





import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


_UpperCAmelCase				:       str	   =				random.Random()

if is_torch_available():
    import torch


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase=1.0						,	_UpperCAmelCase=None						,	_UpperCAmelCase=None )			->     Tuple:
    if rng is None:
        lowerCamelCase__    :      int					 =		global_rng

    lowerCamelCase__    :      Union[str, Any]					 =		[]
    for batch_idx in range(shape[0] ):
        values.append([] )
        for _ in range(shape[1] ):
            values[-1].append(rng.random() * scale )

    return values

class    lowerCAmelCase							(					unittest.TestCase			):
 def __init__(      self  :      int			,     UpperCAmelCase  :      Optional[Any]			,     UpperCAmelCase  :      List[Any]=7			,     UpperCAmelCase  :      Optional[Any]=400			,     UpperCAmelCase  :      Union[str, Any]=2000			,     UpperCAmelCase  :      Optional[int]=1			,     UpperCAmelCase  :      int=0.0			,     UpperCAmelCase  :      List[str]=16000			,     UpperCAmelCase  :      Any=True			,     UpperCAmelCase  :      Dict=True			,     )			->	Tuple:
     lowerCamelCase__    :      List[str]					 =		parent
     lowerCamelCase__    :      Optional[int]					 =		batch_size
     lowerCamelCase__    :      Union[str, Any]					 =		min_seq_length
     lowerCamelCase__    :      Any					 =		max_seq_length
     lowerCamelCase__    :      str					 =		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
     lowerCamelCase__    :      Optional[Any]					 =		feature_size
     lowerCamelCase__    :      Any					 =		padding_value
     lowerCamelCase__    :      int					 =		sampling_rate
     lowerCamelCase__    :      Optional[int]					 =		return_attention_mask
     lowerCamelCase__    :      Tuple					 =		do_normalize
 def      A_	(      self  :      Optional[Any]	)			->	Dict:
     return {
         "feature_size": self.feature_size,
         "padding_value": self.padding_value,
         "sampling_rate": self.sampling_rate,
         "return_attention_mask": self.return_attention_mask,
         "do_normalize": self.do_normalize,
     }







 def      A_	(      self  :      Dict			,     UpperCAmelCase  :      Any=False			,     UpperCAmelCase  :      List[Any]=False	)			->	str:
     def _flatten(UpperCAmelCase  :      int	):
         return list(itertools.chain(*UpperCAmelCase	)	)

     if equal_length:
         lowerCamelCase__    :      int					 =		floats_list((self.batch_size, self.max_seq_length)	)
     else:
         # make sure that inputs increase in size
         lowerCamelCase__    :      List[str]					 =		[
             _flatten(floats_list((x, self.feature_size)	)	)
             for x in range(self.min_seq_length			,     self.max_seq_length			,     self.seq_length_diff	)
         ]

     if numpify:
         lowerCamelCase__    :      Union[str, Any]					 =		[np.asarray(UpperCAmelCase	) for x in speech_inputs]

     return speech_inputs


@require_torch
@require_torchaudio
class    lowerCAmelCase							(					snake_case_,  unittest.TestCase			):
 UpperCAmelCase__													=		ASTFeatureExtractor
 def      A_	(      self  :      Union[str, Any]	)			->	Union[str, Any]:
     lowerCamelCase__    :      Optional[int]					 =		ASTFeatureExtractionTester(self	)
 def      A_	(      self  :      Optional[int]	)			->	Dict:
     # Tests that all call wrap to encode_plus and batch_encode_plus
     lowerCamelCase__    :      List[Any]					 =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
     # create three inputs of length 800, 1000, and 1200
     lowerCamelCase__    :      int					 =		[floats_list((1, x)	)[0] for x in range(800			,     1400			,     200	)]
     lowerCamelCase__    :      str					 =		[np.asarray(UpperCAmelCase	) for speech_input in speech_inputs]

     # Test not batched input
     lowerCamelCase__    :      int					 =		feat_extract(speech_inputs[0]			,     return_tensors='np'	).input_values
     lowerCamelCase__    :      Any					 =		feat_extract(np_speech_inputs[0]			,     return_tensors='np'	).input_values
     self.assertTrue(np.allclose(UpperCAmelCase			,     UpperCAmelCase			,     atol=1e-3	)	)

     # Test batched
     lowerCamelCase__    :      Any					 =		feat_extract(UpperCAmelCase			,     padding=UpperCAmelCase			,     return_tensors='np'	).input_values
     lowerCamelCase__    :      Tuple					 =		feat_extract(UpperCAmelCase			,     padding=UpperCAmelCase			,     return_tensors='np'	).input_values
     for enc_seq_a, enc_seq_a in zip(UpperCAmelCase			,     UpperCAmelCase	):
         self.assertTrue(np.allclose(UpperCAmelCase			,     UpperCAmelCase			,     atol=1e-3	)	)

