
"""simple docstring"""





import unittest

from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    NystromformerForMaskedLM,
		    NystromformerForMultipleChoice,
		    NystromformerForQuestionAnswering,
		    NystromformerForSequenceClassification,
		    NystromformerForTokenClassification,
		    NystromformerModel,
		)
		from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST



class __lowerCAmelCase					:




			"""simple docstring"""







			def __init__(					self    :       List[str]							, _snake_case    :       Optional[Any]							, _snake_case    :       Dict=13							, _snake_case    :       int=7							, _snake_case    :       List[Any]=True							, _snake_case    :       Tuple=True							, _snake_case    :       Optional[Any]=True							, _snake_case    :       int=True							, _snake_case    :       str=99							, _snake_case    :       Union[str, Any]=32							, _snake_case    :       Any=5							, _snake_case    :       int=4							, _snake_case    :       List[Any]=37							, _snake_case    :       List[Any]="gelu"							, _snake_case    :       Tuple=0.1							, _snake_case    :       Union[str, Any]=0.1							, _snake_case    :       List[str]=512							, _snake_case    :       List[str]=16							, _snake_case    :       Any=2							, _snake_case    :       int=0.0_2							, _snake_case    :       str=3							, _snake_case    :       Any=4							, _snake_case    :       Union[str, Any]=None							, )   ->			Optional[int]:







								"""simple docstring"""
								A_								=    parent
								A_								=    batch_size
								A_								=    seq_length
								A_								=    is_training
								A_								=    use_input_mask
								A_								=    use_token_type_ids
								A_								=    use_labels
								A_								=    vocab_size
								A_								=    hidden_size
								A_								=    num_hidden_layers
								A_								=    num_attention_heads
								A_								=    intermediate_size
								A_								=    hidden_act
								A_								=    hidden_dropout_prob
								A_								=    attention_probs_dropout_prob
								A_								=    max_position_embeddings
								A_								=    type_vocab_size
								A_								=    type_sequence_label_size
								A_								=    initializer_range
								A_								=    num_labels
								A_								=    num_choices
								A_								=    scope




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







								"""simple docstring"""
								A_								=    ids_tensor([self.batch_size, self.seq_length]							, self.vocab_size						)

								A_								=    None
								if self.use_input_mask:
													A_								=    random_attention_mask([self.batch_size, self.seq_length]						)

								A_								=    None
								if self.use_token_type_ids:
													A_								=    ids_tensor([self.batch_size, self.seq_length]							, self.type_vocab_size						)

								A_								=    None
								A_								=    None
								A_								=    None
								if self.use_labels:
													A_								=    ids_tensor([self.batch_size]							, self.type_sequence_label_size						)
													A_								=    ids_tensor([self.batch_size, self.seq_length]							, self.num_labels						)
													A_								=    ids_tensor([self.batch_size]							, self.num_choices						)

								A_								=    self.get_config()

								return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




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







								"""simple docstring"""
								return NystromformerConfig(
								    vocab_size=self.vocab_size							, hidden_size=self.hidden_size							, num_hidden_layers=self.num_hidden_layers							, num_attention_heads=self.num_attention_heads							, intermediate_size=self.intermediate_size							, hidden_act=self.hidden_act							, hidden_dropout_prob=self.hidden_dropout_prob							, attention_probs_dropout_prob=self.attention_probs_dropout_prob							, max_position_embeddings=self.max_position_embeddings							, type_vocab_size=self.type_vocab_size							, is_decoder=_snake_case							, initializer_range=self.initializer_range							, )




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







								"""simple docstring"""
								A_								=    NystromformerModel(config=_snake_case						)
								model.to(_snake_case						)
								model.eval()
								A_								=    model(_snake_case							, attention_mask=_snake_case							, token_type_ids=_snake_case						)
								A_								=    model(_snake_case							, token_type_ids=_snake_case						)
								A_								=    model(_snake_case						)
								self.parent.assertEqual(result.last_hidden_state.shape							, (self.batch_size, self.seq_length, self.hidden_size)						)




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







								"""simple docstring"""
								A_								=    NystromformerForMaskedLM(config=_snake_case						)
								model.to(_snake_case						)
								model.eval()
								A_								=    model(_snake_case							, attention_mask=_snake_case							, token_type_ids=_snake_case							, labels=_snake_case						)
								self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.seq_length, self.vocab_size)						)




