



import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class       snake_case__(UpperCAmelCase_				,			unittest.TestCase			):

					"""simple docstring"""






					lowercase_								=		None
					lowercase_								=		BloomTokenizerFast
					lowercase_								=		BloomTokenizerFast
					lowercase_								=		True
					lowercase_								=		False
					lowercase_								=		"""tokenizer_file"""
					lowercase_								=		{"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}







					def  snake_case     (							self					:	int     ):
									super().setUp()
									lowercase__     :    int													=     BloomTokenizerFast.from_pretrained("bigscience/tokenizer"     )
									tokenizer.save_pretrained(self.tmpdirname     )







					def  snake_case     (							self					:	Dict      ,   **SCREAMING_SNAKE_CASE					:	Tuple     ):
									kwargs.update(self.special_tokens_map     )
									return BloomTokenizerFast.from_pretrained(self.tmpdirname      ,   **__lowercase     )







					def  snake_case     (							self					:	str     ):
									lowercase__     :    Union[str, Any]													=     self.get_rust_tokenizer()

									lowercase__     :    str													=     ['''The quick brown fox</s>''', '''jumps over the lazy dog</s>''']
									lowercase__     :    Optional[int]													=     [[2_175, 23_714, 73_173, 144_252, 2], [77, 132_619, 3_478, 368, 109_586, 35_433, 2]]

									lowercase__     :    Dict													=     tokenizer.batch_encode_plus(__lowercase     )['''input_ids''']
									self.assertListEqual(__lowercase      ,   __lowercase     )

									lowercase__     :    Dict													=     tokenizer.batch_decode(__lowercase     )
									self.assertListEqual(__lowercase      ,   __lowercase     )







					def  snake_case     (							self					:	Tuple      ,   SCREAMING_SNAKE_CASE					:	List[str]=6     ):
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
																	lowercase__     :    int													=     self.rust_tokenizer_class.from_pretrained(__lowercase      ,   **__lowercase     )
																	# tokenizer_r.pad_token = None # Hotfixing padding = None
																	# Simple input
																	lowercase__     :    str													=     '''This is a simple input'''
																	lowercase__     :    Optional[int]													=     ['''This is a simple input 1''', '''This is a simple input 2''']
																	lowercase__     :    Any													=     ('''This is a simple input''', '''This is a pair''')
																	lowercase__     :    Optional[Any]													=     [
																	    ('''This is a simple input 1''', '''This is a simple input 2'''),
																	    ('''This is a simple pair 1''', '''This is a simple pair 2'''),
																	]

																	# Simple input tests
																	try:
																					tokenizer_r.encode(__lowercase      ,   max_length=__lowercase     )
																					tokenizer_r.encode_plus(__lowercase      ,   max_length=__lowercase     )

																					tokenizer_r.batch_encode_plus(__lowercase      ,   max_length=__lowercase     )
																					tokenizer_r.encode(__lowercase      ,   max_length=__lowercase     )
																					tokenizer_r.batch_encode_plus(__lowercase      ,   max_length=__lowercase     )
																	except ValueError:
																					self.fail("Bloom Tokenizer should be able to deal with padding"     )

																	lowercase__     :    str													=     None  # Hotfixing padding = None
																	self.assertRaises(__lowercase      ,   tokenizer_r.encode      ,   __lowercase      ,   max_length=__lowercase      ,   padding="max_length"     )

																	# Simple input
																	self.assertRaises(__lowercase      ,   tokenizer_r.encode_plus      ,   __lowercase      ,   max_length=__lowercase      ,   padding="max_length"     )

																	# Simple input
																	self.assertRaises(
																	    __lowercase      ,   tokenizer_r.batch_encode_plus      ,   __lowercase      ,   max_length=__lowercase      ,   padding="max_length"      ,   )

																	# Pair input
																	self.assertRaises(__lowercase      ,   tokenizer_r.encode      ,   __lowercase      ,   max_length=__lowercase      ,   padding="max_length"     )

																	# Pair input
																	self.assertRaises(__lowercase      ,   tokenizer_r.encode_plus      ,   __lowercase      ,   max_length=__lowercase      ,   padding="max_length"     )

