




import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
	import jax
	import jax.numpy as jnp

	from transformers.modeling_flax_pytorch_utils import (
	    convert_pytorch_state_dict_to_flax,
	    load_flax_weights_in_pytorch_model,
	)
	from transformers.models.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
	import torch
class 				__snake_case     :



				def __init__(					self					:						str						,		_UpperCAmelCase					:						Tuple						,		_UpperCAmelCase					:						Union[str, Any]=14						,		_UpperCAmelCase					:						List[Any]=7						,		_UpperCAmelCase					:						List[Any]=True						,		_UpperCAmelCase					:						int=True						,		_UpperCAmelCase					:						Dict=False						,		_UpperCAmelCase					:						Any=True						,		_UpperCAmelCase					:						Dict=99						,		_UpperCAmelCase					:						str=32						,		_UpperCAmelCase					:						Optional[int]=5						,		_UpperCAmelCase					:						Optional[int]=4						,		_UpperCAmelCase					:						List[Any]=37						,		_UpperCAmelCase					:						Tuple="gelu"						,		_UpperCAmelCase					:						Tuple=0.1						,		_UpperCAmelCase					:						Optional[int]=0.1						,		_UpperCAmelCase					:						Optional[int]=512						,		_UpperCAmelCase					:						Dict=0.02						,		)     ->			Dict:





								'''simple docstring'''




								_lowerCAmelCase			:			Tuple        =					parent
								_lowerCAmelCase			:			Union[str, Any]        =					batch_size
								_lowerCAmelCase			:			List[str]        =					seq_length
								_lowerCAmelCase			:			Any        =					is_training
								_lowerCAmelCase			:			Any        =					use_input_mask
								_lowerCAmelCase			:			str        =					use_token_type_ids
								_lowerCAmelCase			:			List[Any]        =					use_labels
								_lowerCAmelCase			:			Any        =					vocab_size
								_lowerCAmelCase			:			int        =					hidden_size
								_lowerCAmelCase			:			List[Any]        =					num_hidden_layers
								_lowerCAmelCase			:			Union[str, Any]        =					num_attention_heads
								_lowerCAmelCase			:			Dict        =					intermediate_size
								_lowerCAmelCase			:			List[Any]        =					hidden_act
								_lowerCAmelCase			:			List[str]        =					hidden_dropout_prob
								_lowerCAmelCase			:			List[Any]        =					attention_probs_dropout_prob
								_lowerCAmelCase			:			Optional[Any]        =					max_position_embeddings
								_lowerCAmelCase			:			Dict        =					initializer_range
								_lowerCAmelCase			:			Union[str, Any]        =					None
								_lowerCAmelCase			:			Any        =					vocab_size - 1
								_lowerCAmelCase			:			Tuple        =					vocab_size - 1
								_lowerCAmelCase			:			str        =					vocab_size - 1



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[Any]					)     ->			int:





								'''simple docstring'''




								_lowerCAmelCase			:			Tuple        =					ids_tensor([self.batch_size, self.seq_length]						,		self.vocab_size					)

								_lowerCAmelCase			:			Optional[Any]        =					None
								if self.use_input_mask:
												_lowerCAmelCase			:			Tuple        =					random_attention_mask([self.batch_size, self.seq_length]					)

								_lowerCAmelCase			:			str        =					GPTaConfig(
								    vocab_size=self.vocab_size						,		n_embd=self.hidden_size						,		n_layer=self.num_hidden_layers						,		n_head=self.num_attention_heads						,		n_positions=self.max_position_embeddings						,		use_cache=_UpperCAmelCase						,		bos_token_id=self.bos_token_id						,		eos_token_id=self.eos_token_id						,		pad_token_id=self.pad_token_id						,		)

								return (config, input_ids, input_mask)



				def 					SCREAMING_SNAKE_CASE				(					self					:						str					)     ->			Dict:





								'''simple docstring'''




