

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		:   List[Any]										=       ["""bart.large""", """bart.large.mnli""", """bart.large.cnn""", """bart_xsum/model.pt"""]
_UpperCAmelCase		:   Dict										=       {"""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		:   Dict										=       logging.get_logger(__name__)

_UpperCAmelCase		:   List[Any]										=       """ Hello world! cécé herlolip"""

_UpperCAmelCase		:   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	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    [
							    'encoder.version',
							    'decoder.version',
							    'model.encoder.version',
							    'model.decoder.version',
							    '_float_tensor',
							]
							for k in ignore_keys:
														state_dict.pop(UpperCamelCase__   ,							UpperCamelCase__      )





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    dct.pop(UpperCamelCase__      )
							snake_case_							=    val





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    torch.load(UpperCamelCase__   ,							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 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_				,    snake_case_							=    emb.weight.shape
							snake_case_							=    nn.Linear(UpperCamelCase__   ,							UpperCamelCase__   ,							bias=UpperCamelCase__      )
							snake_case_							=    emb.weight.data
							return lin_layer





@torch.no_grad()
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__=None      ):







							'''simple docstring'''






							if not os.path.exists(UpperCamelCase__      ):
														snake_case_							=    torch.hub.load('pytorch/fairseq'   ,							UpperCamelCase__      ).eval()
							else:
														snake_case_							=    load_xsum_checkpoint(UpperCamelCase__      )

							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(UpperCamelCase__      )
							snake_case_							=    bart.encode(UpperCamelCase__      ).unsqueeze(0      )
							snake_case_							=    BartTokenizer.from_pretrained(UpperCamelCase__      ).encode(UpperCamelCase__   ,							return_tensors='pt'      ).unsqueeze(0      )
							if not torch.eq(UpperCamelCase__   ,							UpperCamelCase__      ).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_(UpperCamelCase__      )
														snake_case_							=    state_dict['model.decoder.embed_tokens.weight']
														for src, dest in mnli_rename_keys:
																					rename_key(UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      )
														snake_case_							=    BartForSequenceClassification(UpperCamelCase__      ).eval()
														model.load_state_dict(UpperCamelCase__      )
														snake_case_							=    bart.predict('mnli'   ,							UpperCamelCase__   ,							return_logits=UpperCamelCase__      )
														snake_case_							=    model(UpperCamelCase__      )[0]  # logits
							else:  # no classification heads to worry about
														snake_case_							=    bart.model.state_dict()
														remove_ignore_keys_(UpperCamelCase__      )
														snake_case_							=    state_dict['decoder.embed_tokens.weight']
														snake_case_							=    bart.extract_features(UpperCamelCase__      )
														if hf_checkpoint_name == "facebook/bart-large":
																					snake_case_							=    BartModel(UpperCamelCase__      ).eval()
																					model.load_state_dict(UpperCamelCase__      )
																					snake_case_							=    model(UpperCamelCase__      ).model[0]
														else:
																					snake_case_							=    BartForConditionalGeneration(UpperCamelCase__      ).eval()  # an existing summarization ckpt
																					model.model.load_state_dict(UpperCamelCase__      )
																					if hasattr(UpperCamelCase__   ,							'lm_head'      ):
																												snake_case_							=    make_linear_from_emb(model.model.shared      )
																					snake_case_							=    model.model(UpperCamelCase__      )[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(UpperCamelCase__      ).mkdir(exist_ok=UpperCamelCase__      )
							model.save_pretrained(UpperCamelCase__      )


if __name__ == "__main__":
					_UpperCAmelCase		:   Union[str, 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"""
					)
					_UpperCAmelCase		:   str										=       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 tempfile

import datasets
from utils import generate_example_dataset, get_duration


_UpperCAmelCase		:   Optional[int]										=       5_0000
_UpperCAmelCase		:   Dict										=       5000

_UpperCAmelCase				,						_UpperCAmelCase		:   Optional[int]										=       os.path.split(__file__)
_UpperCAmelCase		:   List[str]										=       os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))





@get_duration
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							for i in range(UpperCamelCase__      ):
														snake_case_							=    dataset[i]





@get_duration
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							for i in range(0   ,							len(UpperCamelCase__      )   ,							UpperCamelCase__      ):
														snake_case_							=    dataset[i : i + batch_size]





@get_duration
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							with dataset.formatted_as(type=UpperCamelCase__      ):
														for i in range(UpperCamelCase__      ):
																					snake_case_							=    dataset[i]





@get_duration
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							with dataset.formatted_as(type=UpperCamelCase__      ):
														for i in range(0   ,							UpperCamelCase__   ,							UpperCamelCase__      ):
																					snake_case_							=    dataset[i : i + batch_size]





def 					__lowerCamelCase	( ):







							'''simple docstring'''






							snake_case_							=    {'num examples': SPEED_TEST_N_EXAMPLES}
							snake_case_							=    [
							    (read, {'length': SMALL_TEST}),
							    (read, {'length': SPEED_TEST_N_EXAMPLES}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1000}),
							    (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
							    (read_formatted, {'type': 'pandas', 'length': SMALL_TEST}),
							    (read_formatted, {'type': 'torch', 'length': SMALL_TEST}),
							    (read_formatted, {'type': 'tensorflow', 'length': SMALL_TEST}),
							    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
							    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1000}),
							]

							snake_case_							=    [
							    (read, {'length': SMALL_TEST}),
							    (read, {'length': SPEED_TEST_N_EXAMPLES}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1000}),
							    (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
							    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
							    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1000}),
							]
							with tempfile.TemporaryDirectory() as tmp_dir:
														print('generating dataset'      )
														snake_case_							=    datasets.Features(
														    {'list': datasets.Sequence(datasets.Value('float32'      )      ), 'numbers': datasets.Value('float32'      )}      )
														snake_case_							=    generate_example_dataset(
														    os.path.join(UpperCamelCase__   ,							'dataset.arrow'      )   ,							UpperCamelCase__   ,							num_examples=UpperCamelCase__   ,							seq_shapes={'list': (100,)}   ,							)
														print('first set of iterations'      )
														for func, kwargs in functions:
																					print(func.__name__   ,							str(UpperCamelCase__      )      )
																					snake_case_							=    func(UpperCamelCase__   ,							**UpperCamelCase__      )

														print('shuffling dataset'      )
														snake_case_							=    dataset.shuffle()
														print('Second set of iterations (after shuffling'      )
														for func, kwargs in functions_shuffled:
																					print('shuffled '   ,							func.__name__   ,							str(UpperCamelCase__      )      )
																					snake_case_							=    func(
																					    UpperCamelCase__   ,							**UpperCamelCase__      )

							with open(UpperCamelCase__   ,							'wb'      ) as f:
														f.write(json.dumps(UpperCamelCase__      ).encode('utf-8'      )      )


if __name__ == "__main__":  # useful to run the profiler
					benchmark_iterating()




