

"""simple docstring"""





import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
							import jax
							import jax.numpy as jnp

							from transformers.modeling_flax_pytorch_utils import (
							    convert_pytorch_state_dict_to_flax,
							    load_flax_weights_in_pytorch_model,
							)
							from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
							import torch







class 							SCREAMING_SNAKE_CASE   :






					"""simple docstring"""



					def __init__(			self     :							Tuple  ,lowercase_     :							Optional[int]  ,lowercase_     :							str=1_4  ,lowercase_     :							int=7  ,lowercase_     :							List[str]=True  ,lowercase_     :							List[Any]=True  ,lowercase_     :							Optional[Any]=False  ,lowercase_     :							Optional[int]=True  ,lowercase_     :							Optional[int]=9_9  ,lowercase_     :							Dict=3_2  ,lowercase_     :							List[str]=4  ,lowercase_     :							Optional[int]=4  ,lowercase_     :							Optional[Any]=4  ,lowercase_     :							List[str]=3_7  ,lowercase_     :							List[Any]="gelu"  ,lowercase_     :							Any=0.1  ,lowercase_     :							Optional[int]=0.1  ,lowercase_     :							List[Any]=5_1_2  ,lowercase_     :							Optional[Any]=0.02  ,):
						lowerCAmelCase__      :		List[Any]    							=      parent
						lowerCAmelCase__      :		int    							=      batch_size
						lowerCAmelCase__      :		int    							=      seq_length
						lowerCAmelCase__      :		Union[str, Any]    							=      is_training
						lowerCAmelCase__      :		Optional[int]    							=      use_input_mask
						lowerCAmelCase__      :		List[str]    							=      use_token_type_ids
						lowerCAmelCase__      :		Any    							=      use_labels
						lowerCAmelCase__      :		int    							=      vocab_size
						lowerCAmelCase__      :		int    							=      hidden_size
						lowerCAmelCase__      :		Any    							=      rotary_dim
						lowerCAmelCase__      :		Any    							=      num_hidden_layers
						lowerCAmelCase__      :		str    							=      num_attention_heads
						lowerCAmelCase__      :		Optional[Any]    							=      intermediate_size
						lowerCAmelCase__      :		Optional[Any]    							=      hidden_act
						lowerCAmelCase__      :		Tuple    							=      hidden_dropout_prob
						lowerCAmelCase__      :		Optional[Any]    							=      attention_probs_dropout_prob
						lowerCAmelCase__      :		Optional[int]    							=      max_position_embeddings
						lowerCAmelCase__      :		Union[str, Any]    							=      initializer_range
						lowerCAmelCase__      :		List[Any]    							=      None
						lowerCAmelCase__      :		Tuple    							=      vocab_size - 1
						lowerCAmelCase__      :		List[str]    							=      vocab_size - 1
						lowerCAmelCase__      :		str    							=      vocab_size - 1







					def 			__lowerCAmelCase					(			self     :							Optional[int]						):
						lowerCAmelCase__      :		Union[str, Any]    							=      ids_tensor([self.batch_size, self.seq_length]  ,self.vocab_size						)

						lowerCAmelCase__      :		Optional[int]    							=      None
						if self.use_input_mask:
							lowerCAmelCase__      :		Any    							=      random_attention_mask([self.batch_size, self.seq_length]						)

						lowerCAmelCase__      :		int    							=      GPTJConfig(
						    vocab_size=self.vocab_size  ,n_embd=self.hidden_size  ,n_layer=self.num_hidden_layers  ,n_head=self.num_attention_heads  ,n_positions=self.max_position_embeddings  ,use_cache=__UpperCamelCase  ,bos_token_id=self.bos_token_id  ,eos_token_id=self.eos_token_id  ,pad_token_id=self.pad_token_id  ,rotary_dim=self.rotary_dim  ,)

						return (config, input_ids, input_mask)







					def 			__lowerCAmelCase					(			self     :							List[str]						):
						lowerCAmelCase__      :		Any    							=      self.prepare_config_and_inputs()
						lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__      :		Optional[int]    							=      config_and_inputs
						lowerCAmelCase__      :		Optional[Any]    							=      {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
						return config, inputs_dict