								_lowerCAmelCase			:			Optional[int]        =					self.prepare_config_and_inputs()
								_lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase			:			Dict        =					config_and_inputs
								_lowerCAmelCase			:			Union[str, Any]        =					{"""input_ids""": input_ids, """attention_mask""": attention_mask}
								return config, inputs_dict



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[str]					)     ->			Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase			:			Union[str, Any]        =					self.prepare_config_and_inputs()

								_lowerCAmelCase			:			List[str]        =					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
								_lowerCAmelCase			:			List[str]        =					ids_tensor([self.batch_size, self.seq_length]						,		vocab_size=2					)

								return (
								    config,
								    input_ids,
								    attention_mask,
								    encoder_hidden_states,
								    encoder_attention_mask,
								)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[int]						,		_UpperCAmelCase					:						List[str]						,		_UpperCAmelCase					:						str						,		_UpperCAmelCase					:						Tuple						,		_UpperCAmelCase					:						Optional[int]					)     ->			List[Any]:





								'''simple docstring'''




								_lowerCAmelCase			:			List[Any]        =					20
								_lowerCAmelCase			:			Tuple        =					model_class_name(_UpperCAmelCase					)

								_lowerCAmelCase			:			Optional[Any]        =					model.init_cache(input_ids.shape[0]						,		_UpperCAmelCase					)
								_lowerCAmelCase			:			str        =					jnp.ones((input_ids.shape[0], max_decoder_length)						,		dtype="""i4"""					)

								_lowerCAmelCase			:			Optional[Any]        =					jnp.broadcast_to(
								    jnp.arange(input_ids.shape[-1] - 1					)[None, :]						,		(input_ids.shape[0], input_ids.shape[-1] - 1)					)
								_lowerCAmelCase			:			str        =					model(
								    input_ids[:, :-1]						,		attention_mask=_UpperCAmelCase						,		past_key_values=_UpperCAmelCase						,		position_ids=_UpperCAmelCase						,		)

								_lowerCAmelCase			:			int        =					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						,		dtype="""i4"""					)
								_lowerCAmelCase			:			List[Any]        =					model(
								    input_ids[:, -1:]						,		attention_mask=_UpperCAmelCase						,		past_key_values=outputs_cache.past_key_values						,		position_ids=_UpperCAmelCase						,		)

								_lowerCAmelCase			:			Any        =					model(_UpperCAmelCase					)

								_lowerCAmelCase			:			Dict        =					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 					SCREAMING_SNAKE_CASE				(					self					:						Dict						,		_UpperCAmelCase					:						int						,		_UpperCAmelCase					:						str						,		_UpperCAmelCase					:						Dict						,		_UpperCAmelCase					:						Union[str, Any]					)     ->			List[str]:





								'''simple docstring'''




								_lowerCAmelCase			:			Union[str, Any]        =					20
								_lowerCAmelCase			:			List[Any]        =					model_class_name(_UpperCAmelCase					)

								_lowerCAmelCase			:			str        =					jnp.concatenate(
								    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])					)]						,		axis=-1						,		)

								_lowerCAmelCase			:			Any        =					model.init_cache(input_ids.shape[0]						,		_UpperCAmelCase					)
								_lowerCAmelCase			:			Optional[Any]        =					jnp.broadcast_to(
								    jnp.arange(input_ids.shape[-1] - 1					)[None, :]						,		(input_ids.shape[0], input_ids.shape[-1] - 1)					)

								_lowerCAmelCase			:			List[Any]        =					model(
								    input_ids[:, :-1]						,		attention_mask=_UpperCAmelCase						,		past_key_values=_UpperCAmelCase						,		position_ids=_UpperCAmelCase						,		)
								_lowerCAmelCase			:			Union[str, Any]        =					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						,		dtype="""i4"""					)
								_lowerCAmelCase			:			Optional[int]        =					model(
								    input_ids[:, -1:]						,		past_key_values=outputs_cache.past_key_values						,		attention_mask=_UpperCAmelCase						,		position_ids=_UpperCAmelCase						,		)

								_lowerCAmelCase			:			Optional[Any]        =					model(_UpperCAmelCase						,		attention_mask=_UpperCAmelCase					)

