

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        lowerCamelCase__       (    _a							,	_a=1.0							,	_a=None							,	_a=None):
	if rng is None:
		SCREAMING_SNAKE_CASE      :					int    =				global_rng

	SCREAMING_SNAKE_CASE      :					Tuple    =				[]
	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      ):
			'''simple docstring'''







			def __init__(	self							:       List[str]					,					a							:       Any					,					a							:       Dict=7					,					a							:       int=400					,					a							:       Tuple=2000					,					a							:       Union[str, Any]=2048					,					a							:       Dict=128					,					a							:       Union[str, Any]=1					,					a							:       List[Any]=512					,					a							:       Any=30					,					a							:       int=4_4100					,					)							->				Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Any    =				parent
				SCREAMING_SNAKE_CASE      :					int    =				batch_size
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				min_seq_length
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				max_seq_length
				SCREAMING_SNAKE_CASE      :					List[Any]    =				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				spectrogram_length
				SCREAMING_SNAKE_CASE      :					List[Any]    =				feature_size
				SCREAMING_SNAKE_CASE      :					int    =				num_audio_channels
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				hop_length
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				chunk_length
				SCREAMING_SNAKE_CASE      :					str    =				sampling_rate
			def 							__UpperCamelCase       (	self							:       Optional[Any]		)							->				Any:

				"""simple docstring"""

				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 							__UpperCamelCase       (	self							:       List[Any]					,					a							:       List[str]=False					,					a							:       Dict=False		)							->				Any:

				"""simple docstring"""

				def _flatten(a							:       Optional[Any]		):
					return list(itertools.chain(*a		)		)

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






@require_torch
@require_torchaudio
class _UpperCamelCase							(							__A							,		unittest.TestCase      ):
			'''simple docstring'''







			lowerCamelCase__							=TvltFeatureExtractor
			def 							__UpperCamelCase       (	self							:       int		)							->				Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					int    =				TvltFeatureExtractionTester(self		)
			def 							__UpperCamelCase       (	self							:       Any		)							->				Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Tuple    =				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 							__UpperCamelCase       (	self							:       Optional[Any]		)							->				int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					str    =				self.feature_extraction_class(**self.feat_extract_dict		)

				with tempfile.TemporaryDirectory() as tmpdirname:
					SCREAMING_SNAKE_CASE      :					Tuple    =				feat_extract_first.save_pretrained(a		)[0]
					check_json_file_has_correct_format(a		)
					SCREAMING_SNAKE_CASE      :					List[str]    =				self.feature_extraction_class.from_pretrained(a		)

				SCREAMING_SNAKE_CASE      :					str    =				feat_extract_first.to_dict()
				SCREAMING_SNAKE_CASE      :					List[str]    =				feat_extract_second.to_dict()
				SCREAMING_SNAKE_CASE      :					Tuple    =				dict_first.pop("mel_filters"		)
				SCREAMING_SNAKE_CASE      :					List[Any]    =				dict_second.pop("mel_filters"		)
				self.assertTrue(np.allclose(a					,					a		)		)
				self.assertEqual(a					,					a		)
			def 							__UpperCamelCase       (	self							:       int		)							->				List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				self.feature_extraction_class(**self.feat_extract_dict		)

				with tempfile.TemporaryDirectory() as tmpdirname:
					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				os.path.join(a					,					"feat_extract.json"		)
					feat_extract_first.to_json_file(a		)
					SCREAMING_SNAKE_CASE      :					List[Any]    =				self.feature_extraction_class.from_json_file(a		)

				SCREAMING_SNAKE_CASE      :					int    =				feat_extract_first.to_dict()
				SCREAMING_SNAKE_CASE      :					Tuple    =				feat_extract_second.to_dict()
				SCREAMING_SNAKE_CASE      :					Dict    =				dict_first.pop("mel_filters"		)
				SCREAMING_SNAKE_CASE      :					Any    =				dict_second.pop("mel_filters"		)
				self.assertTrue(np.allclose(a					,					a		)		)
				self.assertEqual(a					,					a		)
			def 							__UpperCamelCase       (	self							:       int		)							->				Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[str]    =				self.feature_extraction_class(**self.feat_extract_dict		)

				# create three inputs of length 800, 1000, and 1200
				SCREAMING_SNAKE_CASE      :					Dict    =				[floats_list((1, x)		)[0] for x in range(800					,					1400					,					200		)]
				SCREAMING_SNAKE_CASE      :					List[Any]    =				[np.asarray(a		) for speech_input in speech_inputs]

				# Test not batched input
				SCREAMING_SNAKE_CASE      :					int    =				feature_extractor(np_speech_inputs[0]					,					return_tensors="np"					,					sampling_rate=4_4100		).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
				SCREAMING_SNAKE_CASE      :					List[str]    =				feature_extractor(a					,					return_tensors="np"					,					sampling_rate=4_4100		).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
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				feature_extractor(
				    a					,					return_tensors="np"					,					sampling_rate=4_4100					,					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.
				SCREAMING_SNAKE_CASE      :					int    =				[floats_list((1, x)		)[0] for x in (800, 800, 800)]
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				np.asarray(a		)
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				feature_extractor(a					,					return_tensors="np"					,					sampling_rate=4_4100		).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 							__UpperCamelCase       (	self							:       Tuple					,					a							:       str		)							->				int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					str    =				load_dataset("hf-internal-testing/librispeech_asr_dummy"					,					"clean"					,					split="validation"		)
				# automatic decoding with librispeech
				SCREAMING_SNAKE_CASE      :					List[str]    =				ds.sort("id"		).select(range(a		)		)[:num_samples]["audio"]

