





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				:



						"""simple docstring"""

						_UpperCamelCase  :	Dict								=	XGLMConfig
						_UpperCamelCase  :	str								=	{}
						_UpperCamelCase  :	Optional[Any]								=	"gelu"







						def __init__(    self			:		Optional[int] ,				lowerCamelCase_			:		List[str] ,				lowerCamelCase_			:		str=14 ,				lowerCamelCase_			:		Optional[int]=7 ,				lowerCamelCase_			:		Optional[Any]=True ,				lowerCamelCase_			:		List[Any]=True ,				lowerCamelCase_			:		List[Any]=True ,				lowerCamelCase_			:		List[Any]=99 ,				lowerCamelCase_			:		Optional[Any]=32 ,				lowerCamelCase_			:		Optional[Any]=2 ,				lowerCamelCase_			:		Tuple=4 ,				lowerCamelCase_			:		Any=37 ,				lowerCamelCase_			:		Tuple="gelu" ,				lowerCamelCase_			:		Optional[Any]=0.1 ,				lowerCamelCase_			:		Tuple=0.1 ,				lowerCamelCase_			:		Any=5_12 ,				lowerCamelCase_			:		List[str]=0.02 ,				):





										'''simple docstring'''







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







						def 		__UpperCAmelCase       (    self			:		Optional[Any]					):





										'''simple docstring'''







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







						def 		__UpperCAmelCase       (    self			:		Union[str, Any]					):





										'''simple docstring'''







										_snake_case :			Tuple  						=   tf.clip_by_value(
										    ids_tensor([self.batch_size, self.seq_length] ,				self.vocab_size					) ,				clip_value_min=0 ,				clip_value_max=3					)

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

										_snake_case :			Dict  						=   self.get_config()

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

										return (
										    config,
										    input_ids,
										    input_mask,
										    head_mask,
										)







						def 		__UpperCAmelCase       (    self			:		Dict					):





										'''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[Any]					):





										'''simple docstring'''







										_snake_case :			Tuple  						=   self.prepare_config_and_inputs()

										(
										    (
										    _snake_case
										)						,			(
										    _snake_case
										)						,			(
										    _snake_case
										)						,			(
										    _snake_case
										)						,			
										) :			Union[str, Any]  						=   config_and_inputs

										_snake_case :			int  						=   {
										    'input_ids': input_ids,
										    'head_mask': head_mask,
										}

										return config, inputs_dict



@require_tf
class 			lowercase				(							a_		,			a_		,			unittest.TestCase     ):



						"""simple docstring"""

						_UpperCamelCase  :	List[str]								=	(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
						_UpperCamelCase  :	Optional[Any]								=	(TFXGLMForCausalLM,) if is_tf_available() else ()
						_UpperCamelCase  :	Tuple								=	(
						    {"feature-extraction": TFXGLMModel, "text-generation": TFXGLMForCausalLM} if is_tf_available() else {}
						)
						_UpperCamelCase  :	List[Any]								=	False
						_UpperCamelCase  :	int								=	False
						_UpperCamelCase  :	List[Any]								=	False







						def 		__UpperCAmelCase       (    self			:		Union[str, Any]					):





										'''simple docstring'''







										_snake_case :			List[str]  						=   TFXGLMModelTester(self					)
										_snake_case :			Optional[Any]  						=   ConfigTester(self ,				config_class=lowerCamelCase_ ,				n_embd=37					)







						def 		__UpperCAmelCase       (    self			:		List[Any]					):





										'''simple docstring'''







										self.config_tester.run_common_tests()







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





										'''simple docstring'''







										for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_snake_case :			str  						=   TFXGLMModel.from_pretrained(lowerCamelCase_					)
														self.assertIsNotNone(lowerCamelCase_					)







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





										'''simple docstring'''







										super().test_resize_token_embeddings()



@require_tf
class 			lowercase				(							unittest.TestCase     ):



						"""simple docstring"""

						@slow
						def 		__UpperCAmelCase       (    self			:		List[str] ,				lowerCamelCase_			:		Dict=True					):





