





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


lowerCamelCase			:					List[Any]         =	['bart.large', 'bart.large.mnli', 'bart.large.cnn', 'bart_xsum/model.pt']
lowerCamelCase			:					Optional[Any]         =	{'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()
lowerCamelCase			:					Optional[Any]         =	logging.get_logger(__name__)

lowerCamelCase			:					Any         =	' Hello world! cécé herlolip'

lowerCamelCase			:					Optional[Any]         =	[
    ('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       __lowerCAmelCase      (						__snake_case       ):
     __lowerCAmelCase								=					[
         "encoder.version",
         "decoder.version",
         "model.encoder.version",
         "model.decoder.version",
         "_float_tensor",
     ]
     for k in ignore_keys:
          state_dict.pop(_lowerCamelCase					, _lowerCamelCase       )





def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case       ):
     __lowerCAmelCase								=					dct.pop(_lowerCamelCase       )
     __lowerCAmelCase								=					val





def       __lowerCAmelCase      (						__snake_case       ):
     __lowerCAmelCase								=					torch.load(_lowerCamelCase					, map_location="cpu"       )
     __lowerCAmelCase								=					torch.hub.load("pytorch/fairseq"					, "bart.large.cnn"       ).eval()
     hub_interface.model.load_state_dict(sd["model"]       )
     return hub_interface





def       __lowerCAmelCase      (						__snake_case       ):
     __lowerCAmelCase								=					emb.weight.shape
     __lowerCAmelCase								=					nn.Linear(_lowerCamelCase					, _lowerCamelCase					, bias=_lowerCamelCase       )
     __lowerCAmelCase								=					emb.weight.data
     return lin_layer





@torch.no_grad()
def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case=None       ):





     if not os.path.exists(_lowerCamelCase       ):
          __lowerCAmelCase								=					torch.hub.load("pytorch/fairseq"					, _lowerCamelCase       ).eval()
     else:
          __lowerCAmelCase								=					load_xsum_checkpoint(_lowerCamelCase       )

     bart.model.upgrade_state_dict(bart.model.state_dict()       )
     if hf_checkpoint_name is None:
          __lowerCAmelCase								=					checkpoint_path.replace("."					, "-"       )
     __lowerCAmelCase								=					BartConfig.from_pretrained(_lowerCamelCase       )
     __lowerCAmelCase								=					bart.encode(_lowerCamelCase       ).unsqueeze(0       )
     __lowerCAmelCase								=					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":
          __lowerCAmelCase								=					bart.state_dict()
          remove_ignore_keys_(_lowerCamelCase       )
          __lowerCAmelCase								=					state_dict["model.decoder.embed_tokens.weight"]
          for src, dest in mnli_rename_keys:
               rename_key(_lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase       )
          __lowerCAmelCase								=					BartForSequenceClassification(_lowerCamelCase       ).eval()
          model.load_state_dict(_lowerCamelCase       )
          __lowerCAmelCase								=					bart.predict("mnli"					, _lowerCamelCase					, return_logits=_lowerCamelCase       )
          __lowerCAmelCase								=					model(_lowerCamelCase       )[0]  # logits
     else:  # no classification heads to worry about
          __lowerCAmelCase								=					bart.model.state_dict()
          remove_ignore_keys_(_lowerCamelCase       )
          __lowerCAmelCase								=					state_dict["decoder.embed_tokens.weight"]
          __lowerCAmelCase								=					bart.extract_features(_lowerCamelCase       )
          if hf_checkpoint_name == "facebook/bart-large":
               __lowerCAmelCase								=					BartModel(_lowerCamelCase       ).eval()
               model.load_state_dict(_lowerCamelCase       )
               __lowerCAmelCase								=					model(_lowerCamelCase       ).model[0]
          else:
               __lowerCAmelCase								=					BartForConditionalGeneration(_lowerCamelCase       ).eval()  # an existing summarization ckpt
               model.model.load_state_dict(_lowerCamelCase       )
               if hasattr(_lowerCamelCase					, "lm_head"       ):
                    __lowerCAmelCase								=					make_linear_from_emb(model.model.shared       )
               __lowerCAmelCase								=					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__":
 lowerCamelCase			:					List[Any]         =	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'''
 )
 lowerCamelCase			:					List[Any]         =	parser.parse_args()
 convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)














