






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


A_       :List[str]       =						logging.get_logger(__name__)  # pylint: disable=invalid-name

A_       :List[str]       =						256




class 				__A     (			__lowerCAmelCase ):





				"""simple docstring"""







				UpperCamelCase__ :      Optional[Any]			 =['''melgan''']


				def __init__( self							,  lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__							,  ):




									"""simple docstring"""

									super().__init__()

									# From MELGAN
									__UpperCamelCase      :					int       		=math.log(1E-5    )  # Matches MelGAN training.
									__UpperCamelCase      :					Optional[int]       		=4.0  # Largest value for most examples
									__UpperCamelCase      :					List[str]       		=128

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


				def       __lowercase							( self							,  lowerCamelCase__							,  lowerCamelCase__=(-1.0, 1.0)							,  lowerCamelCase__=False    ):




									"""simple docstring"""

									__UpperCamelCase		,				__UpperCamelCase      :					Optional[Any]       		=output_range
									if clip:
														__UpperCamelCase      :					Tuple       		=torch.clip(lowerCAmelCase_							,  self.min_value							,  self.max_value    )
									# Scale to [0, 1].
									__UpperCamelCase      :					str       		=(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       __lowercase							( self							,  lowerCamelCase__							,  lowerCamelCase__=(-1.0, 1.0)							,  lowerCamelCase__=False    ):




									"""simple docstring"""

									__UpperCamelCase		,				__UpperCamelCase      :					Dict       		=input_range
									__UpperCamelCase      :					Any       		=torch.clip(lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_    ) if clip else outputs
									# Scale to [0, 1].
									__UpperCamelCase      :					List[Any]       		=(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       __lowercase							( self							,  lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__    ):




									"""simple docstring"""

									__UpperCamelCase      :					Union[str, Any]       		=input_tokens > 0
									__UpperCamelCase		,				__UpperCamelCase      :					List[str]       		=self.notes_encoder(
									    encoder_input_tokens=lowerCAmelCase_							,  encoder_inputs_mask=lowerCAmelCase_    )

									__UpperCamelCase		,				__UpperCamelCase      :					List[Any]       		=self.continuous_encoder(
									    encoder_inputs=lowerCAmelCase_							,  encoder_inputs_mask=lowerCAmelCase_    )

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


				def       __lowercase							( self							,  lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__    ):




									"""simple docstring"""

									__UpperCamelCase      :					List[Any]       		=noise_time
									if not torch.is_tensor(lowerCAmelCase_    ):
														__UpperCamelCase      :					Any       		=torch.tensor([timesteps]							,  dtype=torch.long							,  device=input_tokens.device    )
									elif torch.is_tensor(lowerCAmelCase_    ) and len(timesteps.shape    ) == 0:
														__UpperCamelCase      :					Dict       		=timesteps[None].to(input_tokens.device    )

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

									__UpperCamelCase      :					Optional[int]       		=self.decoder(
									    encodings_and_masks=lowerCAmelCase_							,  decoder_input_tokens=lowerCAmelCase_							,  decoder_noise_time=lowerCAmelCase_    )
									return logits







				@torch.no_grad()
				def __call__( self							,  lowerCamelCase__							,  lowerCamelCase__ = None							,  lowerCamelCase__ = 100							,  lowerCamelCase__ = True							,  lowerCamelCase__ = "numpy"							,  lowerCamelCase__ = None							,  lowerCamelCase__ = 1							,  ):




									"""simple docstring"""

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

									__UpperCamelCase      :					Any       		=np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]							,  dtype=np.floataa    )
									__UpperCamelCase      :					Tuple       		=np.zeros([1, 0, self.n_dims]							,  np.floataa    )
									__UpperCamelCase      :					str       		=torch.ones((1, TARGET_FEATURE_LENGTH)							,  dtype=lowerCAmelCase_							,  device=self.device    )

									for i, encoder_input_tokens in enumerate(lowerCAmelCase_    ):
														if i == 0:
																			__UpperCamelCase      :					Any       		=torch.from_numpy(pred_mel[:1].copy()    ).to(
																			    device=self.device							,  dtype=self.decoder.dtype    )
																			# The first chunk has no previous context.
																			__UpperCamelCase      :					Dict       		=torch.zeros((1, TARGET_FEATURE_LENGTH)							,  dtype=lowerCAmelCase_							,  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.
																			__UpperCamelCase      :					Optional[int]       		=ones