										'''simple docstring'''







										_snake_case :			Union[str, Any]  						=   TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'					)
										_snake_case :			str  						=   tf.convert_to_tensor([[2, 2_68, 98_65]] ,				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
										_snake_case :			int  						=   [2, 2_68, 98_65, 67, 11, 19_88, 5_72_52, 98_65, 5, 9_84, 67, 19_88, 21_38_38, 16_58, 53, 7_04_46, 33, 66_57, 2_78, 15_81]
										# fmt: on
										_snake_case :			Dict  						=   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[Any]					):





										'''simple docstring'''







										_snake_case :			Optional[int]  						=   XGLMTokenizer.from_pretrained('facebook/xglm-564M'					)
										_snake_case :			str  						=   TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'					)

										tf.random.set_seed(0					)
										_snake_case :			List[Any]  						=   tokenizer('Today is a nice day and' ,				return_tensors='tf'					)
										_snake_case :			List[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'					):
														_snake_case :			Any  						=   model.generate(lowerCamelCase_ ,				do_sample=lowerCamelCase_ ,				seed=[7, 0]					)
										_snake_case :			Tuple  						=   tokenizer.decode(output_ids[0] ,				skip_special_tokens=lowerCamelCase_					)

										_snake_case :			Optional[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			:		str					):





										'''simple docstring'''







										_snake_case :			int  						=   TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'					)
										_snake_case :			int  						=   XGLMTokenizer.from_pretrained('facebook/xglm-564M'					)

										_snake_case :			Optional[int]  						=   'left'

										# use different length sentences to test batching
										_snake_case :			Optional[int]  						=   [
										    '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',
										]

										_snake_case :			List[Any]  						=   tokenizer(lowerCamelCase_ ,				return_tensors='tf' ,				padding=lowerCamelCase_					)
										_snake_case :			int  						=   inputs['input_ids']

										_snake_case :			Union[str, Any]  						=   model.generate(input_ids=lowerCamelCase_ ,				attention_mask=inputs['attention_mask'] ,				max_new_tokens=12					)

										_snake_case :			Dict  						=   tokenizer(sentences[0] ,				return_tensors='tf'					).input_ids
										_snake_case :			str  						=   model.generate(input_ids=lowerCamelCase_ ,				max_new_tokens=12					)

										_snake_case :			Optional[int]  						=   tokenizer(sentences[1] ,				return_tensors='tf'					).input_ids
										_snake_case :			Optional[int]  						=   model.generate(input_ids=lowerCamelCase_ ,				max_new_tokens=12					)

										_snake_case :			Optional[Any]  						=   tokenizer.batch_decode(lowerCamelCase_ ,				skip_special_tokens=lowerCamelCase_					)
										_snake_case :			Tuple  						=   tokenizer.decode(output_non_padded[0] ,				skip_special_tokens=lowerCamelCase_					)
										_snake_case :			Tuple  						=   tokenizer.decode(output_padded[0] ,				skip_special_tokens=lowerCamelCase_					)

										_snake_case :			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

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				# Initialise PyTorch model
				_snake_case :			Optional[int]  						=   BertConfig.from_json_file(__lowerCAmelCase )
				print(F'''Building PyTorch model from configuration: {config}''' )
				_snake_case :			List[str]  						=   BertForPreTraining(__lowerCAmelCase )

				# Load weights from tf checkpoint
				load_tf_weights_in_bert(__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase )

				# Save pytorch-model
				print(F'''Save PyTorch model to {pytorch_dump_path}''' )
				torch.save(model.state_dict()	,			__lowerCAmelCase )


if __name__ == "__main__":
		lowercase_		:				Any				=						argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
		)
		parser.add_argument(
		    '''--bert_config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        '''The config json file corresponding to the pre-trained BERT model. \n'''
		        '''This specifies the model architecture.'''
		    ),
		)
		parser.add_argument(
		    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		lowercase_		:				List[str]				=						parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






