



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

a_   :Optional[Any]        =      256




class 					snake_case__						(       _SCREAMING_SNAKE_CASE     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	["melgan"]






     def __init__(       self  :  List[Any], _snake_case  :  Optional[int], _snake_case  :  Dict, _snake_case  :  Any, _snake_case  :  str, _snake_case  :  List[str], )   ->None:
            super().__init__()

            # From MELGAN
            snake_case__							:	int   	=	math.log(1e-5					)  # Matches MelGAN training.
            snake_case__							:	List[str]   	=	4.0  # Largest value for most examples
            snake_case__							:	Dict   	=	1_2_8

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






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

            snake_case__	,				snake_case__							:	Any   	=	self.continuous_encoder(
                encoder_inputs=A_, encoder_inputs_mask=A_					)

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






     def    lowercase_   (       self  :  Optional[int], _snake_case  :  str, _snake_case  :  List[str], _snake_case  :  str					)   ->Union[str, Any]:
            snake_case__							:	str   	=	noise_time
            if not torch.is_tensor(A_					):
                   snake_case__							:	List[str]   	=	torch.tensor([timesteps], dtype=torch.long, device=input_tokens.device					)
            elif torch.is_tensor(A_					) and len(timesteps.shape					) == 0:
                   snake_case__							:	Union[str, Any]   	=	timesteps[None].to(input_tokens.device					)

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

            snake_case__							:	Union[str, Any]   	=	self.decoder(
                encodings_and_masks=A_, decoder_input_tokens=A_, decoder_noise_time=A_					)
            return logits






     @torch.no_grad()
     def __call__(       self  :  Optional[Any], _snake_case  :  Any, _snake_case  :  str = None, _snake_case  :  Optional[Any] = 1_0_0, _snake_case  :  str = True, _snake_case  :  Tuple = "numpy", _snake_case  :  Dict = None, _snake_case  :  Tuple = 1, )   ->Union[AudioPipelineOutput, Tuple]:
            if (callback_steps is None) or (
                callback_steps is not None and (not isinstance(A_, A_					) or callback_steps <= 0)
            ):
                   raise ValueError(
                       F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
                       F''' {type(A_					)}.'''					)

            snake_case__							:	Optional[Any]   	=	np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims], dtype=np.floataa					)
            snake_case__							:	List[str]   	=	np.zeros([1, 0, self.n_dims], np.floataa					)
            snake_case__							:	Tuple   	=	torch.ones((1, TARGET_FEATURE_LENGTH), dtype=A_, device=self.device					)

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

                   snake_case__							:	Dict   	=	self.scale_features(
                       A_, output_range=[-1.0, 1.0], clip=A_					)

                   snake_case__							:	Union[str, Any]   	=	self.encode(
                       input_tokens=torch.IntTensor([encoder_input_tokens]					).to(device=self.device					), continuous_inputs=A_, continuous_mask=A_, )

                   # Sample encoder_continuous_inputs shaped gaussian noise to begin loop
                   snake_case__							:	List[str]   	=	randn_tensor(
                       shape=encoder_continuous_inputs.shape, generator=A_, device=self.device, dtype=self.decoder.dtype, )

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

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

                          # Compute previous output: x_t -> x_t-1
                          snake_case__							:	List[str]   	=	self.scheduler.step(A_, A_, A_, generator=A_					).prev_sample

                   snake_case__							:	Dict   	=	self.scale_to_features(A_, input_range=[-1.0, 1.0]					)
                   snake_case__							:	Optional[int]   	=	mel[:1]
                   snake_case__							:	Optional[int]   	=	mel.cpu().float().numpy()

                   snake_case__							:	List[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(A_, A_					)

                   logger.info('Generated segment', A_					)