																	# Pair input
																	self.assertRaises(
																	    __lowercase      ,   tokenizer_r.batch_encode_plus      ,   __lowercase      ,   max_length=__lowercase      ,   padding="max_length"      ,   )







					def  snake_case     (							self					:	Dict     ):
									lowercase__     :    int													=     self.get_rust_tokenizer()
									lowercase__     :    List[str]													=     load_dataset("xnli"      ,   "all_languages"      ,   split="test"      ,   streaming=__lowercase     )

									lowercase__     :    Optional[int]													=     next(iter(__lowercase     )     )['''premise''']  # pick up one data
									lowercase__     :    Union[str, Any]													=     list(sample_data.values()     )

									lowercase__     :    Optional[int]													=     list(map(tokenizer.encode      ,   __lowercase     )     )
									lowercase__     :    Union[str, Any]													=     [tokenizer.decode(__lowercase      ,   clean_up_tokenization_spaces=__lowercase     ) for x in output_tokens]
									self.assertListEqual(__lowercase      ,   __lowercase     )







					def  snake_case     (							self					:	Any     ):
									# The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
									# any sequence length constraints. This test of the parent class will fail since it relies on the
									# maximum sequence length of the positoonal embeddings.
									self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map     )      ,   1     )
									self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()     )[0]     )      ,   1     )





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


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

__lowercase							 =					256


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						Tuple						=      ["""melgan"""]







    def __init__(	self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , )       ->	None:
        super().__init__()

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

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







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase=(-1.0, 1.0) , __lowercase=False)       ->	Dict:
        __UpperCamelCase  ,       __UpperCamelCase     :str			  =		output_range
        if clip:
            __UpperCamelCase     :Union[str, Any]			  =		torch.clip(__lowercase , self.min_value , self.max_value)
        # Scale to [0, 1].
        __UpperCamelCase     :Union[str, Any]			  =		(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 			UpperCamelCase__   (	self , __lowercase , __lowercase=(-1.0, 1.0) , __lowercase=False)       ->	Optional[int]:
        __UpperCamelCase  ,       __UpperCamelCase     :int			  =		input_range
        __UpperCamelCase     :Optional[int]			  =		torch.clip(__lowercase , __lowercase , __lowercase) if clip else outputs
        # Scale to [0, 1].
        __UpperCamelCase     :List[str]			  =		(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 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase)       ->	List[Any]:
        __UpperCamelCase     :List[str]			  =		input_tokens > 0
        __UpperCamelCase  ,       __UpperCamelCase     :Union[str, Any]			  =		self.notes_encoder(
            encoder_input_tokens=__lowercase , encoder_inputs_mask=__lowercase)

        __UpperCamelCase  ,       __UpperCamelCase     :Union[str, Any]			  =		self.continuous_encoder(
            encoder_inputs=__lowercase , encoder_inputs_mask=__lowercase)

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







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase)       ->	str:
        __UpperCamelCase     :Optional[int]			  =		noise_time
        if not torch.is_tensor(__lowercase):
            __UpperCamelCase     :str			  =		torch.tensor([timesteps] , dtype=torch.long , device=input_tokens.device)
        elif torch.is_tensor(__lowercase) 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     :List[str]			  =		timesteps * torch.ones(input_tokens.shape[0] , dtype=timesteps.dtype , device=timesteps.device)

        __UpperCamelCase     :Tuple			  =		self.decoder(
            encodings_and_masks=__lowercase , decoder_input_tokens=__lowercase , decoder_noise_time=__lowercase)
        return logits







    @torch.no_grad()
    def __call__(	self , __lowercase , __lowercase = None , __lowercase = 100 , __lowercase = True , __lowercase = "numpy" , __lowercase = None , __lowercase = 1 , )       ->	Union[AudioPipelineOutput, Tuple]:
        if (callback_steps is None) or (
            callback_steps is not None and (not isinstance(__lowercase , __lowercase) or callback_steps <= 0)
        ):
            raise ValueError(
                f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
                f""" {type(__lowercase)}.""")

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

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

            __UpperCamelCase     :Optional[Any]			  =		self.scale_features(
                __lowercase , output_range=[-1.0, 1.0] , clip=__lowercase)

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

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

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

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

                # Compute previous output: x_t -> x_t-1
                __UpperCamelCase     :int			  =		self.scheduler.step(__lowercase , __lowercase , __lowercase , generator=__lowercase).prev_sample

            __UpperCamelCase     :Tuple			  =		self.scale_to_features(__lowercase , input_range=[-1.0, 1.0])
            __UpperCamelCase     :List[Any]			  =		mel[:1]
            __UpperCamelCase     :Optional[Any]			  =		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(__lowercase , __lowercase)

            logger.info('''Generated segment''' , __lowercase)

        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     :Optional[Any]			  =		self.melgan(input_features=full_pred_mel.astype(np.floataa))
        else:
            __UpperCamelCase     :List[str]			  =		full_pred_mel

        if not return_dict:
            return (output,)

        return AudioPipelineOutput(audios=__lowercase)





