





import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, 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
					import numpy as np

					from transformers.modeling_flax_pytorch_utils import (
					    convert_pytorch_state_dict_to_flax,
					    load_flax_weights_in_pytorch_model,
					)
					from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
					import torch
@require_flax
class       UpperCamelCase_		:




					def __init__(		self					,    lowerCAmelCase_					,    lowerCAmelCase_=14					,    lowerCAmelCase_=7					,    lowerCAmelCase_=True					,    lowerCAmelCase_=True					,    lowerCAmelCase_=True					,    lowerCAmelCase_=99					,    lowerCAmelCase_=32					,    lowerCAmelCase_=5					,    lowerCAmelCase_=4					,    lowerCAmelCase_=37					,    lowerCAmelCase_="gelu"					,    lowerCAmelCase_=0.1					,    lowerCAmelCase_=0.1					,    lowerCAmelCase_=512					,    lowerCAmelCase_=0.02					,    lowerCAmelCase_=None					,    )		->				int:
										_snake_case						=					parent
										_snake_case						=					batch_size
										_snake_case						=					seq_length
										_snake_case						=					is_training
										_snake_case						=					use_input_mask
										_snake_case						=					use_labels
										_snake_case						=					vocab_size
										_snake_case						=					d_model
										_snake_case						=					num_hidden_layers
										_snake_case						=					num_attention_heads
										_snake_case						=					ffn_dim
										_snake_case						=					activation_function
										_snake_case						=					activation_dropout
										_snake_case						=					attention_dropout
										_snake_case						=					max_position_embeddings
										_snake_case						=					initializer_range
										_snake_case						=					None
										_snake_case						=					0
										_snake_case						=					2
										_snake_case						=					1




					def 						lowerCAmelCase      (		self	)		->				Optional[Any]:
										_snake_case						=					np.clip(ids_tensor([self.batch_size, self.seq_length]					,    self.vocab_size	)					,    3					,    self.vocab_size	)

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

										_snake_case						=					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 (config, input_ids, input_mask)




					def 						lowerCAmelCase      (		self	)		->				List[str]:
										_snake_case						=					self.prepare_config_and_inputs()
										_snake_case				,  _snake_case				,  _snake_case						=					config_and_inputs
										_snake_case						=					{'input_ids': input_ids, 'attention_mask': attention_mask}
										return config, inputs_dict




					def 						lowerCAmelCase      (		self	)		->				Tuple:
										_snake_case				,  _snake_case				,  _snake_case						=					self.prepare_config_and_inputs()

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

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




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_	)		->				Union[str, Any]:
										_snake_case						=					20
										_snake_case						=					model_class_name(lowerCAmelCase_	)

										_snake_case						=					model.init_cache(input_ids.shape[0]					,    lowerCAmelCase_	)
										_snake_case						=					jnp.ones((input_ids.shape[0], max_decoder_length)					,    dtype='i4'	)

										_snake_case						=					jnp.broadcast_to(
										    jnp.arange(input_ids.shape[-1] - 1	)[None, :]					,    (input_ids.shape[0], input_ids.shape[-1] - 1)	)
										_snake_case						=					model(
										    input_ids[:, :-1]					,    attention_mask=lowerCAmelCase_					,    past_key_values=lowerCAmelCase_					,    position_ids=lowerCAmelCase_					,    )

										_snake_case						=					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,    dtype='i4'	)
										_snake_case						=					model(
										    input_ids[:, -1:]					,    attention_mask=lowerCAmelCase_					,    past_key_values=outputs_cache.past_key_values					,    position_ids=lowerCAmelCase_					,    )

										_snake_case						=					model(lowerCAmelCase_	)

										_snake_case						=					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 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_	)		->				Optional[Any]:
										_snake_case						=					20
										_snake_case						=					model_class_name(lowerCAmelCase_	)

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

										_snake_case						=					model.init_cache(input_ids.shape[0]					,    lowerCAmelCase_	)
										_snake_case						=					jnp.broadcast_to(
										    jnp.arange(input_ids.shape[-1] - 1	)[None, :]					,    (input_ids.shape[0], input_ids.shape[-1] - 1)	)

										_snake_case						=					model(
										    input_ids[:, :-1]					,    attention_mask=lowerCAmelCase_					,    past_key_values=lowerCAmelCase_					,    position_ids=lowerCAmelCase_					,    )
										_snake_case						=					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,    dtype='i4'	)
										_snake_case						=					model(
										    input_ids[:, -1:]					,    past_key_values=outputs_cache.past_key_values					,    attention_mask=lowerCAmelCase_					,    position_ids=lowerCAmelCase_					,    )