     # Test 2-D numpy arrays are batched.
     lowerCamelCase__    :      Any					 =		[floats_list((1, x)	)[0] for x in (800, 800, 800)]
     lowerCamelCase__    :      List[str]					 =		np.asarray(UpperCAmelCase	)
     lowerCamelCase__    :      Tuple					 =		feat_extract(UpperCAmelCase			,     return_tensors='np'	).input_values
     lowerCamelCase__    :      int					 =		feat_extract(UpperCAmelCase			,     return_tensors='np'	).input_values
     for enc_seq_a, enc_seq_a in zip(UpperCAmelCase			,     UpperCAmelCase	):
         self.assertTrue(np.allclose(UpperCAmelCase			,     UpperCAmelCase			,     atol=1e-3	)	)
 @require_torch
 def      A_	(      self  :      Any	)			->	List[Any]:
     import torch

     lowerCamelCase__    :      List[Any]					 =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
     lowerCamelCase__    :      List[str]					 =		np.random.rand(100	).astype(np.floataa	)
     lowerCamelCase__    :      Optional[Any]					 =		np_speech_inputs.tolist()

     for inputs in [py_speech_inputs, np_speech_inputs]:
         lowerCamelCase__    :      List[str]					 =		feature_extractor.pad([{'input_values': inputs}]			,     return_tensors='np'	)
         self.assertTrue(np_processed.input_values.dtype == np.floataa	)
         lowerCamelCase__    :      List[str]					 =		feature_extractor.pad([{'input_values': inputs}]			,     return_tensors='pt'	)
         self.assertTrue(pt_processed.input_values.dtype == torch.floataa	)
 def      A_	(      self  :      Optional[Any]			,     UpperCAmelCase  :      Optional[Any]	)			->	Optional[Any]:
     from datasets import load_dataset

     lowerCamelCase__    :      Any					 =		load_dataset('hf-internal-testing/librispeech_asr_dummy'			,     'clean'			,     split='validation'	)
     # automatic decoding with librispeech
     lowerCamelCase__    :      int					 =		ds.sort('id'	).select(range(UpperCAmelCase	)	)[:num_samples]['audio']

     return [x["array"] for x in speech_samples]







 @require_torch
 def      A_	(      self  :      Any	)			->	Optional[int]:
     # fmt: off
     lowerCamelCase__    :      Union[str, Any]					 =		torch.tensor(
         [-0.9_8_9_4, -1.2_7_7_6, -0.9_0_6_6, -1.2_7_7_6, -0.9_3_4_9, -1.2_6_0_9, -1.0_3_8_6, -1.2_7_7_6,
          -1.1_5_6_1, -1.2_7_7_6, -1.2_0_5_2, -1.2_7_2_3, -1.2_1_9_0, -1.2_1_3_2, -1.2_7_7_6, -1.1_1_3_3,
          -1.1_9_5_3, -1.1_3_4_3, -1.1_5_8_4, -1.2_2_0_3, -1.1_7_7_0, -1.2_4_7_4, -1.2_3_8_1, -1.1_9_3_6,
          -0.9_2_7_0, -0.8_3_1_7, -0.8_0_4_9, -0.7_7_0_6, -0.7_5_6_5, -0.7_8_6_9]	)
     # fmt: on

     lowerCamelCase__    :      int					 =		self._load_datasamples(1	)
     lowerCamelCase__    :      int					 =		ASTFeatureExtractor()
     lowerCamelCase__    :      List[str]					 =		feature_extractor(UpperCAmelCase			,     return_tensors='pt'	).input_values
     self.assertEquals(input_values.shape			,     (1, 1024, 128)	)
     self.assertTrue(torch.allclose(input_values[0, 0, :30]			,     UpperCAmelCase			,     atol=1e-4	)	)












'''simple docstring'''

import argparse
import logging
from collections import namedtuple

import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer  # The authors' implementation

from transformers import BertTokenizer


logging.basicConfig(level=logging.INFO)
lowercase_					= logging.getLogger(__name__)


lowercase_					= """Hello world! cécé herlolip"""


lowercase_					= namedtuple(
    """BertAbsConfig""",
    [
        """temp_dir""",
        """large""",
        """use_bert_emb""",
        """finetune_bert""",
        """encoder""",
        """share_emb""",
        """max_pos""",
        """enc_layers""",
        """enc_hidden_size""",
        """enc_heads""",
        """enc_ff_size""",
        """enc_dropout""",
        """dec_layers""",
        """dec_hidden_size""",
        """dec_heads""",
        """dec_ff_size""",
        """dec_dropout""",
    ],
)





