



import argparse
import json
import logging
import os
import sys
from unittest import skip
from unittest.mock import patch

import tensorflow as tf

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


_lowercase				:					Union[str, Any]     					=[
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        "text-generation",
        "text-classification",
        "token-classification",
        "language-modeling",
        "multiple-choice",
        "question-answering",
        "summarization",
        "translation",
        "image-classification",
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
	import run_clm
	import run_image_classification
	import run_mlm
	import run_ner
	import run_qa as run_squad
	import run_summarization
	import run_swag
	import run_text_classification
	import run_translation


logging.basicConfig(level=logging.DEBUG)

_lowercase				:					Tuple     					=logging.getLogger()




def    UpperCAmelCase					(  ):



			'''simple docstring'''





			a__					=  argparse.ArgumentParser()
			parser.add_argument("""-f"""	)
			a__					=  parser.parse_args()
			return args.f




def    UpperCAmelCase					(  lowercase__						:					Any	):



			'''simple docstring'''





			a__					=  {}
			a__					=  os.path.join(__lowerCamelCase				,					"""all_results.json"""	)
			if os.path.exists(__lowerCamelCase	):
						with open(__lowerCamelCase				,					"""r"""	) as f:
									a__					=  json.load(__lowerCamelCase	)
			else:
						raise ValueError(f'can\'t find {path}'	)
			return results




def    UpperCAmelCase					(  ):



			'''simple docstring'''





			return bool(tf.config.list_physical_devices("""GPU"""	)	)


_lowercase				:					Dict     					=logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class lowerCAmelCase_       (   __lowerCamelCase			):







	'''simple docstring'''




	@skip("""Skipping until shape inference for to_tf_dataset PR is merged."""       )
	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.get_auto_remove_tmp_dir()
				a__					=  f'\n            run_text_classification.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --do_train\n            --do_eval\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --max_steps=10\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            '.split()

				if is_cuda_available():
							testargs.append("""--fp16"""       )

				with patch.object(__lowerCAmelCase   ,       """argv"""   ,       __lowerCAmelCase       ):
							run_text_classification.main()
							# Reset the mixed precision policy so we don't break other tests
							tf.keras.mixed_precision.set_global_policy("""float32"""       )
							a__					=  get_results(__lowerCAmelCase       )
							self.assertGreaterEqual(result["""eval_accuracy"""]   ,       0.7_5       )

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.get_auto_remove_tmp_dir()
				a__					=  f'\n            run_clm.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            '.split()

				if len(tf.config.list_physical_devices("""GPU"""       )       ) > 1:
							# Skipping because there are not enough batches to train the model + would need a drop_last to work.
							return

				with patch.object(__lowerCAmelCase   ,       """argv"""   ,       __lowerCAmelCase       ):
							run_clm.main()
							a__					=  get_results(__lowerCAmelCase       )
							self.assertLess(result["""eval_perplexity"""]   ,       100       )

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.get_auto_remove_tmp_dir()
				a__					=  f'\n            run_mlm.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --max_seq_length 64\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --prediction_loss_only\n            --num_train_epochs=1\n            --learning_rate=1e-4\n        '.split()

				with patch.object(__lowerCAmelCase   ,       """argv"""   ,       __lowerCAmelCase       ):
							run_mlm.main()
							a__					=  get_results(__lowerCAmelCase       )
							self.assertLess(result["""eval_perplexity"""]   ,       42       )

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  7 if get_gpu_count() > 1 else 2

				a__					=  self.get_auto_remove_tmp_dir()
				a__					=  f'\n            run_ner.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n        '.split()

				with patch.object(__lowerCAmelCase   ,       """argv"""   ,       __lowerCAmelCase       ):
							run_ner.main()
							a__					=  get_results(__lowerCAmelCase       )
							self.assertGreaterEqual(result["""accuracy"""]   ,       0.7_5       )

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.get_auto_remove_tmp_dir()
				a__					=  f'\n            run_qa.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=10\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()

				with patch.object(__lowerCAmelCase   ,       """argv"""   ,       __lowerCAmelCase       ):
							run_squad.main()
							a__					=  get_results(__lowerCAmelCase       )
							self.assertGreaterEqual(result["""f1"""]   ,       30       )
							self.assertGreaterEqual(result["""exact"""]   ,       30       )