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

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


lowerCamelCase			:					List[Any]         =	logging.get_logger(__name__)

lowerCamelCase			:					Union[str, Any]         =	{
    '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json''',
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}



class 							_UpperCamelCase			(a_			):
      snake_case_  =							"""blenderbot-small"""
      snake_case_  =							["""past_key_values"""]
      snake_case_  =							{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}


      def __init__(	self					,						__UpperCamelCase=5_0_2_6_5					,						__UpperCamelCase=5_1_2					,						__UpperCamelCase=8					,						__UpperCamelCase=2_0_4_8					,						__UpperCamelCase=1_6					,						__UpperCamelCase=8					,						__UpperCamelCase=2_0_4_8					,						__UpperCamelCase=1_6					,						__UpperCamelCase=0.0					,						__UpperCamelCase=0.0					,						__UpperCamelCase=True					,						__UpperCamelCase=True					,						__UpperCamelCase="gelu"					,						__UpperCamelCase=5_1_2					,						__UpperCamelCase=0.1					,						__UpperCamelCase=0.0					,						__UpperCamelCase=0.0					,						__UpperCamelCase=0.0_2					,						__UpperCamelCase=1					,						__UpperCamelCase=False					,						__UpperCamelCase=0					,						__UpperCamelCase=1					,						__UpperCamelCase=2					,						__UpperCamelCase=2					,						**__UpperCamelCase					,						)->	Any:
           __lowerCAmelCase								=					vocab_size
           __lowerCAmelCase								=					max_position_embeddings
           __lowerCAmelCase								=					d_model
           __lowerCAmelCase								=					encoder_ffn_dim
           __lowerCAmelCase								=					encoder_layers
           __lowerCAmelCase								=					encoder_attention_heads
           __lowerCAmelCase								=					decoder_ffn_dim
           __lowerCAmelCase								=					decoder_layers
           __lowerCAmelCase								=					decoder_attention_heads
           __lowerCAmelCase								=					dropout
           __lowerCAmelCase								=					attention_dropout
           __lowerCAmelCase								=					activation_dropout
           __lowerCAmelCase								=					activation_function
           __lowerCAmelCase								=					init_std
           __lowerCAmelCase								=					encoder_layerdrop
           __lowerCAmelCase								=					decoder_layerdrop
           __lowerCAmelCase								=					use_cache
           __lowerCAmelCase								=					encoder_layers
           __lowerCAmelCase								=					scale_embedding  # scale factor will be sqrt(d_model) if True

           super().__init__(
               pad_token_id=__UpperCamelCase					,						bos_token_id=__UpperCamelCase					,						eos_token_id=__UpperCamelCase					,						is_encoder_decoder=__UpperCamelCase					,						decoder_start_token_id=__UpperCamelCase					,						forced_eos_token_id=__UpperCamelCase					,						**__UpperCamelCase					,						)



class 							_UpperCamelCase			(a_			):


      @property
      def  __UpperCAmelCase		(	self	)->	Mapping[str, Mapping[int, str]]:
           if self.task in ["default", "seq2seq-lm"]:
                __lowerCAmelCase								=					OrderedDict(
                    [
                        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                    ]	)

                if self.use_past:
                     __lowerCAmelCase								=					{0: "batch"}
                     __lowerCAmelCase								=					{0: "batch", 1: "past_decoder_sequence + sequence"}
                else:
                     __lowerCAmelCase								=					{0: "batch", 1: "decoder_sequence"}
                     __lowerCAmelCase								=					{0: "batch", 1: "decoder_sequence"}

