

import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_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_speech_available():
				from transformers import WhisperFeatureExtractor

if is_torch_available():
				import torch

_a:    Union[str, Any] 							=			random.Random()



def       __lowerCAmelCase      (						A       ,     A=1.0       ,     A=None       ,     A=None    ):
	if rng is None:
		UpperCAmelCase_       				=			global_rng

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

	return values

@require_torch
@require_torchaudio
class 						__UpperCamelCase    (					unittest.TestCase						):




	def __init__(		self	:						Optional[Any]		,	lowerCAmelCase	:						Optional[Any]		,	lowerCAmelCase	:						Optional[Any]=7		,	lowerCAmelCase	:						Optional[Any]=400		,	lowerCAmelCase	:						Dict=2_000		,	lowerCAmelCase	:						Union[str, Any]=10		,	lowerCAmelCase	:						int=160		,	lowerCAmelCase	:						Tuple=8		,	lowerCAmelCase	:						Optional[Any]=0.0		,	lowerCAmelCase	:						Dict=4_000		,	lowerCAmelCase	:						List[Any]=False		,	lowerCAmelCase	:						Dict=True		,	):


		'''simple docstring'''







		UpperCAmelCase_       				=			parent
		UpperCAmelCase_       				=			batch_size
		UpperCAmelCase_       				=			min_seq_length
		UpperCAmelCase_       				=			max_seq_length
		UpperCAmelCase_       				=			(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
		UpperCAmelCase_       				=			padding_value
		UpperCAmelCase_       				=			sampling_rate
		UpperCAmelCase_       				=			return_attention_mask
		UpperCAmelCase_       				=			do_normalize
		UpperCAmelCase_       				=			feature_size
		UpperCAmelCase_       				=			chunk_length
		UpperCAmelCase_       				=			hop_length




	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







		return {
		    "feature_size": self.feature_size,
		    "hop_length": self.hop_length,
		    "chunk_length": self.chunk_length,
		    "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	:						Union[str, Any]		,	lowerCAmelCase	:						Optional[int]=False		,	lowerCAmelCase	:						Tuple=False							):


		'''simple docstring'''







		def _flatten(lowerCAmelCase	:						int							):
			return list(itertools.chain(*A__							)							)

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


@require_torch
@require_torchaudio
class 						__UpperCamelCase    (					lowercase     ,     unittest.TestCase						):
	SCREAMING_SNAKE_CASE__    				=     WhisperFeatureExtractor if is_speech_available() else None




	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		UpperCAmelCase_       				=			WhisperFeatureExtractionTester(self							)




	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.feature_extraction_class(**self.feat_extract_dict							)

		with tempfile.TemporaryDirectory() as tmpdirname:
			UpperCAmelCase_       				=			feat_extract_first.save_pretrained(A__							)[0]
			check_json_file_has_correct_format(A__							)
			UpperCAmelCase_       				=			self.feature_extraction_class.from_pretrained(A__							)

		UpperCAmelCase_       				=			feat_extract_first.to_dict()
		UpperCAmelCase_       				=			feat_extract_second.to_dict()
		UpperCAmelCase_       				=			feat_extract_first.mel_filters
		UpperCAmelCase_       				=			feat_extract_second.mel_filters
		self.assertTrue(np.allclose(A__		,	A__							)							)
		self.assertEqual(A__		,	A__							)




	def       __A					(		self	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.feature_extraction_class(**self.feat_extract_dict							)

		with tempfile.TemporaryDirectory() as tmpdirname:
			UpperCAmelCase_       				=			os.path.join(A__		,	"feat_extract.json"							)
			feat_extract_first.to_json_file(A__							)
			UpperCAmelCase_       				=			self.feature_extraction_class.from_json_file(A__							)

		UpperCAmelCase_       				=			feat_extract_first.to_dict()
		UpperCAmelCase_       				=			feat_extract_second.to_dict()
		UpperCAmelCase_       				=			feat_extract_first.mel_filters
		UpperCAmelCase_       				=			feat_extract_second.mel_filters
		self.assertTrue(np.allclose(A__		,	A__							)							)
		self.assertEqual(A__		,	A__							)




	def       __A					(		self	:						int							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
		# create three inputs of length 800, 1000, and 1200
		UpperCAmelCase_       				=			[floats_list((1, x)							)[0] for x in range(800		,	1_400		,	200							)]
		UpperCAmelCase_       				=			[np.asarray(A__							) for speech_input in speech_inputs]

		# Test feature size
		UpperCAmelCase_       				=			feature_extractor(A__		,	padding="max_length"		,	return_tensors="np"							).input_features
		self.assertTrue(input_features.ndim == 3							)
		self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames							)
		self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size							)

		# Test not batched input
		UpperCAmelCase_       				=			feature_extractor(speech_inputs[0]		,	return_tensors="np"							).input_features
		UpperCAmelCase_       				=			feature_extractor(np_speech_inputs[0]		,	return_tensors="np"							).input_features
		self.assertTrue(np.allclose(A__		,	A__		,	atol=1e-3							)							)

		# Test batched
		UpperCAmelCase_       				=			feature_extractor(A__		,	return_tensors="np"							).input_features
		UpperCAmelCase_       				=			feature_extractor(A__		,	return_tensors="np"							).input_features
		for enc_seq_a, enc_seq_a in zip(A__		,	A__							):
			self.assertTrue(np.allclose(A__		,	A__		,	atol=1e-3							)							)