														__UpperCamelCase      :					str       		=self.scale_features(
														    lowerCAmelCase_							,  output_range=[-1.0, 1.0]							,  clip=lowerCAmelCase_    )

														__UpperCamelCase      :					int       		=self.encode(
														    input_tokens=torch.IntTensor([encoder_input_tokens]    ).to(device=self.device    )							,  continuous_inputs=lowerCAmelCase_							,  continuous_mask=lowerCAmelCase_							,  )

														# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
														__UpperCamelCase      :					Tuple       		=randn_tensor(
														    shape=encoder_continuous_inputs.shape							,  generator=lowerCAmelCase_							,  device=self.device							,  dtype=self.decoder.dtype							,  )

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

														# Denoising diffusion loop
														for j, t in enumerate(self.progress_bar(self.scheduler.timesteps    )    ):
																			__UpperCamelCase      :					List[Any]       		=self.decode(
																			    encodings_and_masks=lowerCAmelCase_							,  input_tokens=lowerCAmelCase_							,  noise_time=t / self.scheduler.config.num_train_timesteps							,  )

																			# Compute previous output: x_t -> x_t-1
																			__UpperCamelCase      :					List[Any]       		=self.scheduler.step(lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  generator=lowerCAmelCase_    ).prev_sample

														__UpperCamelCase      :					Union[str, Any]       		=self.scale_to_features(lowerCAmelCase_							,  input_range=[-1.0, 1.0]    )
														__UpperCamelCase      :					List[Any]       		=mel[:1]
														__UpperCamelCase      :					List[str]       		=mel.cpu().float().numpy()

														__UpperCamelCase      :					Any       		=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(lowerCAmelCase_							,  lowerCAmelCase_    )

														logger.info('Generated segment'							,  lowerCAmelCase_    )

									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":
														__UpperCamelCase      :					Dict       		=self.melgan(input_features=full_pred_mel.astype(np.floataa    )    )
									else:
														__UpperCamelCase      :					Union[str, Any]       		=full_pred_mel

									if not return_dict:
														return (output,)

									return AudioPipelineOutput(audios=lowerCAmelCase_    )


import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import (
        AutoProcessor,
        BertTokenizerFast,
        BlipImageProcessor,
        GPTaTokenizer,
        InstructBlipProcessor,
        PreTrainedTokenizerFast,
    )



@require_vision
class 				a							(			unittest.TestCase       ):







 """simple docstring"""






 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        _A        =  tempfile.mkdtemp()

        _A        =  BlipImageProcessor()
        _A        =  GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""							)
        _A        =  BertTokenizerFast.from_pretrained("""hf-internal-testing/tiny-random-bert"""							)

        _A        =  InstructBlipProcessor(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)

        processor.save_pretrained(self.tmpdirname							)



 def      UpperCAmelCase							(					self		,				**lowerCAmelCase_							)  ->					Any:
        return AutoProcessor.from_pretrained(self.tmpdirname		,				**lowerCAmelCase_							).tokenizer



 def      UpperCAmelCase							(					self		,				**lowerCAmelCase_							)  ->					Dict:
        return AutoProcessor.from_pretrained(self.tmpdirname		,				**lowerCAmelCase_							).image_processor



 def      UpperCAmelCase							(					self		,				**lowerCAmelCase_							)  ->					Union[str, Any]:
        return AutoProcessor.from_pretrained(self.tmpdirname		,				**lowerCAmelCase_							).qformer_tokenizer



 def      UpperCAmelCase							(					self							)  ->					int:
        shutil.rmtree(self.tmpdirname							)



 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A        =  [np.random.randint(2_55		,				size=(3, 30, 4_00)		,				dtype=np.uinta							)]

        _A        =  [Image.fromarray(np.moveaxis(lowerCAmelCase_		,				0		,				-1							)							) for x in image_inputs]

        return image_inputs



 def      UpperCAmelCase							(					self							)  ->					str:
        _A        =  InstructBlipProcessor(
            tokenizer=self.get_tokenizer()		,				image_processor=self.get_image_processor()		,				qformer_tokenizer=self.get_qformer_tokenizer()		,				)
        processor.save_pretrained(self.tmpdirname							)

        _A        =  self.get_tokenizer(bos_token="""(BOS)"""		,				eos_token="""(EOS)"""							)
        _A        =  self.get_image_processor(do_normalize=lowerCAmelCase_		,				padding_value=1.0							)

