





import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
 import torch


__A       : int						    = random.Random()





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE=1.0       ,    _SCREAMING_SNAKE_CASE=None       ,    _SCREAMING_SNAKE_CASE=None							)       ->   Union[str, Any]:




    """simple docstring"""




    if rng is None:
        _A    =       global_rng

    _A    =       []
    for batch_idx in range(shape[0]							):
        values.append([]							)
        for _ in range(shape[1]							):
            values[-1].append(rng.random() * scale							)

    return values







@require_torch
class     lowerCamelCase(	unittest.TestCase  ):
     '''simple docstring'''


     def __init__(						self							,    snake_case_							,    snake_case_=7							,    snake_case_=400							,    snake_case_=2000							,    snake_case_=1							,    snake_case_=0.0							,    snake_case_=2_4000							,    snake_case_=True							,    ):
         _A    =       parent
         _A    =       batch_size
         _A    =       min_seq_length
         _A    =       max_seq_length
         _A    =       (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
         _A    =       feature_size
         _A    =       padding_value
         _A    =       sampling_rate
         _A    =       return_attention_mask




     def 					lowerCAmelCase__		(						self       ):
         return {
             "feature_size": self.feature_size,
             "padding_value": self.padding_value,
             "sampling_rate": self.sampling_rate,
             "return_attention_mask": self.return_attention_mask,
         }




     def 					lowerCAmelCase__		(						self							,    snake_case_=False							,    snake_case_=False       ):
         def _flatten(snake_case_       ):
             return list(itertools.chain(*snake_case_       )       )

         if equal_length:
             _A    =       floats_list((self.batch_size, self.max_seq_length)       )
         else:
             # make sure that inputs increase in size
             _A    =       [
                 _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:
             _A    =       [np.asarray(snake_case_       ) for x in audio_inputs]

         return audio_inputs







@require_torch
class     lowerCamelCase(	__snake_case ,   unittest.TestCase  ):
     '''simple docstring'''


     __magic_name__        = EncodecFeatureExtractor




     def 					lowerCAmelCase__		(						self       ):
         _A    =       EnCodecFeatureExtractionTester(self       )




     def 					lowerCAmelCase__		(						self       ):
         # Tests that all call wrap to encode_plus and batch_encode_plus
         _A    =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
         # create three inputs of length 800, 1000, and 1200
         _A    =       [floats_list((1, x)       )[0] for x in range(800							,    1400							,    200       )]
         _A    =       [np.asarray(snake_case_       ) for audio_input in audio_inputs]

         # Test not batched input
         _A    =       feat_extract(audio_inputs[0]							,    return_tensors='np'       ).input_values
         _A    =       feat_extract(np_audio_inputs[0]							,    return_tensors='np'       ).input_values
         self.assertTrue(np.allclose(snake_case_							,    snake_case_							,    atol=1E-3       )       )

         # Test batched
         _A    =       feat_extract(snake_case_							,    padding=snake_case_							,    return_tensors='np'       ).input_values
         _A    =       feat_extract(snake_case_							,    padding=snake_case_							,    return_tensors='np'       ).input_values
         for enc_seq_a, enc_seq_a in zip(snake_case_							,    snake_case_       ):
             self.assertTrue(np.allclose(snake_case_							,    snake_case_							,    atol=1E-3       )       )




     def 					lowerCAmelCase__		(						self       ):
         _A    =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
         _A    =       np.random.rand(100       ).astype(np.floataa       )
         _A    =       np_audio_inputs.tolist()

         for inputs in [py_audio_inputs, np_audio_inputs]:
             _A    =       feature_extractor.pad([{'input_values': inputs}]							,    return_tensors='np'       )
             self.assertTrue(np_processed.input_values.dtype == np.floataa       )
             _A    =       feature_extractor.pad([{'input_values': inputs}]							,    return_tensors='pt'       )
             self.assertTrue(pt_processed.input_values.dtype == torch.floataa       )




     def 					lowerCAmelCase__		(						self							,    snake_case_       ):
         from datasets import load_dataset

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

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




     def 					lowerCAmelCase__		(						self       ):
         # fmt: off
         _A    =       torch.tensor(
             [2.3_804E-03, 2.0_752E-03, 1.9_836E-03, 2.1_057E-03, 1.6_174E-03,
              3.0_518E-04, 9.1_553E-05, 3.3_569E-04, 9.7_656E-04, 1.8_311E-03,
              2.0_142E-03, 2.1_057E-03, 1.7_395E-03, 4.5_776E-04, -3.9_673E-04,
              4.5_776E-04, 1.0_071E-03, 9.1_553E-05, 4.8_828E-04, 1.1_597E-03,
              7.3_242E-04, 9.4_604E-04, 1.8_005E-03, 1.8_311E-03, 8.8_501E-04,
              4.2_725E-04, 4.8_828E-04, 7.3_242E-04, 1.0_986E-03, 2.1_057E-03]       )
         # fmt: on
         _A    =       self._load_datasamples(1       )
         _A    =       EncodecFeatureExtractor()
         _A    =       feature_extractor(snake_case_							,    return_tensors='pt'       ).input_values
         self.assertEquals(input_values.shape							,    (1, 1, 9_3680)       )
         self.assertTrue(torch.allclose(input_values[0, 0, :30]							,    snake_case_							,    atol=1E-6       )       )