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







								"""simple docstring"""
								A_								=    NystromformerForQuestionAnswering(config=_snake_case						)
								model.to(_snake_case						)
								model.eval()
								A_								=    model(
								    _snake_case							, attention_mask=_snake_case							, token_type_ids=_snake_case							, start_positions=_snake_case							, end_positions=_snake_case							, )
								self.parent.assertEqual(result.start_logits.shape							, (self.batch_size, self.seq_length)						)
								self.parent.assertEqual(result.end_logits.shape							, (self.batch_size, self.seq_length)						)




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







								"""simple docstring"""
								A_								=    self.num_labels
								A_								=    NystromformerForSequenceClassification(_snake_case						)
								model.to(_snake_case						)
								model.eval()
								A_								=    model(_snake_case							, attention_mask=_snake_case							, token_type_ids=_snake_case							, labels=_snake_case						)
								self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.num_labels)						)




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







								"""simple docstring"""
								A_								=    self.num_labels
								A_								=    NystromformerForTokenClassification(config=_snake_case						)
								model.to(_snake_case						)
								model.eval()
								A_								=    model(_snake_case							, attention_mask=_snake_case							, token_type_ids=_snake_case							, labels=_snake_case						)
								self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.seq_length, self.num_labels)						)




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







								"""simple docstring"""
								A_								=    self.num_choices
								A_								=    NystromformerForMultipleChoice(config=_snake_case						)
								model.to(_snake_case						)
								model.eval()
								A_								=    input_ids.unsqueeze(1						).expand(-1							, self.num_choices							, -1						).contiguous()
								A_								=    token_type_ids.unsqueeze(1						).expand(-1							, self.num_choices							, -1						).contiguous()
								A_								=    input_mask.unsqueeze(1						).expand(-1							, self.num_choices							, -1						).contiguous()
								A_								=    model(
								    _snake_case							, attention_mask=_snake_case							, token_type_ids=_snake_case							, labels=_snake_case							, )
								self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.num_choices)						)






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







								"""simple docstring"""
								A_								=    self.prepare_config_and_inputs()
								(
								    (
								    A_
								)			,     (
								    A_
								)			,     (
								    A_
								)			,     (
								    A_
								)			,     (
								    A_
								)			,     (
								    A_
								)			,     (
								    A_
								)			,     
								)								=    config_and_inputs
								A_								=    {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
								return config, inputs_dict





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




			"""simple docstring"""







			snake_case						   =					(
			    (
			        NystromformerModel,
			        NystromformerForMaskedLM,
			        NystromformerForMultipleChoice,
			        NystromformerForQuestionAnswering,
			        NystromformerForSequenceClassification,
			        NystromformerForTokenClassification,
			    )
			    if is_torch_available()
			    else ()
			)
			snake_case						   =					(
			    {
			        "feature-extraction": NystromformerModel,
			        "fill-mask": NystromformerForMaskedLM,
			        "question-answering": NystromformerForQuestionAnswering,
			        "text-classification": NystromformerForSequenceClassification,
			        "token-classification": NystromformerForTokenClassification,
			        "zero-shot": NystromformerForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			snake_case						   =					False
			snake_case						   =					False




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







								"""simple docstring"""
								A_								=    NystromformerModelTester(self						)
								A_								=    ConfigTester(self							, config_class=_snake_case							, hidden_size=37						)




			def   lowerCamelCase__  (					self    :       Any						)   ->			int:







								"""simple docstring"""
								self.config_tester.run_common_tests()




			def   lowerCamelCase__  (					self    :       str						)   ->			Any:







								"""simple docstring"""
								A_								=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_snake_case						)




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







								"""simple docstring"""
								A_								=    self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
													A_								=    type
													self.model_tester.create_and_check_model(*_snake_case						)




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







								"""simple docstring"""
								A_								=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*_snake_case						)




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







								"""simple docstring"""
								A_								=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_multiple_choice(*_snake_case						)




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







								"""simple docstring"""
								A_								=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*_snake_case						)




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







								"""simple docstring"""
								A_								=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*_snake_case						)




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







								"""simple docstring"""
								A_								=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*_snake_case						)






			@slow
			def   lowerCamelCase__  (					self    :       List[str]						)   ->			Dict:







								"""simple docstring"""
								for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													A_								=    NystromformerModel.from_pretrained(_snake_case						)
													self.assertIsNotNone(_snake_case						)





@require_torch
class __lowerCAmelCase					(       unittest.TestCase   ):




			"""simple docstring"""







			@slow
			def   lowerCamelCase__  (					self    :       int						)   ->			str:







								"""simple docstring"""
								A_								=    NystromformerModel.from_pretrained("uw-madison/nystromformer-512"						)
								A_								=    torch.tensor([[0, 1, 2, 3, 4, 5]]						)

								with torch.no_grad():
													A_								=    model(_snake_case						)[0]

								A_								=    torch.Size((1, 6, 768)						)
								self.assertEqual(output.shape							, _snake_case						)

								A_								=    torch.tensor(
								    [[[-0.4_5_3_2, -0.0_9_3_6, 0.5_1_3_7], [-0.2_6_7_6, 0.0_6_2_8, 0.6_1_8_6], [-0.3_6_2_9, -0.1_7_2_6, 0.4_7_1_6]]]						)

								self.assertTrue(torch.allclose(output[:, :3, :3]							, _snake_case							, atol=1e-4						)						)






			@slow
			def   lowerCamelCase__  (					self    :       Optional[int]						)   ->			Dict:







								"""simple docstring"""
								A_								=    "the [MASK] of Belgium is Brussels"

								A_								=    AutoTokenizer.from_pretrained("uw-madison/nystromformer-512"						)
								A_								=    NystromformerForMaskedLM.from_pretrained("uw-madison/nystromformer-512"						)

								A_								=    tokenizer(_snake_case							, return_tensors="pt"						)

								with torch.no_grad():
													A_								=    model(encoding.input_ids						).logits

								A_								=    token_logits[:, 2, :].argmax(-1						)[0]

								self.assertEqual(tokenizer.decode(_snake_case						)							, "capital"						)






"""simple docstring"""





import math
from typing import Any, Callable, List, Optional, Tuple, Union

import numpy as np
import torch

from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor


if is_onnx_available():
		from ..onnx_utils import OnnxRuntimeModel

from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder


UpperCamelCase_       :    Any						      =			logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCamelCase_       :    List[Any]						      =			256



class __lowerCAmelCase					(       _lowercase   ):




			"""simple docstring"""







			snake_case						   =					["melgan"]




			def __init__(					self    :       Dict							, _snake_case    :       SpectrogramNotesEncoder							, _snake_case    :       SpectrogramContEncoder							, _snake_case    :       TaFilmDecoder							, _snake_case    :       DDPMScheduler							, _snake_case    :       OnnxRuntimeModel if is_onnx_available() else Any							, )   ->			None:







								"""simple docstring"""
								super().__init__()

								# From MELGAN
								A_								=    math.log(1e-5						)  # Matches MelGAN training.
								A_								=    4.0  # Largest value for most examples
								A_								=    128

								self.register_modules(
								    notes_encoder=_snake_case							, continuous_encoder=_snake_case							, decoder=_snake_case							, scheduler=_snake_case							, melgan=_snake_case							, )




			def   lowerCamelCase__  (					self    :       Union[str, Any]							, _snake_case    :       Tuple							, _snake_case    :       str=(-1.0, 1.0)							, _snake_case    :       int=False						)   ->			str:







								"""simple docstring"""
								A_			,     A_								=    output_range
								if clip:
													A_								=    torch.clip(_snake_case							, self.min_value							, self.max_value						)
								# Scale to [0, 1].
								A_								=    (features - self.min_value) / (self.max_value - self.min_value)
								# Scale to [min_out, max_out].
								return zero_one * (max_out - min_out) + min_out




			def   lowerCamelCase__  (					self    :       Dict							, _snake_case    :       Tuple							, _snake_case    :       Optional[Any]=(-1.0, 1.0)							, _snake_case    :       List[str]=False						)   ->			List[str]:







								"""simple docstring"""
								A_			,     A_								=    input_range
								A_								=    torch.clip(_snake_case							, _snake_case							, _snake_case						) if clip else outputs
								# Scale to [0, 1].
								A_								=    (outputs - min_out) / (max_out - min_out)
								# Scale to [self.min_value, self.max_value].
								return zero_one * (self.max_value - self.min_value) + self.min_value




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







								"""simple docstring"""
								A_								=    input_tokens > 0
								A_			,     A_								=    self.notes_encoder(
								    encoder_input_tokens=_snake_case							, encoder_inputs_mask=_snake_case						)

								A_			,     A_								=    self.continuous_encoder(
								    encoder_inputs=_snake_case							, encoder_inputs_mask=_snake_case						)

								return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]




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







								"""simple docstring"""
								A_								=    noise_time
								if not torch.is_tensor(_snake_case						):
													A_								=    torch.tensor([timesteps]							, dtype=torch.long							, device=input_tokens.device						)
								elif torch.is_tensor(_snake_case						) and len(timesteps.shape						) == 0:
													A_								=    timesteps[None].to(input_tokens.device						)

								# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
								A_								=    timesteps * torch.ones(input_tokens.shape[0]							, dtype=timesteps.dtype							, device=timesteps.device						)

								A_								=    self.decoder(
								    encodings_and_masks=_snake_case							, decoder_input_tokens=_snake_case							, decoder_noise_time=_snake_case						)
								return logits






			@torch.no_grad()
			def __call__(					self    :       List[Any]							, _snake_case    :       List[List[int]]							, _snake_case    :       Optional[torch.Generator] = None							, _snake_case    :       int = 100							, _snake_case    :       bool = True							, _snake_case    :       str = "numpy"							, _snake_case    :       Optional[Callable[[int, int, torch.FloatTensor], None]] = None							, _snake_case    :       int = 1							, )   ->			Union[AudioPipelineOutput, Tuple]:







								"""simple docstring"""
								if (callback_steps is None) or (
								    callback_steps is not None and (not isinstance(_snake_case							, _snake_case						) or callback_steps <= 0)
								):
													raise ValueError(
													    F'`callback_steps` has to be a positive integer but is {callback_steps} of type'
													    F' {type(_snake_case						)}.'						)

								A_								=    np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]							, dtype=np.floataa						)
								A_								=    np.zeros([1, 0, self.n_dims]							, np.floataa						)
								A_								=    torch.ones((1, TARGET_FEATURE_LENGTH)							, dtype=_snake_case							, device=self.device						)

								for i, encoder_input_tokens in enumerate(_snake_case						):
													if i == 0:
																		A_								=    torch.from_numpy(pred_mel[:1].copy()						).to(
																		    device=self.device							, dtype=self.decoder.dtype						)
																		# The first chunk has no previous context.
																		A_								=    torch.zeros((1, TARGET_FEATURE_LENGTH)							, dtype=_snake_case							, device=self.device						)
													else:
																		# The full song pipeline does not feed in a context feature, so the mask
																		# will be all 0s after the feature converter. Because we know we're
																		# feeding in a full context chunk from the previous prediction, set it
																		# to all 1s.
																		A_								=    ones

													A_								=    self.scale_features(
													    _snake_case							, output_range=[-1.0, 1.0]							, clip=_snake_case						)

													A_								=    self.encode(
													    input_tokens=torch.IntTensor([encoder_input_tokens]						).to(device=self.device						)							, continuous_inputs=_snake_case							, continuous_mask=_snake_case							, )

													# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
													A_								=    randn_tensor(
													    shape=encoder_continuous_inputs.shape							, generator=_snake_case							, device=self.device							, dtype=self.decoder.dtype							, )

													# set step values
													self.scheduler.set_timesteps(_snake_case						)

													# Denoising diffusion loop
													for j, t in enumerate(self.progress_bar(self.scheduler.timesteps						)						):
																		A_								=    self.decode(
																		    encodings_and_masks=_snake_case							, input_tokens=_snake_case							, noise_time=t / self.scheduler.config.num_train_timesteps							, )

																		# Compute previous output: x_t -> x_t-1
																		A_								=    self.scheduler.step(_snake_case							, _snake_case							, _snake_case							, generator=_snake_case						).prev_sample

													A_								=    self.scale_to_features(_snake_case							, input_range=[-1.0, 1.0]						)
													A_								=    mel[:1]
													A_								=    mel.cpu().float().numpy()

													A_								=    np.concatenate([full_pred_mel, pred_mel[:1]]							, axis=1						)

													# call the callback, if provided
													if callback is not None and i % callback_steps == 0:
																		callback(_snake_case							, _snake_case						)

													logger.info("Generated segment"							, _snake_case						)

								if output_type == "numpy" and not is_onnx_available():
													raise ValueError(
													    "Cannot return output in 'np' format if ONNX is not available. Make sure to have ONNX installed or set 'output_type' to 'mel'."						)
								elif output_type == "numpy" and self.melgan is None:
													raise ValueError(
													    "Cannot return output in 'np' format if melgan component is not defined. Make sure to define `self.melgan` or set 'output_type' to 'mel'."						)

								if output_type == "numpy":
													A_								=    self.melgan(input_features=full_pred_mel.astype(np.floataa						)						)
								else:
													A_								=    full_pred_mel

								if not return_dict:
													return (output,)

								return AudioPipelineOutput(audios=_snake_case						)




