
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


lowercase  : Optional[int]						   =							["""bart.large""", """bart.large.mnli""", """bart.large.cnn""", """bart_xsum/model.pt"""]
lowercase  : 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()
lowercase  : Union[str, Any]						   =							logging.get_logger(__name__)

lowercase  : Optional[Any]						   =							""" Hello world! cécé herlolip"""

lowercase  : str						   =							[
    ("""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 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   List[Any]:
       lowercase							:					str	     =				[
           """encoder.version""",
           """decoder.version""",
           """model.encoder.version""",
           """model.decoder.version""",
           """_float_tensor""",
       ]
       for k in ignore_keys:
              state_dict.pop(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   List[str]:
       lowercase							:					Dict	     =				dct.pop(SCREAMING_SNAKE_CASE__    )
       lowercase							:					List[str]	     =				val







def 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   Optional[Any]:
       lowercase							:					Dict	     =				torch.load(SCREAMING_SNAKE_CASE__ ,     map_location="""cpu"""    )
       lowercase							:					Union[str, Any]	     =				torch.hub.load("""pytorch/fairseq""" ,     """bart.large.cnn"""    ).eval()
       hub_interface.model.load_state_dict(sd["""model"""]    )
       return hub_interface







def 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   Dict:
       lowercase     ,      lowercase							:					List[str]	     =				emb.weight.shape
       lowercase							:					Union[str, Any]	     =				nn.Linear(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     bias=SCREAMING_SNAKE_CASE__    )
       lowercase							:					Optional[int]	     =				emb.weight.data
       return lin_layer







@torch.no_grad()
def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__=None    )			->   Optional[Any]:
       if not os.path.exists(SCREAMING_SNAKE_CASE__    ):
              lowercase							:					Any	     =				torch.hub.load("""pytorch/fairseq""" ,     SCREAMING_SNAKE_CASE__    ).eval()
       else:
              lowercase							:					int	     =				load_xsum_checkpoint(SCREAMING_SNAKE_CASE__    )

       bart.model.upgrade_state_dict(bart.model.state_dict()    )
       if hf_checkpoint_name is None:
              lowercase							:					Tuple	     =				checkpoint_path.replace(""".""" ,     """-"""    )
       lowercase							:					Any	     =				BartConfig.from_pretrained(SCREAMING_SNAKE_CASE__    )
       lowercase							:					Optional[Any]	     =				bart.encode(SCREAMING_SNAKE_CASE__    ).unsqueeze(0    )
       lowercase							:					Optional[int]	     =				BartTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__    ).encode(SCREAMING_SNAKE_CASE__ ,     return_tensors="""pt"""    ).unsqueeze(0    )
       if not torch.eq(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    ).all():
              raise ValueError(
                  f"converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}"    )

       if checkpoint_path == "bart.large.mnli":
              lowercase							:					List[str]	     =				bart.state_dict()
              remove_ignore_keys_(SCREAMING_SNAKE_CASE__    )
              lowercase							:					Dict	     =				state_dict["""model.decoder.embed_tokens.weight"""]
              for src, dest in mnli_rename_keys:
                     rename_key(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
              lowercase							:					str	     =				BartForSequenceClassification(SCREAMING_SNAKE_CASE__    ).eval()
              model.load_state_dict(SCREAMING_SNAKE_CASE__    )
              lowercase							:					Optional[Any]	     =				bart.predict("""mnli""" ,     SCREAMING_SNAKE_CASE__ ,     return_logits=SCREAMING_SNAKE_CASE__    )
              lowercase							:					Tuple	     =				model(SCREAMING_SNAKE_CASE__    )[0]  # logits
       else:  # no classification heads to worry about
              lowercase							:					int	     =				bart.model.state_dict()
              remove_ignore_keys_(SCREAMING_SNAKE_CASE__    )
              lowercase							:					Optional[Any]	     =				state_dict["""decoder.embed_tokens.weight"""]
              lowercase							:					Tuple	     =				bart.extract_features(SCREAMING_SNAKE_CASE__    )
              if hf_checkpoint_name == "facebook/bart-large":
                     lowercase							:					Optional[Any]	     =				BartModel(SCREAMING_SNAKE_CASE__    ).eval()
                     model.load_state_dict(SCREAMING_SNAKE_CASE__    )
                     lowercase							:					str	     =				model(SCREAMING_SNAKE_CASE__    ).model[0]
              else:
                     lowercase							:					Dict	     =				BartForConditionalGeneration(SCREAMING_SNAKE_CASE__    ).eval()  # an existing summarization ckpt
                     model.model.load_state_dict(SCREAMING_SNAKE_CASE__    )
                     if hasattr(SCREAMING_SNAKE_CASE__ ,     """lm_head"""    ):
                            lowercase							:					Optional[int]	     =				make_linear_from_emb(model.model.shared    )
                     lowercase							:					Optional[int]	     =				model.model(SCREAMING_SNAKE_CASE__    )[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(SCREAMING_SNAKE_CASE__    ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__    )
       model.save_pretrained(SCREAMING_SNAKE_CASE__    )


