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


SCREAMING_SNAKE_CASE__   =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   =					{
    "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json",
    # See all Marian models at https://huggingface.co/models?filter=marian
}



class 					lowerCAmelCase_      (     lowerCAmelCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      Optional[Any]     					=  """marian"""
				_lowerCAmelCase       :      List[str]     					=  ["""past_key_values"""]
				_lowerCAmelCase       :      Tuple     					=  {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

				def __init__(			self						,      lowerCAmelCase=5_81_01						,      lowerCAmelCase=None						,      lowerCAmelCase=10_24						,      lowerCAmelCase=12						,      lowerCAmelCase=40_96						,      lowerCAmelCase=16						,      lowerCAmelCase=12						,      lowerCAmelCase=40_96						,      lowerCAmelCase=16						,      lowerCAmelCase=0.0						,      lowerCAmelCase=0.0						,      lowerCAmelCase=True						,      lowerCAmelCase=True						,      lowerCAmelCase="gelu"						,      lowerCAmelCase=10_24						,      lowerCAmelCase=0.1						,      lowerCAmelCase=0.0						,      lowerCAmelCase=0.0						,      lowerCAmelCase=0.02						,      lowerCAmelCase=5_81_00						,      lowerCAmelCase=False						,      lowerCAmelCase=5_81_00						,      lowerCAmelCase=0						,      lowerCAmelCase=0						,      lowerCAmelCase=True						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										snake_case        =     vocab_size
										snake_case        =     decoder_vocab_size or vocab_size
										snake_case        =     max_position_embeddings
										snake_case        =     d_model
										snake_case        =     encoder_ffn_dim
										snake_case        =     encoder_layers
										snake_case        =     encoder_attention_heads
										snake_case        =     decoder_ffn_dim
										snake_case        =     decoder_layers
										snake_case        =     decoder_attention_heads
										snake_case        =     dropout
										snake_case        =     attention_dropout
										snake_case        =     activation_dropout
										snake_case        =     activation_function
										snake_case        =     init_std
										snake_case        =     encoder_layerdrop
										snake_case        =     decoder_layerdrop
										snake_case        =     use_cache
										snake_case        =     encoder_layers
										snake_case        =     scale_embedding  # scale factor will be sqrt(d_model) if True
										snake_case        =     share_encoder_decoder_embeddings
										super().__init__(
										    pad_token_id=lowerCAmelCase						,      eos_token_id=lowerCAmelCase						,      is_encoder_decoder=lowerCAmelCase						,      decoder_start_token_id=lowerCAmelCase						,      forced_eos_token_id=lowerCAmelCase						,      **lowerCAmelCase						,      )





class 					lowerCAmelCase_      (     lowerCAmelCase   ):



				"""simple docstring"""

				@property
				# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
				def 						snake_case	(			self      ):






										"""simple docstring"""

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

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

																if self.use_past:
																						self.fill_with_past_key_values_(lowerCAmelCase						,      direction='inputs'      )
										elif self.task == "causal-lm":
																# TODO: figure this case out.
																snake_case        =     OrderedDict(
																    [
																        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
																        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
																    ]      )
																if self.use_past:
																						snake_case   ,snake_case        =     self.num_layers
																						for i in range(lowerCAmelCase      ):
																												snake_case        =     {0: 'batch', 2: 'past_sequence + sequence'}
																												snake_case        =     {0: 'batch', 2: 'past_sequence + sequence'}
										else:
																snake_case        =     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
				# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
				def 						snake_case	(			self      ):






										"""simple docstring"""

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

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase = -1						,      lowerCAmelCase = -1						,      lowerCAmelCase = False						,      lowerCAmelCase = None						,      ):






										"""simple docstring"""

										snake_case        =     self._generate_dummy_inputs_for_encoder_and_decoder(
										    lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      )