		# Test 2-D numpy arrays are batched.
		UpperCAmelCase_       				=			[floats_list((1, x)							)[0] for x in (800, 800, 800)]
		UpperCAmelCase_       				=			np.asarray(A__							)
		UpperCAmelCase_       				=			feature_extractor(A__		,	return_tensors="np"							).input_features
		UpperCAmelCase_       				=			feature_extractor(A__		,	return_tensors="np"							).input_features
		for enc_seq_a, enc_seq_a in zip(A__		,	A__							):
			self.assertTrue(np.allclose(A__		,	A__		,	atol=1e-3							)							)

		# Test truncation required
		UpperCAmelCase_       				=			[floats_list((1, x)							)[0] for x in range(200		,	(feature_extractor.n_samples + 500)		,	200							)]
		UpperCAmelCase_       				=			[np.asarray(A__							) for speech_input in speech_inputs]

		UpperCAmelCase_       				=			[x[: feature_extractor.n_samples] for x in speech_inputs]
		UpperCAmelCase_       				=			[np.asarray(A__							) for speech_input in speech_inputs_truncated]

		UpperCAmelCase_       				=			feature_extractor(A__		,	return_tensors="np"							).input_features
		UpperCAmelCase_       				=			feature_extractor(A__		,	return_tensors="np"							).input_features
		for enc_seq_a, enc_seq_a in zip(A__		,	A__							):
			self.assertTrue(np.allclose(A__		,	A__		,	atol=1e-3							)							)




	def       __A					(		self	:						str							):


		'''simple docstring'''







		import torch

		UpperCAmelCase_       				=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
		UpperCAmelCase_       				=			np.random.rand(100		,	32							).astype(np.floataa							)
		UpperCAmelCase_       				=			np_speech_inputs.tolist()

		for inputs in [py_speech_inputs, np_speech_inputs]:
			UpperCAmelCase_       				=			feature_extractor.pad([{"input_features": inputs}]		,	return_tensors="np"							)
			self.assertTrue(np_processed.input_features.dtype == np.floataa							)
			UpperCAmelCase_       				=			feature_extractor.pad([{"input_features": inputs}]		,	return_tensors="pt"							)
			self.assertTrue(pt_processed.input_features.dtype == torch.floataa							)




	def       __A					(		self	:						Any		,	lowerCAmelCase	:						Tuple							):


		'''simple docstring'''







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

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




	def       __A					(		self	:						Union[str, Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			torch.tensor(
		    [
		        0.1_193, -0.0_946, -0.1_098, -0.0_196, 0.0_225, -0.0_690, -0.1_736, 0.0_951,
		        0.0_971, -0.0_817, -0.0_702, 0.0_162, 0.0_260, 0.0_017, -0.0_192, -0.1_678,
		        0.0_709, -0.1_867, -0.0_655, -0.0_274, -0.0_234, -0.1_884, -0.0_516, -0.0_554,
		        -0.0_274, -0.1_425, -0.1_423, 0.0_837, 0.0_377, -0.0_854
		    ]							)
		# fmt: on

		UpperCAmelCase_       				=			self._load_datasamples(1							)
		UpperCAmelCase_       				=			WhisperFeatureExtractor()
		UpperCAmelCase_       				=			feature_extractor(A__		,	return_tensors="pt"							).input_features
		self.assertEqual(input_features.shape		,	(1, 80, 3_000)							)
		self.assertTrue(torch.allclose(input_features[0, 0, :30]		,	A__		,	atol=1e-4							)							)




	def       __A					(		self	:						int							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
		UpperCAmelCase_       				=			self._load_datasamples(1							)[0]
		UpperCAmelCase_       				=			((audio - audio.min()) / (audio.max() - audio.min())) * 65_535  # Rescale to [0, 65535] to show issue
		UpperCAmelCase_       				=			feat_extract.zero_mean_unit_var_norm([audio]		,	attention_mask=A__							)[0]

		self.assertTrue(np.all(np.mean(A__							) < 1e-3							)							)
		self.assertTrue(np.all(np.abs(np.var(A__							) - 1							) < 1e-3							)							)







from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a:    List[str] 							=			logging.get_logger(__name__)

_a:    Any 							=			{
    """sayakpaul/vit-msn-base""": """https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json""",
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}

class 						__UpperCamelCase    (					lowercase						):
	SCREAMING_SNAKE_CASE__    				=     'vit_msn'




	def __init__(		self	:						Union[str, Any]		,	lowerCAmelCase	:						Optional[int]=768		,	lowerCAmelCase	:						Dict=12		,	lowerCAmelCase	:						str=12		,	lowerCAmelCase	:						List[str]=3_072		,	lowerCAmelCase	:						Tuple="gelu"		,	lowerCAmelCase	:						int=0.0		,	lowerCAmelCase	:						Optional[int]=0.0		,	lowerCAmelCase	:						Union[str, Any]=0.02		,	lowerCAmelCase	:						str=1e-06		,	lowerCAmelCase	:						Dict=224		,	lowerCAmelCase	:						str=16		,	lowerCAmelCase	:						List[Any]=3		,	lowerCAmelCase	:						Union[str, Any]=True		,	**lowerCAmelCase	:						List[str]		,	):


		'''simple docstring'''







		super().__init__(**lowerCAmelCase							)

		UpperCAmelCase_       				=			hidden_size
		UpperCAmelCase_       				=			num_hidden_layers
		UpperCAmelCase_       				=			num_attention_heads
		UpperCAmelCase_       				=			intermediate_size
		UpperCAmelCase_       				=			hidden_act
		UpperCAmelCase_       				=			hidden_dropout_prob
		UpperCAmelCase_       				=			attention_probs_dropout_prob
		UpperCAmelCase_       				=			initializer_range
		UpperCAmelCase_       				=			layer_norm_eps
		UpperCAmelCase_       				=			image_size
		UpperCAmelCase_       				=			patch_size
		UpperCAmelCase_       				=			num_channels
		UpperCAmelCase_       				=			qkv_bias