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






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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					str    =				self._load_datasamples(1		)
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				TvltFeatureExtractor()
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				feature_extractor(a					,					return_tensors="pt"		).audio_values

				self.assertEquals(audio_values.shape					,					(1, 1, 192, 128)		)

				SCREAMING_SNAKE_CASE      :					List[str]    =				torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]		)
				self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]					,					a					,					atol=1e-4		)		)


from __future__ import annotations

from typing import Any


class       SCREAMING_SNAKE_CASE__					:





    def __init__(      self						,   a						,   a						,   a = 0):
         lowercase__      , lowercase__    :   Dict      =       row, column
         lowercase__    :   Optional[Any]      =       [[default_value for c in range(a)] for r in range(a)]





    def __str__(      self):
         lowercase__    :   Tuple      =       f"""Matrix consist of {self.row} rows and {self.column} columns\n"""

         # Make string identifier
         lowercase__    :   Optional[int]      =       0
         for row_vector in self.array:
              for obj in row_vector:
                   lowercase__    :   List[str]      =       max(a						,   len(str(a)))
         lowercase__    :   Optional[int]      =       f"""%{max_element_length}s"""

         # Make string and return
         def single_line(a) -> str:
              nonlocal string_format_identifier
              lowercase__    :   Tuple      =       '['
              line += ", ".join(string_format_identifier % (obj,) for obj in row_vector)
              line += "]"
              return line

         s += "\n".join(single_line(a) for row_vector in self.array)
         return s





    def __repr__(      self):
         return str(self)





    def       snake_case_				(      self						,   a):
         if not (isinstance(a						,   (list, tuple)) and len(a) == 2):
              return False
         elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
              return False
         else:
              return True





    def __getitem__(      self						,   a):
         assert self.validate_indicies(a)
         return self.array[loc[0]][loc[1]]





    def __setitem__(      self						,   a						,   a):
         assert self.validate_indicies(a)
         lowercase__    :   List[str]      =       value





    def __add__(      self						,   a):
         assert isinstance(a						,   a)
         assert self.row == another.row and self.column == another.column

         # Add
         lowercase__    :   List[str]      =       Matrix(self.row						,   self.column)
         for r in range(self.row):
              for c in range(self.column):
                   lowercase__    :   Dict      =       self[r, c] + another[r, c]
         return result





    def __neg__(      self):
         lowercase__    :   List[Any]      =       Matrix(self.row						,   self.column)
         for r in range(self.row):
              for c in range(self.column):
                   lowercase__    :   int      =       -self[r, c]
         return result





    def __sub__(      self						,   a):
         return self + (-another)





    def __mul__(      self						,   a):

         if isinstance(a						,   (int, float)):  # Scalar multiplication
              lowercase__    :   Optional[Any]      =       Matrix(self.row						,   self.column)
              for r in range(self.row):
                   for c in range(self.column):
                        lowercase__    :   Union[str, Any]      =       self[r, c] * another
              return result
         elif isinstance(a						,   a):  # Matrix multiplication
              assert self.column == another.row
              lowercase__    :   List[Any]      =       Matrix(self.row						,   another.column)
              for r in range(self.row):
                   for c in range(another.column):
                        for i in range(self.column):
                             result[r, c] += self[r, i] * another[i, c]
              return result
         else:
              lowercase__    :   Optional[Any]      =       f"""Unsupported type given for another ({type(a)})"""
              raise TypeError(a)





    def       snake_case_				(      self):
         lowercase__    :   Optional[int]      =       Matrix(self.column						,   self.row)
         for r in range(self.row):
              for c in range(self.column):
                   lowercase__    :   str      =       self[r, c]
         return result





    def       snake_case_				(      self						,   a						,   a):
         assert isinstance(a						,   a) and isinstance(a						,   a)
         assert self.row == self.column == u.row == v.row  # u, v should be column vector
         assert u.column == v.column == 1  # u, v should be column vector

         # Calculate
         lowercase__    :   int      =       v.transpose()
         lowercase__    :   Tuple      =       (v_t * self * u)[0, 0] + 1
         if numerator_factor == 0:
              return None  # It's not invertable
         return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":

       def 	snake_case__			(    ):



            '''simple docstring'''





            lowercase__    :   Any      =       Matrix(3	,     3	,     0			)
            for i in range(3			):
                 lowercase__    :   str      =       1
            print(f"""a^(-1) is {ainv}"""			)
            # u, v
            lowercase__    :   Optional[Any]      =       Matrix(3	,     1	,     0			)
            lowercase__      , lowercase__      , lowercase__    :   List[Any]      =       1, 2, -3
            lowercase__    :   Union[str, Any]      =       Matrix(3	,     1	,     0			)
            lowercase__      , lowercase__      , lowercase__    :   Any      =       4, -2, 5
            print(f"""u is {u}"""			)
            print(f"""v is {v}"""			)
            print(f"""uv^T is {u * v.transpose()}"""			)
            # Sherman Morrison
            print(f"""(a + uv^T)^(-1) is {ainv.sherman_morrison(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			)}"""			)

       def 	snake_case__			(    ):



            '''simple docstring'''





            import doctest

            doctest.testmod()

       testa()

