



import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("0.12.2"):
       raise Exception("requires fairseq >= 0.12.2")
if version.parse(fairseq.__version__) > version.parse("2"):
       raise Exception("requires fairseq < v2")

logging.set_verbosity_info()
a__      :				Tuple   	=			logging.get_logger(__name__)

a__      :				List[str]   	=			"Hello, World!"
a__      :				str   	=			"en_XX"

def   _lowerCAmelCase   (						A__						,  A__						,  A__	):
      lowercase__     =  Path('data_bin'	)
      lowercase__     =  FairseqXmodModel.from_pretrained(
          model_name_or_path=str(Path(A__	).parent	)						,  checkpoint_file=Path(A__	).name						,  _name='xmod_base'						,  arch='xmod_base'						,  task='multilingual_masked_lm'						,  data_name_or_path=str(A__	)						,  bpe='sentencepiece'						,  sentencepiece_model=str(Path(A__	).parent / 'sentencepiece.bpe.model'	)						,  src_dict=str(data_dir / 'dict.txt'	)						,  )
      xmod.eval()  # disable dropout
      print(A__	)

      lowercase__     =  xmod.model.encoder.sentence_encoder
      lowercase__     =  XmodConfig(
          vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings						,  hidden_size=xmod.cfg.model.encoder_embed_dim						,  num_hidden_layers=xmod.cfg.model.encoder_layers						,  num_attention_heads=xmod.cfg.model.encoder_attention_heads						,  intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim						,  max_position_embeddings=514						,  type_vocab_size=1						,  layer_norm_eps=1E-5						,  pre_norm=xmod.cfg.model.encoder_normalize_before						,  adapter_reduction_factor=getattr(xmod.cfg.model						,  'bottleneck'						,  2	)						,  adapter_layer_norm=xmod.cfg.model.adapter_layer_norm						,  adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm						,  ln_before_adapter=xmod.cfg.model.ln_before_adapter						,  languages=xmod.cfg.model.languages						,  )
      if classification_head:
            lowercase__     =  xmod.model.classification_heads['mnli'].out_proj.weight.shape[0]

      print('Our X-MOD config:'						,  A__	)

      lowercase__     =  XmodForSequenceClassification(A__	) if classification_head else XmodForMaskedLM(A__	)
      model.eval()

      # Now let's copy all the weights.
      # Embeddings
      lowercase__     =  xmod_sent_encoder.embed_tokens.weight
      lowercase__     =  xmod_sent_encoder.embed_positions.weight
      lowercase__     =  torch.zeros_like(
          model.roberta.embeddings.token_type_embeddings.weight	)  # just zero them out b/c xmod doesn't use them.

      lowercase__     =  xmod_sent_encoder.layernorm_embedding.weight
      lowercase__     =  xmod_sent_encoder.layernorm_embedding.bias

      for i in range(config.num_hidden_layers	):
            # Encoder: start of layer
            lowercase__     =  model.roberta.encoder.layer[i]
            lowercase__     =  xmod_sent_encoder.layers[i]

            # self attention
            lowercase__     =  layer.attention.self
            if not (
                xmod_layer.self_attn.k_proj.weight.data.shape
                == xmod_layer.self_attn.q_proj.weight.data.shape
                == xmod_layer.self_attn.v_proj.weight.data.shape
                == torch.Size((config.hidden_size, config.hidden_size)	)
            ):
                  raise AssertionError('Dimensions of self-attention weights do not match.'	)

            lowercase__     =  xmod_layer.self_attn.q_proj.weight
            lowercase__     =  xmod_layer.self_attn.q_proj.bias
            lowercase__     =  xmod_layer.self_attn.k_proj.weight
            lowercase__     =  xmod_layer.self_attn.k_proj.bias
            lowercase__     =  xmod_layer.self_attn.v_proj.weight
            lowercase__     =  xmod_layer.self_attn.v_proj.bias