								_lowerCAmelCase			:			List[str]        =					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}"					)






@require_flax
class 				__snake_case     (_a  ,		_a  ,		unittest.TestCase						):
				lowerCAmelCase__			=   (FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
				lowerCAmelCase__			=   (FlaxGPTaLMHeadModel,) if is_flax_available() else ()



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[Any]					)     ->			Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase			:			List[str]        =					FlaxGPTaModelTester(self					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Union[str, Any]					)     ->			Dict:





								'''simple docstring'''




								for model_class_name in self.all_model_classes:
												_lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase			:			List[str]        =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.check_use_cache_forward(_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase					)



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





								'''simple docstring'''




								for model_class_name in self.all_model_classes:
												_lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase			:			Union[str, Any]        =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.check_use_cache_forward_with_attn_mask(
												    _UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase					)



				@slow
				def 					SCREAMING_SNAKE_CASE				(					self					:						int					)     ->			Dict:





								'''simple docstring'''




								_lowerCAmelCase			:			Union[str, Any]        =					GPTaTokenizer.from_pretrained("""gpt2"""						,		pad_token="""</s>"""						,		padding_side="""left"""					)
								_lowerCAmelCase			:			int        =					tokenizer(["""Hello this is a long string""", """Hey"""]						,		return_tensors="""np"""						,		padding=_UpperCAmelCase						,		truncation=_UpperCAmelCase					)

								_lowerCAmelCase			:			Tuple        =					FlaxGPTaLMHeadModel.from_pretrained("""gpt2"""					)
								_lowerCAmelCase			:			List[Any]        =					False
								_lowerCAmelCase			:			int        =					model.config.eos_token_id

								_lowerCAmelCase			:			List[str]        =					jax.jit(model.generate					)

								_lowerCAmelCase			:			int        =					jit_generate(inputs["""input_ids"""]						,		attention_mask=inputs["""attention_mask"""]					).sequences

								_lowerCAmelCase			:			str        =					tokenizer.batch_decode(_UpperCAmelCase						,		skip_special_tokens=_UpperCAmelCase					)

								_lowerCAmelCase			:			List[str]        =					[
								    """Hello this is a long string of words. I'm going to start with the first one.\n""",
								    """Hey, I'm not sure if I'm going to be able to do""",
								]

								self.assertListEqual(_UpperCAmelCase						,		_UpperCAmelCase					)



				@is_pt_flax_cross_test
				def 					SCREAMING_SNAKE_CASE				(					self					:						List[str]					)     ->			str:





								'''simple docstring'''




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

								for model_class in self.all_model_classes:
												with self.subTest(model_class.__name__					):
																# prepare inputs
																_lowerCAmelCase			:			Tuple        =					self._prepare_for_class(_UpperCAmelCase						,		_UpperCAmelCase					)
																_lowerCAmelCase			:			Dict        =					{k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

																# load corresponding PyTorch class
																_lowerCAmelCase			:			Union[str, Any]        =					model_class.__name__[4:]  # Skip the "Flax" at the beginning
																_lowerCAmelCase			:			str        =					getattr(_UpperCAmelCase						,		_UpperCAmelCase					)

																_lowerCAmelCase    ,			_lowerCAmelCase			:			Union[str, Any]        =					pt_inputs["""input_ids"""].shape
																_lowerCAmelCase			:			Tuple        =					np.random.randint(0						,		seq_length - 1						,		size=(batch_size,)					)
																for batch_idx, start_index in enumerate(_UpperCAmelCase					):
																				_lowerCAmelCase			:			Dict        =					0
																				_lowerCAmelCase			:			Tuple        =					1
																				_lowerCAmelCase			:			Union[str, Any]        =					0
																				_lowerCAmelCase			:			List[Any]        =					1
																_lowerCAmelCase			:			List[str]        =					pt_model_class(_UpperCAmelCase					).eval()
																_lowerCAmelCase			:			Union[str, Any]        =					model_class(_UpperCAmelCase						,		dtype=jnp.floataa					)

