
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


UpperCamelCase_					:						Optional[int]				= logging.get_logger(__name__)

UpperCamelCase_					:						Dict				= {
    '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/config.json''',
    # See all BART models at https://huggingface.co/models?filter=bart
}



class __lowerCAmelCase				(					_lowercase      ):




			"""simple docstring"""







			snake_case	      =				"bart"
			snake_case	      =				["past_key_values"]
			snake_case	      =				{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




			def __init__(				self						:					Optional[int]       ,   _snake_case						:					Optional[Any]=50_265       ,   _snake_case						:					Optional[int]=1_024       ,   _snake_case						:					List[Any]=12       ,   _snake_case						:					Optional[Any]=4_096       ,   _snake_case						:					Dict=16       ,   _snake_case						:					Tuple=12       ,   _snake_case						:					Dict=4_096       ,   _snake_case						:					Tuple=16       ,   _snake_case						:					Union[str, Any]=0.0       ,   _snake_case						:					Optional[int]=0.0       ,   _snake_case						:					str="gelu"       ,   _snake_case						:					Union[str, Any]=1_024       ,   _snake_case						:					Tuple=0.1       ,   _snake_case						:					Any=0.0       ,   _snake_case						:					Union[str, Any]=0.0       ,   _snake_case						:					Optional[Any]=0.0_2       ,   _snake_case						:					Optional[Any]=0.0       ,   _snake_case						:					Union[str, Any]=False       ,   _snake_case						:					int=True       ,   _snake_case						:					List[Any]=3       ,   _snake_case						:					Dict=1       ,   _snake_case						:					Any=0       ,   _snake_case						:					Any=2       ,   _snake_case						:					int=True       ,   _snake_case						:					Any=2       ,   _snake_case						:					int=2       ,   **_snake_case						:					Union[str, Any]       ,   )      -> int:







								"""simple docstring"""
								A_				 =						vocab_size
								A_				 =						max_position_embeddings
								A_				 =						d_model
								A_				 =						encoder_ffn_dim
								A_				 =						encoder_layers
								A_				 =						encoder_attention_heads
								A_				 =						decoder_ffn_dim
								A_				 =						decoder_layers
								A_				 =						decoder_attention_heads
								A_				 =						dropout
								A_				 =						attention_dropout
								A_				 =						activation_dropout
								A_				 =						activation_function
								A_				 =						init_std
								A_				 =						encoder_layerdrop
								A_				 =						decoder_layerdrop
								A_				 =						classifier_dropout
								A_				 =						use_cache
								A_				 =						encoder_layers
								A_				 =						scale_embedding  # scale factor will be sqrt(d_model) if True

								super().__init__(
								    num_labels=_snake_case       ,   pad_token_id=_snake_case       ,   bos_token_id=_snake_case       ,   eos_token_id=_snake_case       ,   is_encoder_decoder=_snake_case       ,   decoder_start_token_id=_snake_case       ,   forced_eos_token_id=_snake_case       ,   **_snake_case       ,   )

								# ensure backward compatibility for BART CNN models
								if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated"       ,   _snake_case	):
													A_				 =						self.bos_token_id
													warnings.warn(
													    F'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
													    "The config can simply be saved and uploaded again to be fixed."	)





class __lowerCAmelCase				(					_lowercase      ):




			"""simple docstring"""







			@property
			def      lowerCamelCase__     (				self						:					Optional[int]	)      -> Mapping[str, Mapping[int, str]]:







								"""simple docstring"""
								if self.task in ["default", "seq2seq-lm"]:
													A_				 =						OrderedDict(
													    [
													        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
													        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
													    ]	)

													if self.use_past:
																		A_				 =						{0: "batch"}
																		A_				 =						{0: "batch", 1: "past_decoder_sequence + sequence"}
													else:
																		A_				 =						{0: "batch", 1: "decoder_sequence"}
																		A_				 =						{0: "batch", 1: "decoder_sequence"}

													if self.use_past:
																		self.fill_with_past_key_values_(_snake_case       ,   direction="inputs"	)
								elif self.task == "causal-lm":
													# TODO: figure this case out.
													A_				 =						OrderedDict(
													    [
													        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
													        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
													    ]	)
													if self.use_past:
																		A_ ,    A_				 =						self.num_layers
																		for i in range(_snake_case	):
																							A_				 =						{0: "batch", 2: "past_sequence + sequence"}
																							A_				 =						{0: "batch", 2: "past_sequence + sequence"}
								else:
													A_				 =						OrderedDict(
													    [
													        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
													        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
													        ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
													        ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
													    ]	)

								return common_inputs




			@property
			def      lowerCamelCase__     (				self						:					Optional[int]	)      -> Mapping[str, Mapping[int, str]]:







								"""simple docstring"""
								if self.task in ["default", "seq2seq-lm"]:
													A_				 =						super().outputs
								else:
													A_				 =						super(_snake_case       ,   self	).outputs
													if self.use_past:
																		A_ ,    A_				 =						self.num_layers
																		for i in range(_snake_case	):
																							A_				 =						{0: "batch", 2: "past_sequence + sequence"}
																							A_				 =						{0: "batch", 2: "past_sequence + sequence"}
								return common_outputs




			def      lowerCamelCase__     (				self						:					Union[str, Any]       ,   _snake_case						:					PreTrainedTokenizer       ,   _snake_case						:					int = -1       ,   _snake_case						:					int = -1       ,   _snake_case						:					bool = False       ,   _snake_case						:					Optional[TensorType] = None       ,   )      -> Mapping[str, Any]:







								"""simple docstring"""
								A_				 =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    _snake_case       ,   _snake_case       ,   _snake_case       ,   _snake_case       ,   _snake_case	)

								# Generate decoder inputs
								A_				 =						seq_length if not self.use_past else 1
								A_				 =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    _snake_case       ,   _snake_case       ,   _snake_case       ,   _snake_case       ,   _snake_case	)
								A_				 =						{F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
								A_				 =						dict(**_snake_case       ,   **_snake_case	)

								if self.use_past:
													if not is_torch_available():
																		raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."	)
													else:
																		import torch
													A_ ,    A_				 =						common_inputs["input_ids"].shape
													A_				 =						common_inputs["decoder_input_ids"].shape[1]
													A_ ,    A_				 =						self.num_attention_heads
													A_				 =						(
													    batch,
													    num_encoder_attention_heads,
													    encoder_seq_length,
													    self._config.hidden_size // num_encoder_attention_heads,
													)
													A_				 =						decoder_seq_length + 3
													A_				 =						(
													    batch,
													    num_decoder_attention_heads,
													    decoder_past_length,
													    self._config.hidden_size // num_decoder_attention_heads,
													)

													A_				 =						torch.cat(
													    [common_inputs["decoder_attention_mask"], torch.ones(_snake_case       ,   _snake_case	)]       ,   dim=1	)

													A_				 =						[]
													# If the number of encoder and decoder layers are present in the model configuration, both are considered
													A_ ,    A_				 =						self.num_layers
													A_				 =						min(_snake_case       ,   _snake_case	)
													A_				 =						max(_snake_case       ,   _snake_case	) - min_num_layers
													A_				 =						"encoder" if num_encoder_layers > num_decoder_layers else "decoder"

													for _ in range(_snake_case	):
																		common_inputs["past_key_values"].append(
																		    (
																		        torch.zeros(_snake_case	),
																		        torch.zeros(_snake_case	),
																		        torch.zeros(_snake_case	),
																		        torch.zeros(_snake_case	),
																		    )	)
													# TODO: test this.
													A_				 =						encoder_shape if remaining_side_name == "encoder" else decoder_shape
													for _ in range(_snake_case       ,   _snake_case	):
																		common_inputs["past_key_values"].append((torch.zeros(_snake_case	), torch.zeros(_snake_case	))	)
								return common_inputs




			def      lowerCamelCase__     (				self						:					Any       ,   _snake_case						:					PreTrainedTokenizer       ,   _snake_case						:					int = -1       ,   _snake_case						:					int = -1       ,   _snake_case						:					bool = False       ,   _snake_case						:					Optional[TensorType] = None       ,   )      -> Mapping[str, Any]:







								"""simple docstring"""
								A_				 =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    _snake_case       ,   _snake_case       ,   _snake_case       ,   _snake_case       ,   _snake_case	)

								if self.use_past:
													if not is_torch_available():
																		raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."	)
													else:
																		import torch
													A_ ,    A_				 =						common_inputs["input_ids"].shape
													# Not using the same length for past_key_values
													A_				 =						seqlen + 2
													A_ ,    A_				 =						self.num_layers
													A_ ,    A_				 =						self.num_attention_heads
													A_				 =						(
													    batch,
													    num_encoder_attention_heads,
													    past_key_values_length,
													    self._config.hidden_size // num_encoder_attention_heads,
													)

