
'''simple docstring'''







import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
							import os

							# The slow tests are often failing with OOM error on GPU
							# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
							# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
							_lowercase :  Any    							=       '''platform'''
							import jax
							import jax.numpy as jnp
							import numpy as np

							from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel






@require_flax
class  lowerCAmelCase__   :
					lowerCAmelCase_        =     PegasusConfig
					lowerCAmelCase_        =     {}
					lowerCAmelCase_        =     '''gelu'''






					def __init__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE=13				,							__SCREAMING_SNAKE_CASE=7				,							__SCREAMING_SNAKE_CASE=True				,							__SCREAMING_SNAKE_CASE=False				,							__SCREAMING_SNAKE_CASE=99				,							__SCREAMING_SNAKE_CASE=32				,							__SCREAMING_SNAKE_CASE=5				,							__SCREAMING_SNAKE_CASE=4				,							__SCREAMING_SNAKE_CASE=37				,							__SCREAMING_SNAKE_CASE=0.1				,							__SCREAMING_SNAKE_CASE=0.1				,							__SCREAMING_SNAKE_CASE=20				,							__SCREAMING_SNAKE_CASE=2				,							__SCREAMING_SNAKE_CASE=1				,							__SCREAMING_SNAKE_CASE=0				,							):


											"""simple docstring"""





											lowercase_	:     Union[str, Any]													=       parent
											lowercase_	:     str													=       batch_size
											lowercase_	:     str													=       seq_length
											lowercase_	:     str													=       is_training
											lowercase_	:     int													=       use_labels
											lowercase_	:     int													=       vocab_size
											lowercase_	:     Optional[int]													=       hidden_size
											lowercase_	:     Union[str, Any]													=       num_hidden_layers
											lowercase_	:     Tuple													=       num_attention_heads
											lowercase_	:     str													=       intermediate_size

											lowercase_	:     Dict													=       hidden_dropout_prob
											lowercase_	:     Tuple													=       attention_probs_dropout_prob
											lowercase_	:     int													=       max_position_embeddings
											lowercase_	:     Tuple													=       eos_token_id
											lowercase_	:     Any													=       pad_token_id
											lowercase_	:     List[Any]													=       bos_token_id






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Optional[Any]													=       ids_tensor([self.batch_size, self.seq_length - 1]				,							self.vocab_size		).clip(3				,							self.vocab_size		)
											lowercase_	:     Optional[int]													=       np.expand_dims(np.array([self.eos_token_id] * self.batch_size		)				,							1		)
											lowercase_	:     List[Any]													=       np.concatenate([input_ids, eos_tensor]				,							axis=1		)

											lowercase_	:     Optional[Any]													=       ids_tensor([self.batch_size, self.seq_length]				,							self.vocab_size		)

											lowercase_	:     Optional[Any]													=       self.config_cls(
											    vocab_size=self.vocab_size				,							d_model=self.hidden_size				,							encoder_layers=self.num_hidden_layers				,							decoder_layers=self.num_hidden_layers				,							encoder_attention_heads=self.num_attention_heads				,							decoder_attention_heads=self.num_attention_heads				,							encoder_ffn_dim=self.intermediate_size				,							decoder_ffn_dim=self.intermediate_size				,							dropout=self.hidden_dropout_prob				,							attention_dropout=self.attention_probs_dropout_prob				,							max_position_embeddings=self.max_position_embeddings				,							eos_token_ids=[2]				,							bos_token_id=self.bos_token_id				,							pad_token_id=self.pad_token_id				,							decoder_start_token_id=self.pad_token_id				,							**self.config_updates				,							)
											lowercase_	:     str													=       prepare_pegasus_inputs_dict(__snake_case				,							__snake_case				,							__snake_case		)
											return config, inputs_dict






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     int													=       20
											lowercase_	:     Union[str, Any]													=       model_class_name(__snake_case		)

											lowercase_	:     List[str]													=       model.encode(inputs_dict['''input_ids''']		)

											lowercase_						, lowercase_	:     Tuple													=       (
											    inputs_dict['''decoder_input_ids'''],
											    inputs_dict['''decoder_attention_mask'''],
											)

											lowercase_	:     List[Any]													=       model.init_cache(decoder_input_ids.shape[0]				,							__snake_case				,							__snake_case		)
											lowercase_	:     str													=       jnp.ones((decoder_input_ids.shape[0], max_decoder_length)				,							dtype='''i4'''		)

											lowercase_	:     int													=       jnp.broadcast_to(
											    jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]				,							(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,							)
											lowercase_	:     Any													=       model.decode(
											    decoder_input_ids[:, :-1]				,							__snake_case				,							decoder_attention_mask=__snake_case				,							past_key_values=__snake_case				,							decoder_position_ids=__snake_case				,							)