                if self.use_past:
                     self.fill_with_past_key_values_(__UpperCamelCase					,						direction="inputs"	)
           elif self.task == "causal-lm":
                # TODO: figure this case out.
                __lowerCAmelCase								=					OrderedDict(
                    [
                        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                    ]	)
                if self.use_past:
                     __lowerCAmelCase			, __lowerCAmelCase								=					self.num_layers
                     for i in range(__UpperCamelCase	):
                          __lowerCAmelCase								=					{0: "batch", 2: "past_sequence + sequence"}
                          __lowerCAmelCase								=					{0: "batch", 2: "past_sequence + sequence"}
           else:
                __lowerCAmelCase								=					OrderedDict(
                    [
                        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                        ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
                        ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
                    ]	)

           return common_inputs


      @property
      def  __UpperCAmelCase		(	self	)->	Mapping[str, Mapping[int, str]]:
           if self.task in ["default", "seq2seq-lm"]:
                __lowerCAmelCase								=					super().outputs
           else:
                __lowerCAmelCase								=					super(__UpperCamelCase					,						self	).outputs
                if self.use_past:
                     __lowerCAmelCase			, __lowerCAmelCase								=					self.num_layers
                     for i in range(__UpperCamelCase	):
                          __lowerCAmelCase								=					{0: "batch", 2: "past_sequence + sequence"}
                          __lowerCAmelCase								=					{0: "batch", 2: "past_sequence + sequence"}
           return common_outputs


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase = -1					,						__UpperCamelCase = -1					,						__UpperCamelCase = False					,						__UpperCamelCase = None					,						)->	Mapping[str, Any]:
           __lowerCAmelCase								=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               __UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)

           # Generate decoder inputs
           __lowerCAmelCase								=					seq_length if not self.use_past else 1
           __lowerCAmelCase								=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               __UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)
           __lowerCAmelCase								=					{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
           __lowerCAmelCase								=					dict(**__UpperCamelCase					,						**__UpperCamelCase	)

           if self.use_past:
                if not is_torch_available():
                     raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."	)
                else:
                     import torch
                __lowerCAmelCase			, __lowerCAmelCase								=					common_inputs["input_ids"].shape
                __lowerCAmelCase								=					common_inputs["decoder_input_ids"].shape[1]
                __lowerCAmelCase			, __lowerCAmelCase								=					self.num_attention_heads
                __lowerCAmelCase								=					(
                    batch,
                    num_encoder_attention_heads,
                    encoder_seq_length,
                    self._config.hidden_size // num_encoder_attention_heads,
                )
                __lowerCAmelCase								=					decoder_seq_length + 3
                __lowerCAmelCase								=					(
                    batch,
                    num_decoder_attention_heads,
                    decoder_past_length,
                    self._config.hidden_size // num_decoder_attention_heads,
                )

                __lowerCAmelCase								=					torch.cat(
                    [common_inputs["decoder_attention_mask"], torch.ones(__UpperCamelCase					,						__UpperCamelCase	)]					,						dim=1	)

                __lowerCAmelCase								=					[]
                # If the number of encoder and decoder layers are present in the model configuration, both are considered
                __lowerCAmelCase			, __lowerCAmelCase								=					self.num_layers
                __lowerCAmelCase								=					min(__UpperCamelCase					,						__UpperCamelCase	)
                __lowerCAmelCase								=					max(__UpperCamelCase					,						__UpperCamelCase	) - min_num_layers
                __lowerCAmelCase								=					"encoder" if num_encoder_layers > num_decoder_layers else "decoder"

                for _ in range(__UpperCamelCase	):
                     common_inputs["past_key_values"].append(
                         (
                             torch.zeros(__UpperCamelCase	),
                             torch.zeros(__UpperCamelCase	),
                             torch.zeros(__UpperCamelCase	),
                             torch.zeros(__UpperCamelCase	),
                         )	)
                # TODO: test this.
                __lowerCAmelCase								=					encoder_shape if remaining_side_name == "encoder" else decoder_shape
                for _ in range(__UpperCamelCase					,						__UpperCamelCase	):
                     common_inputs["past_key_values"].append((torch.zeros(__UpperCamelCase	), torch.zeros(__UpperCamelCase	))	)
           return common_inputs