					def 			__lowerCAmelCase					(			self     :							Dict  ,lowercase_     :							Union[str, Any]  ,lowercase_     :							Any  ,lowercase_     :							Union[str, Any]  ,lowercase_     :							int						):
						lowerCAmelCase__      :		List[str]    							=      2_0
						lowerCAmelCase__      :		List[str]    							=      model_class_name(__UpperCamelCase						)

						lowerCAmelCase__      :		int    							=      model.init_cache(input_ids.shape[0]  ,__UpperCamelCase						)
						lowerCAmelCase__      :		Optional[int]    							=      jnp.ones((input_ids.shape[0], max_decoder_length)  ,dtype='''i4'''						)

						lowerCAmelCase__      :		Tuple    							=      jnp.broadcast_to(
						    jnp.arange(input_ids.shape[-1] - 1						)[None, :]  ,(input_ids.shape[0], input_ids.shape[-1] - 1)						)
						lowerCAmelCase__      :		Union[str, Any]    							=      model(
						    input_ids[:, :-1]  ,attention_mask=__UpperCamelCase  ,past_key_values=__UpperCamelCase  ,position_ids=__UpperCamelCase  ,)

						lowerCAmelCase__      :		Union[str, Any]    							=      jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,dtype='''i4'''						)
						lowerCAmelCase__      :		List[Any]    							=      model(
						    input_ids[:, -1:]  ,attention_mask=__UpperCamelCase  ,past_key_values=outputs_cache.past_key_values  ,position_ids=__UpperCamelCase  ,)

						lowerCAmelCase__      :		List[Any]    							=      model(__UpperCamelCase						)

						lowerCAmelCase__      :		Dict    							=      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
						self.parent.assertTrue(diff < 1E-3  ,msg=F'Max diff is {diff}'						)







					def 			__lowerCAmelCase					(			self     :							Dict  ,lowercase_     :							List[Any]  ,lowercase_     :							Tuple  ,lowercase_     :							Any  ,lowercase_     :							str						):
						lowerCAmelCase__      :		Dict    							=      2_0
						lowerCAmelCase__      :		Optional[Any]    							=      model_class_name(__UpperCamelCase						)

						lowerCAmelCase__      :		List[Any]    							=      jnp.concatenate(
						    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])						)]  ,axis=-1  ,)

						lowerCAmelCase__      :		Dict    							=      model.init_cache(input_ids.shape[0]  ,__UpperCamelCase						)
						lowerCAmelCase__      :		Any    							=      jnp.broadcast_to(
						    jnp.arange(input_ids.shape[-1] - 1						)[None, :]  ,(input_ids.shape[0], input_ids.shape[-1] - 1)						)

						lowerCAmelCase__      :		Union[str, Any]    							=      model(
						    input_ids[:, :-1]  ,attention_mask=__UpperCamelCase  ,past_key_values=__UpperCamelCase  ,position_ids=__UpperCamelCase  ,)
						lowerCAmelCase__      :		Optional[int]    							=      jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,dtype='''i4'''						)
						lowerCAmelCase__      :		List[Any]    							=      model(
						    input_ids[:, -1:]  ,past_key_values=outputs_cache.past_key_values  ,attention_mask=__UpperCamelCase  ,position_ids=__UpperCamelCase  ,)

						lowerCAmelCase__      :		str    							=      model(__UpperCamelCase  ,attention_mask=__UpperCamelCase						)

						lowerCAmelCase__      :		Union[str, Any]    							=      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
						self.parent.assertTrue(diff < 1E-3  ,msg=F'Max diff is {diff}'						)







@require_flax
class 							SCREAMING_SNAKE_CASE   (						_a       ,				_a       ,				unittest.TestCase			):






					"""simple docstring"""



					lowercase__												=			(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
					lowercase__												=			(FlaxGPTJForCausalLM,) if is_flax_available() else ()







					def 			__lowerCAmelCase					(			self     :							int						):
						lowerCAmelCase__      :		str    							=      FlaxGPTJModelTester(self						)







					def 			__lowerCAmelCase					(			self     :							Any						):
						for model_class_name in self.all_model_classes:
							lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__      :		Union[str, Any]    							=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.check_use_cache_forward(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase						)







					def 			__lowerCAmelCase					(			self     :							List[str]						):
						for model_class_name in self.all_model_classes:
							lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__      :		Optional[Any]    							=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.check_use_cache_forward_with_attn_mask(
							    __UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase						)