            # self-attention output
            lowercase__     =  layer.attention.output
            if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
                  raise AssertionError('Dimensions of self-attention output weights do not match.'	)
            lowercase__     =  xmod_layer.self_attn.out_proj.weight
            lowercase__     =  xmod_layer.self_attn.out_proj.bias
            lowercase__     =  xmod_layer.self_attn_layer_norm.weight
            lowercase__     =  xmod_layer.self_attn_layer_norm.bias

            # intermediate
            lowercase__     =  layer.intermediate
            if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
                  raise AssertionError('Dimensions of intermediate weights do not match.'	)
            lowercase__     =  xmod_layer.fca.weight
            lowercase__     =  xmod_layer.fca.bias

            # output
            lowercase__     =  layer.output
            if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
                  raise AssertionError('Dimensions of feed-forward weights do not match.'	)
            lowercase__     =  xmod_layer.fca.weight
            lowercase__     =  xmod_layer.fca.bias
            lowercase__     =  xmod_layer.final_layer_norm.weight
            lowercase__     =  xmod_layer.final_layer_norm.bias
            if bert_output.adapter_layer_norm is not None:
                  lowercase__     =  xmod_layer.adapter_layer_norm.weight
                  lowercase__     =  xmod_layer.adapter_layer_norm.bias

            if sorted(bert_output.adapter_modules.keys()	) != sorted(xmod_layer.adapter_modules.keys()	):
                  raise AssertionError('Lists of language adapters do not match.'	)
            for lang_code, adapter in xmod_layer.adapter_modules.items():
                  lowercase__     =  bert_output.adapter_modules[lang_code]
                  lowercase__     =  xmod_layer.adapter_modules[lang_code]
                  lowercase__     =  from_adapter.fca.weight
                  lowercase__     =  from_adapter.fca.bias
                  lowercase__     =  from_adapter.fca.weight
                  lowercase__     =  from_adapter.fca.bias

            # end of layer

      if xmod_sent_encoder.layer_norm is not None:
            lowercase__     =  xmod_sent_encoder.layer_norm.weight
            lowercase__     =  xmod_sent_encoder.layer_norm.bias

      if classification_head:
            lowercase__     =  xmod.model.classification_heads['mnli'].dense.weight
            lowercase__     =  xmod.model.classification_heads['mnli'].dense.bias
            lowercase__     =  xmod.model.classification_heads['mnli'].out_proj.weight
            lowercase__     =  xmod.model.classification_heads['mnli'].out_proj.bias
      else:
            # LM Head
            lowercase__     =  xmod.model.encoder.lm_head.dense.weight
            lowercase__     =  xmod.model.encoder.lm_head.dense.bias
            lowercase__     =  xmod.model.encoder.lm_head.layer_norm.weight
            lowercase__     =  xmod.model.encoder.lm_head.layer_norm.bias
            lowercase__     =  xmod.model.encoder.lm_head.weight
            lowercase__     =  xmod.model.encoder.lm_head.bias

      # Let's check that we get the same results.
      lowercase__     =  xmod.encode(A__	).unsqueeze(0	)  # batch of size 1
      model.roberta.set_default_language(A__	)

      lowercase__     =  model(A__	)[0]
      if classification_head:
            lowercase__     =  xmod.model.classification_heads['mnli'](xmod.extract_features(A__	)	)
      else:
            lowercase__     =  xmod.model(A__						,  lang_id=[SAMPLE_LANGUAGE]	)[0]
      print(our_output.shape						,  their_output.shape	)
      lowercase__     =  torch.max(torch.abs(our_output - their_output	)	).item()
      print(F'''max_absolute_diff = {max_absolute_diff}'''	)  # ~ 1e-7
      lowercase__     =  torch.allclose(A__						,  A__						,  atol=1E-3	)
      print('Do both models output the same tensors?'						,  '🔥' if success else '💩'	)
      if not success:
            raise Exception('Something went wRoNg'	)

      Path(A__	).mkdir(parents=A__						,  exist_ok=A__	)
      print(F'''Saving model to {pytorch_dump_folder_path}'''	)
      model.save_pretrained(A__	)


if __name__ == "__main__":
       a__      :				Tuple   	=			argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--xmod_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."
       )
       parser.add_argument(
           "--classification_head", action="store_true", help="Whether to convert a final classification head."
       )
       a__      :				int   	=			parser.parse_args()
       convert_xmod_checkpoint_to_pytorch(
           args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
       )





import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  List[Any]       				=    None
   A    :  Optional[int]       				=    None