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase = -1					,						__UpperCamelCase = -1					,						__UpperCamelCase = False					,						__UpperCamelCase = None					,						)->	Mapping[str, Any]:
           __lowerCAmelCase								=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               __UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)

           if self.use_past:
                if not is_torch_available():
                     raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."	)
                else:
                     import torch
                __lowerCAmelCase			, __lowerCAmelCase								=					common_inputs["input_ids"].shape
                # Not using the same length for past_key_values
                __lowerCAmelCase								=					seqlen + 2
                __lowerCAmelCase			, __lowerCAmelCase								=					self.num_layers
                __lowerCAmelCase			, __lowerCAmelCase								=					self.num_attention_heads
                __lowerCAmelCase								=					(
                    batch,
                    num_encoder_attention_heads,
                    past_key_values_length,
                    self._config.hidden_size // num_encoder_attention_heads,
                )

                __lowerCAmelCase								=					common_inputs["attention_mask"].dtype
                __lowerCAmelCase								=					torch.cat(
                    [common_inputs["attention_mask"], torch.ones(__UpperCamelCase					,						__UpperCamelCase					,						dtype=__UpperCamelCase	)]					,						dim=1	)
                __lowerCAmelCase								=					[
                    (torch.zeros(__UpperCamelCase	), torch.zeros(__UpperCamelCase	)) for _ in range(__UpperCamelCase	)
                ]
           return common_inputs


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase = -1					,						__UpperCamelCase = -1					,						__UpperCamelCase = False					,						__UpperCamelCase = None					,						)->	Mapping[str, Any]:
           # Copied from OnnxConfig.generate_dummy_inputs
           # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
           # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
           __lowerCAmelCase								=					compute_effective_axis_dimension(
               __UpperCamelCase					,						fixed_dimension=OnnxConfig.default_fixed_batch					,						num_token_to_add=0	)

           # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
           __lowerCAmelCase								=					tokenizer.num_special_tokens_to_add(__UpperCamelCase	)
           __lowerCAmelCase								=					compute_effective_axis_dimension(
               __UpperCamelCase					,						fixed_dimension=OnnxConfig.default_fixed_sequence					,						num_token_to_add=__UpperCamelCase	)

           # Generate dummy inputs according to compute batch and sequence
           __lowerCAmelCase								=					[" ".join([tokenizer.unk_token]	) * seq_length] * batch_size
           __lowerCAmelCase								=					dict(tokenizer(__UpperCamelCase					,						return_tensors=__UpperCamelCase	)	)
           return common_inputs


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase = -1					,						__UpperCamelCase = -1					,						__UpperCamelCase = False					,						__UpperCamelCase = None					,						)->	Mapping[str, Any]:
           if self.task in ["default", "seq2seq-lm"]:
                __lowerCAmelCase								=					self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                    __UpperCamelCase					,						batch_size=__UpperCamelCase					,						seq_length=__UpperCamelCase					,						is_pair=__UpperCamelCase					,						framework=__UpperCamelCase	)

           elif self.task == "causal-lm":
                __lowerCAmelCase								=					self._generate_dummy_inputs_for_causal_lm(
                    __UpperCamelCase					,						batch_size=__UpperCamelCase					,						seq_length=__UpperCamelCase					,						is_pair=__UpperCamelCase					,						framework=__UpperCamelCase	)
           else:
                __lowerCAmelCase								=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                    __UpperCamelCase					,						batch_size=__UpperCamelCase					,						seq_length=__UpperCamelCase					,						is_pair=__UpperCamelCase					,						framework=__UpperCamelCase	)

           return common_inputs


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)->	List[Any]:
           if self.task in ["default", "seq2seq-lm"]:
                __lowerCAmelCase								=					super()._flatten_past_key_values_(__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)
           else:
                __lowerCAmelCase								=					super(__UpperCamelCase					,						self	)._flatten_past_key_values_(
                    __UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)