																_lowerCAmelCase			:			List[str]        =					convert_pytorch_state_dict_to_flax(pt_model.state_dict()						,		_UpperCAmelCase					)
																_lowerCAmelCase			:			Tuple        =					fx_state

																with torch.no_grad():
																				_lowerCAmelCase			:			str        =					pt_model(**_UpperCAmelCase					).to_tuple()

																_lowerCAmelCase			:			Union[str, Any]        =					fx_model(**_UpperCAmelCase					).to_tuple()
																self.assertEqual(len(_UpperCAmelCase					)						,		len(_UpperCAmelCase					)						,		"""Output lengths differ between Flax and PyTorch"""					)
																for fx_output, pt_output in zip(_UpperCAmelCase						,		_UpperCAmelCase					):
																				self.assert_almost_equals(fx_output[:, -1]						,		pt_output[:, -1].numpy()						,		4E-2					)

																with tempfile.TemporaryDirectory() as tmpdirname:
																				pt_model.save_pretrained(_UpperCAmelCase					)
																				_lowerCAmelCase			:			Optional[int]        =					model_class.from_pretrained(_UpperCAmelCase						,		from_pt=_UpperCAmelCase					)

																_lowerCAmelCase			:			List[str]        =					fx_model_loaded(**_UpperCAmelCase					).to_tuple()
																self.assertEqual(
																    len(_UpperCAmelCase					)						,		len(_UpperCAmelCase					)						,		"""Output lengths differ between Flax and PyTorch"""					)
																for fx_output_loaded, pt_output in zip(_UpperCAmelCase						,		_UpperCAmelCase					):
																				self.assert_almost_equals(fx_output_loaded[:, -1]						,		pt_output[:, -1].numpy()						,		4E-2					)



				@is_pt_flax_cross_test
				def 					SCREAMING_SNAKE_CASE				(					self					:						int					)     ->			Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase    ,			_lowerCAmelCase			:			Tuple        =					self.model_tester.prepare_config_and_inputs_for_common()
								for model_class in self.all_model_classes:
												with self.subTest(model_class.__name__					):
																# prepare inputs
																_lowerCAmelCase			:			Tuple        =					self._prepare_for_class(_UpperCAmelCase						,		_UpperCAmelCase					)
																_lowerCAmelCase			:			Any        =					{k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

																# load corresponding PyTorch class
																_lowerCAmelCase			:			Union[str, Any]        =					model_class.__name__[4:]  # Skip the "Flax" at the beginning
																_lowerCAmelCase			:			Tuple        =					getattr(_UpperCAmelCase						,		_UpperCAmelCase					)

																_lowerCAmelCase			:			List[Any]        =					pt_model_class(_UpperCAmelCase					).eval()
																_lowerCAmelCase			:			int        =					model_class(_UpperCAmelCase						,		dtype=jnp.floataa					)

																_lowerCAmelCase			:			Dict        =					load_flax_weights_in_pytorch_model(_UpperCAmelCase						,		fx_model.params					)
																_lowerCAmelCase    ,			_lowerCAmelCase			:			str        =					pt_inputs["""input_ids"""].shape
																_lowerCAmelCase			:			List[Any]        =					np.random.randint(0						,		seq_length - 1						,		size=(batch_size,)					)
																for batch_idx, start_index in enumerate(_UpperCAmelCase					):
																				_lowerCAmelCase			:			Optional[int]        =					0
																				_lowerCAmelCase			:			str        =					1
																				_lowerCAmelCase			:			List[Any]        =					0
																				_lowerCAmelCase			:			List[Any]        =					1

																# make sure weights are tied in PyTorch
																pt_model.tie_weights()

																with torch.no_grad():
																				_lowerCAmelCase			:			str        =					pt_model(**_UpperCAmelCase					).to_tuple()