   @property
   def 			UpperCAmelCase		(						self  :			str)	->				Union[str, Any]:




         """simple docstring"""

         return self.feat_extract_tester.prepare_feat_extract_dict()


   def 			UpperCAmelCase		(						self  :			int)	->				Any:




         """simple docstring"""

         lowercase__     =  self.feature_extraction_class(**self.feat_extract_dict)
         self.assertTrue(hasattr(lowerCAmelCase			,				'feature_size'))
         self.assertTrue(hasattr(lowerCAmelCase			,				'sampling_rate'))
         self.assertTrue(hasattr(lowerCAmelCase			,				'padding_value'))


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




         """simple docstring"""

         lowercase__     =  self.feat_extract_tester.prepare_inputs_for_common()
         lowercase__     =  self.feature_extraction_class(**self.feat_extract_dict)
         lowercase__     =  feat_extract.model_input_names[0]

         lowercase__     =  BatchFeature({input_name: speech_inputs})

         self.assertTrue(all(len(lowerCAmelCase) == len(lowerCAmelCase) for x, y in zip(lowerCAmelCase			,				processed_features[input_name])))

         lowercase__     =  self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCAmelCase)
         lowercase__     =  BatchFeature({input_name: speech_inputs}			,				tensor_type='np')

         lowercase__     =  processed_features[input_name]

         if len(batch_features_input.shape) < 3:
               lowercase__     =  batch_features_input[:, :, None]

         self.assertTrue(
             batch_features_input.shape
             == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size))


   @require_torch
   def 			UpperCAmelCase		(						self  :			Dict)	->				int:




         """simple docstring"""

         lowercase__     =  self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCAmelCase)
         lowercase__     =  self.feature_extraction_class(**self.feat_extract_dict)
         lowercase__     =  feat_extract.model_input_names[0]

         lowercase__     =  BatchFeature({input_name: speech_inputs}			,				tensor_type='pt')

         lowercase__     =  processed_features[input_name]

         if len(batch_features_input.shape) < 3:
               lowercase__     =  batch_features_input[:, :, None]

         self.assertTrue(
             batch_features_input.shape
             == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size))


   @require_tf
   def 			UpperCAmelCase		(						self  :			Optional[Any])	->				Optional[int]:




         """simple docstring"""

         lowercase__     =  self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCAmelCase)
         lowercase__     =  self.feature_extraction_class(**self.feat_extract_dict)
         lowercase__     =  feat_extract.model_input_names[0]

         lowercase__     =  BatchFeature({input_name: speech_inputs}			,				tensor_type='tf')

         lowercase__     =  processed_features[input_name]

         if len(batch_features_input.shape) < 3:
               lowercase__     =  batch_features_input[:, :, None]

         self.assertTrue(
             batch_features_input.shape
             == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size))


   def 			UpperCAmelCase		(						self  :			str			,				lowerCAmelCase  :			str=False)	->				Union[str, Any]:




         """simple docstring"""

         def _inputs_have_equal_length(lowerCAmelCase  :			int):
               lowercase__     =  len(input[0])
               for input_slice in input[1:]:
                     if len(lowerCAmelCase) != length:
                           return False
               return True

         def _inputs_are_equal(lowerCAmelCase  :			Optional[Any]			,				lowerCAmelCase  :			Tuple):
               if len(lowerCAmelCase) != len(lowerCAmelCase):
                     return False

               for input_slice_a, input_slice_a in zip(lowerCAmelCase			,				lowerCAmelCase):
                     if not np.allclose(np.asarray(lowerCAmelCase)			,				np.asarray(lowerCAmelCase)			,				atol=1E-3):
                           return False
               return True

         lowercase__     =  self.feature_extraction_class(**self.feat_extract_dict)
         lowercase__     =  self.feat_extract_tester.prepare_inputs_for_common(numpify=lowerCAmelCase)
         lowercase__     =  feat_extract.model_input_names[0]

         lowercase__     =  BatchFeature({input_name: speech_inputs})

         lowercase__     =  self.feat_extract_tester.seq_length_diff
         lowercase__     =  self.feat_extract_tester.max_seq_length + pad_diff
         lowercase__     =  self.feat_extract_tester.min_seq_length
         lowercase__     =  self.feat_extract_tester.batch_size
         lowercase__     =  self.feat_extract_tester.feature_size

