
"""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


UpperCAmelCase__   :					Optional[int]												=				random.Random()

if is_torch_available():
					import torch

def lowercase_						(     _snake_case     ,_snake_case=1.0     ,_snake_case=None     ,_snake_case=None		):
							if rng is None:
														SCREAMING_SNAKE_CASE__		: Tuple     =     global_rng

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

							return values



class 			lowerCAmelCase_		(unittest.TestCase						):


				"""simple docstring"""



				def __init__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__=7     , SCREAMING_SNAKE_CASE__=4_00     , SCREAMING_SNAKE_CASE__=20_00     , SCREAMING_SNAKE_CASE__=1     , SCREAMING_SNAKE_CASE__=0.0     , SCREAMING_SNAKE_CASE__=1_60_00     , SCREAMING_SNAKE_CASE__=True     , SCREAMING_SNAKE_CASE__=True     , )      ->	Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[int]     =     parent
											SCREAMING_SNAKE_CASE__		: List[Any]     =     batch_size
											SCREAMING_SNAKE_CASE__		: int     =     min_seq_length
											SCREAMING_SNAKE_CASE__		: Dict     =     max_seq_length
											SCREAMING_SNAKE_CASE__		: str     =     (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
											SCREAMING_SNAKE_CASE__		: Dict     =     feature_size
											SCREAMING_SNAKE_CASE__		: Any     =     padding_value
											SCREAMING_SNAKE_CASE__		: List[str]     =     sampling_rate
											SCREAMING_SNAKE_CASE__		: List[Any]     =     return_attention_mask
											SCREAMING_SNAKE_CASE__		: Tuple     =     do_normalize



				def 			__magic_name__					(self					)      ->	Dict:



											"""simple docstring"""




											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 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__=False     , SCREAMING_SNAKE_CASE__=False					)      ->	Any:



											"""simple docstring"""




											def _flatten(SCREAMING_SNAKE_CASE__					):
																		return list(itertools.chain(*SCREAMING_SNAKE_CASE__					)					)

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

											return speech_inputs






@require_torch
@require_torchaudio
class 			lowerCAmelCase_		(a__				,      unittest.TestCase						):


				"""simple docstring"""



				__UpperCamelCase   :		Dict       =     ASTFeatureExtractor



				def 			__magic_name__					(self					)      ->	List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     ASTFeatureExtractionTester(self					)



				def 			__magic_name__					(self					)      ->	Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Any     =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
											# create three inputs of length 800, 1000, and 1200
											SCREAMING_SNAKE_CASE__		: Tuple     =     [floats_list((1, x)					)[0] for x in range(8_00     , 14_00     , 2_00					)]
											SCREAMING_SNAKE_CASE__		: List[Any]     =     [np.asarray(SCREAMING_SNAKE_CASE__					) for speech_input in speech_inputs]

											# Test not batched input
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     feat_extract(speech_inputs[0]     , return_tensors="""np"""					).input_values
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     feat_extract(np_speech_inputs[0]     , return_tensors="""np"""					).input_values
											self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , atol=1E-3					)					)

											# Test batched
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     feat_extract(SCREAMING_SNAKE_CASE__     , padding=SCREAMING_SNAKE_CASE__     , return_tensors="""np"""					).input_values
											SCREAMING_SNAKE_CASE__		: int     =     feat_extract(SCREAMING_SNAKE_CASE__     , padding=SCREAMING_SNAKE_CASE__     , return_tensors="""np"""					).input_values
											for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					):
																		self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , atol=1E-3					)					)

											# Test 2-D numpy arrays are batched.
											SCREAMING_SNAKE_CASE__		: Dict     =     [floats_list((1, x)					)[0] for x in (8_00, 8_00, 8_00)]
											SCREAMING_SNAKE_CASE__		: List[Any]     =     np.asarray(SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     feat_extract(SCREAMING_SNAKE_CASE__     , return_tensors="""np"""					).input_values
											SCREAMING_SNAKE_CASE__		: Tuple     =     feat_extract(SCREAMING_SNAKE_CASE__     , return_tensors="""np"""					).input_values
											for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					):
																		self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , atol=1E-3					)					)



				@require_torch
				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											import torch

											SCREAMING_SNAKE_CASE__		: str     =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     np.random.rand(1_00					).astype(np.floataa					)
											SCREAMING_SNAKE_CASE__		: Tuple     =     np_speech_inputs.tolist()