					@tooslow
					def 			__lowerCAmelCase					(			self     :							Any						):
						lowerCAmelCase__      :		Tuple    							=      GPTaTokenizer.from_pretrained('''gpt2'''  ,pad_token='''<|endoftext|>'''  ,padding_side='''left'''						)
						lowerCAmelCase__      :		Tuple    							=      tokenizer(['''Hello this is a long string''', '''Hey''']  ,return_tensors='''np'''  ,padding=__UpperCamelCase  ,truncation=__UpperCamelCase						)

						lowerCAmelCase__      :		Any    							=      FlaxGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B'''						)
						lowerCAmelCase__      :		int    							=      False
						lowerCAmelCase__      :		Optional[Any]    							=      model.config.eos_token_id

						lowerCAmelCase__      :		int    							=      jax.jit(model.generate						)

						lowerCAmelCase__      :		List[Any]    							=      jit_generate(
						    inputs['''input_ids''']  ,attention_mask=inputs['''attention_mask''']  ,pad_token_id=tokenizer.pad_token_id						).sequences

						lowerCAmelCase__      :		str    							=      tokenizer.batch_decode(__UpperCamelCase  ,skip_special_tokens=__UpperCamelCase						)

						lowerCAmelCase__      :		Optional[Any]    							=      [
						    '''Hello this is a long string of text.\n\nI\'m trying to get the text of the''',
						    '''Hey, I\'m a little late to the party. I\'m going to''',
						]

						self.assertListEqual(__UpperCamelCase  ,__UpperCamelCase						)







					@is_pt_flax_cross_test
					def 			__lowerCAmelCase					(			self     :							str						):
						lowerCAmelCase__							,lowerCAmelCase__      :		str    							=      self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__						):
								# prepare inputs
								lowerCAmelCase__      :		Dict    							=      self._prepare_for_class(__UpperCamelCase  ,__UpperCamelCase						)
								lowerCAmelCase__      :		Dict    							=      {k: torch.tensor(v.tolist()						) for k, v in prepared_inputs_dict.items()}

								# load corresponding PyTorch class
								lowerCAmelCase__      :		Union[str, Any]    							=      model_class.__name__[4:]  # Skip the "Flax" at the beginning
								lowerCAmelCase__      :		Optional[Any]    							=      getattr(__UpperCamelCase  ,__UpperCamelCase						)

								lowerCAmelCase__							,lowerCAmelCase__      :		Any    							=      pt_inputs['''input_ids'''].shape
								lowerCAmelCase__      :		Optional[int]    							=      np.random.randint(0  ,seq_length - 1  ,size=(batch_size,)						)
								for batch_idx, start_index in enumerate(__UpperCamelCase						):
									lowerCAmelCase__      :		int    							=      0
									lowerCAmelCase__      :		Union[str, Any]    							=      1
									lowerCAmelCase__      :		str    							=      0
									lowerCAmelCase__      :		Any    							=      1
								lowerCAmelCase__      :		Optional[Any]    							=      pt_model_class(__UpperCamelCase						).eval()
								lowerCAmelCase__      :		Any    							=      model_class(__UpperCamelCase  ,dtype=jnp.floataa						)

								lowerCAmelCase__      :		List[str]    							=      convert_pytorch_state_dict_to_flax(pt_model.state_dict()  ,__UpperCamelCase						)
								lowerCAmelCase__      :		Dict    							=      fx_state

								with torch.no_grad():
									lowerCAmelCase__      :		Optional[Any]    							=      pt_model(**__UpperCamelCase						).to_tuple()

