



"""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

SCREAMING_SNAKE_CASE_				= random.Random()



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__=1.0, SCREAMING_SNAKE_CASE__=None, SCREAMING_SNAKE_CASE__=None	)			->      Tuple:
		if rng is None:
				a_     :      Any 						=     global_rng

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

		return values




class 			snake_case_						(						unittest.TestCase				):






							def __init__(   self			,     a_			,     a_=7			,     a_=4_0_0			,     a_=2_0_0_0			,     a_=2_0_4_8			,     a_=1_2_8			,     a_=1			,     a_=5_1_2			,     a_=3_0			,     a_=4_4_1_0_0			,     ):
									a_     :      Optional[Any] 						=     parent
									a_     :      Tuple 						=     batch_size
									a_     :      Union[str, Any] 						=     min_seq_length
									a_     :      Dict 						=     max_seq_length
									a_     :      Union[str, Any] 						=     (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
									a_     :      int 						=     spectrogram_length
									a_     :      Optional[Any] 						=     feature_size
									a_     :      Optional[int] 						=     num_audio_channels
									a_     :      List[str] 						=     hop_length
									a_     :      Optional[int] 						=     chunk_length
									a_     :      List[str] 						=     sampling_rate






							def snake_case_  (   self      ):
									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 snake_case_  (   self			,     a_=False			,     a_=False      ):
									def _flatten(a_      ):
											return list(itertools.chain(*a_      )      )

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




@require_torch
@require_torchaudio
class 			snake_case_						(						a_     ,unittest.TestCase				):
							__lowerCAmelCase    					=					TvltFeatureExtractor






							def snake_case_  (   self      ):
									a_     :      Dict 						=     TvltFeatureExtractionTester(self      )






							def snake_case_  (   self      ):
									a_     :      Optional[Any] 						=     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 snake_case_  (   self      ):
									a_     :      List[Any] 						=     self.feature_extraction_class(**self.feat_extract_dict      )

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

									a_     :      Dict 						=     feat_extract_first.to_dict()
									a_     :      List[str] 						=     feat_extract_second.to_dict()
									a_     :      List[Any] 						=     dict_first.pop("mel_filters"      )
									a_     :      str 						=     dict_second.pop("mel_filters"      )
									self.assertTrue(np.allclose(a_			,     a_      )      )
									self.assertEqual(a_			,     a_      )






							def snake_case_  (   self      ):
									a_     :      List[str] 						=     self.feature_extraction_class(**self.feat_extract_dict      )

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

									a_     :      Dict 						=     feat_extract_first.to_dict()
									a_     :      str 						=     feat_extract_second.to_dict()
									a_     :      int 						=     dict_first.pop("mel_filters"      )
									a_     :      List[Any] 						=     dict_second.pop("mel_filters"      )
									self.assertTrue(np.allclose(a_			,     a_      )      )
									self.assertEqual(a_			,     a_      )






							def snake_case_  (   self      ):
									# Initialize feature_extractor
									a_     :      str 						=     self.feature_extraction_class(**self.feat_extract_dict      )

									# create three inputs of length 800, 1000, and 1200
									a_     :      Optional[Any] 						=     [floats_list((1, x)      )[0] for x in range(8_0_0			,     1_4_0_0			,     2_0_0      )]
									a_     :      Union[str, Any] 						=     [np.asarray(a_      ) for speech_input in speech_inputs]

									# Test not batched input
									a_     :      Dict 						=     feature_extractor(np_speech_inputs[0]			,     return_tensors="np"			,     sampling_rate=4_4_1_0_0      ).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_     :      int 						=     feature_extractor(a_			,     return_tensors="np"			,     sampling_rate=4_4_1_0_0      ).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_     :      Optional[int] 						=     feature_extractor(
									    a_			,     return_tensors="np"			,     sampling_rate=4_4_1_0_0			,     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.
									a_     :      List[str] 						=     [floats_list((1, x)      )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
									a_     :      Union[str, Any] 						=     np.asarray(a_      )
									a_     :      Dict 						=     feature_extractor(a_			,     return_tensors="np"			,     sampling_rate=4_4_1_0_0      ).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 snake_case_  (   self			,     a_      ):
									a_     :      Optional[int] 						=     load_dataset("hf-internal-testing/librispeech_asr_dummy"			,     "clean"			,     split="validation"      )
									# automatic decoding with librispeech
									a_     :      List[str] 						=     ds.sort("id"      ).select(range(a_      )      )[:num_samples]["audio"]