										_snake_case						=					model(lowerCAmelCase_					,    attention_mask=lowerCAmelCase_	)
										_snake_case						=					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_sentencepiece
@require_flax
class       UpperCamelCase_		(	_lowerCamelCase				,	_lowerCamelCase				,	unittest.TestCase       ):
					lowerCAmelCase_				=						(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
					lowerCAmelCase_				=						(FlaxXGLMForCausalLM,) if is_flax_available() else ()




					def 						lowerCAmelCase      (		self	)		->				int:
										_snake_case						=					FlaxXGLMModelTester(self	)




					def 						lowerCAmelCase      (		self	)		->				Dict:
										for model_class_name in self.all_model_classes:
															_snake_case				,  _snake_case				,  _snake_case						=					self.model_tester.prepare_config_and_inputs()
															self.model_tester.check_use_cache_forward(lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self	)		->				Dict:
										for model_class_name in self.all_model_classes:
															_snake_case				,  _snake_case				,  _snake_case						=					self.model_tester.prepare_config_and_inputs()
															self.model_tester.check_use_cache_forward_with_attn_mask(
															    lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_	)




					@slow
					def 						lowerCAmelCase      (		self	)		->				List[str]:
										_snake_case						=					XGLMTokenizer.from_pretrained('XGLM'					,    padding_side='left'	)
										_snake_case						=					tokenizer(['Hello this is a long string', 'Hey']					,    return_tensors='np'					,    padding=lowerCAmelCase_					,    truncation=lowerCAmelCase_	)

										_snake_case						=					FlaxXGLMForCausalLM.from_pretrained('facebook/xglm-564M'	)
										_snake_case						=					1
										_snake_case						=					False

										_snake_case						=					jax.jit(model.generate	)

										_snake_case						=					jit_generate(inputs['input_ids']					,    attention_mask=inputs['attention_mask']	).sequences

										_snake_case						=					tokenizer.batch_decode(lowerCAmelCase_					,    skip_special_tokens=lowerCAmelCase_	)

										_snake_case						=					[
										    'Hello this is a long string of questions, but I\'m not sure if I\'m',
										    'Hey, I\'m a newbie to the forum and I\'',
										]

										self.assertListEqual(lowerCAmelCase_					,    lowerCAmelCase_	)




					@is_pt_flax_cross_test
					def 						lowerCAmelCase      (		self	)		->				Any:
										_snake_case				,  _snake_case						=					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
																				_snake_case						=					self._prepare_for_class(lowerCAmelCase_					,    lowerCAmelCase_	)
																				_snake_case						=					{k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

																				# load corresponding PyTorch class
																				_snake_case						=					model_class.__name__[4:]  # Skip the "Flax" at the beginning
																				_snake_case						=					getattr(lowerCAmelCase_					,    lowerCAmelCase_	)

																				_snake_case				,  _snake_case						=					pt_inputs['input_ids'].shape
																				_snake_case						=					np.random.randint(0					,    seq_length - 1					,    size=(batch_size,)	)
																				for batch_idx, start_index in enumerate(lowerCAmelCase_	):
																									_snake_case						=					0
																									_snake_case						=					1
																									_snake_case						=					0
																									_snake_case						=					1
																				_snake_case						=					pt_model_class(lowerCAmelCase_	).eval()
																				# Flax models don't use the `use_cache` option and cache is not returned as a default.
																				# So we disable `use_cache` here for PyTorch model.
																				_snake_case						=					False
																				_snake_case						=					model_class(lowerCAmelCase_					,    dtype=jnp.floataa	)
																				_snake_case						=					convert_pytorch_state_dict_to_flax(pt_model.state_dict()					,    lowerCAmelCase_	)
																				_snake_case						=					fx_state

																				with torch.no_grad():
																									_snake_case						=					pt_model(**lowerCAmelCase_	).to_tuple()

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

																				with tempfile.TemporaryDirectory() as tmpdirname:
																									pt_model.save_pretrained(lowerCAmelCase_	)
																									_snake_case						=					model_class.from_pretrained(lowerCAmelCase_					,    from_pt=lowerCAmelCase_	)

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




					@is_pt_flax_cross_test
					def 						lowerCAmelCase      (		self	)		->				List[str]:
										_snake_case				,  _snake_case						=					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
																				_snake_case						=					self._prepare_for_class(lowerCAmelCase_					,    lowerCAmelCase_	)
																				_snake_case						=					{k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

																				# load corresponding PyTorch class
																				_snake_case						=					model_class.__name__[4:]  # Skip the "Flax" at the beginning
																				_snake_case						=					getattr(lowerCAmelCase_					,    lowerCAmelCase_	)

																				_snake_case						=					pt_model_class(lowerCAmelCase_	).eval()
																				_snake_case						=					False
																				_snake_case						=					model_class(lowerCAmelCase_					,    dtype=jnp.floataa	)