											lowercase_	:     int													=       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,							dtype='''i4'''		)
											lowercase_	:     Any													=       model.decode(
											    decoder_input_ids[:, -1:]				,							__snake_case				,							decoder_attention_mask=__snake_case				,							past_key_values=outputs_cache.past_key_values				,							decoder_position_ids=__snake_case				,							)

											lowercase_	:     Dict													=       model.decode(__snake_case				,							__snake_case		)

											lowercase_	:     int													=       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])		)		)
											self.parent.assertTrue(diff < 1E-3				,							msg=F'''Max diff is {diff}'''		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Tuple													=       20
											lowercase_	:     List[Any]													=       model_class_name(__snake_case		)

											lowercase_	:     str													=       model.encode(inputs_dict['''input_ids''']		)

											lowercase_						, lowercase_	:     List[str]													=       (
											    inputs_dict['''decoder_input_ids'''],
											    inputs_dict['''decoder_attention_mask'''],
											)

											lowercase_	:     Optional[Any]													=       jnp.concatenate(
											    [
											        decoder_attention_mask,
											        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])		),
											    ]				,							axis=-1				,							)

											lowercase_	:     Optional[Any]													=       model.init_cache(decoder_input_ids.shape[0]				,							__snake_case				,							__snake_case		)
											lowercase_	:     Dict													=       jnp.broadcast_to(
											    jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]				,							(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,							)

											lowercase_	:     List[Any]													=       model.decode(
											    decoder_input_ids[:, :-1]				,							__snake_case				,							decoder_attention_mask=__snake_case				,							past_key_values=__snake_case				,							decoder_position_ids=__snake_case				,							)
											lowercase_	:     Union[str, Any]													=       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,							dtype='''i4'''		)
											lowercase_	:     Optional[int]													=       model.decode(
											    decoder_input_ids[:, -1:]				,							__snake_case				,							past_key_values=outputs_cache.past_key_values				,							decoder_attention_mask=__snake_case				,							decoder_position_ids=__snake_case				,							)

											lowercase_	:     int													=       model.decode(__snake_case				,							__snake_case				,							decoder_attention_mask=__snake_case		)

											lowercase_	:     int													=       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])		)		)
											self.parent.assertTrue(diff < 1E-3				,							msg=F'''Max diff is {diff}'''		)







def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Any      ,      __SCREAMING_SNAKE_CASE					: List[str]      ,      __SCREAMING_SNAKE_CASE					: Any      ,      __SCREAMING_SNAKE_CASE					: Optional[int]=None      ,      __SCREAMING_SNAKE_CASE					: Optional[Any]=None      ,      ):



						"""simple docstring"""





						if attention_mask is None:
												lowercase_	:     Optional[int]													=       np.not_equal(__lowerCAmelCase      ,      config.pad_token_id							).astype(np.inta							)
						if decoder_attention_mask is None:
												lowercase_	:     Optional[Any]													=       np.concatenate(
												    [
												        np.ones(decoder_input_ids[:, :1].shape      ,      dtype=np.inta							),
												        np.not_equal(decoder_input_ids[:, 1:]      ,      config.pad_token_id							).astype(np.inta							),
												    ]      ,      axis=-1      ,      )
						return {
						    "input_ids": input_ids,
						    "decoder_input_ids": decoder_input_ids,
						    "attention_mask": attention_mask,
						    "decoder_attention_mask": decoder_attention_mask,
						}






@require_flax
class  lowerCAmelCase__   (  lowerCamelCase_    ,						unittest.TestCase     ):
					lowerCAmelCase_        =     (
					    (
					        FlaxPegasusForConditionalGeneration,
					        FlaxPegasusModel,
					    )
					    if is_flax_available()
					    else ()
					)
					lowerCAmelCase_        =     (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
					lowerCAmelCase_        =     True
					lowerCAmelCase_        =     False
					lowerCAmelCase_        =     False
					lowerCAmelCase_        =     False






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     List[Any]													=       FlaxPegasusModelTester(self		)
											lowercase_	:     int													=       ConfigTester(self				,							config_class=__snake_case		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											self.config_tester.run_common_tests()






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_						, lowercase_	:     Tuple													=       self.model_tester.prepare_config_and_inputs_for_common()
											for model_class in self.all_model_classes:
																	self.model_tester.check_use_cache_forward(__snake_case				,							__snake_case				,							__snake_case		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_						, lowercase_	:     Tuple													=       self.model_tester.prepare_config_and_inputs_for_common()
											for model_class in self.all_model_classes:
																	self.model_tester.check_use_cache_forward_with_attn_mask(__snake_case				,							__snake_case				,							__snake_case		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_						, lowercase_	:     Optional[Any]													=       self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	with self.subTest(model_class.__name__		):
																							lowercase_	:     str													=       self._prepare_for_class(__snake_case				,							__snake_case		)
																							lowercase_	:     Optional[int]													=       model_class(__snake_case		)

