


"""simple docstring"""


import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
		import tensorflow as tf

if is_keras_nlp_available():
		from transformers.models.gpta import TFGPTaTokenizer


a_       							=    ["gpt2"]
a_       							=    "gpt2"

if is_tf_available():
		class snake_case      (  tf.Module):







				def __init__(					self      :       Optional[Any]    ,   a__      :       str		)       ->						str:





								'''simple docstring'''







								super().__init__()
								_A					  =			tokenizer
								_A					  =			AutoConfig.from_pretrained(a__		)
								_A					  =			TFGPTaLMHeadModel.from_config(a__		)







				@tf.function(input_signature=(tf.TensorSpec((None,)    ,   tf.string    ,   name="text"		),)		)
				def  a_ (					self      :       List[str]    ,   a__      :       Dict		)       ->						Union[str, Any]:





								'''simple docstring'''







								_A					  =			self.tokenizer(a__		)
								_A					  =			tokenized["input_ids"].to_tensor()

								_A					  =			tf.cast(input_ids_dense > 0    ,   tf.intaa		)
								# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

								_A					  =			self.model(input_ids=a__    ,   attention_mask=a__		)["logits"]

								return outputs
@require_tf
@require_keras_nlp
class snake_case      (  unittest.TestCase):







		def  a_ (					self      :       List[Any]		)       ->						str:





						'''simple docstring'''







						super().setUp()

						_A					  =			[GPTaTokenizer.from_pretrained(a__		) for checkpoint in (TOKENIZER_CHECKPOINTS)]
						_A					  =			[TFGPTaTokenizer.from_pretrained(a__		) for checkpoint in TOKENIZER_CHECKPOINTS]
						assert len(self.tokenizers		) == len(self.tf_tokenizers		)

						_A					  =			[
						    "This is a straightforward English test sentence.",
						    "This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.",
						    "Now we're going to add some Chinese: 一 二 三 一二三",
						    "And some much more rare Chinese: 齉 堃 齉堃",
						    "Je vais aussi écrire en français pour tester les accents",
						    "Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ",
						]
						_A					  =			list(zip(self.test_sentences    ,   self.test_sentences[::-1]		)		)







		def  a_ (					self      :       Optional[int]		)       ->						Tuple:





						'''simple docstring'''







						for tokenizer, tf_tokenizer in zip(self.tokenizers    ,   self.tf_tokenizers		):
										for test_inputs in self.test_sentences:
														_A					  =			tokenizer([test_inputs]    ,   return_tensors="tf"		)
														_A					  =			tf_tokenizer([test_inputs]		)

														for key in python_outputs.keys():
																		# convert them to numpy to avoid messing with ragged tensors
																		_A					  =			python_outputs[key].numpy()
																		_A					  =			tf_outputs[key].numpy()

																		self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape		)		)
																		self.assertTrue(tf.reduce_all(tf.cast(a__    ,   tf.intaa		) == tf_outputs_values		)		)







		@slow
		def  a_ (					self      :       Any		)       ->						List[Any]:





						'''simple docstring'''







						for tf_tokenizer in self.tf_tokenizers:
										_A					  =			tf.function(a__		)
										for test_inputs in self.test_sentences:
														_A					  =			tf.constant(a__		)
														_A					  =			compiled_tokenizer(a__		)
														_A					  =			tf_tokenizer(a__		)

														for key in eager_outputs.keys():
																		self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]		)		)







		@slow
		def  a_ (					self      :       List[Any]		)       ->						str:





						'''simple docstring'''







						for tf_tokenizer in self.tf_tokenizers:
										_A					  =			ModelToSave(tokenizer=a__		)
										_A					  =			tf.convert_to_tensor([self.test_sentences[0]]		)
										_A					  =			model.serving(a__		)  # Build model with some sample inputs
										with TemporaryDirectory() as tempdir:
														_A					  =			Path(a__		) / "saved.model"
														tf.saved_model.save(a__    ,   a__    ,   signatures={"serving_default": model.serving}		)
														_A					  =			tf.saved_model.load(a__		)
										_A					  =			loaded_model.signatures["serving_default"](a__		)["output_0"]
										# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
										self.assertTrue(tf.reduce_all(out == loaded_output		)		)







		@slow
		def  a_ (					self      :       int		)       ->						Union[str, Any]:





						'''simple docstring'''







						for tf_tokenizer in self.tf_tokenizers:
										_A					  =			tf.convert_to_tensor([self.test_sentences[0]]		)
										_A					  =			tf_tokenizer(a__		)  # Build model with some sample inputs

										_A					  =			tf_tokenizer.get_config()
										_A					  =			TFGPTaTokenizer.from_config(a__		)
										_A					  =			model_from_config(a__		)

										for key in from_config_output.keys():
														self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]		)		)







		@slow
		def  a_ (					self      :       Dict		)       ->						Dict:





						'''simple docstring'''







						for tf_tokenizer in self.tf_tokenizers:
										# for the test to run
										_A					  =			12_31_23

										for max_length in [3, 5, 10_24]:
														_A					  =			tf.convert_to_tensor([self.test_sentences[0]]		)
														_A					  =			tf_tokenizer(a__    ,   max_length=a__		)

														_A					  =			out["input_ids"].numpy().shape[1]

														assert out_length == max_length



"""simple docstring"""


import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def      a__ (				__lowercase   )	->	Optional[int]:
				_A					  =			[
				    "encoder.version",
				    "decoder.version",
				    "model.encoder.version",
				    "model.decoder.version",
				    "_float_tensor",
				    "decoder.output_projection.weight",
				]
				for k in ignore_keys:
								state_dict.pop(__lowercase     ,			__lowercase   )






def      a__ (				__lowercase   )	->	List[Any]:
				_A  ,		_A					  =			emb.weight.shape
				_A					  =			nn.Linear(__lowercase     ,			__lowercase     ,			bias=__lowercase   )
				_A					  =			emb.weight.data
				return lin_layer






def      a__ (				__lowercase     ,			__lowercase="facebook/mbart-large-en-ro"     ,			__lowercase=False     ,			__lowercase=False   )	->	List[str]:
				_A					  =			torch.load(__lowercase     ,			map_location="cpu"   )["model"]
				remove_ignore_keys_(__lowercase   )
				_A					  =			state_dict["encoder.embed_tokens.weight"].shape[0]

				_A					  =			MBartConfig.from_pretrained(__lowercase     ,			vocab_size=__lowercase   )
				if mbart_aa and finetuned:
								_A					  =			"relu"

				_A					  =			state_dict["decoder.embed_tokens.weight"]
				_A					  =			MBartForConditionalGeneration(__lowercase   )
				model.model.load_state_dict(__lowercase   )

				if finetuned:
								_A					  =			make_linear_from_emb(model.model.shared   )

				return model


if __name__ == "__main__":
		a_       							=    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="facebook/mbart-large-cc25",
		    type=str,
		    help="Which huggingface architecture to use: mbart-large",
		)
		parser.add_argument("--mbart_50", action="store_true", help="whether the model is mMART-50 checkpoint")
		parser.add_argument("--finetuned", action="store_true", help="whether the model is a fine-tuned checkpoint")
		a_       							=    parser.parse_args()
		a_       							=    convert_fairseq_mbart_checkpoint_from_disk(
		    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
		)
		model.save_pretrained(args.pytorch_dump_folder_path)