
'''simple docstring'''
import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
  import torch

if is_datasets_available():
  from datasets import load_dataset

a_											=						random.Random()

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE=1.0,		__SCREAMING_SNAKE_CASE=None,		__SCREAMING_SNAKE_CASE=None       ):
    """simple docstring"""






    if rng is None:
        snake_case_ :    Any     			=						global_rng

    snake_case_ :    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    UpperCAmelCase_				(	unittest.TestCase					):





 def __init__(    self ,       lowercase_ ,       lowercase_=7 ,       lowercase_=4_00 ,       lowercase_=20_00 ,       lowercase_=20_48 ,       lowercase_=1_28 ,       lowercase_=1 ,       lowercase_=5_12 ,       lowercase_=30 ,       lowercase_=4_41_00 ,       ):
     snake_case_ :    str     			=						parent
     snake_case_ :    List[str]     			=						batch_size
     snake_case_ :    str     			=						min_seq_length
     snake_case_ :    Any     			=						max_seq_length
     snake_case_ :    List[Any]     			=						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
     snake_case_ :    Dict     			=						spectrogram_length
     snake_case_ :    Any     			=						feature_size
     snake_case_ :    List[str]     			=						num_audio_channels
     snake_case_ :    int     			=						hop_length
     snake_case_ :    Dict     			=						chunk_length
     snake_case_ :    Any     			=						sampling_rate





 def        snake_case__		(    self):
     return {
         "spectrogram_length": self.spectrogram_length,
         "feature_size": self.feature_size,
         "num_audio_channels": self.num_audio_channels,
         "hop_length": self.hop_length,
         "chunk_length": self.chunk_length,
         "sampling_rate": self.sampling_rate,
     }





 def        snake_case__		(    self ,       lowercase_=False ,       lowercase_=False):
     def _flatten(lowercase_):
         return list(itertools.chain(*_a))

     if equal_length:
         snake_case_ :    Union[str, Any]     			=						[floats_list((self.max_seq_length, self.feature_size)) for _ in range(self.batch_size)]
     else:
         # make sure that inputs increase in size
         snake_case_ :    Optional[int]     			=						[
             floats_list((x, self.feature_size))
             for x in range(self.min_seq_length ,       self.max_seq_length ,       self.seq_length_diff)
         ]
     if numpify:
         snake_case_ :    Optional[Any]     			=						[np.asarray(_a) for x in speech_inputs]
     return speech_inputs






@require_torch
@require_torchaudio
class    UpperCAmelCase_				(	__SCREAMING_SNAKE_CASE    ,						unittest.TestCase					):
 UpperCAmelCase_		    =						TvltFeatureExtractor





 def        snake_case__		(    self):
     snake_case_ :    Optional[int]     			=						TvltFeatureExtractionTester(self)





 def        snake_case__		(    self):
     snake_case_ :    Any     			=						self.feature_extraction_class(**self.feat_extract_dict)
     self.assertTrue(hasattr(_a ,       "spectrogram_length"))
     self.assertTrue(hasattr(_a ,       "feature_size"))
     self.assertTrue(hasattr(_a ,       "num_audio_channels"))
     self.assertTrue(hasattr(_a ,       "hop_length"))
     self.assertTrue(hasattr(_a ,       "chunk_length"))
     self.assertTrue(hasattr(_a ,       "sampling_rate"))





 def        snake_case__		(    self):
     snake_case_ :    Tuple     			=						self.feature_extraction_class(**self.feat_extract_dict)

     with tempfile.TemporaryDirectory() as tmpdirname:
         snake_case_ :    List[Any]     			=						feat_extract_first.save_pretrained(_a)[0]
         check_json_file_has_correct_format(_a)
         snake_case_ :    Optional[int]     			=						self.feature_extraction_class.from_pretrained(_a)

     snake_case_ :    int     			=						feat_extract_first.to_dict()
     snake_case_ :    Optional[Any]     			=						feat_extract_second.to_dict()
     snake_case_ :    List[Any]     			=						dict_first.pop("mel_filters")
     snake_case_ :    Any     			=						dict_second.pop("mel_filters")
     self.assertTrue(np.allclose(_a ,       _a))
     self.assertEqual(_a ,       _a)





 def        snake_case__		(    self):
     snake_case_ :    Optional[Any]     			=						self.feature_extraction_class(**self.feat_extract_dict)

     with tempfile.TemporaryDirectory() as tmpdirname:
         snake_case_ :    Optional[int]     			=						os.path.join(_a ,       "feat_extract.json")
         feat_extract_first.to_json_file(_a)
         snake_case_ :    Union[str, Any]     			=						self.feature_extraction_class.from_json_file(_a)

     snake_case_ :    List[str]     			=						feat_extract_first.to_dict()
     snake_case_ :    Optional[int]     			=						feat_extract_second.to_dict()
     snake_case_ :    Optional[Any]     			=						dict_first.pop("mel_filters")
     snake_case_ :    Union[str, Any]     			=						dict_second.pop("mel_filters")
     self.assertTrue(np.allclose(_a ,       _a))
     self.assertEqual(_a ,       _a)





 def        snake_case__		(    self):
     # Initialize feature_extractor
     snake_case_ :    Union[str, Any]     			=						self.feature_extraction_class(**self.feat_extract_dict)

     # create three inputs of length 800, 1000, and 1200
     snake_case_ :    str     			=						[floats_list((1, x))[0] for x in range(8_00 ,       14_00 ,       2_00)]
     snake_case_ :    Any     			=						[np.asarray(_a) for speech_input in speech_inputs]