     def 					lowerCAmelCase__		(						self       ):
         # fmt: off
         _A    =       torch.tensor(
             [2.3_804E-03, 2.0_752E-03, 1.9_836E-03, 2.1_057E-03, 1.6_174E-03,
              3.0_518E-04, 9.1_553E-05, 3.3_569E-04, 9.7_656E-04, 1.8_311E-03,
              2.0_142E-03, 2.1_057E-03, 1.7_395E-03, 4.5_776E-04, -3.9_673E-04,
              4.5_776E-04, 1.0_071E-03, 9.1_553E-05, 4.8_828E-04, 1.1_597E-03,
              7.3_242E-04, 9.4_604E-04, 1.8_005E-03, 1.8_311E-03, 8.8_501E-04,
              4.2_725E-04, 4.8_828E-04, 7.3_242E-04, 1.0_986E-03, 2.1_057E-03]       )
         # fmt: on
         _A    =       self._load_datasamples(1       )
         _A    =       [np.tile(input_audio[0][None]							,    reps=(2, 1)       )]
         input_audio[0][1] *= 0.5
         _A    =       EncodecFeatureExtractor(feature_size=2       )
         _A    =       feature_extractor(snake_case_							,    return_tensors='pt'       ).input_values
         self.assertEquals(input_values.shape							,    (1, 2, 9_3680)       )
         self.assertTrue(torch.allclose(input_values[0, 0, :30]							,    snake_case_							,    atol=1E-6       )       )
         self.assertTrue(torch.allclose(input_values[0, 1, :30]							,    EXPECTED_INPUT_VALUES * 0.5							,    atol=1E-6       )       )




     def 					lowerCAmelCase__		(						self       ):
         _A    =       self._load_datasamples(2       )
         # would be easier if the stride was like
         _A    =       EncodecFeatureExtractor(feature_size=1							,    chunk_length_s=1							,    overlap=0.01       )

         # pad and trunc raise an error ?
         with self.assertRaisesRegex(
             snake_case_							,    '^Both padding and truncation were set. Make sure you only set one.$'							,    ):
             _A    =       feature_extractor(
                 snake_case_							,    padding='max_length'							,    truncation=snake_case_							,    return_tensors='pt'       ).input_values

         # truncate to chunk
         _A    =       feature_extractor(snake_case_							,    truncation=snake_case_							,    return_tensors='pt'       ).input_values
         self.assertEquals(truncated_outputs.shape							,    (2, 1, 7_1520)       )  # 2 chunks

         # force truncate to max_length
         _A    =       feature_extractor(
             snake_case_							,    truncation=snake_case_							,    max_length=4_8000							,    return_tensors='pt'       ).input_values
         self.assertEquals(truncated_outputs.shape							,    (2, 1, 4_8000)       )

         # pad to chunk
         _A    =       feature_extractor(snake_case_							,    padding=snake_case_							,    return_tensors='pt'       ).input_values
         self.assertEquals(padded_outputs.shape							,    (2, 1, 9_5280)       )

         # pad to chunk
         _A    =       feature_extractor(snake_case_							,    return_tensors='pt'       ).input_values
         self.assertEquals(truncated_outputs.shape							,    (2, 1, 9_5280)       )

         # force pad to max length
         _A    =       feature_extractor(
             snake_case_							,    padding='max_length'							,    max_length=10_0000							,    return_tensors='pt'       ).input_values
         self.assertEquals(truncated_outputs.shape							,    (2, 1, 10_0000)       )

         # force no pad
         with self.assertRaisesRegex(
             snake_case_							,    '^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'							,    ):
             _A    =       feature_extractor(snake_case_							,    padding=snake_case_							,    return_tensors='pt'       ).input_values

         _A    =       feature_extractor(input_audio[0]							,    padding=snake_case_							,    return_tensors='pt'       ).input_values
         self.assertEquals(truncated_outputs.shape							,    (1, 1, 9_3680)       )

         # no pad if no chunk_length_s
         _A    =       None
         with self.assertRaisesRegex(
             snake_case_							,    '^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'							,    ):
             _A    =       feature_extractor(snake_case_							,    padding=snake_case_							,    return_tensors='pt'       ).input_values

         _A    =       feature_extractor(input_audio[0]							,    padding=snake_case_							,    return_tensors='pt'       ).input_values
         self.assertEquals(truncated_outputs.shape							,    (1, 1, 9_3680)       )

