





import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()





def 	lowerCAmelCase_		(lowercase__						:       Any     ,					lowercase__						:       Optional[Any]     ,					lowercase__						:       List[str]       )		->    int:


				'''simple docstring'''




				lowerCAmelCase__      			=						AlbertConfig.from_json_file(lowercase__       )
				print(f'Building PyTorch model from configuration: {config}'       )
				lowerCAmelCase__      			=						AlbertForPreTraining(lowercase__       )

				# Load weights from tf checkpoint
				load_tf_weights_in_albert(lowercase__     ,					lowercase__     ,					lowercase__       )

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


if __name__ == "__main__":
			_UpperCAmelCase			:		Tuple   				=    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(
			    "--albert_config_file",
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        "The config json file corresponding to the pre-trained ALBERT 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."
			)
			_UpperCAmelCase			:		Any   				=    parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)













import unittest

from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import BertGenerationDecoder, BertGenerationEncoder
class  lowerCAmelCase_       :




							def __init__(   self     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   str ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=13 ,							SCREAMING_SNAKE_CASE_     :   Dict=7 ,							SCREAMING_SNAKE_CASE_     :   Dict=True ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=True ,							SCREAMING_SNAKE_CASE_     :   List[str]=99 ,							SCREAMING_SNAKE_CASE_     :   str=32 ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=5 ,							SCREAMING_SNAKE_CASE_     :   str=4 ,							SCREAMING_SNAKE_CASE_     :   Tuple=37 ,							SCREAMING_SNAKE_CASE_     :   List[Any]="gelu" ,							SCREAMING_SNAKE_CASE_     :   Tuple=0.1 ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=0.1 ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=50 ,							SCREAMING_SNAKE_CASE_     :   List[str]=0.02 ,							SCREAMING_SNAKE_CASE_     :   List[str]=True ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=None ,							):
											lowerCAmelCase__      			=						parent
											lowerCAmelCase__      			=						batch_size
											lowerCAmelCase__      			=						seq_length
											lowerCAmelCase__      			=						is_training
											lowerCAmelCase__      			=						use_input_mask
											lowerCAmelCase__      			=						vocab_size
											lowerCAmelCase__      			=						hidden_size
											lowerCAmelCase__      			=						num_hidden_layers
											lowerCAmelCase__      			=						num_attention_heads
											lowerCAmelCase__      			=						intermediate_size
											lowerCAmelCase__      			=						hidden_act
											lowerCAmelCase__      			=						hidden_dropout_prob
											lowerCAmelCase__      			=						attention_probs_dropout_prob
											lowerCAmelCase__      			=						max_position_embeddings
											lowerCAmelCase__      			=						initializer_range
											lowerCAmelCase__      			=						use_labels
											lowerCAmelCase__      			=						scope




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

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

											if self.use_labels:
															lowerCAmelCase__      			=						ids_tensor([self.batch_size, self.seq_length] ,							self.vocab_size	)

											lowerCAmelCase__      			=						self.get_config()

											return config, input_ids, input_mask, token_labels




							def 						__snake_case					(   self     :   List[str]	):
											return BertGenerationConfig(
											    vocab_size=self.vocab_size ,							hidden_size=self.hidden_size ,							num_hidden_layers=self.num_hidden_layers ,							num_attention_heads=self.num_attention_heads ,							intermediate_size=self.intermediate_size ,							hidden_act=self.hidden_act ,							hidden_dropout_prob=self.hidden_dropout_prob ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob ,							max_position_embeddings=self.max_position_embeddings ,							is_decoder=SCREAMING_SNAKE_CASE_ ,							initializer_range=self.initializer_range ,							)




							def 						__snake_case					(   self     :   str	):
											(
											    (
											    lowerCAmelCase__
											)			,		(
											    lowerCAmelCase__
											)			,		(
											    lowerCAmelCase__
											)			,		(
											    lowerCAmelCase__
											)			,		
											)      			=						self.prepare_config_and_inputs()

											lowerCAmelCase__      			=						True
											lowerCAmelCase__      			=						floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
											lowerCAmelCase__      			=						ids_tensor([self.batch_size, self.seq_length] ,							vocab_size=2	)

											return (
											    config,
											    input_ids,
											    input_mask,
											    token_labels,
											    encoder_hidden_states,
											    encoder_attention_mask,
											)




							def 						__snake_case					(   self     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   List[str] ,							SCREAMING_SNAKE_CASE_     :   str ,							SCREAMING_SNAKE_CASE_     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   List[Any] ,							**SCREAMING_SNAKE_CASE_     :   Union[str, Any] ,							):
											lowerCAmelCase__      			=						BertGenerationEncoder(config=SCREAMING_SNAKE_CASE_	)
											model.to(SCREAMING_SNAKE_CASE_	)
											model.eval()
											lowerCAmelCase__      			=						model(SCREAMING_SNAKE_CASE_ ,							attention_mask=SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						model(SCREAMING_SNAKE_CASE_	)
											self.parent.assertEqual(result.last_hidden_state.shape ,							(self.batch_size, self.seq_length, self.hidden_size)	)