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.get_auto_remove_tmp_dir()
				a__					=  f'\n            run_swag.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=20\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()

				with patch.object(__lowerCAmelCase   ,       """argv"""   ,       __lowerCAmelCase       ):
							run_swag.main()
							a__					=  get_results(__lowerCAmelCase       )
							self.assertGreaterEqual(result["""val_accuracy"""]   ,       0.8       )

	@slow
	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.get_auto_remove_tmp_dir()
				a__					=  f'\n            run_summarization.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=50\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()

				with patch.object(__lowerCAmelCase   ,       """argv"""   ,       __lowerCAmelCase       ):
							run_summarization.main()
							a__					=  get_results(__lowerCAmelCase       )
							self.assertGreaterEqual(result["""rouge1"""]   ,       10       )
							self.assertGreaterEqual(result["""rouge2"""]   ,       2       )
							self.assertGreaterEqual(result["""rougeL"""]   ,       7       )
							self.assertGreaterEqual(result["""rougeLsum"""]   ,       7       )

	@slow
	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.get_auto_remove_tmp_dir()
				a__					=  f'\n            run_translation.py\n            --model_name_or_path Rocketknight1/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=3e-3\n            --num_train_epochs 12\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n        '.split()

				with patch.object(__lowerCAmelCase   ,       """argv"""   ,       __lowerCAmelCase       ):
							run_translation.main()
							a__					=  get_results(__lowerCAmelCase       )
							self.assertGreaterEqual(result["""bleu"""]   ,       30       )







	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.get_auto_remove_tmp_dir()
				a__					=  f'\n            run_image_classification.py\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --model_name_or_path microsoft/resnet-18\n            --do_train\n            --do_eval\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --dataloader_num_workers 16\n            --num_train_epochs 2\n            --train_val_split 0.1\n            --seed 42\n            --ignore_mismatched_sizes True\n            '.split()

				with patch.object(__lowerCAmelCase   ,       """argv"""   ,       __lowerCAmelCase       ):
							run_image_classification.main()
							a__					=  get_results(__lowerCAmelCase       )
							self.assertGreaterEqual(result["""accuracy"""]   ,       0.7       )


'''simple docstring'''

import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

lowerCAmelCase_			:			Optional[Any]    	=   {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "mask_emb": "masked_spec_embed",
}


def __a			(						__lowerCamelCase      :    Dict			,		__lowerCamelCase      :    Optional[int]			,		__lowerCamelCase      :    str			,		__lowerCamelCase      :    int			,		__lowerCamelCase      :    Union[str, Any]					)						->				Optional[Any]:
      '''simple docstring'''


      for attribute in key.split("."					):
            lowercase_     			=   getattr(__lowerCamelCase			,		__lowerCamelCase					)

      if weight_type is not None:
            lowercase_     			=   getattr(__lowerCamelCase			,		__lowerCamelCase					).shape
      else:
            lowercase_     			=   hf_pointer.shape

      assert hf_shape == value.shape, (
          f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
          f' {value.shape} for {full_name}'
      )

      if weight_type == "weight":
            lowercase_     			=   value
      elif weight_type == "weight_g":
            lowercase_     			=   value
      elif weight_type == "weight_v":
            lowercase_     			=   value
      elif weight_type == "bias":
            lowercase_     			=   value
      else:
            lowercase_     			=   value

      logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'					)


def __a			(						__lowerCamelCase      :    List[str]			,		__lowerCamelCase      :    Optional[int]					)						->				List[Any]:
      '''simple docstring'''


      lowercase_     			=   []
      lowercase_     			=   fairseq_model.state_dict()

      lowercase_     			=   hf_model.feature_extractor

      for name, value in fairseq_dict.items():
            lowercase_     			=   False
            if "conv_layers" in name:
                  load_conv_layer(
                      __lowerCamelCase			,		__lowerCamelCase			,		__lowerCamelCase			,		__lowerCamelCase			,		hf_model.config.feat_extract_norm == "group"			,		)
                  lowercase_     			=   True
            else:
                  for key, mapped_key in MAPPING.items():
                        lowercase_     			=   mapped_key