         # no pad if no overlap
         _A    =       2
         _A    =       None
         with self.assertRaisesRegex(
             snake_case_							,    '^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'							,    ):
             _A    =       feature_extractor(snake_case_							,    padding=snake_case_							,    return_tensors='pt'       ).input_values

         _A    =       feature_extractor(input_audio[0]							,    padding=snake_case_							,    return_tensors='pt'       ).input_values
         self.assertEquals(truncated_outputs.shape							,    (1, 1, 9_3680)       )












import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   List[str]:




    """simple docstring"""




    _A    =       FNetConfig.from_json_file(_SCREAMING_SNAKE_CASE							)
    print(F"Building PyTorch model from configuration: {config}"							)
    _A    =       FNetForPreTraining(_SCREAMING_SNAKE_CASE							)

    _A    =       restore_checkpoint(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)
    _A    =       checkpoint_dict['target']

    # Embeddings
    # Position IDs
    _A    =       fnet_pretraining_model.state_dict()

    _A    =       state_dict['fnet.embeddings.position_ids']
    _A    =       {'fnet.embeddings.position_ids': position_ids}
    # Embedding Layers
    _A    =       torch.tensor(
        pretrained_model_params['encoder']['embedder']['word']['embedding']							)
    _A    =       torch.tensor(
        pretrained_model_params['encoder']['embedder']['position']['embedding'][0]							)
    _A    =       torch.tensor(
        pretrained_model_params['encoder']['embedder']['type']['embedding']							)
    _A    =       torch.tensor(
        pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['kernel']							).T
    _A    =       torch.tensor(
        pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['bias']							)
    _A    =       torch.tensor(
        pretrained_model_params['encoder']['embedder']['layer_norm']['scale']							)
    _A    =       torch.tensor(
        pretrained_model_params['encoder']['embedder']['layer_norm']['bias']							)

    # Encoder Layers
    for layer in range(config.num_hidden_layers							):
        _A    =       torch.tensor(
            pretrained_model_params['encoder'][F"encoder_{layer}"]['mixing_layer_norm']['scale']							)
        _A    =       torch.tensor(
            pretrained_model_params['encoder'][F"encoder_{layer}"]['mixing_layer_norm']['bias']							)

        _A    =       torch.tensor(
            pretrained_model_params['encoder'][F"feed_forward_{layer}"]['intermediate']['kernel']							).T
        _A    =       torch.tensor(
            pretrained_model_params['encoder'][F"feed_forward_{layer}"]['intermediate']['bias']							)

        _A    =       torch.tensor(
            pretrained_model_params['encoder'][F"feed_forward_{layer}"]['output']['kernel']							).T
        _A    =       torch.tensor(
            pretrained_model_params['encoder'][F"feed_forward_{layer}"]['output']['bias']							)

        _A    =       torch.tensor(
            pretrained_model_params['encoder'][F"encoder_{layer}"]['output_layer_norm']['scale']							)
        _A    =       torch.tensor(
            pretrained_model_params['encoder'][F"encoder_{layer}"]['output_layer_norm']['bias']							)

    # Pooler Layers
    _A    =       torch.tensor(pretrained_model_params['encoder']['pooler']['kernel']							).T
    _A    =       torch.tensor(pretrained_model_params['encoder']['pooler']['bias']							)

    # Masked LM Layers
    _A    =       torch.tensor(
        pretrained_model_params['predictions_dense']['kernel']							).T
    _A    =       torch.tensor(
        pretrained_model_params['predictions_dense']['bias']							)
    _A    =       torch.tensor(
        pretrained_model_params['predictions_layer_norm']['scale']							)
    _A    =       torch.tensor(
        pretrained_model_params['predictions_layer_norm']['bias']							)
    _A    =       torch.tensor(
        pretrained_model_params['encoder']['embedder']['word']['embedding']							)
    _A    =       torch.tensor(
        pretrained_model_params['predictions_output']['output_bias']							)
    _A    =       torch.tensor(pretrained_model_params['predictions_output']['output_bias']							)

    # Seq Relationship Layers
    _A    =       torch.tensor(
        pretrained_model_params['classification']['output_kernel']							)
    _A    =       torch.tensor(
        pretrained_model_params['classification']['output_bias']							)

    # Load State Dict
    fnet_pretraining_model.load_state_dict(_SCREAMING_SNAKE_CASE							)

    # Save PreTrained
    print(F"Saving pretrained model to {save_path}"							)
    fnet_pretraining_model.save_pretrained(_SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
 __A       : str						    = argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--flax_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
 )
 parser.add_argument(
     "--fnet_config_file",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained FNet model. \n"
         "This specifies the model architecture."
     ),
 )
 parser.add_argument("--save_path", default=None, type=str, required=True, help="Path to the output model.")
 __A       : Any						    = parser.parse_args()
 convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)