								lowerCAmelCase__      :		List[Any]    							=      fx_model(**__UpperCamelCase						).to_tuple()
								self.assertEqual(len(__UpperCamelCase						)  ,len(__UpperCamelCase						)  ,'''Output lengths differ between Flax and PyTorch'''						)
								for fx_output, pt_output in zip(__UpperCamelCase  ,__UpperCamelCase						):
									self.assert_almost_equals(fx_output[:, -1]  ,pt_output[:, -1].numpy()  ,4E-2						)

								with tempfile.TemporaryDirectory() as tmpdirname:
									pt_model.save_pretrained(__UpperCamelCase						)
									lowerCAmelCase__      :		Any    							=      model_class.from_pretrained(__UpperCamelCase  ,from_pt=__UpperCamelCase						)

								lowerCAmelCase__      :		Tuple    							=      fx_model_loaded(**__UpperCamelCase						).to_tuple()
								self.assertEqual(
								    len(__UpperCamelCase						)  ,len(__UpperCamelCase						)  ,'''Output lengths differ between Flax and PyTorch'''						)
								for fx_output_loaded, pt_output in zip(__UpperCamelCase  ,__UpperCamelCase						):
									self.assert_almost_equals(fx_output_loaded[:, -1]  ,pt_output[:, -1].numpy()  ,4E-2						)







					@is_pt_flax_cross_test
					def 			__lowerCAmelCase					(			self     :							Union[str, Any]						):
						lowerCAmelCase__							,lowerCAmelCase__      :		Dict    							=      self.model_tester.prepare_config_and_inputs_for_common()
						for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__						):
								# prepare inputs
								lowerCAmelCase__      :		Union[str, Any]    							=      self._prepare_for_class(__UpperCamelCase  ,__UpperCamelCase						)
								lowerCAmelCase__      :		Dict    							=      {k: torch.tensor(v.tolist()						) for k, v in prepared_inputs_dict.items()}

								# load corresponding PyTorch class
								lowerCAmelCase__      :		str    							=      model_class.__name__[4:]  # Skip the "Flax" at the beginning
								lowerCAmelCase__      :		Tuple    							=      getattr(__UpperCamelCase  ,__UpperCamelCase						)

								lowerCAmelCase__      :		Optional[Any]    							=      pt_model_class(__UpperCamelCase						).eval()
								lowerCAmelCase__      :		Optional[int]    							=      model_class(__UpperCamelCase  ,dtype=jnp.floataa						)

								lowerCAmelCase__      :		List[str]    							=      load_flax_weights_in_pytorch_model(__UpperCamelCase  ,fx_model.params						)
								lowerCAmelCase__							,lowerCAmelCase__      :		Optional[int]    							=      pt_inputs['''input_ids'''].shape
								lowerCAmelCase__      :		Dict    							=      np.random.randint(0  ,seq_length - 1  ,size=(batch_size,)						)
								for batch_idx, start_index in enumerate(__UpperCamelCase						):
									lowerCAmelCase__      :		Any    							=      0
									lowerCAmelCase__      :		int    							=      1
									lowerCAmelCase__      :		Union[str, Any]    							=      0
									lowerCAmelCase__      :		str    							=      1

								# make sure weights are tied in PyTorch
								pt_model.tie_weights()

								with torch.no_grad():
									lowerCAmelCase__      :		Tuple    							=      pt_model(**__UpperCamelCase						).to_tuple()

								lowerCAmelCase__      :		Any    							=      fx_model(**__UpperCamelCase						).to_tuple()
								self.assertEqual(len(__UpperCamelCase						)  ,len(__UpperCamelCase						)  ,'''Output lengths differ between Flax and PyTorch'''						)
								for fx_output, pt_output in zip(__UpperCamelCase  ,__UpperCamelCase						):
									self.assert_almost_equals(fx_output[:, -1]  ,pt_output[:, -1].numpy()  ,4E-2						)

								with tempfile.TemporaryDirectory() as tmpdirname:
									fx_model.save_pretrained(__UpperCamelCase						)
									lowerCAmelCase__      :		Union[str, Any]    							=      pt_model_class.from_pretrained(__UpperCamelCase  ,from_flax=__UpperCamelCase						)

								with torch.no_grad():
									lowerCAmelCase__      :		List[Any]    							=      pt_model_loaded(**__UpperCamelCase						).to_tuple()

								self.assertEqual(
								    len(__UpperCamelCase						)  ,len(__UpperCamelCase						)  ,'''Output lengths differ between Flax and PyTorch'''						)
								for fx_output, pt_output in zip(__UpperCamelCase  ,__UpperCamelCase						):
									self.assert_almost_equals(fx_output[:, -1]  ,pt_output[:, -1].numpy()  ,4E-2						)