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






							def snake_case_  (   self      ):
									a_     :      List[str] 						=     self._load_datasamples(1      )
									a_     :      Any 						=     TvltFeatureExtractor()
									a_     :      Union[str, Any] 						=     feature_extractor(a_			,     return_tensors="pt"      ).audio_values

									self.assertEquals(audio_values.shape			,     (1, 1, 1_9_2, 1_2_8)      )

									a_     :      Dict 						=     torch.tensor([[-0.3_032, -0.2_708], [-0.4_434, -0.4_007]]      )
									self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]			,     a_			,     atol=1e-4      )      )




"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional

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


SCREAMING_SNAKE_CASE_				= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_				= {
    """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json""",
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}




class 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					"blenderbot-small"
							__lowerCAmelCase    					=					["past_key_values"]
							__lowerCAmelCase    					=					{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






							def __init__(   self			,     a_=5_0_2_6_5			,     a_=5_1_2			,     a_=8			,     a_=2_0_4_8			,     a_=1_6			,     a_=8			,     a_=2_0_4_8			,     a_=1_6			,     a_=0.0			,     a_=0.0			,     a_=True			,     a_=True			,     a_="gelu"			,     a_=5_1_2			,     a_=0.1			,     a_=0.0			,     a_=0.0			,     a_=0.02			,     a_=1			,     a_=False			,     a_=0			,     a_=1			,     a_=2			,     a_=2			,     **a_			,     ):
									a_     :      int 						=     vocab_size
									a_     :      Any 						=     max_position_embeddings
									a_     :      Optional[int] 						=     d_model
									a_     :      Tuple 						=     encoder_ffn_dim
									a_     :      List[Any] 						=     encoder_layers
									a_     :      Optional[int] 						=     encoder_attention_heads
									a_     :      Optional[int] 						=     decoder_ffn_dim
									a_     :      List[str] 						=     decoder_layers
									a_     :      Dict 						=     decoder_attention_heads
									a_     :      List[str] 						=     dropout
									a_     :      List[Any] 						=     attention_dropout
									a_     :      List[str] 						=     activation_dropout
									a_     :      Optional[Any] 						=     activation_function
									a_     :      List[Any] 						=     init_std
									a_     :      int 						=     encoder_layerdrop
									a_     :      Optional[int] 						=     decoder_layerdrop
									a_     :      List[str] 						=     use_cache
									a_     :      Optional[int] 						=     encoder_layers
									a_     :      Optional[int] 						=     scale_embedding  # scale factor will be sqrt(d_model) if True

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




class 			snake_case_						(						a_				):






							@property
							def snake_case_  (   self      ):
									if self.task in ["default", "seq2seq-lm"]:
											a_     :      Optional[int] 						=     OrderedDict(
											    [
											        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
											        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
											    ]      )

											if self.use_past:
													a_     :      Tuple 						=     {0: "batch"}
													a_     :      int 						=     {0: "batch", 1: "past_decoder_sequence + sequence"}
											else:
													a_     :      List[str] 						=     {0: "batch", 1: "decoder_sequence"}
													a_     :      Optional[int] 						=     {0: "batch", 1: "decoder_sequence"}

											if self.use_past:
													self.fill_with_past_key_values_(a_			,     direction="inputs"      )
									elif self.task == "causal-lm":
											# TODO: figure this case out.
											a_     :      Dict 						=     OrderedDict(
											    [
											        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
											        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
											    ]      )
											if self.use_past:
													a_						,				a_     :      Optional[int] 						=     self.num_layers
													for i in range(a_      ):
															a_     :      Union[str, Any] 						=     {0: "batch", 2: "past_sequence + sequence"}
															a_     :      List[Any] 						=     {0: "batch", 2: "past_sequence + sequence"}
									else:
											a_     :      List[str] 						=     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 snake_case_  (   self      ):
									if self.task in ["default", "seq2seq-lm"]:
											a_     :      List[Any] 						=     super().outputs
									else:
											a_     :      Tuple 						=     super(a_			,     self      ).outputs
											if self.use_past:
													a_						,				a_     :      Dict 						=     self.num_layers
													for i in range(a_      ):
															a_     :      List[Any] 						=     {0: "batch", 2: "past_sequence + sequence"}
															a_     :      List[Any] 						=     {0: "batch", 2: "past_sequence + sequence"}
									return common_outputs






							def snake_case_  (   self			,     a_			,     a_ = -1			,     a_ = -1			,     a_ = False			,     a_ = None			,     ):
									a_     :      List[str] 						=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    a_			,     a_			,     a_			,     a_			,     a_      )

