zeros(_lowerCamelCase			), torch.zeros(_lowerCamelCase			)) for _ in range(self.num_layers			)
           ]

       _snake_case					     =       common_inputs['''attention_mask''']
       if self.use_past:
         _snake_case					     =       ordered_inputs['''attention_mask'''].dtype
         _snake_case					     =       torch.cat(
             [ordered_inputs['''attention_mask'''], torch.ones(_lowerCamelCase    ,      _lowerCamelCase    ,      dtype=_lowerCamelCase			)]    ,      dim=1			)

       return ordered_inputs







     @property
     def      lowercase	(							self			:			Dict			):
       return 13





"""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)