							def 						__snake_case					(   self     :   Dict ,							SCREAMING_SNAKE_CASE_     :   Any ,							SCREAMING_SNAKE_CASE_     :   Any ,							SCREAMING_SNAKE_CASE_     :   Optional[int] ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   Tuple ,							SCREAMING_SNAKE_CASE_     :   Optional[int] ,							**SCREAMING_SNAKE_CASE_     :   str ,							):
											lowerCAmelCase__      			=						True
											lowerCAmelCase__      			=						BertGenerationEncoder(config=SCREAMING_SNAKE_CASE_	)
											model.to(SCREAMING_SNAKE_CASE_	)
											model.eval()
											lowerCAmelCase__      			=						model(
											    SCREAMING_SNAKE_CASE_ ,							attention_mask=SCREAMING_SNAKE_CASE_ ,							encoder_hidden_states=SCREAMING_SNAKE_CASE_ ,							encoder_attention_mask=SCREAMING_SNAKE_CASE_ ,							)
											lowerCAmelCase__      			=						model(
											    SCREAMING_SNAKE_CASE_ ,							attention_mask=SCREAMING_SNAKE_CASE_ ,							encoder_hidden_states=SCREAMING_SNAKE_CASE_ ,							)
											self.parent.assertEqual(result.last_hidden_state.shape ,							(self.batch_size, self.seq_length, self.hidden_size)	)




							def 						__snake_case					(   self     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   Dict ,							SCREAMING_SNAKE_CASE_     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   Tuple ,							SCREAMING_SNAKE_CASE_     :   Optional[int] ,							**SCREAMING_SNAKE_CASE_     :   Optional[Any] ,							):
											lowerCAmelCase__      			=						True
											lowerCAmelCase__      			=						True
											lowerCAmelCase__      			=						BertGenerationDecoder(config=SCREAMING_SNAKE_CASE_	).to(SCREAMING_SNAKE_CASE_	).eval()

											# first forward pass
											lowerCAmelCase__      			=						model(
											    SCREAMING_SNAKE_CASE_ ,							attention_mask=SCREAMING_SNAKE_CASE_ ,							encoder_hidden_states=SCREAMING_SNAKE_CASE_ ,							encoder_attention_mask=SCREAMING_SNAKE_CASE_ ,							use_cache=SCREAMING_SNAKE_CASE_ ,							)
											lowerCAmelCase__      			=						outputs.past_key_values

											# create hypothetical multiple next token and extent to next_input_ids
											lowerCAmelCase__      			=						ids_tensor((self.batch_size, 3) ,							config.vocab_size	)
											lowerCAmelCase__      			=						ids_tensor((self.batch_size, 3) ,							vocab_size=2	)

											# append to next input_ids and
											lowerCAmelCase__      			=						torch.cat([input_ids, next_tokens] ,							dim=-1	)
											lowerCAmelCase__      			=						torch.cat([input_mask, next_mask] ,							dim=-1	)

											lowerCAmelCase__      			=						model(
											    SCREAMING_SNAKE_CASE_ ,							attention_mask=SCREAMING_SNAKE_CASE_ ,							encoder_hidden_states=SCREAMING_SNAKE_CASE_ ,							encoder_attention_mask=SCREAMING_SNAKE_CASE_ ,							output_hidden_states=SCREAMING_SNAKE_CASE_ ,							)['''hidden_states'''][0]
											lowerCAmelCase__      			=						model(
											    SCREAMING_SNAKE_CASE_ ,							attention_mask=SCREAMING_SNAKE_CASE_ ,							encoder_hidden_states=SCREAMING_SNAKE_CASE_ ,							encoder_attention_mask=SCREAMING_SNAKE_CASE_ ,							past_key_values=SCREAMING_SNAKE_CASE_ ,							output_hidden_states=SCREAMING_SNAKE_CASE_ ,							)['''hidden_states'''][0]

											# select random slice
											lowerCAmelCase__      			=						ids_tensor((1,) ,							output_from_past.shape[-1]	).item()
											lowerCAmelCase__      			=						output_from_no_past[:, -3:, random_slice_idx].detach()
											lowerCAmelCase__      			=						output_from_past[:, :, random_slice_idx].detach()

											self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]	)

											# test that outputs are equal for slice
											self.parent.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							atol=1e-3	)	)




							def 						__snake_case					(   self     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   Any ,							SCREAMING_SNAKE_CASE_     :   int ,							SCREAMING_SNAKE_CASE_     :   Optional[Any] ,							SCREAMING_SNAKE_CASE_     :   Optional[Any] ,							*SCREAMING_SNAKE_CASE_     :   Tuple ,							):
											lowerCAmelCase__      			=						BertGenerationDecoder(SCREAMING_SNAKE_CASE_	)
											model.to(SCREAMING_SNAKE_CASE_	)
											model.eval()
											lowerCAmelCase__      			=						model(SCREAMING_SNAKE_CASE_ ,							attention_mask=SCREAMING_SNAKE_CASE_ ,							labels=SCREAMING_SNAKE_CASE_	)
											self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.seq_length, self.vocab_size)	)




							def 						__snake_case					(   self     :   List[Any]	):
											lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						self.prepare_config_and_inputs()
											lowerCAmelCase__      			=						{'''input_ids''': input_ids, '''attention_mask''': input_mask}
											return config, inputs_dict