																				_snake_case						=					load_flax_weights_in_pytorch_model(lowerCAmelCase_					,    fx_model.params	)
																				_snake_case				,  _snake_case						=					pt_inputs['input_ids'].shape
																				_snake_case						=					np.random.randint(0					,    seq_length - 1					,    size=(batch_size,)	)
																				for batch_idx, start_index in enumerate(lowerCAmelCase_	):
																									_snake_case						=					0
																									_snake_case						=					1
																									_snake_case						=					0
																									_snake_case						=					1

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

																				with torch.no_grad():
																									_snake_case						=					pt_model(**lowerCAmelCase_	).to_tuple()

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

																				with tempfile.TemporaryDirectory() as tmpdirname:
																									fx_model.save_pretrained(lowerCAmelCase_	)
																									_snake_case						=					pt_model_class.from_pretrained(lowerCAmelCase_					,    from_flax=lowerCAmelCase_	)

																				with torch.no_grad():
																									_snake_case						=					pt_model_loaded(**lowerCAmelCase_	).to_tuple()

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






					@slow
					def 						lowerCAmelCase      (		self	)		->				Optional[int]:
										for model_class_name in self.all_model_classes:
															_snake_case						=					model_class_name.from_pretrained('facebook/xglm-564M'	)
															_snake_case						=					model(np.ones((1, 1)	)	)
															self.assertIsNotNone(lowerCAmelCase_	)










import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase_   				=  logging.get_logger(__name__)





def     lowerCamelCase__  (  UpperCamelCase__      :							List[Any]							,     UpperCamelCase__      :							Dict							,     UpperCamelCase__      :							Optional[Any]    )      ->			Optional[int]:



					'''simple docstring'''




					_snake_case						=					WavLMForSequenceClassification.from_pretrained(UpperCamelCase__							,     config=UpperCamelCase__    )
					_snake_case						=					downstream_dict['projector.weight']
					_snake_case						=					downstream_dict['projector.bias']
					_snake_case						=					downstream_dict['model.post_net.linear.weight']
					_snake_case						=					downstream_dict['model.post_net.linear.bias']
					return model





def     lowerCamelCase__  (  UpperCamelCase__      :							Optional[Any]							,     UpperCamelCase__      :							str							,     UpperCamelCase__      :							List[Any]    )      ->			Optional[Any]:



					'''simple docstring'''




					_snake_case						=					WavLMForAudioFrameClassification.from_pretrained(UpperCamelCase__							,     config=UpperCamelCase__    )
					_snake_case						=					downstream_dict['model.linear.weight']
					_snake_case						=					downstream_dict['model.linear.bias']
					return model





def     lowerCamelCase__  (  UpperCamelCase__      :							Dict							,     UpperCamelCase__      :							List[Any]							,     UpperCamelCase__      :							Dict    )      ->			Tuple:



					'''simple docstring'''




					_snake_case						=					WavLMForXVector.from_pretrained(UpperCamelCase__							,     config=UpperCamelCase__    )
					_snake_case						=					downstream_dict['connector.weight']
					_snake_case						=					downstream_dict['connector.bias']
					for i, kernel_size in enumerate(hf_config.tdnn_kernel    ):
										_snake_case						=					downstream_dict[
										    F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
										]
										_snake_case						=					downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

					_snake_case						=					downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
					_snake_case						=					downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
					_snake_case						=					downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
					_snake_case						=					downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
					_snake_case						=					downstream_dict['objective.W']
					return model





@torch.no_grad()
def     lowerCamelCase__  (  UpperCamelCase__      :							Dict							,     UpperCamelCase__      :							Union[str, Any]							,     UpperCamelCase__      :							List[str]							,     UpperCamelCase__      :							Optional[int]    )      ->			int:



					'''simple docstring'''




					_snake_case						=					torch.load(UpperCamelCase__							,     map_location='cpu'    )

					_snake_case						=					checkpoint['Downstream']

					_snake_case						=					WavLMConfig.from_pretrained(UpperCamelCase__    )
					_snake_case						=					WavaVecaFeatureExtractor.from_pretrained(
					    UpperCamelCase__							,     return_attention_mask=UpperCamelCase__							,     do_normalize=UpperCamelCase__    )

					_snake_case						=					hf_config.architectures[0]
					if arch.endswith('ForSequenceClassification'    ):
										_snake_case						=					convert_classification(UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__    )
					elif arch.endswith('ForAudioFrameClassification'    ):
										_snake_case						=					convert_diarization(UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__    )
					elif arch.endswith('ForXVector'    ):
										_snake_case						=					convert_xvector(UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__    )
					else:
										raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''    )

					if hf_config.use_weighted_layer_sum:
										_snake_case						=					checkpoint['Featurizer']['weights']

					hf_feature_extractor.save_pretrained(UpperCamelCase__    )
					hf_model.save_pretrained(UpperCamelCase__    )


if __name__ == "__main__":
					UpperCAmelCase_   				=  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.""")
					UpperCAmelCase_   				=  parser.parse_args()
					convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)



