



'''simple docstring'''
from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers.models.xglm.modeling_tf_xglm import (
					    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
					    TFXGLMForCausalLM,
					    TFXGLMModel,
					)






@require_tf
class 							lowercase__	:
	lowercase__  				=			XGLMConfig
	lowercase__  				=			{}
	lowercase__  				=			"""gelu"""





	def __init__( self					:				List[str] ,lowerCamelCase__					:				Union[str, Any] ,lowerCamelCase__					:				Optional[Any]=14 ,lowerCamelCase__					:				Optional[int]=7 ,lowerCamelCase__					:				Union[str, Any]=True ,lowerCamelCase__					:				Any=True ,lowerCamelCase__					:				Tuple=True ,lowerCamelCase__					:				Union[str, Any]=99 ,lowerCamelCase__					:				List[Any]=32 ,lowerCamelCase__					:				int=2 ,lowerCamelCase__					:				Tuple=4 ,lowerCamelCase__					:				str=37 ,lowerCamelCase__					:				List[Any]="gelu" ,lowerCamelCase__					:				List[Any]=0.1 ,lowerCamelCase__					:				Dict=0.1 ,lowerCamelCase__					:				int=512 ,lowerCamelCase__					:				List[str]=0.0_2 ,):







							'''simple docstring'''
							_UpperCamelCase					:		str 				=   parent
							_UpperCamelCase					:		Optional[int] 				=   batch_size
							_UpperCamelCase					:		str 				=   seq_length
							_UpperCamelCase					:		Tuple 				=   is_training
							_UpperCamelCase					:		Dict 				=   use_input_mask
							_UpperCamelCase					:		str 				=   use_labels
							_UpperCamelCase					:		Union[str, Any] 				=   vocab_size
							_UpperCamelCase					:		Tuple 				=   d_model
							_UpperCamelCase					:		Any 				=   num_hidden_layers
							_UpperCamelCase					:		Union[str, Any] 				=   num_attention_heads
							_UpperCamelCase					:		Optional[Any] 				=   ffn_dim
							_UpperCamelCase					:		Optional[Any] 				=   activation_function
							_UpperCamelCase					:		List[Any] 				=   activation_dropout
							_UpperCamelCase					:		Union[str, Any] 				=   attention_dropout
							_UpperCamelCase					:		Optional[int] 				=   max_position_embeddings
							_UpperCamelCase					:		Tuple 				=   initializer_range
							_UpperCamelCase					:		int 				=   None
							_UpperCamelCase					:		Optional[int] 				=   0
							_UpperCamelCase					:		Optional[int] 				=   2
							_UpperCamelCase					:		int 				=   1





	def 	UpperCamelCase_  ( self					:				str ):







							'''simple docstring'''
							return XGLMConfig.from_pretrained('facebook/xglm-564M' )





	def 	UpperCamelCase_  ( self					:				int ):







							'''simple docstring'''
							_UpperCamelCase					:		List[Any] 				=   tf.clip_by_value(
							    ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size ) ,clip_value_min=0 ,clip_value_max=3 )

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

							_UpperCamelCase					:		Union[str, Any] 				=   self.get_config()

							_UpperCamelCase					:		str 				=   floats_tensor([self.num_hidden_layers, self.num_attention_heads] ,2 )

							return (
							    config,
							    input_ids,
							    input_mask,
							    head_mask,
							)





	def 	UpperCamelCase_  ( self					:				Any ):







							'''simple docstring'''
							return XGLMConfig(
							    vocab_size=self.vocab_size ,d_model=self.hidden_size ,num_layers=self.num_hidden_layers ,attention_heads=self.num_attention_heads ,ffn_dim=self.ffn_dim ,activation_function=self.activation_function ,activation_dropout=self.activation_dropout ,attention_dropout=self.attention_dropout ,max_position_embeddings=self.max_position_embeddings ,initializer_range=self.initializer_range ,use_cache=lowerCamelCase__ ,bos_token_id=self.bos_token_id ,eos_token_id=self.eos_token_id ,pad_token_id=self.pad_token_id ,return_dict=lowerCamelCase__ ,)





	def 	UpperCamelCase_  ( self					:				Optional[int] ):