         # test padding for List[int] + numpy
         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				padding=lowerCAmelCase)
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				padding='longest')
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				padding='max_length'			,				max_length=len(speech_inputs[-1]))
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				padding='longest'			,				return_tensors='np')
         lowercase__     =  input_a[input_name]

         # max_length parameter has to be provided when setting `padding="max_length"`
         with self.assertRaises(lowerCAmelCase):
               feat_extract.pad(lowerCAmelCase			,				padding='max_length')[input_name]

         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				max_length=lowerCAmelCase			,				return_tensors='np')
         lowercase__     =  input_a[input_name]

         self.assertFalse(_inputs_have_equal_length(lowerCAmelCase))
         self.assertTrue(_inputs_have_equal_length(lowerCAmelCase))
         self.assertTrue(_inputs_have_equal_length(lowerCAmelCase))
         self.assertTrue(_inputs_are_equal(lowerCAmelCase			,				lowerCAmelCase))
         self.assertTrue(len(input_a[0]) == pad_min_length)
         self.assertTrue(len(input_a[1]) == pad_min_length + pad_diff)
         self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0])))
         self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length))

         if feature_size > 1:
               self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size)

         # test padding for `pad_to_multiple_of` for List[int] + numpy
         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				pad_to_multiple_of=10)
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				padding='longest'			,				pad_to_multiple_of=10)
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				pad_to_multiple_of=10			,				max_length=lowerCAmelCase)
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				pad_to_multiple_of=10			,				max_length=lowerCAmelCase			,				return_tensors='np'			,				)
         lowercase__     =  input_a[input_name]

         self.assertTrue(all(len(lowerCAmelCase) % 10 == 0 for x in input_a))
         self.assertTrue(_inputs_are_equal(lowerCAmelCase			,				lowerCAmelCase))

         lowercase__     =  pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
         self.assertTrue(all(len(lowerCAmelCase) == expected_mult_pad_length for x in input_a))
         self.assertEqual(input_a.shape[:2]			,				(batch_size, expected_mult_pad_length))

         if feature_size > 1:
               self.assertTrue(input_a.shape[2] == feature_size)

         # Check padding value is correct
         lowercase__     =  (np.ones(self.feat_extract_tester.feature_size) * feat_extract.padding_value).sum()
         self.assertTrue(
             abs(np.asarray(input_a[0])[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length))
             < 1E-3)
         self.assertTrue(
             abs(
                 np.asarray(input_a[1])[pad_min_length + pad_diff :].sum()
                 - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff))
             < 1E-3)
         self.assertTrue(
             abs(
                 np.asarray(input_a[2])[pad_min_length + 2 * pad_diff :].sum()
                 - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff))
             < 1E-3)
         self.assertTrue(
             abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)) < 1E-3)
         self.assertTrue(
             abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length))
             < 1E-3)


   def 			UpperCAmelCase		(						self  :			Tuple			,				lowerCAmelCase  :			Dict=False)	->				str:




         """simple docstring"""

         def _inputs_have_equal_length(lowerCAmelCase  :			int):
               lowercase__     =  len(input[0])
               for input_slice in input[1:]:
                     if len(lowerCAmelCase) != length:
                           return False
               return True

         def _inputs_are_equal(lowerCAmelCase  :			str			,				lowerCAmelCase  :			Optional[Any]):
               if len(lowerCAmelCase) != len(lowerCAmelCase):
                     return False

               for input_slice_a, input_slice_a in zip(lowerCAmelCase			,				lowerCAmelCase):
                     if not np.allclose(np.asarray(lowerCAmelCase)			,				np.asarray(lowerCAmelCase)			,				atol=1E-3):
                           return False
               return True

         lowercase__     =  self.feature_extraction_class(**self.feat_extract_dict)
         lowercase__     =  self.feat_extract_tester.prepare_inputs_for_common(numpify=lowerCAmelCase)
         lowercase__     =  feat_extract.model_input_names[0]

         lowercase__     =  BatchFeature({input_name: speech_inputs})