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



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	Optional[int]:



											"""simple docstring"""




											from datasets import load_dataset

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

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



				@require_torch
				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: List[Any]     =     torch.tensor(
											    [-0.9894, -1.2776, -0.9066, -1.2776, -0.9349, -1.2609, -1.0386, -1.2776,
											     -1.1561, -1.2776, -1.2052, -1.2723, -1.2190, -1.2132, -1.2776, -1.1133,
											     -1.1953, -1.1343, -1.1584, -1.2203, -1.1770, -1.2474, -1.2381, -1.1936,
											     -0.9270, -0.8317, -0.8049, -0.7706, -0.7565, -0.7869]					)
											# fmt: on

											SCREAMING_SNAKE_CASE__		: Optional[int]     =     self._load_datasamples(1					)
											SCREAMING_SNAKE_CASE__		: int     =     ASTFeatureExtractor()
											SCREAMING_SNAKE_CASE__		: Dict     =     feature_extractor(SCREAMING_SNAKE_CASE__     , return_tensors="""pt"""					).input_values
											self.assertEquals(input_values.shape     , (1, 10_24, 1_28)					)
											self.assertTrue(torch.allclose(input_values[0, 0, :30]     , SCREAMING_SNAKE_CASE__     , atol=1E-4					)					)






"""simple docstring"""




from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput



class 			lowerCAmelCase_		(nn.Module						):


				"""simple docstring"""



				def __init__(self     , SCREAMING_SNAKE_CASE__ = 16     , SCREAMING_SNAKE_CASE__ = 88     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = 1     , SCREAMING_SNAKE_CASE__ = 0.0     , SCREAMING_SNAKE_CASE__ = 32     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = False     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = "geglu"     , SCREAMING_SNAKE_CASE__ = None     , )      ->	Optional[Any]:



											"""simple docstring"""




											super().__init__()
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     nn.ModuleList(
											    [
											        TransformeraDModel(
											            num_attention_heads=SCREAMING_SNAKE_CASE__     , attention_head_dim=SCREAMING_SNAKE_CASE__     , in_channels=SCREAMING_SNAKE_CASE__     , num_layers=SCREAMING_SNAKE_CASE__     , dropout=SCREAMING_SNAKE_CASE__     , norm_num_groups=SCREAMING_SNAKE_CASE__     , cross_attention_dim=SCREAMING_SNAKE_CASE__     , attention_bias=SCREAMING_SNAKE_CASE__     , sample_size=SCREAMING_SNAKE_CASE__     , num_vector_embeds=SCREAMING_SNAKE_CASE__     , activation_fn=SCREAMING_SNAKE_CASE__     , num_embeds_ada_norm=SCREAMING_SNAKE_CASE__     , )
											        for _ in range(2					)
											    ]					)

											# Variables that can be set by a pipeline:

											# The ratio of transformer1 to transformer2's output states to be combined during inference
											SCREAMING_SNAKE_CASE__		: Tuple     =     0.5

											# The shape of `encoder_hidden_states` is expected to be
											# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     [77, 2_57]

											# Which transformer to use to encode which condition.
											# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
											SCREAMING_SNAKE_CASE__		: int     =     [1, 0]



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__ = True     , )      ->	Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Any     =     hidden_states

											SCREAMING_SNAKE_CASE__		: Tuple     =     []
											SCREAMING_SNAKE_CASE__		: List[str]     =     0
											# attention_mask is not used yet
											for i in range(2					):
																		# for each of the two transformers, pass the corresponding condition tokens
																		SCREAMING_SNAKE_CASE__		: str     =     encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
																		SCREAMING_SNAKE_CASE__		: Any     =     self.transformer_index_for_condition[i]
																		SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     self.transformers[transformer_index](
																		    SCREAMING_SNAKE_CASE__     , encoder_hidden_states=SCREAMING_SNAKE_CASE__     , timestep=SCREAMING_SNAKE_CASE__     , cross_attention_kwargs=SCREAMING_SNAKE_CASE__     , return_dict=SCREAMING_SNAKE_CASE__     , )[0]
																		encoded_states.append(encoded_state - input_states					)
																		tokens_start += self.condition_lengths[i]

											SCREAMING_SNAKE_CASE__		: Tuple     =     encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     output_states + input_states

											if not return_dict:
																		return (output_states,)

											return TransformeraDModelOutput(sample=SCREAMING_SNAKE_CASE__					)