        _A        =  InstructBlipProcessor.from_pretrained(
            self.tmpdirname		,				bos_token="""(BOS)"""		,				eos_token="""(EOS)"""		,				do_normalize=lowerCAmelCase_		,				padding_value=1.0							)

        self.assertEqual(processor.tokenizer.get_vocab()		,				tokenizer_add_kwargs.get_vocab()							)
        self.assertIsInstance(processor.tokenizer		,				lowerCAmelCase_							)

        self.assertEqual(processor.image_processor.to_json_string()		,				image_processor_add_kwargs.to_json_string()							)
        self.assertIsInstance(processor.image_processor		,				lowerCAmelCase_							)
        self.assertIsInstance(processor.qformer_tokenizer		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					int:
        _A        =  self.get_image_processor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_qformer_tokenizer()

        _A        =  InstructBlipProcessor(
            tokenizer=lowerCAmelCase_		,				image_processor=lowerCAmelCase_		,				qformer_tokenizer=lowerCAmelCase_							)

        _A        =  self.prepare_image_inputs()

        _A        =  image_processor(lowerCAmelCase_		,				return_tensors="""np"""							)
        _A        =  processor(images=lowerCAmelCase_		,				return_tensors="""np"""							)

        for key in input_feat_extract.keys():
               self.assertAlmostEqual(input_feat_extract[key].sum()		,				input_processor[key].sum()		,				delta=1E-2							)



 def      UpperCAmelCase							(					self							)  ->					Dict:
        _A        =  self.get_image_processor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_qformer_tokenizer()

        _A        =  InstructBlipProcessor(
            tokenizer=lowerCAmelCase_		,				image_processor=lowerCAmelCase_		,				qformer_tokenizer=lowerCAmelCase_							)

        _A        =  """lower newer"""

        _A        =  processor(text=lowerCAmelCase_							)

        _A        =  tokenizer(lowerCAmelCase_		,				return_token_type_ids=lowerCAmelCase_							)
        _A        =  qformer_tokenizer(lowerCAmelCase_		,				return_token_type_ids=lowerCAmelCase_							)

        for key in encoded_tokens.keys():
               self.assertListEqual(encoded_tokens[key]		,				encoded_processor[key]							)

        for key in encoded_tokens_qformer.keys():
               self.assertListEqual(encoded_tokens_qformer[key]		,				encoded_processor["""qformer_""" + key]							)



 def      UpperCAmelCase							(					self							)  ->					List[str]:
        _A        =  self.get_image_processor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_qformer_tokenizer()

        _A        =  InstructBlipProcessor(
            tokenizer=lowerCAmelCase_		,				image_processor=lowerCAmelCase_		,				qformer_tokenizer=lowerCAmelCase_							)

        _A        =  """lower newer"""
        _A        =  self.prepare_image_inputs()

        _A        =  processor(text=lowerCAmelCase_		,				images=lowerCAmelCase_							)

        self.assertListEqual(
            list(inputs.keys()							)		,				["""input_ids""", """attention_mask""", """qformer_input_ids""", """qformer_attention_mask""", """pixel_values"""]		,				)

        # test if it raises when no input is passed
        with pytest.raises(lowerCAmelCase_							):
               processor()



 def      UpperCAmelCase							(					self							)  ->					int:
        _A        =  self.get_image_processor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_qformer_tokenizer()

        _A        =  InstructBlipProcessor(
            tokenizer=lowerCAmelCase_		,				image_processor=lowerCAmelCase_		,				qformer_tokenizer=lowerCAmelCase_							)

        _A        =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        _A        =  processor.batch_decode(lowerCAmelCase_							)
        _A        =  tokenizer.batch_decode(lowerCAmelCase_							)

        self.assertListEqual(lowerCAmelCase_		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        _A        =  self.get_image_processor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_qformer_tokenizer()

        _A        =  InstructBlipProcessor(
            tokenizer=lowerCAmelCase_		,				image_processor=lowerCAmelCase_		,				qformer_tokenizer=lowerCAmelCase_							)

        _A        =  """lower newer"""
        _A        =  self.prepare_image_inputs()

        _A        =  processor(text=lowerCAmelCase_		,				images=lowerCAmelCase_							)

        self.assertListEqual(
            list(inputs.keys()							)		,				["""input_ids""", """attention_mask""", """qformer_input_ids""", """qformer_attention_mask""", """pixel_values"""]		,				)