         # truncate to smallest
         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				max_length=len(speech_inputs[0])			,				truncation=lowerCAmelCase)
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				padding='max_length'			,				max_length=len(speech_inputs[0]))
         lowercase__     =  input_a[input_name]

         self.assertTrue(_inputs_have_equal_length(lowerCAmelCase))
         self.assertFalse(_inputs_have_equal_length(lowerCAmelCase))

         # truncate to smallest with np
         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				max_length=len(speech_inputs[0])			,				return_tensors='np'			,				truncation=lowerCAmelCase			,				)
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				max_length=len(speech_inputs[0])			,				return_tensors='np')
         lowercase__     =  input_a[input_name]

         self.assertTrue(_inputs_have_equal_length(lowerCAmelCase))
         self.assertTrue(input_a.shape[1] == len(speech_inputs[0]))

         # since truncation forces padding to be smaller than longest input
         # function can't return `np.ndarray`, but has to return list
         self.assertFalse(_inputs_have_equal_length(lowerCAmelCase))

         # truncate to middle
         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				max_length=len(speech_inputs[1])			,				truncation=lowerCAmelCase			,				return_tensors='np'			,				)
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				max_length=len(speech_inputs[1])			,				truncation=lowerCAmelCase)
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				max_length=len(speech_inputs[1])			,				return_tensors='np')
         lowercase__     =  input_a[input_name]

         self.assertTrue(input_a.shape[1] == len(speech_inputs[1]))
         self.assertTrue(_inputs_have_equal_length(lowerCAmelCase))
         self.assertTrue(_inputs_have_equal_length(lowerCAmelCase))
         self.assertTrue(_inputs_are_equal(lowerCAmelCase			,				lowerCAmelCase))

         # since truncation forces padding to be smaller than longest input
         # function can't return `np.ndarray`, but has to return list
         self.assertFalse(_inputs_have_equal_length(lowerCAmelCase))
         self.assertTrue(len(input_a[-1]) == len(speech_inputs[-1]))

         # padding has to be max_length when setting `truncation=True`
         with self.assertRaises(lowerCAmelCase):
               feat_extract.pad(lowerCAmelCase			,				truncation=lowerCAmelCase)[input_name]

         # padding has to be max_length when setting `truncation=True`
         with self.assertRaises(lowerCAmelCase):
               feat_extract.pad(lowerCAmelCase			,				padding='longest'			,				truncation=lowerCAmelCase)[input_name]

         # padding has to be max_length when setting `truncation=True`
         with self.assertRaises(lowerCAmelCase):
               feat_extract.pad(lowerCAmelCase			,				padding='longest'			,				truncation=lowerCAmelCase)[input_name]

         # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
         with self.assertRaises(lowerCAmelCase):
               feat_extract.pad(lowerCAmelCase			,				padding='max_length'			,				truncation=lowerCAmelCase)[input_name]

         # test truncation for `pad_to_multiple_of` for List[int] + numpy
         lowercase__     =  12
         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				max_length=len(speech_inputs[0])			,				pad_to_multiple_of=lowerCAmelCase			,				truncation=lowerCAmelCase			,				)
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				max_length=len(speech_inputs[0])			,				pad_to_multiple_of=lowerCAmelCase			,				)
         lowercase__     =  input_a[input_name]