					@tooslow
					def 			__lowerCAmelCase					(			self     :							Optional[Any]						):
						for model_class_name in self.all_model_classes:
							lowerCAmelCase__      :		List[Any]    							=      model_class_name.from_pretrained('''EleutherAI/gpt-j-6B'''						)
							lowerCAmelCase__      :		Optional[int]    							=      model(np.ones((1, 1)						)						)
							self.assertIsNotNone(__UpperCamelCase						)






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


A		: int								=     ['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
A		: 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()
A		: Optional[int]								=     logging.get_logger(__name__)

A		: int								=     ''' Hello world! cécé herlolip'''

A		: List[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 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Union[str, Any]       )  ->     Tuple:
						_lowercase					       =					[
						    """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 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Union[str, Any]		,   SCREAMING_SNAKE_CASE_	:	Optional[int]		,   SCREAMING_SNAKE_CASE_	:	List[Any]       )  ->     List[str]:
						_lowercase					       =					dct.pop(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					val




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Any       )  ->     Any:
						_lowercase					       =					torch.load(SCREAMING_SNAKE_CASE_		,   map_location="""cpu"""       )
						_lowercase					       =					torch.hub.load("""pytorch/fairseq"""		,   """bart.large.cnn"""       ).eval()
						hub_interface.model.load_state_dict(sd["""model"""]       )
						return hub_interface




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[str]       )  ->     Optional[int]:
						_lowercase       ,						_lowercase					       =					emb.weight.shape
						_lowercase					       =					nn.Linear(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   bias=SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					emb.weight.data
						return lin_layer




@torch.no_grad()
def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Any		,   SCREAMING_SNAKE_CASE_	:	Tuple		,   SCREAMING_SNAKE_CASE_	:	int=None       )  ->     int:
						if not os.path.exists(SCREAMING_SNAKE_CASE_       ):
												_lowercase					       =					torch.hub.load("""pytorch/fairseq"""		,   SCREAMING_SNAKE_CASE_       ).eval()
						else:
												_lowercase					       =					load_xsum_checkpoint(SCREAMING_SNAKE_CASE_       )

						bart.model.upgrade_state_dict(bart.model.state_dict()       )
						if hf_checkpoint_name is None:
												_lowercase					       =					checkpoint_path.replace("""."""		,   """-"""       )
						_lowercase					       =					BartConfig.from_pretrained(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					bart.encode(SCREAMING_SNAKE_CASE_       ).unsqueeze(0       )
						_lowercase					       =					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					       =					bart.state_dict()
												remove_ignore_keys_(SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					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					       =					BartForSequenceClassification(SCREAMING_SNAKE_CASE_       ).eval()
												model.load_state_dict(SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					bart.predict("""mnli"""		,   SCREAMING_SNAKE_CASE_		,   return_logits=SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					model(SCREAMING_SNAKE_CASE_       )[0]  # logits
						else:  # no classification heads to worry about
												_lowercase					       =					bart.model.state_dict()
												remove_ignore_keys_(SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					state_dict["""decoder.embed_tokens.weight"""]
												_lowercase					       =					bart.extract_features(SCREAMING_SNAKE_CASE_       )
												if hf_checkpoint_name == "facebook/bart-large":
																		_lowercase					       =					BartModel(SCREAMING_SNAKE_CASE_       ).eval()
																		model.load_state_dict(SCREAMING_SNAKE_CASE_       )
																		_lowercase					       =					model(SCREAMING_SNAKE_CASE_       ).model[0]
												else:
																		_lowercase					       =					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					       =					make_linear_from_emb(model.model.shared       )
																		_lowercase					       =					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__":
		A		: 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'''
		)
		A		: Any								=     parser.parse_args()
		convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)