													A_				 =						common_inputs["attention_mask"].dtype
													A_				 =						torch.cat(
													    [common_inputs["attention_mask"], torch.ones(_snake_case       ,   _snake_case       ,   dtype=_snake_case	)]       ,   dim=1	)
													A_				 =						[
													    (torch.zeros(_snake_case	), torch.zeros(_snake_case	)) for _ in range(_snake_case	)
													]
								return common_inputs




			def      lowerCamelCase__     (				self						:					Union[str, Any]       ,   _snake_case						:					PreTrainedTokenizer       ,   _snake_case						:					int = -1       ,   _snake_case						:					int = -1       ,   _snake_case						:					bool = False       ,   _snake_case						:					Optional[TensorType] = None       ,   )      -> Mapping[str, Any]:







								"""simple docstring"""
								A_				 =						compute_effective_axis_dimension(
								    _snake_case       ,   fixed_dimension=OnnxConfig.default_fixed_batch       ,   num_token_to_add=0	)

								# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
								A_				 =						tokenizer.num_special_tokens_to_add(_snake_case	)
								A_				 =						compute_effective_axis_dimension(
								    _snake_case       ,   fixed_dimension=OnnxConfig.default_fixed_sequence       ,   num_token_to_add=_snake_case	)

								# Generate dummy inputs according to compute batch and sequence
								A_				 =						[" ".join([tokenizer.unk_token]	) * seq_length] * batch_size
								A_				 =						dict(tokenizer(_snake_case       ,   return_tensors=_snake_case	)	)
								return common_inputs




			def      lowerCamelCase__     (				self						:					str       ,   _snake_case						:					PreTrainedTokenizer       ,   _snake_case						:					int = -1       ,   _snake_case						:					int = -1       ,   _snake_case						:					bool = False       ,   _snake_case						:					Optional[TensorType] = None       ,   )      -> Mapping[str, Any]:







								"""simple docstring"""
								if self.task in ["default", "seq2seq-lm"]:
													A_				 =						self._generate_dummy_inputs_for_default_and_seqaseq_lm(
													    _snake_case       ,   batch_size=_snake_case       ,   seq_length=_snake_case       ,   is_pair=_snake_case       ,   framework=_snake_case	)

								elif self.task == "causal-lm":
													A_				 =						self._generate_dummy_inputs_for_causal_lm(
													    _snake_case       ,   batch_size=_snake_case       ,   seq_length=_snake_case       ,   is_pair=_snake_case       ,   framework=_snake_case	)
								else:
													A_				 =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    _snake_case       ,   batch_size=_snake_case       ,   seq_length=_snake_case       ,   is_pair=_snake_case       ,   framework=_snake_case	)

								return common_inputs






			def      lowerCamelCase__     (				self						:					Optional[Any]       ,   _snake_case						:					List[str]       ,   _snake_case						:					Tuple       ,   _snake_case						:					Any       ,   _snake_case						:					Union[str, Any]	)      -> List[str]:







								"""simple docstring"""
								if self.task in ["default", "seq2seq-lm"]:
													A_				 =						super()._flatten_past_key_values_(_snake_case       ,   _snake_case       ,   _snake_case       ,   _snake_case	)
								else:
													A_				 =						super(_snake_case       ,   self	)._flatten_past_key_values_(
													    _snake_case       ,   _snake_case       ,   _snake_case       ,   _snake_case	)






"""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

UpperCamelCase_					:						Union[str, Any]				= random.Random()
def 							A_     (__a				,   __a=1.0				,   __a=None				,   __a=None				):

					'''simple docstring'''





					if rng is None:
										A_				 =						global_rng

					A_				 =						[]
					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						:					Optional[Any]       ,   _snake_case						:					str       ,   _snake_case						:					Union[str, Any]=7       ,   _snake_case						:					Any=400       ,   _snake_case						:					Tuple=2_000       ,   _snake_case						:					str=2_048       ,   _snake_case						:					List[Any]=128       ,   _snake_case						:					List[Any]=1       ,   _snake_case						:					Union[str, Any]=512       ,   _snake_case						:					List[str]=30       ,   _snake_case						:					List[str]=44_100       ,   )      -> Optional[int]:







								"""simple docstring"""
								A_				 =						parent
								A_				 =						batch_size
								A_				 =						min_seq_length
								A_				 =						max_seq_length
								A_				 =						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								A_				 =						spectrogram_length
								A_				 =						feature_size
								A_				 =						num_audio_channels
								A_				 =						hop_length
								A_				 =						chunk_length
								A_				 =						sampling_rate




			def      lowerCamelCase__     (				self						:					Any	)      -> Optional[int]:







								"""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      lowerCamelCase__     (				self						:					Dict       ,   _snake_case						:					str=False       ,   _snake_case						:					Optional[Any]=False	)      -> List[str]:







								"""simple docstring"""
								def _flatten(_snake_case						:					List[Any]	):
													return list(itertools.chain(*_snake_case	)	)

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





@require_torch
@require_torchaudio
class __lowerCAmelCase				(					_lowercase     ,				unittest.TestCase      ):




			"""simple docstring"""







			snake_case	      =				TvltFeatureExtractor




			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> List[Any]:







								"""simple docstring"""
								A_				 =						TvltFeatureExtractionTester(self	)




			def      lowerCamelCase__     (				self						:					Tuple	)      -> Union[str, Any]:







								"""simple docstring"""
								A_				 =						self.feature_extraction_class(**self.feat_extract_dict	)
								self.assertTrue(hasattr(_snake_case       ,   "spectrogram_length"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "feature_size"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "num_audio_channels"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "hop_length"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "chunk_length"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "sampling_rate"	)	)




			def      lowerCamelCase__     (				self						:					Tuple	)      -> Union[str, Any]:







								"""simple docstring"""
								A_				 =						self.feature_extraction_class(**self.feat_extract_dict	)

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

								A_				 =						feat_extract_first.to_dict()
								A_				 =						feat_extract_second.to_dict()
								A_				 =						dict_first.pop("mel_filters"	)
								A_				 =						dict_second.pop("mel_filters"	)
								self.assertTrue(np.allclose(_snake_case       ,   _snake_case	)	)
								self.assertEqual(_snake_case       ,   _snake_case	)




			def      lowerCamelCase__     (				self						:					Optional[int]	)      -> Tuple:







								"""simple docstring"""
								A_				 =						self.feature_extraction_class(**self.feat_extract_dict	)

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

								A_				 =						feat_extract_first.to_dict()
								A_				 =						feat_extract_second.to_dict()
								A_				 =						dict_first.pop("mel_filters"	)
								A_				 =						dict_second.pop("mel_filters"	)
								self.assertTrue(np.allclose(_snake_case       ,   _snake_case	)	)
								self.assertEqual(_snake_case       ,   _snake_case	)




			def      lowerCamelCase__     (				self						:					int	)      -> List[Any]:







								"""simple docstring"""
								# Initialize feature_extractor
								A_				 =						self.feature_extraction_class(**self.feat_extract_dict	)

								# create three inputs of length 800, 1000, and 1200
								A_				 =						[floats_list((1, x)	)[0] for x in range(800       ,   1_400       ,   200	)]
								A_				 =						[np.asarray(_snake_case	) for speech_input in speech_inputs]

								# Test not batched input
								A_				 =						feature_extractor(np_speech_inputs[0]       ,   return_tensors="np"       ,   sampling_rate=44_100	).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
								A_				 =						feature_extractor(_snake_case       ,   return_tensors="np"       ,   sampling_rate=44_100	).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
								A_				 =						feature_extractor(
								    _snake_case       ,   return_tensors="np"       ,   sampling_rate=44_100       ,   mask_audio=_snake_case	).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.
								A_				 =						[floats_list((1, x)	)[0] for x in (800, 800, 800)]
								A_				 =						np.asarray(_snake_case	)
								A_				 =						feature_extractor(_snake_case       ,   return_tensors="np"       ,   sampling_rate=44_100	).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      lowerCamelCase__     (				self						:					Dict       ,   _snake_case						:					int	)      -> List[str]:







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

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






			def      lowerCamelCase__     (				self						:					List[str]	)      -> int:







								"""simple docstring"""
								A_				 =						self._load_datasamples(1	)
								A_				 =						TvltFeatureExtractor()
								A_				 =						feature_extractor(_snake_case       ,   return_tensors="pt"	).audio_values

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

								A_				 =						torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]]	)
								self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]       ,   _snake_case       ,   atol=1e-4	)	)