         # retrieve expected_length as multiple of pad_to_multiple_of
         lowercase__     =  len(speech_inputs[0])
         if expected_length % pad_to_multiple_of != 0:
               lowercase__     =  ((len(speech_inputs[0]) // pad_to_multiple_of) + 1) * pad_to_multiple_of

         self.assertTrue(len(input_a[0]) == expected_length)
         self.assertTrue(_inputs_have_equal_length(lowerCAmelCase))
         self.assertFalse(_inputs_have_equal_length(lowerCAmelCase))


   def 			UpperCAmelCase		(						self  :			List[str])	->				List[str]:




         """simple docstring"""

         self._check_padding(numpify=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Any)	->				Optional[Any]:




         """simple docstring"""

         self._check_padding(numpify=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			List[Any])	->				int:




         """simple docstring"""

         self._check_truncation(numpify=lowerCAmelCase)


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




         """simple docstring"""

         self._check_truncation(numpify=lowerCAmelCase)


   @require_torch
   def 			UpperCAmelCase		(						self  :			Dict)	->				List[str]:




         """simple docstring"""

         lowercase__     =  self.feature_extraction_class(**self.feat_extract_dict)
         lowercase__     =  self.feat_extract_tester.prepare_inputs_for_common()
         lowercase__     =  feat_extract.model_input_names[0]

         lowercase__     =  BatchFeature({input_name: speech_inputs})

         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				padding='longest'			,				return_tensors='np')[input_name]
         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				padding='longest'			,				return_tensors='pt')[input_name]

         self.assertTrue(abs(input_np.astype(np.floataa).sum() - input_pt.numpy().astype(np.floataa).sum()) < 1E-2)


   @require_tf
   def 			UpperCAmelCase		(						self  :			str)	->				str:




         """simple docstring"""

         lowercase__     =  self.feature_extraction_class(**self.feat_extract_dict)
         lowercase__     =  self.feat_extract_tester.prepare_inputs_for_common()
         lowercase__     =  feat_extract.model_input_names[0]

         lowercase__     =  BatchFeature({input_name: speech_inputs})

         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				padding='longest'			,				return_tensors='np')[input_name]
         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				padding='longest'			,				return_tensors='tf')[input_name]

         self.assertTrue(abs(input_np.astype(np.floataa).sum() - input_tf.numpy().astype(np.floataa).sum()) < 1E-2)


   def 			UpperCAmelCase		(						self  :			Optional[Any])	->				Tuple:




         """simple docstring"""

         lowercase__     =  self.feat_extract_dict
         lowercase__     =  True
         lowercase__     =  self.feature_extraction_class(**lowerCAmelCase)
         lowercase__     =  self.feat_extract_tester.prepare_inputs_for_common()
         lowercase__     =  [len(lowerCAmelCase) for x in speech_inputs]
         lowercase__     =  feat_extract.model_input_names[0]

         lowercase__     =  BatchFeature({input_name: speech_inputs})

         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				padding='longest'			,				return_tensors='np')
         self.assertIn('attention_mask'			,				lowerCAmelCase)
         self.assertListEqual(list(processed.attention_mask.shape)			,				list(processed[input_name].shape[:2]))
         self.assertListEqual(processed.attention_mask.sum(-1).tolist()			,				lowerCAmelCase)



   def 			UpperCAmelCase		(						self  :			str)	->				Tuple:




         """simple docstring"""

         lowercase__     =  self.feat_extract_dict
         lowercase__     =  True
         lowercase__     =  self.feature_extraction_class(**lowerCAmelCase)
         lowercase__     =  self.feat_extract_tester.prepare_inputs_for_common()
         lowercase__     =  [len(lowerCAmelCase) for x in speech_inputs]
         lowercase__     =  feat_extract.model_input_names[0]

         lowercase__     =  BatchFeature({input_name: speech_inputs})
         lowercase__     =  min(lowerCAmelCase)

         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				max_length=lowerCAmelCase			,				truncation=lowerCAmelCase			,				return_tensors='np')
         self.assertIn('attention_mask'			,				lowerCAmelCase)
         self.assertListEqual(
             list(processed_pad.attention_mask.shape)			,				[processed_pad[input_name].shape[0], max_length])
         self.assertListEqual(
             processed_pad.attention_mask[:, :max_length].sum(-1).tolist()			,				[max_length for x in speech_inputs])
