
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
		import tensorflow as tf

if is_tensorflow_text_available():
		from transformers.models.bert import TFBertTokenizer


lowerCamelCase__	=							["bert-base-uncased", "bert-base-cased"]
lowerCamelCase__	=							"hf-internal-testing/tiny-bert-tf-only"

if is_tf_available():







		class        lowerCAmelCase__      (   tf.keras.Model	):

								def __init__(			self       ,							a				)					->      List[str]:


										'''simple docstring'''

										super().__init__()
										_UpperCamelCase						    =	tokenizer
										_UpperCamelCase						    =	AutoConfig.from_pretrained(a				)
										_UpperCamelCase						    =	TFAutoModel.from_config(a				)







								def  A_				(			self       ,							a				)					->      Any:


										'''simple docstring'''

										_UpperCamelCase						    =	self.tokenizer(a				)
										_UpperCamelCase						    =	self.bert(**a				)
										return out["pooler_output"]







@require_tf
@require_tensorflow_text
class        lowerCAmelCase__      (   unittest.TestCase	):

						def  A_				(			self				)					->      Optional[int]:


								'''simple docstring'''

								super().setUp()

								_UpperCamelCase						    =	[
								    BertTokenizer.from_pretrained(a				) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
								]  # repeat for when fast_bert_tokenizer=false
								_UpperCamelCase						    =	[TFBertTokenizer.from_pretrained(a				) for checkpoint in TOKENIZER_CHECKPOINTS] + [
								    TFBertTokenizer.from_pretrained(a       ,							use_fast_bert_tokenizer=a				)
								    for checkpoint in TOKENIZER_CHECKPOINTS
								]
								assert len(self.tokenizers				) == len(self.tf_tokenizers				)

								_UpperCamelCase						    =	[
								    """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""",
								]
								_UpperCamelCase						    =	list(zip(self.test_sentences       ,							self.test_sentences[::-1]				)				)

						def  A_				(			self				)					->      Any:


								'''simple docstring'''

								for tokenizer, tf_tokenizer in zip(self.tokenizers       ,							self.tf_tokenizers				):
										for test_inputs in (self.test_sentences, self.paired_sentences):
												_UpperCamelCase						    =	tokenizer(a       ,							return_tensors="""tf"""       ,							padding="""longest"""				)
												_UpperCamelCase						    =	tf_tokenizer(a				)

												for key in python_outputs.keys():
														self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape				)				)
														self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]       ,							tf.intaa				) == tf_outputs[key]				)				)

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


								'''simple docstring'''

								for tf_tokenizer in self.tf_tokenizers:
										_UpperCamelCase						    =	tf_tokenizer(self.paired_sentences				)
										_UpperCamelCase						    =	tf_tokenizer(
										    text=[sentence[0] for sentence in self.paired_sentences]       ,							text_pair=[sentence[1] for sentence in self.paired_sentences]       ,							)
										for key in merged_outputs.keys():
												self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]       ,							tf.intaa				) == separated_outputs[key]				)				)

						@slow
						def  A_				(			self				)					->      str:


								'''simple docstring'''

								for tf_tokenizer in self.tf_tokenizers:
										_UpperCamelCase						    =	tf.function(a				)
										for test_inputs in (self.test_sentences, self.paired_sentences):
												_UpperCamelCase						    =	tf.constant(a				)
												_UpperCamelCase						    =	compiled_tokenizer(a				)
												_UpperCamelCase						    =	tf_tokenizer(a				)

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







						@slow
						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

								for tf_tokenizer in self.tf_tokenizers:
										_UpperCamelCase						    =	ModelToSave(tokenizer=a				)
										_UpperCamelCase						    =	tf.convert_to_tensor(self.test_sentences				)
										_UpperCamelCase						    =	model(a				)  # Build model with some sample inputs
										with TemporaryDirectory() as tempdir:
												_UpperCamelCase						    =	Path(a				) / """saved.model"""
												model.save(a				)
												_UpperCamelCase						    =	tf.keras.models.load_model(a				)
										_UpperCamelCase						    =	loaded_model(a				)
										# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
										self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output				)				)       ,							1e-5				)





import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def 							__A(lowerCAmelCase )		->				Any:

		"""simple docstring"""






		_UpperCamelCase						    =	[
		    """decoder.version""",
		    """decoder.output_projection.weight""",
		    """_float_tensor""",
		    """decoder.embed_positions._float_tensor""",
		]
		for k in ignore_keys:
				state_dict.pop(lowerCAmelCase						,  lowerCAmelCase )


def 							__A(lowerCAmelCase )		->				Optional[int]:

		"""simple docstring"""






		_UpperCamelCase    ,	_UpperCamelCase						    =	emb.weight.shape
		_UpperCamelCase						    =	nn.Linear(lowerCAmelCase						,  lowerCAmelCase						,  bias=lowerCAmelCase )
		_UpperCamelCase						    =	emb.weight.data
		return lin_layer


def 							__A(lowerCAmelCase )		->				Tuple:

		"""simple docstring"""






		_UpperCamelCase						    =	torch.load(lowerCAmelCase						,  map_location="""cpu""" )
		_UpperCamelCase						    =	Namespace(**checkpoint["""cfg"""]["""model"""] )
		_UpperCamelCase						    =	checkpoint["""model"""]
		remove_ignore_keys_(lowerCAmelCase )
		_UpperCamelCase						    =	state_dict["""decoder.embed_tokens.weight"""].shape[0]

		_UpperCamelCase						    =	{key.replace("""decoder"""						,  """model""" ): val for key, val in state_dict.items()}

		_UpperCamelCase						    =	XGLMConfig(
		    vocab_size=lowerCAmelCase						,  max_position_embeddings=args.max_target_positions						,  num_layers=args.decoder_layers						,  attention_heads=args.decoder_attention_heads						,  ffn_dim=args.decoder_ffn_embed_dim						,  d_model=args.decoder_embed_dim						,  layerdrop=args.decoder_layerdrop						,  dropout=args.dropout						,  attention_dropout=args.attention_dropout						,  activation_dropout=args.activation_dropout						,  activation_function="""gelu"""						,  scale_embedding=not args.no_scale_embedding						,  tie_word_embeddings=args.share_decoder_input_output_embed						,  )

		_UpperCamelCase						    =	XGLMForCausalLM(lowerCAmelCase )
		_UpperCamelCase						    =	model.load_state_dict(lowerCAmelCase						,  strict=lowerCAmelCase )
		print(lowerCAmelCase )
		_UpperCamelCase						    =	make_linear_from_emb(model.model.embed_tokens )

		return model


if __name__ == "__main__":
		lowerCamelCase__	=							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument("fairseq_path", type=str, help="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.")
		lowerCamelCase__	=							parser.parse_args()
		lowerCamelCase__	=							convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
		model.save_pretrained(args.pytorch_dump_folder_path)