            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":
                   snake_case__							:	int   	=	self.melgan(input_features=full_pred_mel.astype(np.floataa					)					)
            else:
                   snake_case__							:	Optional[Any]   	=	full_pred_mel

            if not return_dict:
                   return (output,)

            return AudioPipelineOutput(audios=A_					)








import inspect
import unittest
from math import floor

from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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


if is_torch_available():
					import torch

					from transformers import CvtForImageClassification, CvtModel
					from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor


class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):



		def __UpperCamelCase ( self       )  ->					int:
				"""simple docstring"""
				UpperCamelCase		=     self.config_class(**self.inputs_dict       )
				self.parent.assertTrue(hasattr(A_				,      'embed_dim'       )       )
				self.parent.assertTrue(hasattr(A_				,      'num_heads'       )       )






class 				lowercase					:



		def __init__( self				,      A_				,      A_=13				,      A_=64				,      A_=3				,      A_=[16, 48, 96]				,      A_=[1, 3, 6]				,      A_=[1, 2, 10]				,      A_=[7, 3, 3]				,      A_=[4, 2, 2]				,      A_=[2, 1, 1]				,      A_=[2, 2, 2]				,      A_=[False, False, True]				,      A_=[0.0, 0.0, 0.0]				,      A_=0.02				,      A_=1e-12				,      A_=True				,      A_=True				,      A_=2				,      )  ->					int:
				"""simple docstring"""
				UpperCamelCase		=     parent
				UpperCamelCase		=     batch_size
				UpperCamelCase		=     image_size
				UpperCamelCase		=     patch_sizes
				UpperCamelCase		=     patch_stride
				UpperCamelCase		=     patch_padding
				UpperCamelCase		=     is_training
				UpperCamelCase		=     use_labels
				UpperCamelCase		=     num_labels
				UpperCamelCase		=     num_channels
				UpperCamelCase		=     embed_dim
				UpperCamelCase		=     num_heads
				UpperCamelCase		=     stride_kv
				UpperCamelCase		=     depth
				UpperCamelCase		=     cls_token
				UpperCamelCase		=     attention_drop_rate
				UpperCamelCase		=     initializer_range
				UpperCamelCase		=     layer_norm_eps



		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				UpperCamelCase		=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

				UpperCamelCase		=     None
				if self.use_labels:
						UpperCamelCase		=     ids_tensor([self.batch_size]				,      self.num_labels       )

				UpperCamelCase		=     self.get_config()
				return config, pixel_values, labels



		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				return CvtConfig(
				    image_size=self.image_size				,      num_labels=self.num_labels				,      num_channels=self.num_channels				,      embed_dim=self.embed_dim				,      num_heads=self.num_heads				,      patch_sizes=self.patch_sizes				,      patch_padding=self.patch_padding				,      patch_stride=self.patch_stride				,      stride_kv=self.stride_kv				,      depth=self.depth				,      cls_token=self.cls_token				,      attention_drop_rate=self.attention_drop_rate				,      initializer_range=self.initializer_range				,      )



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     CvtModel(config=A_       )
				model.to(A_       )
				model.eval()
				UpperCamelCase		=     model(A_       )
				UpperCamelCase		=     (self.image_size, self.image_size)
				UpperCamelCase	,  UpperCamelCase		=     image_size[0], image_size[1]
				for i in range(len(self.depth       )       ):
						UpperCamelCase		=     floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1       )
						UpperCamelCase		=     floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1       )
				self.parent.assertEqual(result.last_hidden_state.shape				,      (self.batch_size, self.embed_dim[-1], height, width)       )



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     self.num_labels
				UpperCamelCase		=     CvtForImageClassification(A_       )
				model.to(A_       )
				model.eval()
				UpperCamelCase		=     model(A_				,      labels=A_       )
				self.parent.assertEqual(result.logits.shape				,      (self.batch_size, self.num_labels)       )



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     self.prepare_config_and_inputs()
				UpperCamelCase	,  UpperCamelCase	,  UpperCamelCase		=     config_and_inputs
				UpperCamelCase		=     {'pixel_values': pixel_values}
				return config, inputs_dict






@require_torch
class 				lowercase					(		_SCREAMING_SNAKE_CASE  ,   _SCREAMING_SNAKE_CASE  ,   unittest.TestCase   ):
		__lowercase       :       List[str]	     =				(CvtModel, CvtForImageClassification) if is_torch_available() else ()
		__lowercase       :       Tuple	     =				(
		    {"feature-extraction": CvtModel, "image-classification": CvtForImageClassification}
		    if is_torch_available()
		    else {}
		)

