






"""simple docstring"""
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


lowerCamelCase_       			=   random.Random()

if is_torch_available():
			import torch

def        snake_case   (     A__			,A__=1.0			,A__=None			,A__=None		):
			if rng is None:
						UpperCAmelCase_  :				int      =      global_rng

			UpperCAmelCase_  :				List[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      :				Optional[Any]			,			lowerCAmelCase_      :				Any			,			lowerCAmelCase_      :				Union[str, Any]=7			,			lowerCAmelCase_      :				Optional[int]=400			,			lowerCAmelCase_      :				Any=2_000			,			lowerCAmelCase_      :				List[Any]=1			,			lowerCAmelCase_      :				Any=0.0			,			lowerCAmelCase_      :				Union[str, Any]=16_000			,			lowerCAmelCase_      :				Optional[int]=True			,			lowerCAmelCase_      :				List[str]=True			,			)		->    List[str]:
				UpperCAmelCase_  :				List[str]      =      parent
				UpperCAmelCase_  :				int      =      batch_size
				UpperCAmelCase_  :				int      =      min_seq_length
				UpperCAmelCase_  :				Dict      =      max_seq_length
				UpperCAmelCase_  :				Dict      =      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
				UpperCAmelCase_  :				Union[str, Any]      =      feature_size
				UpperCAmelCase_  :				Union[str, Any]      =      padding_value
				UpperCAmelCase_  :				Tuple      =      sampling_rate
				UpperCAmelCase_  :				List[Any]      =      return_attention_mask
				UpperCAmelCase_  :				Optional[Any]      =      do_normalize





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    Optional[Any]:
				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   _SCREAMING_SNAKE_CASE   (   self      :				Dict			,			lowerCAmelCase_      :				List[str]=False			,			lowerCAmelCase_      :				str=False		)		->    Tuple:
				def _flatten(lowerCAmelCase_      :				Any		):
							return list(itertools.chain(*lowerCAmelCase_		)		)

				if equal_length:
							UpperCAmelCase_  :				List[str]      =      floats_list((self.batch_size, self.max_seq_length)		)
				else:
							# make sure that inputs increase in size
							UpperCAmelCase_  :				Tuple      =      [
							    _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:
							UpperCAmelCase_  :				Any      =      [np.asarray(lowerCAmelCase_		) for x in speech_inputs]

				return speech_inputs







@require_torch
@require_torchaudio
class     UpperCamelCase_ (__A       ,		unittest.TestCase  ):
	__magic_name__			   =       ASTFeatureExtractor





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    int:
				UpperCAmelCase_  :				Union[str, Any]      =      ASTFeatureExtractionTester(self		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    Optional[Any]:
				# Tests that all call wrap to encode_plus and batch_encode_plus
				UpperCAmelCase_  :				Tuple      =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
				# create three inputs of length 800, 1000, and 1200
				UpperCAmelCase_  :				Any      =      [floats_list((1, x)		)[0] for x in range(800			,			1_400			,			200		)]
				UpperCAmelCase_  :				int      =      [np.asarray(lowerCAmelCase_		) for speech_input in speech_inputs]

				# Test not batched input
				UpperCAmelCase_  :				Dict      =      feat_extract(speech_inputs[0]			,			return_tensors="np"		).input_values
				UpperCAmelCase_  :				List[str]      =      feat_extract(np_speech_inputs[0]			,			return_tensors="np"		).input_values
				self.assertTrue(np.allclose(lowerCAmelCase_			,			lowerCAmelCase_			,			atol=1e-3		)		)

				# Test batched
				UpperCAmelCase_  :				Tuple      =      feat_extract(lowerCAmelCase_			,			padding=lowerCAmelCase_			,			return_tensors="np"		).input_values
				UpperCAmelCase_  :				Dict      =      feat_extract(lowerCAmelCase_			,			padding=lowerCAmelCase_			,			return_tensors="np"		).input_values
				for enc_seq_a, enc_seq_a in zip(lowerCAmelCase_			,			lowerCAmelCase_		):
							self.assertTrue(np.allclose(lowerCAmelCase_			,			lowerCAmelCase_			,			atol=1e-3		)		)

				# Test 2-D numpy arrays are batched.
				UpperCAmelCase_  :				Dict      =      [floats_list((1, x)		)[0] for x in (800, 800, 800)]
				UpperCAmelCase_  :				Union[str, Any]      =      np.asarray(lowerCAmelCase_		)
				UpperCAmelCase_  :				Any      =      feat_extract(lowerCAmelCase_			,			return_tensors="np"		).input_values
				UpperCAmelCase_  :				List[str]      =      feat_extract(lowerCAmelCase_			,			return_tensors="np"		).input_values
				for enc_seq_a, enc_seq_a in zip(lowerCAmelCase_			,			lowerCAmelCase_		):
							self.assertTrue(np.allclose(lowerCAmelCase_			,			lowerCAmelCase_			,			atol=1e-3		)		)





	@require_torch
	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    List[Any]:
				import torch

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

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





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]			,			lowerCAmelCase_      :				Any		)		->    Any:
				from datasets import load_dataset

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

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





	@require_torch
	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    List[Any]:
				# fmt: off
				UpperCAmelCase_  :				Optional[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

				UpperCAmelCase_  :				List[str]      =      self._load_datasamples(1		)
				UpperCAmelCase_  :				Tuple      =      ASTFeatureExtractor()
				UpperCAmelCase_  :				List[str]      =      feature_extractor(lowerCAmelCase_			,			return_tensors="pt"		).input_values
				self.assertEquals(input_values.shape			,			(1, 1_024, 128)		)
				self.assertTrue(torch.allclose(input_values[0, 0, :30]			,			lowerCAmelCase_			,			atol=1e-4		)		)











"""simple docstring"""
from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
			import numpy as np
			import tensorflow as tf

			from transformers import TFCamembertModel







@require_tf
@require_sentencepiece
@require_tokenizers
class     UpperCamelCase_ (unittest.TestCase  ):





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    Optional[Any]:
				UpperCAmelCase_  :				List[Any]      =      TFCamembertModel.from_pretrained("jplu/tf-camembert-base"		)

				UpperCAmelCase_  :				Tuple      =      tf.convert_to_tensor(
				    [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]			,			dtype=tf.intaa			,			)  # J'aime le camembert !"

				UpperCAmelCase_  :				Any      =      model(lowerCAmelCase_		)["last_hidden_state"]
				UpperCAmelCase_  :				str      =      tf.TensorShape((1, 10, 768)		)
				self.assertEqual(output.shape			,			lowerCAmelCase_		)
				# compare the actual values for a slice.
				UpperCAmelCase_  :				Tuple      =      tf.convert_to_tensor(
				    [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]			,			dtype=tf.floataa			,			)
				# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
				# camembert.eval()
				# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

				self.assertTrue(np.allclose(output[:, :3, :3].numpy()			,			expected_slice.numpy()			,			atol=1e-4		)		)