     # Test not batched input
     snake_case_ :    Optional[int]     			=						feature_extractor(np_speech_inputs[0] ,       return_tensors="np" ,       sampling_rate=4_41_00).audio_values

     self.assertTrue(encoded_audios.ndim == 4)
     self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
     self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
     self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)

     # Test batched
     snake_case_ :    Optional[Any]     			=						feature_extractor(_a ,       return_tensors="np" ,       sampling_rate=4_41_00).audio_values

     self.assertTrue(encoded_audios.ndim == 4)
     self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
     self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
     self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)

     # Test audio masking
     snake_case_ :    Optional[int]     			=						feature_extractor(
         _a ,       return_tensors="np" ,       sampling_rate=4_41_00 ,       mask_audio=_a).audio_values

     self.assertTrue(encoded_audios.ndim == 4)
     self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
     self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
     self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)

     # Test 2-D numpy arrays are batched.
     snake_case_ :    Optional[Any]     			=						[floats_list((1, x))[0] for x in (8_00, 8_00, 8_00)]
     snake_case_ :    List[Any]     			=						np.asarray(_a)
     snake_case_ :    Optional[Any]     			=						feature_extractor(_a ,       return_tensors="np" ,       sampling_rate=4_41_00).audio_values
     self.assertTrue(encoded_audios.ndim == 4)
     self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
     self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
     self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)





 def        snake_case__		(    self ,       lowercase_):
     snake_case_ :    int     			=						load_dataset("hf-internal-testing/librispeech_asr_dummy" ,       "clean" ,       split="validation")
     # automatic decoding with librispeech
     snake_case_ :    Optional[Any]     			=						ds.sort("id").select(range(_a))[:num_samples]["audio"]

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





 def        snake_case__		(    self):
     snake_case_ :    Tuple     			=						self._load_datasamples(1)
     snake_case_ :    Tuple     			=						TvltFeatureExtractor()
     snake_case_ :    Optional[Any]     			=						feature_extractor(_a ,       return_tensors="pt").audio_values

     self.assertEquals(audio_values.shape ,       (1, 1, 1_92, 1_28))

     snake_case_ :    List[str]     			=						torch.tensor([[-0.3_032, -0.2_708], [-0.4_434, -0.4_007]])
     self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2] ,       _a ,       atol=1E-4))


'''simple docstring'''
import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


a_											=						logging.get_logger(__name__)

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    List[Any]     			=						r"\w+[.]\d+"
    snake_case_ :    Tuple     			=						re.findall(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
    for pat in pats:
        snake_case_ :    Union[str, Any]     			=						key.replace(__SCREAMING_SNAKE_CASE,		"_".join(pat.split("."       )       )       )
    return key

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Tuple     			=						pt_tuple_key[:-1] + ("scale",)
    if (
        any("norm" in str_ for str_ in pt_tuple_key       )
        and (pt_tuple_key[-1] == "bias")
        and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
        and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
    ):
        snake_case_ :    int     			=						pt_tuple_key[:-1] + ("scale",)
        return renamed_pt_tuple_key, pt_tensor
    elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
        snake_case_ :    Dict     			=						pt_tuple_key[:-1] + ("scale",)
        return renamed_pt_tuple_key, pt_tensor

    # embedding
    if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
        snake_case_ :    List[Any]     			=						pt_tuple_key[:-1] + ("embedding",)
        return renamed_pt_tuple_key, pt_tensor

    # conv layer
    snake_case_ :    List[str]     			=						pt_tuple_key[:-1] + ("kernel",)
    if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
        snake_case_ :    str     			=						pt_tensor.transpose(2,		3,		1,		0       )
        return renamed_pt_tuple_key, pt_tensor

    # linear layer
    snake_case_ :    str     			=						pt_tuple_key[:-1] + ("kernel",)
    if pt_tuple_key[-1] == "weight":
        snake_case_ :    Any     			=						pt_tensor.T
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm weight
    snake_case_ :    Union[str, Any]     			=						pt_tuple_key[:-1] + ("weight",)
    if pt_tuple_key[-1] == "gamma":
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm bias
    snake_case_ :    Union[str, Any]     			=						pt_tuple_key[:-1] + ("bias",)
    if pt_tuple_key[-1] == "beta":
        return renamed_pt_tuple_key, pt_tensor

    return pt_tuple_key, pt_tensor


def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE=4_2       ):
    """simple docstring"""






    snake_case_ :    int     			=						{k: v.numpy() for k, v in pt_state_dict.items()}

    # Step 2: Since the model is stateless, get random Flax params
    snake_case_ :    Optional[int]     			=						flax_model.init_weights(PRNGKey(__SCREAMING_SNAKE_CASE       )       )

    snake_case_ :    Union[str, Any]     			=						flatten_dict(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    str     			=						{}

    # Need to change some parameters name to match Flax names
    for pt_key, pt_tensor in pt_state_dict.items():
        snake_case_ :    List[str]     			=						rename_key(__SCREAMING_SNAKE_CASE       )
        snake_case_ :    Tuple     			=						tuple(renamed_pt_key.split("."       )       )

        # Correctly rename weight parameters
        snake_case_			, snake_case_ :    Dict     			=						rename_key_and_reshape_tensor(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )

        if flax_key in random_flax_state_dict:
            if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                raise ValueError(
                    f'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
                    f'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'       )

        # also add unexpected weight so that warning is thrown
        snake_case_ :    Any     			=						jnp.asarray(__SCREAMING_SNAKE_CASE       )

    return unflatten_dict(__SCREAMING_SNAKE_CASE       )




