






import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


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







def      snake_case		(							UpperCAmelCase	:       str,						UpperCAmelCase	:       str   ):
				if "xprophetnet" in prophetnet_checkpoint_path:
								A	 =      XLMProphetNetForConditionalGenerationOld.from_pretrained(UpperCAmelCase   )
								A ,		A	 =      XLMProphetNetForConditionalGeneration.from_pretrained(
								    UpperCAmelCase,						output_loading_info=UpperCAmelCase   )
				else:
								A	 =      ProphetNetForConditionalGenerationOld.from_pretrained(UpperCAmelCase   )
								A ,		A	 =      ProphetNetForConditionalGeneration.from_pretrained(
								    UpperCAmelCase,						output_loading_info=UpperCAmelCase   )

				A	 =      ['key_proj', 'value_proj', 'query_proj']

				A	 =      {
				    'self_attn': 'ngram_self_attn',
				    'cross_attn': 'encoder_attn',
				    'cross_attn_layer_norm': 'encoder_attn_layer_norm',
				    'feed_forward_layer_norm': 'final_layer_norm',
				    'feed_forward': '',
				    'intermediate': 'fc1',
				    'output': 'fc2',
				    'key_proj': 'k_proj',
				    'query_proj': 'q_proj',
				    'value_proj': 'v_proj',
				    'word_embeddings': 'embed_tokens',
				    'embeddings_layer_norm': 'emb_layer_norm',
				    'relative_pos_embeddings': 'relative_linear',
				    'ngram_embeddings': 'ngram_input_embed',
				    'position_embeddings': 'embed_positions',
				}

				for key in loading_info["missing_keys"]:
								A	 =      key.split('.'   )

								if attributes[0] == "lm_head":
												A	 =      prophet
												A	 =      prophet_old
								else:
												A	 =      prophet.prophetnet
												A	 =      prophet_old.model

								A	 =      False
								for attribute in attributes:
												if attribute in mapping:
																A	 =      mapping[attribute]
																if not hasattr(UpperCAmelCase,						UpperCAmelCase   ) and len(UpperCAmelCase   ) > 0:
																				A	 =      attribute
												elif hasattr(UpperCAmelCase,						UpperCAmelCase   ):
																A	 =      attribute

												if attribute == "weight":
																assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
																A	 =      old_model.weight
																logger.info(f'{attribute} is initialized.'   )
																A	 =      True
																break
												elif attribute == "bias":
																assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
																A	 =      old_model.bias
																logger.info(f'{attribute} is initialized'   )
																A	 =      True
																break
												elif attribute in special_keys and hasattr(UpperCAmelCase,						'in_proj_weight'   ):
																A	 =      old_model.in_proj_weight.shape[0] // 3
																A	 =      getattr(UpperCAmelCase,						UpperCAmelCase   )
																param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
																param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
																if attribute == "query_proj":
																				A	 =      nn.Parameter(old_model.in_proj_weight[:embed_dim, :]   )
																				A	 =      nn.Parameter(old_model.in_proj_bias[:embed_dim]   )

																elif attribute == "key_proj":
																				A	 =      nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]   )
																				A	 =      nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]   )
																elif attribute == "value_proj":
																				A	 =      nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]   )
																				A	 =      nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]   )
																A	 =      True
																break
												elif attribute == "position_embeddings":
																assert (
																    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
																), "Hidden size has to match"
																assert model.position_embeddings.weight.shape[0] == 5_12, "We want 512 position_embeddings."
																A	 =      nn.Parameter(old_model.embed_positions.weight[:5_12, :]   )
																A	 =      True
																break

												if attribute.isdigit():
																A	 =      model[int(UpperCAmelCase   )]
																A	 =      old_model[int(UpperCAmelCase   )]
												else:
																A	 =      getattr(UpperCAmelCase,						UpperCAmelCase   )

																if old_attribute == "":
																				A	 =      old_model
																else:
																				if not hasattr(UpperCAmelCase,						UpperCAmelCase   ):
																								raise ValueError(f'{old_model} does not have {old_attribute}'   )
																				A	 =      getattr(UpperCAmelCase,						UpperCAmelCase   )

								if not is_key_init:
												raise ValueError(f'{key} was not correctly initialized!'   )

				print(f'Saving model to {pytorch_dump_folder_path}'   )
				prophet.save_pretrained(UpperCAmelCase   )


if __name__ == "__main__":
					lowerCAmelCase_           = argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--prophetnet_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
					)
					parser.add_argument(
					    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					lowerCAmelCase_           = parser.parse_args()
					convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)








import unittest

import numpy as np

from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torchaudio,
    slow,
)

from .test_pipelines_common import ANY
@is_pipeline_test
class      UpperCamelCase (				unittest.TestCase ):







		"""simple docstring"""



		snake_case    =   MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
		snake_case    =   TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
		def       A(  self		:       int						,_SCREAMING_SNAKE_CASE		:       Any						,_SCREAMING_SNAKE_CASE		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       Tuple		)    ->   Dict:


						'''simple docstring'''
						A	 =      AudioClassificationPipeline(model=_SCREAMING_SNAKE_CASE						,feature_extractor=_SCREAMING_SNAKE_CASE		)

						# test with a raw waveform
						A	 =      np.zeros((3_4_0_0_0,)		)
						A	 =      np.zeros((1_4_0_0_0,)		)
						return audio_classifier, [audioa, audio]
		def       A(  self		:       int						,_SCREAMING_SNAKE_CASE		:       Tuple						,_SCREAMING_SNAKE_CASE		:       List[Any]		)    ->   Union[str, Any]:


						'''simple docstring'''
						A ,		A	 =      examples
						A	 =      audio_classifier(_SCREAMING_SNAKE_CASE		)
						# by default a model is initialized with num_labels=2
						self.assertEqual(
						    _SCREAMING_SNAKE_CASE						,[
						        {'score': ANY(_SCREAMING_SNAKE_CASE		), 'label': ANY(_SCREAMING_SNAKE_CASE		)},
						        {'score': ANY(_SCREAMING_SNAKE_CASE		), 'label': ANY(_SCREAMING_SNAKE_CASE		)},
						    ]						,)
						A	 =      audio_classifier(_SCREAMING_SNAKE_CASE						,top_k=1		)
						self.assertEqual(
						    _SCREAMING_SNAKE_CASE						,[
						        {'score': ANY(_SCREAMING_SNAKE_CASE		), 'label': ANY(_SCREAMING_SNAKE_CASE		)},
						    ]						,)

						self.run_torchaudio(_SCREAMING_SNAKE_CASE		)
		@require_torchaudio
		def       A(  self		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       int		)    ->   Optional[Any]:


						'''simple docstring'''
						import datasets

						# test with a local file
						A	 =      datasets.load_dataset('hf-internal-testing/librispeech_asr_dummy'						,'clean'						,split='validation'		)
						A	 =      dataset[0]['audio']['array']
						A	 =      audio_classifier(_SCREAMING_SNAKE_CASE		)
						self.assertEqual(
						    _SCREAMING_SNAKE_CASE						,[
						        {'score': ANY(_SCREAMING_SNAKE_CASE		), 'label': ANY(_SCREAMING_SNAKE_CASE		)},
						        {'score': ANY(_SCREAMING_SNAKE_CASE		), 'label': ANY(_SCREAMING_SNAKE_CASE		)},
						    ]						,)
		@require_torch
		def       A(  self		:       int		)    ->   str:


						'''simple docstring'''
						A	 =      'anton-l/wav2vec2-random-tiny-classifier'

						A	 =      pipeline('audio-classification'						,model=_SCREAMING_SNAKE_CASE		)

						A	 =      np.ones((8_0_0_0,)		)
						A	 =      audio_classifier(_SCREAMING_SNAKE_CASE						,top_k=4		)

						A	 =      [
						    {'score': 0.0842, 'label': 'no'},
						    {'score': 0.0838, 'label': 'up'},
						    {'score': 0.0837, 'label': 'go'},
						    {'score': 0.0834, 'label': 'right'},
						]
						A	 =      [
						    {'score': 0.0845, 'label': 'stop'},
						    {'score': 0.0844, 'label': 'on'},
						    {'score': 0.0841, 'label': 'right'},
						    {'score': 0.0834, 'label': 'left'},
						]
						self.assertIn(nested_simplify(_SCREAMING_SNAKE_CASE						,decimals=4		)						,[EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]		)

						A	 =      {'array': np.ones((8_0_0_0,)		), 'sampling_rate': audio_classifier.feature_extractor.sampling_rate}
						A	 =      audio_classifier(_SCREAMING_SNAKE_CASE						,top_k=4		)
						self.assertIn(nested_simplify(_SCREAMING_SNAKE_CASE						,decimals=4		)						,[EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]		)
		@require_torch
		@slow
		def       A(  self		:       Optional[int]		)    ->   Union[str, Any]:


						'''simple docstring'''
						import datasets

						A	 =      'superb/wav2vec2-base-superb-ks'

						A	 =      pipeline('audio-classification'						,model=_SCREAMING_SNAKE_CASE		)
						A	 =      datasets.load_dataset('anton-l/superb_dummy'						,'ks'						,split='test'		)

						A	 =      np.array(dataset[3]['speech']						,dtype=np.floataa		)
						A	 =      audio_classifier(_SCREAMING_SNAKE_CASE						,top_k=4		)
						self.assertEqual(
						    nested_simplify(_SCREAMING_SNAKE_CASE						,decimals=3		)						,[
						        {'score': 0.981, 'label': 'go'},
						        {'score': 0.007, 'label': 'up'},
						        {'score': 0.006, 'label': '_unknown_'},
						        {'score': 0.001, 'label': 'down'},
						    ]						,)







		@require_tf
		@unittest.skip('Audio classification is not implemented for TF'		)
		def       A(  self		:       Any		)    ->   int:


						'''simple docstring'''
						pass