							'''simple docstring'''
							_UpperCamelCase					:		List[Any] 				=   self.prepare_config_and_inputs()

							(
							    (
							    _UpperCamelCase
							)				,			(
							    _UpperCamelCase
							)				,			(
							    _UpperCamelCase
							)				,			(
							    _UpperCamelCase
							)				,			
							)					:		int 				=   config_and_inputs

							_UpperCamelCase					:		str 				=   {
							    'input_ids': input_ids,
							    'head_mask': head_mask,
							}

							return config, inputs_dict






@require_tf
class 							lowercase__	(							lowercase						,					lowercase						,					unittest.TestCase   ):
	lowercase__  				=			(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
	lowercase__  				=			(TFXGLMForCausalLM,) if is_tf_available() else ()
	lowercase__  				=			(
	    {"""feature-extraction""": TFXGLMModel, """text-generation""": TFXGLMForCausalLM} if is_tf_available() else {}
	)
	lowercase__  				=			False
	lowercase__  				=			False
	lowercase__  				=			False





	def 	UpperCamelCase_  ( self					:				Optional[Any] ):







							'''simple docstring'''
							_UpperCamelCase					:		List[str] 				=   TFXGLMModelTester(self )
							_UpperCamelCase					:		str 				=   ConfigTester(self ,config_class=lowerCamelCase__ ,n_embd=37 )





	def 	UpperCamelCase_  ( self					:				Any ):







							'''simple docstring'''
							self.config_tester.run_common_tests()





	@slow
	def 	UpperCamelCase_  ( self					:				Dict ):







							'''simple docstring'''
							for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													_UpperCamelCase					:		int 				=   TFXGLMModel.from_pretrained(lowerCamelCase__ )
													self.assertIsNotNone(lowerCamelCase__ )





	@unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.' )
	def 	UpperCamelCase_  ( self					:				Union[str, Any] ):







							'''simple docstring'''
							super().test_resize_token_embeddings()






@require_tf
class 							lowercase__	(							unittest.TestCase   ):





	@slow
	def 	UpperCamelCase_  ( self					:				Any ,lowerCamelCase__					:				Optional[Any]=True ):







							'''simple docstring'''
							_UpperCamelCase					:		int 				=   TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
							_UpperCamelCase					:		Optional[int] 				=   tf.convert_to_tensor([[2, 268, 9865]] ,dtype=tf.intaa )  # The dog
							# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
							# fmt: off
							_UpperCamelCase					:		Union[str, Any] 				=   [2, 268, 9865, 67, 11, 1988, 57252, 9865, 5, 984, 67, 1988, 213838, 1658, 53, 70446, 33, 6657, 278, 1581]
							# fmt: on
							_UpperCamelCase					:		Tuple 				=   model.generate(lowerCamelCase__ ,do_sample=lowerCamelCase__ ,num_beams=1 )
							if verify_outputs:
													self.assertListEqual(output_ids[0].numpy().tolist() ,lowerCamelCase__ )





	@slow
	def 	UpperCamelCase_  ( self					:				List[str] ):







							'''simple docstring'''
							_UpperCamelCase					:		Optional[int] 				=   XGLMTokenizer.from_pretrained('facebook/xglm-564M' )
							_UpperCamelCase					:		int 				=   TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )

							tf.random.set_seed(0 )
							_UpperCamelCase					:		List[str] 				=   tokenizer('Today is a nice day and' ,return_tensors='tf' )
							_UpperCamelCase					:		Any 				=   tokenized.input_ids
							# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
							with tf.device(':/CPU:0' ):
													_UpperCamelCase					:		Optional[int] 				=   model.generate(lowerCamelCase__ ,do_sample=lowerCamelCase__ ,seed=[7, 0] )
							_UpperCamelCase					:		Any 				=   tokenizer.decode(output_ids[0] ,skip_special_tokens=lowerCamelCase__ )

							_UpperCamelCase					:		List[Any] 				=   (
							    'Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'
							)
							self.assertEqual(lowerCamelCase__ ,lowerCamelCase__ )