if __name__ == "__main__":
  lowercase  : Dict						   =							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"""
  )
  lowercase  : Any						   =							parser.parse_args()
  convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor

@require_vision
class __snake_case		(				unittest.TestCase					):
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					str	     =				tempfile.mkdtemp()

           lowercase							:					List[str]	     =				[
               """[UNK]""",
               """[CLS]""",
               """[SEP]""",
               """[PAD]""",
               """[MASK]""",
               """的""",
               """价""",
               """格""",
               """是""",
               """15""",
               """便""",
               """alex""",
               """##andra""",
               """，""",
               """。""",
               """-""",
               """t""",
               """shirt""",
           ]
           lowercase							:					Dict	     =				os.path.join(self.tmpdirname		,VOCAB_FILES_NAMES["""vocab_file"""]				)
           with open(self.vocab_file		,"""w"""		,encoding="""utf-8"""				) as vocab_writer:
                  vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]				)				)

           lowercase							:					Any	     =				{
               """do_resize""": True,
               """size""": {"""height""": 224, """width""": 224},
               """do_center_crop""": True,
               """crop_size""": {"""height""": 18, """width""": 18},
               """do_normalize""": True,
               """image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
               """image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
               """do_convert_rgb""": True,
           }
           lowercase							:					List[str]	     =				os.path.join(self.tmpdirname		,snake_case				)
           with open(self.image_processor_file		,"""w"""		,encoding="""utf-8"""				) as fp:
                  json.dump(snake_case		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,**snake_case				):



           '''simple docstring'''







           return BertTokenizer.from_pretrained(self.tmpdirname		,**snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,**snake_case				):



           '''simple docstring'''







           return BertTokenizerFast.from_pretrained(self.tmpdirname		,**snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,**snake_case				):



           '''simple docstring'''







           return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname		,**snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           shutil.rmtree(self.tmpdirname				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				[np.random.randint(255		,size=(3, 30, 400)		,dtype=np.uinta				)]

           lowercase							:					str	     =				[Image.fromarray(np.moveaxis(snake_case		,0		,-1				)				) for x in image_inputs]

           return image_inputs
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					List[str]	     =				self.get_tokenizer()
           lowercase							:					Dict	     =				self.get_rust_tokenizer()
           lowercase							:					Union[str, Any]	     =				self.get_image_processor()

           lowercase							:					Optional[Any]	     =				ChineseCLIPProcessor(tokenizer=snake_case		,image_processor=snake_case				)
           processor_slow.save_pretrained(self.tmpdirname				)
           lowercase							:					Any	     =				ChineseCLIPProcessor.from_pretrained(self.tmpdirname		,use_fast=snake_case				)

           lowercase							:					List[Any]	     =				ChineseCLIPProcessor(tokenizer=snake_case		,image_processor=snake_case				)
           processor_fast.save_pretrained(self.tmpdirname				)
           lowercase							:					Tuple	     =				ChineseCLIPProcessor.from_pretrained(self.tmpdirname				)

           self.assertEqual(processor_slow.tokenizer.get_vocab()		,tokenizer_slow.get_vocab()				)
           self.assertEqual(processor_fast.tokenizer.get_vocab()		,tokenizer_fast.get_vocab()				)
           self.assertEqual(tokenizer_slow.get_vocab()		,tokenizer_fast.get_vocab()				)
           self.assertIsInstance(processor_slow.tokenizer		,snake_case				)
           self.assertIsInstance(processor_fast.tokenizer		,snake_case				)