																							@jax.jit
																							def encode_jitted(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE=None				,							**__SCREAMING_SNAKE_CASE		):
																													return model.encode(input_ids=__snake_case				,							attention_mask=__snake_case		)

																							with self.subTest('''JIT Enabled'''		):
																													lowercase_	:     Any													=       encode_jitted(**__snake_case		).to_tuple()

																							with self.subTest('''JIT Disabled'''		):
																													with jax.disable_jit():
																																			lowercase_	:     List[Any]													=       encode_jitted(**__snake_case		).to_tuple()

																							self.assertEqual(len(__snake_case		)				,							len(__snake_case		)		)
																							for jitted_output, output in zip(__snake_case				,							__snake_case		):
																													self.assertEqual(jitted_output.shape				,							output.shape		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_						, lowercase_	:     int													=       self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	with self.subTest(model_class.__name__		):
																							lowercase_	:     int													=       model_class(__snake_case		)
																							lowercase_	:     Tuple													=       model.encode(inputs_dict['''input_ids''']				,							inputs_dict['''attention_mask''']		)

																							lowercase_	:     Dict													=       {
																							    '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
																							    '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
																							    '''encoder_outputs''': encoder_outputs,
																							}

																							@jax.jit
																							def decode_jitted(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):
																													return model.decode(
																													    decoder_input_ids=__snake_case				,							decoder_attention_mask=__snake_case				,							encoder_outputs=__snake_case				,							)

																							with self.subTest('''JIT Enabled'''		):
																													lowercase_	:     Optional[Any]													=       decode_jitted(**__snake_case		).to_tuple()

																							with self.subTest('''JIT Disabled'''		):
																													with jax.disable_jit():
																																			lowercase_	:     Optional[int]													=       decode_jitted(**__snake_case		).to_tuple()

																							self.assertEqual(len(__snake_case		)				,							len(__snake_case		)		)
																							for jitted_output, output in zip(__snake_case				,							__snake_case		):
																													self.assertEqual(jitted_output.shape				,							output.shape		)






					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





											for model_class_name in self.all_model_classes:
																	lowercase_	:     Union[str, Any]													=       model_class_name.from_pretrained('''google/pegasus-large'''				,							from_pt=__snake_case		)
																	lowercase_	:     Tuple													=       np.ones((1, 1)		)
																	lowercase_	:     Union[str, Any]													=       model(__snake_case		)
																	self.assertIsNotNone(__snake_case		)






					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     str													=       FlaxPegasusForConditionalGeneration.from_pretrained('''google/pegasus-xsum'''		)
											lowercase_	:     List[Any]													=       PegasusTokenizer.from_pretrained('''google/pegasus-xsum'''		)

											lowercase_	:     List[Any]													=       [
											    ''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
											    ''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
											]

											lowercase_	:     Union[str, Any]													=       [
											    '''California\'s largest electricity provider has turned off power to hundreds of thousands of customers.''',
											    '''Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.''',
											]

											lowercase_	:     Optional[Any]													=       tokenizer(__snake_case				,							return_tensors='''np'''				,							truncation=__snake_case				,							max_length=5_12				,							padding=__snake_case		)
											lowercase_	:     Union[str, Any]													=       model.generate(**__snake_case				,							num_beams=2		).sequences
											lowercase_	:     Tuple													=       tokenizer.batch_decode(__snake_case				,							skip_special_tokens=__snake_case		)
											assert tgt_text == decoded







import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def 							__lowercase   ( __lowerCAmelCase							:				str					,    __lowerCAmelCase							:				Any					,    __lowerCAmelCase							:				int   ):
							# Construct model
							if gpta_config_file == "":
														a__             =     GPTaConfig()
							else:
														a__             =     GPTaConfig.from_json_file(__lowerCAmelCase   )
							a__             =     GPTaModel(__lowerCAmelCase   )

							# Load weights from numpy
							load_tf_weights_in_gpta(__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase   )

							# Save pytorch-model
							a__             =     pytorch_dump_folder_path + '/' + WEIGHTS_NAME
							a__             =     pytorch_dump_folder_path + '/' + CONFIG_NAME
							print(F'Save PyTorch model to {pytorch_weights_dump_path}'   )
							torch.save(model.state_dict()					,    __lowerCAmelCase   )
							print(F'Save configuration file to {pytorch_config_dump_path}'   )
							with open(__lowerCAmelCase					,    'w'					,    encoding='utf-8'   ) as f:
														f.write(config.to_json_string()   )


if __name__ == "__main__":
						snake_case							:       List[Any]     			=      argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--gpt2_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
						)
						parser.add_argument(
						    '''--gpt2_config_file''',
						    default='''''',
						    type=str,
						    help=(
						        '''An optional config json file corresponding to the pre-trained OpenAI model. \n'''
						        '''This specifies the model architecture.'''
						    ),
						)
						snake_case							:       Any     			=      parser.parse_args()
						convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)