def lowerCamelCase						(     __lowerCamelCase :    Optional[Any]					,       __lowerCamelCase :    Union[str, Any]    )						->List[Any]:
    _SCREAMING_SNAKE_CASE									=			BertAbsConfig(
        temp_dir="""."""					,       finetune_bert=__lowerCamelCase					,       large=__lowerCamelCase					,       share_emb=__lowerCamelCase					,       use_bert_emb=__lowerCamelCase					,       encoder="""bert"""					,       max_pos=512					,       enc_layers=6					,       enc_hidden_size=512					,       enc_heads=8					,       enc_ff_size=512					,       enc_dropout=0.2					,       dec_layers=6					,       dec_hidden_size=768					,       dec_heads=8					,       dec_ff_size=2048					,       dec_dropout=0.2					,       )
    _SCREAMING_SNAKE_CASE									=			torch.load(__lowerCamelCase					,       lambda __lowerCamelCase					,       __lowerCamelCase    : storage    )
    _SCREAMING_SNAKE_CASE									=			AbsSummarizer(__lowerCamelCase					,       torch.device("""cpu"""    )					,       __lowerCamelCase    )
    original.eval()

    _SCREAMING_SNAKE_CASE									=			BertAbsSummarizer(__lowerCamelCase					,       torch.device("""cpu"""    )    )
    new_model.eval()

    # -------------------
    # Convert the weights
    # -------------------

    logging.info("""convert the model"""    )
    new_model.bert.load_state_dict(original.bert.state_dict()    )
    new_model.decoder.load_state_dict(original.decoder.state_dict()    )
    new_model.generator.load_state_dict(original.generator.state_dict()    )

    # ----------------------------------
    # Make sure the outpus are identical
    # ----------------------------------

    logging.info("""Make sure that the models' outputs are identical"""    )
    _SCREAMING_SNAKE_CASE									=			BertTokenizer.from_pretrained("""bert-base-uncased"""    )

    # prepare the model inputs
    _SCREAMING_SNAKE_CASE									=			tokenizer.encode("""This is sample éàalj'-."""    )
    encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(__lowerCamelCase    ))    )
    _SCREAMING_SNAKE_CASE									=			torch.tensor(__lowerCamelCase    ).unsqueeze(0    )
    _SCREAMING_SNAKE_CASE									=			tokenizer.encode("""This is sample 3 éàalj'-."""    )
    decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(__lowerCamelCase    ))    )
    _SCREAMING_SNAKE_CASE									=			torch.tensor(__lowerCamelCase    ).unsqueeze(0    )

    # failsafe to make sure the weights reset does not affect the
    # loaded weights.
    assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight    )    ) == 0

    # forward pass
    _SCREAMING_SNAKE_CASE									=			encoder_input_ids
    _SCREAMING_SNAKE_CASE									=			decoder_input_ids
    _SCREAMING_SNAKE_CASE									=			_SCREAMING_SNAKE_CASE									=			None
    _SCREAMING_SNAKE_CASE									=			None
    _SCREAMING_SNAKE_CASE									=			_SCREAMING_SNAKE_CASE									=			None
    _SCREAMING_SNAKE_CASE									=			_SCREAMING_SNAKE_CASE									=			None
    _SCREAMING_SNAKE_CASE									=			None

    # The original model does not apply the geneator layer immediatly but rather in
    # the beam search (where it combines softmax + linear layer). Since we already
    # apply the softmax in our generation process we only apply the linear layer here.
    # We make sure that the outputs of the full stack are identical
    _SCREAMING_SNAKE_CASE									=			original(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )[0]
    _SCREAMING_SNAKE_CASE									=			original.generator(__lowerCamelCase    )

    _SCREAMING_SNAKE_CASE									=			new_model(
        __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )[0]
    _SCREAMING_SNAKE_CASE									=			new_model.generator(__lowerCamelCase    )

    _SCREAMING_SNAKE_CASE									=			torch.max(torch.abs(output_converted_model - output_original_model    )    ).item()
    print("""Maximum absolute difference beween weights: {:.2f}""".format(__lowerCamelCase    )    )
    _SCREAMING_SNAKE_CASE									=			torch.max(torch.abs(output_converted_generator - output_original_generator    )    ).item()
    print("""Maximum absolute difference beween weights: {:.2f}""".format(__lowerCamelCase    )    )

    _SCREAMING_SNAKE_CASE									=			torch.allclose(__lowerCamelCase					,       __lowerCamelCase					,       atol=1e-3    )
    if are_identical:
        logging.info("""all weights are equal up to 1e-3"""    )
    else:
        raise ValueError("""the weights are different. The new model is likely different from the original one."""    )

    # The model has been saved with torch.save(model) and this is bound to the exact
    # directory structure. We save the state_dict instead.
    logging.info("""saving the model's state dictionary"""    )
    torch.save(
        new_model.state_dict()					,       """./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin"""    )


if __name__ == "__main__":
      lowercase_					= argparse.ArgumentParser()
      parser.add_argument(
          """--bertabs_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.""",
      )
      lowercase_					= parser.parse_args()

      convert_bertabs_checkpoints(
          args.bertabs_checkpoint_path,
          args.pytorch_dump_folder_path,
      )