           self.assertEqual(processor_slow.image_processor.to_json_string()		,image_processor.to_json_string()				)
           self.assertEqual(processor_fast.image_processor.to_json_string()		,image_processor.to_json_string()				)
           self.assertIsInstance(processor_slow.image_processor		,snake_case				)
           self.assertIsInstance(processor_fast.image_processor		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					List[Any]	     =				ChineseCLIPProcessor(tokenizer=self.get_tokenizer()		,image_processor=self.get_image_processor()				)
           processor.save_pretrained(self.tmpdirname				)

           lowercase							:					int	     =				self.get_tokenizer(cls_token="""(CLS)"""		,sep_token="""(SEP)"""				)
           lowercase							:					Dict	     =				self.get_image_processor(do_normalize=snake_case				)

           lowercase							:					Optional[Any]	     =				ChineseCLIPProcessor.from_pretrained(
               self.tmpdirname		,cls_token="""(CLS)"""		,sep_token="""(SEP)"""		,do_normalize=snake_case				)

           self.assertEqual(processor.tokenizer.get_vocab()		,tokenizer_add_kwargs.get_vocab()				)
           self.assertIsInstance(processor.tokenizer		,snake_case				)

           self.assertEqual(processor.image_processor.to_json_string()		,image_processor_add_kwargs.to_json_string()				)
           self.assertIsInstance(processor.image_processor		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					str	     =				self.get_image_processor()
           lowercase							:					List[str]	     =				self.get_tokenizer()

           lowercase							:					Optional[Any]	     =				ChineseCLIPProcessor(tokenizer=snake_case		,image_processor=snake_case				)

           lowercase							:					Optional[int]	     =				self.prepare_image_inputs()

           lowercase							:					Tuple	     =				image_processor(snake_case		,return_tensors="""np"""				)
           lowercase							:					Optional[int]	     =				processor(images=snake_case		,return_tensors="""np"""				)

           for key in input_feat_extract.keys():
                  self.assertAlmostEqual(input_feat_extract[key].sum()		,input_processor[key].sum()		,delta=1e-2				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					str	     =				self.get_image_processor()
           lowercase							:					Union[str, Any]	     =				self.get_tokenizer()

           lowercase							:					Any	     =				ChineseCLIPProcessor(tokenizer=snake_case		,image_processor=snake_case				)

           lowercase							:					Any	     =				"""Alexandra，T-shirt的价格是15便士。"""

           lowercase							:					int	     =				processor(text=snake_case				)

           lowercase							:					Dict	     =				tokenizer(snake_case				)

           for key in encoded_tok.keys():
                  self.assertListEqual(encoded_tok[key]		,encoded_processor[key]				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Optional[Any]	     =				self.get_image_processor()
           lowercase							:					Optional[Any]	     =				self.get_tokenizer()

           lowercase							:					Dict	     =				ChineseCLIPProcessor(tokenizer=snake_case		,image_processor=snake_case				)

           lowercase							:					List[str]	     =				"""Alexandra，T-shirt的价格是15便士。"""
           lowercase							:					Any	     =				self.prepare_image_inputs()

           lowercase							:					Optional[Any]	     =				processor(text=snake_case		,images=snake_case				)

           self.assertListEqual(list(inputs.keys()				)		,["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]				)

           # test if it raises when no input is passed
           with pytest.raises(snake_case				):
                  processor()
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					str	     =				self.get_image_processor()
           lowercase							:					str	     =				self.get_tokenizer()

           lowercase							:					Dict	     =				ChineseCLIPProcessor(tokenizer=snake_case		,image_processor=snake_case				)

           lowercase							:					str	     =				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           lowercase							:					Any	     =				processor.batch_decode(snake_case				)
           lowercase							:					str	     =				tokenizer.batch_decode(snake_case				)

           self.assertListEqual(snake_case		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Dict	     =				self.get_image_processor()
           lowercase							:					Dict	     =				self.get_tokenizer()

           lowercase							:					List[Any]	     =				ChineseCLIPProcessor(tokenizer=snake_case		,image_processor=snake_case				)

           lowercase							:					str	     =				"""Alexandra，T-shirt的价格是15便士。"""
           lowercase							:					Optional[Any]	     =				self.prepare_image_inputs()

           lowercase							:					Dict	     =				processor(text=snake_case		,images=snake_case				)

           self.assertListEqual(list(inputs.keys()				)		,processor.model_input_names				)