                        if key in name:
                              lowercase_     			=   True
                              if "*" in mapped_key:
                                    lowercase_     			=   name.split(__lowerCamelCase					)[0].split("."					)[-2]
                                    lowercase_     			=   mapped_key.replace("*"			,		__lowerCamelCase					)
                              if "weight_g" in name:
                                    lowercase_     			=   "weight_g"
                              elif "weight_v" in name:
                                    lowercase_     			=   "weight_v"
                              elif "weight" in name:
                                    lowercase_     			=   "weight"
                              elif "bias" in name:
                                    lowercase_     			=   "bias"
                              else:
                                    lowercase_     			=   None
                              set_recursively(__lowerCamelCase			,		__lowerCamelCase			,		__lowerCamelCase			,		__lowerCamelCase			,		__lowerCamelCase					)
                        continue
            if not is_used:
                  unused_weights.append(__lowerCamelCase					)

      logger.warning(f'Unused weights: {unused_weights}'					)


def __a			(						__lowerCamelCase      :    Any			,		__lowerCamelCase      :    Optional[int]			,		__lowerCamelCase      :    List[str]			,		__lowerCamelCase      :    List[str]			,		__lowerCamelCase      :    Optional[Any]					)						->				Tuple:
      '''simple docstring'''


      lowercase_     			=   full_name.split("conv_layers."					)[-1]
      lowercase_     			=   name.split("."					)
      lowercase_     			=   int(items[0]					)
      lowercase_     			=   int(items[1]					)

      if type_id == 0:
            if "bias" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                      f'{full_name} has size {value.shape}, but'
                      f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
                  )
                  lowercase_     			=   value
                  logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'					)
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                      f'{full_name} has size {value.shape}, but'
                      f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
                  )
                  lowercase_     			=   value
                  logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'					)
      elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
            if "bias" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                      f'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
                      " found."
                  )
                  lowercase_     			=   value
                  logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'					)
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                      f'{full_name} has size {value.shape}, but'
                      f' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
                  )
                  lowercase_     			=   value
                  logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'					)
      else:
            unused_weights.append(__lowerCamelCase					)


def __a			(						__lowerCamelCase      :    Optional[int]					)						->				Dict:
      '''simple docstring'''


      lowercase_     			=   HubertConfig()
      lowercase_     			=   model.config

      lowercase_     			=   fs_config.activation_dropout
      lowercase_     			=   False
      lowercase_     			=   fs_config.attention_dropout
      lowercase_     			=   False
      lowercase_     			=   eval(fs_config.extractor_conv_feature_layers					)
      lowercase_     			=   [x[0] for x in conv_layers]
      lowercase_     			=   [x[1] for x in conv_layers]
      lowercase_     			=   [x[2] for x in conv_layers]
      lowercase_     			=   "gelu"
      lowercase_     			=   "layer" if fs_config.extractor_mode == "layer_norm" else "group"
      lowercase_     			=   False
      lowercase_     			=   0.0
      lowercase_     			=   0.0
      lowercase_     			=   fs_config.activation_fn
      lowercase_     			=   fs_config.dropout
      lowercase_     			=   fs_config.encoder_embed_dim
      lowercase_     			=   0.0_2
      lowercase_     			=   fs_config.encoder_ffn_embed_dim
      lowercase_     			=   1e-5
      lowercase_     			=   0.0
      lowercase_     			=   fs_config.encoder_attention_heads
      lowercase_     			=   fs_config.conv_pos_groups
      lowercase_     			=   fs_config.conv_pos
      lowercase_     			=   len(__lowerCamelCase					)
      lowercase_     			=   fs_config.encoder_layers

      return config


@torch.no_grad()
def __a			(						__lowerCamelCase      :    Optional[Any]			,		__lowerCamelCase      :    Any=None					)						->				Any:
      '''simple docstring'''


      lowercase_     			=   distilhubert().model.model

      if config_path is not None:
            lowercase_     			=   HubertConfig.from_pretrained(__lowerCamelCase					)
      else:
            lowercase_     			=   convert_config(__lowerCamelCase					)
      lowercase_     			=   model.eval()

      lowercase_     			=   WavaVecaFeatureExtractor(
          feature_size=1			,		sampling_rate=16_000			,		padding_value=0			,		do_normalize=__lowerCamelCase			,		return_attention_mask=__lowerCamelCase			,		)
      lowercase_     			=   HubertModel(__lowerCamelCase					)

      recursively_load_weights(__lowerCamelCase			,		__lowerCamelCase					)

      feature_extractor.save_pretrained(__lowerCamelCase					)
      hf_model.save_pretrained(__lowerCamelCase					)


if __name__ == "__main__":
       lowerCAmelCase_			:			str    	=   argparse.ArgumentParser()
       parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
       parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
       lowerCAmelCase_			:			str    	=   parser.parse_args()
       convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)