		__lowercase       :       Tuple	     =				False
		__lowercase       :       Union[str, Any]	     =				False
		__lowercase       :       Optional[Any]	     =				False
		__lowercase       :       List[str]	     =				False
		__lowercase       :       Dict	     =				False



		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     CvtModelTester(self       )
				UpperCamelCase		=     ConfigTester(self				,      config_class=A_				,      has_text_modality=A_				,      hidden_size=37       )



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				self.create_and_test_config_common_properties()
				self.config_tester.create_and_test_config_to_json_string()
				self.config_tester.create_and_test_config_to_json_file()
				self.config_tester.create_and_test_config_from_and_save_pretrained()
				self.config_tester.create_and_test_config_with_num_labels()
				self.config_tester.check_config_can_be_init_without_params()
				self.config_tester.check_config_arguments_init()



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				return



		@unittest.skip(reason='Cvt does not output attentions'       )
		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				pass



		@unittest.skip(reason='Cvt does not use inputs_embeds'       )
		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				pass



		@unittest.skip(reason='Cvt does not support input and output embeddings'       )
		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				pass



		def __UpperCamelCase ( self       )  ->					Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase	,  UpperCamelCase		=     self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						UpperCamelCase		=     model_class(A_       )
						UpperCamelCase		=     inspect.signature(model.forward       )
						# signature.parameters is an OrderedDict => so arg_names order is deterministic
						UpperCamelCase		=     [*signature.parameters.keys()]

						UpperCamelCase		=     ['pixel_values']
						self.assertListEqual(arg_names[:1]				,      A_       )



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*A_       )



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				def check_hidden_states_output(A_				,      A_				,      A_       ):
						UpperCamelCase		=     model_class(A_       )
						model.to(A_       )
						model.eval()

						with torch.no_grad():
								UpperCamelCase		=     model(**self._prepare_for_class(A_				,      A_       )       )

						UpperCamelCase		=     outputs.hidden_states

						UpperCamelCase		=     len(self.model_tester.depth       )
						self.assertEqual(len(A_       )				,      A_       )

						# verify the first hidden states (first block)
						self.assertListEqual(
						    list(hidden_states[0].shape[-3:]       )				,      [
						        self.model_tester.embed_dim[0],
						        self.model_tester.image_size // 4,
						        self.model_tester.image_size // 4,
						    ]				,      )

				UpperCamelCase	,  UpperCamelCase		=     self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						UpperCamelCase		=     True
						check_hidden_states_output(A_				,      A_				,      A_       )

						# check that output_hidden_states also work using config
						del inputs_dict["output_hidden_states"]
						UpperCamelCase		=     True

						check_hidden_states_output(A_				,      A_				,      A_       )



		def __UpperCamelCase ( self       )  ->					Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*A_       )



		@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'       )
		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				pass



		@slow
		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						UpperCamelCase		=     CvtModel.from_pretrained(A_       )
						self.assertIsNotNone(A_       )






def       A       (					)   ->  Tuple:
		'''simple docstring'''

		UpperCamelCase		=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
		return image


@require_torch
@require_vision
class 				lowercase					(		unittest.TestCase   ):



		@cached_property
		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )



		@slow
		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       ).to(A_       )

				UpperCamelCase		=     self.default_image_processor
				UpperCamelCase		=     prepare_img()
				UpperCamelCase		=     image_processor(images=A_				,      return_tensors='pt'       ).to(A_       )

				# forward pass
				with torch.no_grad():
						UpperCamelCase		=     model(**A_       )

				# verify the logits
				UpperCamelCase		=     torch.Size((1, 1_000)       )
				self.assertEqual(outputs.logits.shape				,      A_       )

				UpperCamelCase		=     torch.tensor([0.9285, 0.9015, -0.3150]       ).to(A_       )

				self.assertTrue(torch.allclose(outputs.logits[0, :3]				,      A_				,      atol=1e-4       )       )