	@slow
	def 	UpperCamelCase_  ( self					:				List[Any] ):







							'''simple docstring'''
							_UpperCamelCase					:		List[str] 				=   TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
							_UpperCamelCase					:		List[str] 				=   XGLMTokenizer.from_pretrained('facebook/xglm-564M' )

							_UpperCamelCase					:		Optional[Any] 				=   'left'

							# use different length sentences to test batching
							_UpperCamelCase					:		List[str] 				=   [
							    'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
							    'left-padding, such as in batched generation. The output for the sequence below should be the same '
							    'regardless of whether left padding is applied or not. When',
							    'Hello, my dog is a little',
							]

							_UpperCamelCase					:		Dict 				=   tokenizer(lowerCamelCase__ ,return_tensors='tf' ,padding=lowerCamelCase__ )
							_UpperCamelCase					:		List[Any] 				=   inputs['input_ids']

							_UpperCamelCase					:		int 				=   model.generate(input_ids=lowerCamelCase__ ,attention_mask=inputs['attention_mask'] ,max_new_tokens=12 )

							_UpperCamelCase					:		int 				=   tokenizer(sentences[0] ,return_tensors='tf' ).input_ids
							_UpperCamelCase					:		List[str] 				=   model.generate(input_ids=lowerCamelCase__ ,max_new_tokens=12 )

							_UpperCamelCase					:		List[str] 				=   tokenizer(sentences[1] ,return_tensors='tf' ).input_ids
							_UpperCamelCase					:		Union[str, Any] 				=   model.generate(input_ids=lowerCamelCase__ ,max_new_tokens=12 )

							_UpperCamelCase					:		List[str] 				=   tokenizer.batch_decode(lowerCamelCase__ ,skip_special_tokens=lowerCamelCase__ )
							_UpperCamelCase					:		Optional[int] 				=   tokenizer.decode(output_non_padded[0] ,skip_special_tokens=lowerCamelCase__ )
							_UpperCamelCase					:		List[Any] 				=   tokenizer.decode(output_padded[0] ,skip_special_tokens=lowerCamelCase__ )

							_UpperCamelCase					:		Any 				=   [
							    'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
							    'left-padding, such as in batched generation. The output for the sequence below should be the same '
							    'regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '
							    'a single',
							    'Hello, my dog is a little bit of a shy one, but he is very friendly',
							]
							self.assertListEqual(lowerCamelCase__ ,lowerCamelCase__ )
							self.assertListEqual(lowerCamelCase__ ,[non_padded_sentence, padded_sentence] )








import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  [
						    '''decoder.version''',
						    '''decoder.output_projection.weight''',
						    '''_float_tensor''',
						    '''decoder.embed_positions._float_tensor''',
						]
						for k in ignore_keys:
												state_dict.pop(_A ,  _A  )





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_			,					lowerCAmelCase_      			=  emb.weight.shape
						lowerCAmelCase_      			=  nn.Linear(_A ,  _A ,  bias=_A  )
						lowerCAmelCase_      			=  emb.weight.data
						return lin_layer





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  torch.load(_A ,  map_location='''cpu'''  )
						lowerCAmelCase_      			=  Namespace(**checkpoint['''cfg''']['''model''']  )
						lowerCAmelCase_      			=  checkpoint['''model''']
						remove_ignore_keys_(_A  )
						lowerCAmelCase_      			=  state_dict['''decoder.embed_tokens.weight'''].shape[0]

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

						lowerCAmelCase_      			=  XGLMConfig(
						    vocab_size=_A ,  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 ,  )

						lowerCAmelCase_      			=  XGLMForCausalLM(_A  )
						lowerCAmelCase_      			=  model.load_state_dict(_A ,  strict=_A  )
						print(_A  )
						lowerCAmelCase_      			=  make_linear_from_emb(model.model.embed_tokens  )

						return model


if __name__ == "__main__":
						_A     =			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.''')
						_A     =			parser.parse_args()
						_A     =			convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
						model.save_pretrained(args.pytorch_dump_folder_path)