										# Generate decoder inputs
										snake_case        =     seq_length if not self.use_past else 1
										snake_case        =     self._generate_dummy_inputs_for_encoder_and_decoder(
										    lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      )
										snake_case        =     {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
										snake_case        =     dict(**lowerCAmelCase						,      **lowerCAmelCase      )

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

																snake_case        =     torch.cat(
																    [common_inputs['decoder_attention_mask'], torch.ones(lowerCAmelCase						,      lowerCAmelCase      )]						,      dim=1      )

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

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

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase = -1						,      lowerCAmelCase = -1						,      lowerCAmelCase = False						,      lowerCAmelCase = None						,      ):






										"""simple docstring"""

										snake_case        =     self._generate_dummy_inputs_for_encoder_and_decoder(
										    lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      )

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

																snake_case        =     common_inputs['attention_mask'].dtype
																snake_case        =     torch.cat(
																    [common_inputs['attention_mask'], torch.ones(lowerCAmelCase						,      lowerCAmelCase						,      dtype=lowerCAmelCase      )]						,      dim=1      )
																snake_case        =     [
																    (torch.zeros(lowerCAmelCase      ), torch.zeros(lowerCAmelCase      )) for _ in range(lowerCAmelCase      )
																]
										return common_inputs

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase = -1						,      lowerCAmelCase = -1						,      lowerCAmelCase = False						,      lowerCAmelCase = None						,      ):






										"""simple docstring"""

										snake_case        =     compute_effective_axis_dimension(
										    lowerCAmelCase						,      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
										snake_case        =     tokenizer.num_special_tokens_to_add(lowerCAmelCase      )
										snake_case        =     compute_effective_axis_dimension(
										    lowerCAmelCase						,      fixed_dimension=OnnxConfig.default_fixed_sequence						,      num_token_to_add=lowerCAmelCase      )

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

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase = -1						,      lowerCAmelCase = -1						,      lowerCAmelCase = False						,      lowerCAmelCase = None						,      ):






										"""simple docstring"""

										if self.task in ["default", "seq2seq-lm"]:
																snake_case        =     self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																    lowerCAmelCase						,      batch_size=lowerCAmelCase						,      seq_length=lowerCAmelCase						,      is_pair=lowerCAmelCase						,      framework=lowerCAmelCase      )

										else:
																snake_case        =     self._generate_dummy_inputs_for_causal_lm(
																    lowerCAmelCase						,      batch_size=lowerCAmelCase						,      seq_length=lowerCAmelCase						,      is_pair=lowerCAmelCase						,      framework=lowerCAmelCase      )

										return common_inputs

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										if self.task in ["default", "seq2seq-lm"]:
																snake_case        =     super()._flatten_past_key_values_(lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      )
										else:
																snake_case        =     super(lowerCAmelCase						,      self      )._flatten_past_key_values_(
																    lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      )

				@property
				def 						snake_case	(			self      ):






										"""simple docstring"""

										return 1E-4



"""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__      (     _UpperCamelCase    :			List[Any]	,     _UpperCamelCase    :			Optional[Any]=1.0	,     _UpperCamelCase    :			Optional[int]=None	,     _UpperCamelCase    :			Optional[int]=None   )   ->			Optional[int]:





						"""simple docstring"""






						if rng is None:
												snake_case        =     global_rng

						snake_case        =     []
						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						,      lowerCAmelCase						,      lowerCAmelCase=7						,      lowerCAmelCase=4_00						,      lowerCAmelCase=20_00						,      lowerCAmelCase=20_48						,      lowerCAmelCase=1_28						,      lowerCAmelCase=1						,      lowerCAmelCase=5_12						,      lowerCAmelCase=30						,      lowerCAmelCase=4_41_00						,      ):






										"""simple docstring"""

										snake_case        =     parent
										snake_case        =     batch_size
										snake_case        =     min_seq_length
										snake_case        =     max_seq_length
										snake_case        =     (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
										snake_case        =     spectrogram_length
										snake_case        =     feature_size
										snake_case        =     num_audio_channels
										snake_case        =     hop_length
										snake_case        =     chunk_length
										snake_case        =     sampling_rate

				def 						snake_case	(			self      ):