																_lowerCAmelCase			:			str        =					fx_model(**_UpperCAmelCase					).to_tuple()
																self.assertEqual(len(_UpperCAmelCase					)						,		len(_UpperCAmelCase					)						,		"""Output lengths differ between Flax and PyTorch"""					)
																for fx_output, pt_output in zip(_UpperCAmelCase						,		_UpperCAmelCase					):
																				self.assert_almost_equals(fx_output[:, -1]						,		pt_output[:, -1].numpy()						,		4E-2					)

																with tempfile.TemporaryDirectory() as tmpdirname:
																				fx_model.save_pretrained(_UpperCAmelCase					)
																				_lowerCAmelCase			:			Optional[int]        =					pt_model_class.from_pretrained(_UpperCAmelCase						,		from_flax=_UpperCAmelCase					)

																with torch.no_grad():
																				_lowerCAmelCase			:			int        =					pt_model_loaded(**_UpperCAmelCase					).to_tuple()

																self.assertEqual(
																    len(_UpperCAmelCase					)						,		len(_UpperCAmelCase					)						,		"""Output lengths differ between Flax and PyTorch"""					)
																for fx_output, pt_output in zip(_UpperCAmelCase						,		_UpperCAmelCase					):
																				self.assert_almost_equals(fx_output[:, -1]						,		pt_output[:, -1].numpy()						,		4E-2					)



				@slow
				def 					SCREAMING_SNAKE_CASE				(					self					:						Any					)     ->			Tuple:





								'''simple docstring'''




								for model_class_name in self.all_model_classes:
												_lowerCAmelCase			:			str        =					model_class_name.from_pretrained("""gpt2"""						,		from_pt=_UpperCAmelCase					)
												_lowerCAmelCase			:			List[str]        =					model(np.ones((1, 1)					)					)
												self.assertIsNotNone(_UpperCAmelCase					)







import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_lowerCamelCase					:     List[str]					  =  logging.get_logger(__name__)

_lowerCamelCase					:     Tuple					  =  ["UtteranceLevel"]




@torch.no_grad()
def 							_UpperCAmelCase     (UpperCamelCase_       :       Optional[Any]					,			UpperCamelCase_       :       List[Any]					,			UpperCamelCase_       :       Tuple					,			UpperCamelCase_       :       Dict				):
				'''simple docstring'''
				_lowerCAmelCase			:			List[str]        =					torch.load(UpperCamelCase_					,			map_location="""cpu"""				)
				if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
								raise NotImplementedError(F"The supported s3prl models are {SUPPORTED_MODELS}"				)

				_lowerCAmelCase			:			List[str]        =					checkpoint["""Downstream"""]

				_lowerCAmelCase			:			Optional[int]        =					HubertConfig.from_pretrained(UpperCamelCase_				)
				_lowerCAmelCase			:			str        =					HubertForSequenceClassification.from_pretrained(UpperCamelCase_					,			config=UpperCamelCase_				)
				_lowerCAmelCase			:			Union[str, Any]        =					WavaVecaFeatureExtractor.from_pretrained(
				    UpperCamelCase_					,			return_attention_mask=UpperCamelCase_					,			do_normalize=UpperCamelCase_				)

				if hf_congfig.use_weighted_layer_sum:
								_lowerCAmelCase			:			int        =					checkpoint["""Featurizer"""]["""weights"""]

				_lowerCAmelCase			:			Optional[int]        =					downstream_dict["""projector.weight"""]
				_lowerCAmelCase			:			int        =					downstream_dict["""projector.bias"""]
				_lowerCAmelCase			:			Dict        =					downstream_dict["""model.post_net.linear.weight"""]
				_lowerCAmelCase			:			Dict        =					downstream_dict["""model.post_net.linear.bias"""]

				hf_feature_extractor.save_pretrained(UpperCamelCase_				)
				hf_model.save_pretrained(UpperCamelCase_				)


if __name__ == "__main__":
	_lowerCamelCase					:     Any					  =  argparse.ArgumentParser()
	parser.add_argument(
	    "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
	)
	parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
	parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
	parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
	_lowerCamelCase					:     Dict					  =  parser.parse_args()
	convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