@require_torch
class  lowerCAmelCase_       (				snake_case__     ,    snake_case__     ,    snake_case__     ,    unittest.TestCase					):
							UpperCamelCase_		:Dict		      =    (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
							UpperCamelCase_		:str		      =    (BertGenerationDecoder,) if is_torch_available() else ()
							UpperCamelCase_		:List[str]		      =    (
							    {'feature-extraction': BertGenerationEncoder, 'text-generation': BertGenerationDecoder}
							    if is_torch_available()
							    else {}
							)




							def 						__snake_case					(   self     :   List[str]	):
											lowerCAmelCase__      			=						BertGenerationEncoderTester(self	)
											lowerCAmelCase__      			=						ConfigTester(self ,							config_class=SCREAMING_SNAKE_CASE_ ,							hidden_size=37	)




							def 						__snake_case					(   self     :   Optional[Any]	):
											self.config_tester.run_common_tests()




							def 						__snake_case					(   self     :   List[Any]	):
											lowerCAmelCase__      			=						self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   str	):
											lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						self.model_tester.prepare_config_and_inputs()
											lowerCAmelCase__      			=						'''bert'''
											self.model_tester.create_and_check_model(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   int	):
											lowerCAmelCase__      			=						self.model_tester.prepare_config_and_inputs_for_decoder()
											self.model_tester.create_and_check_model_as_decoder(*SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   List[Any]	):
											lowerCAmelCase__      			=						self.model_tester.prepare_config_and_inputs_for_decoder()
											self.model_tester.create_and_check_decoder_model_past_large_inputs(*SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   Any	):
											# This regression test was failing with PyTorch < 1.3
											(
											    (
											    lowerCAmelCase__
											)			,		(
											    lowerCAmelCase__
											)			,		(
											    lowerCAmelCase__
											)			,		(
											    lowerCAmelCase__
											)			,		(
											    lowerCAmelCase__
											)			,		(
											    lowerCAmelCase__
											)			,		
											)      			=						self.model_tester.prepare_config_and_inputs_for_decoder()

											lowerCAmelCase__      			=						None

											self.model_tester.create_and_check_model_as_decoder(
											    SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							)




							def 						__snake_case					(   self     :   int	):
											lowerCAmelCase__      			=						self.model_tester.prepare_config_and_inputs_for_decoder()
											self.model_tester.create_and_check_for_causal_lm(*SCREAMING_SNAKE_CASE_	)




							@slow
							def 						__snake_case					(   self     :   str	):
											lowerCAmelCase__      			=						BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''	)
											self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)



@require_torch
class  lowerCAmelCase_       (				unittest.TestCase					):




							@slow
							def 						__snake_case					(   self     :   Tuple	):
											lowerCAmelCase__      			=						BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''	)
											lowerCAmelCase__      			=						torch.tensor([[101, 7_592, 1_010, 2_026, 3_899, 2_003, 10_140, 102]]	)
											with torch.no_grad():
															lowerCAmelCase__      			=						model(SCREAMING_SNAKE_CASE_	)[0]
											lowerCAmelCase__      			=						torch.Size([1, 8, 1_024]	)
											self.assertEqual(output.shape ,							SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						torch.tensor(
											    [[[0.1_775, 0.0_083, -0.0_321], [1.6_002, 0.1_287, 0.3_912], [2.1_473, 0.5_791, 0.6_066]]]	)
											self.assertTrue(torch.allclose(output[:, :3, :3] ,							SCREAMING_SNAKE_CASE_ ,							atol=1e-4	)	)



@require_torch
class  lowerCAmelCase_       (				unittest.TestCase					):




							@slow
							def 						__snake_case					(   self     :   Any	):
											lowerCAmelCase__      			=						BertGenerationDecoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''	)
											lowerCAmelCase__      			=						torch.tensor([[101, 7_592, 1_010, 2_026, 3_899, 2_003, 10_140, 102]]	)
											with torch.no_grad():
															lowerCAmelCase__      			=						model(SCREAMING_SNAKE_CASE_	)[0]
											lowerCAmelCase__      			=						torch.Size([1, 8, 50_358]	)
											self.assertEqual(output.shape ,							SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						torch.tensor(
											    [[[-0.5_788, -2.5_994, -3.7_054], [0.0_438, 4.7_997, 1.8_795], [1.5_862, 6.6_409, 4.4_638]]]	)
											self.assertTrue(torch.allclose(output[:, :3, :3] ,							SCREAMING_SNAKE_CASE_ ,							atol=1e-4	)	)