										"""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 						snake_case	(			self						,      lowerCAmelCase=False						,      lowerCAmelCase=False      ):






										"""simple docstring"""

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

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





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



				"""simple docstring"""
				_lowerCAmelCase       :      Union[str, Any]     					=  TvltFeatureExtractor

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     TvltFeatureExtractionTester(self      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.feature_extraction_class(**self.feat_extract_dict      )
										self.assertTrue(hasattr(lowerCAmelCase						,      'spectrogram_length'      )      )
										self.assertTrue(hasattr(lowerCAmelCase						,      'feature_size'      )      )
										self.assertTrue(hasattr(lowerCAmelCase						,      'num_audio_channels'      )      )
										self.assertTrue(hasattr(lowerCAmelCase						,      'hop_length'      )      )
										self.assertTrue(hasattr(lowerCAmelCase						,      'chunk_length'      )      )
										self.assertTrue(hasattr(lowerCAmelCase						,      'sampling_rate'      )      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

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

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

										snake_case        =     feat_extract_first.to_dict()
										snake_case        =     feat_extract_second.to_dict()
										snake_case        =     dict_first.pop('mel_filters'      )
										snake_case        =     dict_second.pop('mel_filters'      )
										self.assertTrue(np.allclose(lowerCAmelCase						,      lowerCAmelCase      )      )
										self.assertEqual(lowerCAmelCase						,      lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

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

										with tempfile.TemporaryDirectory() as tmpdirname:
																snake_case        =     os.path.join(lowerCAmelCase						,      'feat_extract.json'      )
																feat_extract_first.to_json_file(lowerCAmelCase      )
																snake_case        =     self.feature_extraction_class.from_json_file(lowerCAmelCase      )

										snake_case        =     feat_extract_first.to_dict()
										snake_case        =     feat_extract_second.to_dict()
										snake_case        =     dict_first.pop('mel_filters'      )
										snake_case        =     dict_second.pop('mel_filters'      )
										self.assertTrue(np.allclose(lowerCAmelCase						,      lowerCAmelCase      )      )
										self.assertEqual(lowerCAmelCase						,      lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

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

										# create three inputs of length 800, 1000, and 1200
										snake_case        =     [floats_list((1, x)      )[0] for x in range(8_00						,      14_00						,      2_00      )]
										snake_case        =     [np.asarray(lowerCAmelCase      ) for speech_input in speech_inputs]

										# Test not batched input
										snake_case        =     feature_extractor(np_speech_inputs[0]						,      return_tensors='np'						,      sampling_rate=4_41_00      ).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
										snake_case        =     feature_extractor(lowerCAmelCase						,      return_tensors='np'						,      sampling_rate=4_41_00      ).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
										snake_case        =     feature_extractor(
										    lowerCAmelCase						,      return_tensors='np'						,      sampling_rate=4_41_00						,      mask_audio=lowerCAmelCase      ).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.
										snake_case        =     [floats_list((1, x)      )[0] for x in (8_00, 8_00, 8_00)]
										snake_case        =     np.asarray(lowerCAmelCase      )
										snake_case        =     feature_extractor(lowerCAmelCase						,      return_tensors='np'						,      sampling_rate=4_41_00      ).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						,      lowerCAmelCase      ):






										"""simple docstring"""

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

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

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self._load_datasamples(1      )
										snake_case        =     TvltFeatureExtractor()
										snake_case        =     feature_extractor(lowerCAmelCase						,      return_tensors='pt'      ).audio_values

										self.assertEquals(audio_values.shape						,      (1, 1, 1_92, 1_28)      )

										snake_case        =     torch.tensor([[-0.30_32, -0.27_08], [-0.44_34, -0.40_07]]      )
										self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]						,      lowerCAmelCase						,      atol=1E-4      )      )