									# Generate decoder inputs
									a_     :      Optional[int] 						=     seq_length if not self.use_past else 1
									a_     :      str 						=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    a_			,     a_			,     a_			,     a_			,     a_      )
									a_     :      int 						=     {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
									a_     :      Tuple 						=     dict(**a_			,     **a_      )

									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_     :      Optional[int] 						=     common_inputs["input_ids"].shape
											a_     :      str 						=     common_inputs["decoder_input_ids"].shape[1]
											a_						,				a_     :      Dict 						=     self.num_attention_heads
											a_     :      List[str] 						=     (
											    batch,
											    num_encoder_attention_heads,
											    encoder_seq_length,
											    self._config.hidden_size // num_encoder_attention_heads,
											)
											a_     :      Optional[Any] 						=     decoder_seq_length + 3
											a_     :      str 						=     (
											    batch,
											    num_decoder_attention_heads,
											    decoder_past_length,
											    self._config.hidden_size // num_decoder_attention_heads,
											)

											a_     :      Dict 						=     torch.cat(
											    [common_inputs["decoder_attention_mask"], torch.ones(a_			,     a_      )]			,     dim=1      )

											a_     :      Optional[int] 						=     []
											# If the number of encoder and decoder layers are present in the model configuration, both are considered
											a_						,				a_     :      Tuple 						=     self.num_layers
											a_     :      str 						=     min(a_			,     a_      )
											a_     :      Dict 						=     max(a_			,     a_      ) - min_num_layers
											a_     :      Union[str, Any] 						=     "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

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






							def snake_case_  (   self			,     a_			,     a_ = -1			,     a_ = -1			,     a_ = False			,     a_ = None			,     ):
									a_     :      Optional[int] 						=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    a_			,     a_			,     a_			,     a_			,     a_      )

									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_     :      Dict 						=     common_inputs["input_ids"].shape
											# Not using the same length for past_key_values
											a_     :      int 						=     seqlen + 2
											a_						,				a_     :      Optional[int] 						=     self.num_layers
											a_						,				a_     :      Optional[int] 						=     self.num_attention_heads
											a_     :      Optional[Any] 						=     (
											    batch,
											    num_encoder_attention_heads,
											    past_key_values_length,
											    self._config.hidden_size // num_encoder_attention_heads,
											)

											a_     :      str 						=     common_inputs["attention_mask"].dtype
											a_     :      Tuple 						=     torch.cat(
											    [common_inputs["attention_mask"], torch.ones(a_			,     a_			,     dtype=a_      )]			,     dim=1      )
											a_     :      Optional[int] 						=     [
											    (torch.zeros(a_      ), torch.zeros(a_      )) for _ in range(a_      )
											]
									return common_inputs






							def snake_case_  (   self			,     a_			,     a_ = -1			,     a_ = -1			,     a_ = False			,     a_ = None			,     ):
									# Copied from OnnxConfig.generate_dummy_inputs
									# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
									# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
									a_     :      Optional[Any] 						=     compute_effective_axis_dimension(
									    a_			,     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_     :      Tuple 						=     tokenizer.num_special_tokens_to_add(a_      )
									a_     :      Union[str, Any] 						=     compute_effective_axis_dimension(
									    a_			,     fixed_dimension=OnnxConfig.default_fixed_sequence			,     num_token_to_add=a_      )

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






							def snake_case_  (   self			,     a_			,     a_ = -1			,     a_ = -1			,     a_ = False			,     a_ = None			,     ):
									if self.task in ["default", "seq2seq-lm"]:
											a_     :      List[str] 						=     self._generate_dummy_inputs_for_default_and_seqaseq_lm(
											    a_			,     batch_size=a_			,     seq_length=a_			,     is_pair=a_			,     framework=a_      )

									elif self.task == "causal-lm":
											a_     :      List[Any] 						=     self._generate_dummy_inputs_for_causal_lm(
											    a_			,     batch_size=a_			,     seq_length=a_			,     is_pair=a_			,     framework=a_      )
									else:
											a_     :      Union[str, Any] 						=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    a_			,     batch_size=a_			,     seq_length=a_			,     is_pair=a_			,     framework=a_      )

									return common_inputs






							def snake_case_  (   self			,     a_			,     a_			,     a_			,     a_      ):
									if self.task in ["default", "seq2seq-lm"]:
											a_     :      Optional[int] 						=     super()._flatten_past_key_values_(a_			,     a_			,     a_			,     a_      )
									else:
											a_     :      int 						=     super(a_			,     self      )._flatten_past_key_values_(
											    a_			,     a_			,     a_			,     a_      )