



import argparse
import glob
import inspect
import os
import re
import unittest

from get_test_info import get_test_classes

from tests.test_pipeline_mixin import pipeline_test_mapping


UpperCAmelCase							=       {}
for task, _ in pipeline_test_mapping.items():
	UpperCAmelCase							=       {"""pt""": None, """tf""": None}


# DO **NOT** add item to this set (unless the reason is approved)
UpperCAmelCase							=       {
    """tests/models/esm/test_modeling_esmfold.py""",  # The pipeline test mapping is added to `test_modeling_esm.py`
}

def       A_   (						__a  :					Optional[Any]		):

				"""simple docstring"""

				if "ModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
								return "pt"
				elif "TFModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
								return "tf"
				elif "FlaxModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
								return "flax"
				else:
								return None

def       A_   (						__a  :					Union[str, Any]			,		__a  :					Tuple		):

				"""simple docstring"""
				# Use the cached results
				if PIPELINE_TEST_MAPPING[task].get(__a			,		__a		) is not None:
								return PIPELINE_TEST_MAPPING[task][framework]

				a__					   =		pipeline_test_mapping[task]["""test"""]
				a__					   =		None

				if framework == "pt":
								a__					   =		getattr(__a			,		"""model_mapping"""			,		__a		)
				elif framework == "tf":
								a__					   =		getattr(__a			,		"""tf_model_mapping"""			,		__a		)

				if mapping is not None:
								a__					   =		dict(mapping.items()		)

				# cache the results
				a__					   =		mapping
				return mapping

def       A_   (						__a  :					str			,		__a  :					Optional[int]		):

				"""simple docstring"""
				a__					   =		get_framework(__a		)
				if framework is None:
								return None
				a__					   =		get_mapping_for_task(__a			,		__a		)
				if mapping is None:
								return None

				a__					   =		list({model_class.config_class for model_class in test_class.all_model_classes}		)
				if len(__a		) != 1:
								raise ValueError("""There should be exactly one configuration class from `test_class.all_model_classes`."""		)

				# This could be a list/tuple of model classes, but it's rare.
				a__					   =		mapping.get(config_classes[0]			,		__a		)
				if isinstance(__a			,		(tuple, list)		):
								a__					   =		sorted(__a			,		key=lambda __a		: x.__name__		)

				return model_class

def       A_   (						__a  :					str		):

				"""simple docstring"""
				a__					   =		[(task, get_model_for_pipeline_test(__a			,		__a		)) for task in pipeline_test_mapping]
				a__					   =		sorted([(task, model) for task, model in mapping if model is not None]			,		key=lambda __a		: x[0]		)

				return dict(__a		)

def       A_   (						__a  :					Dict		):

				"""simple docstring"""
				a__					   =		get_framework(__a		)
				if framework == "pt":
								a__					   =		"""torch"""
				a__					   =		"""{}"""

				a__					   =		get_pipeline_model_mapping(__a		)
				if len(__a		) == 0:
								return ""

				a__					   =		[]
				for task, model_classes in mapping.items():
								if isinstance(__a			,		(tuple, list)		):
												# A list/tuple of model classes
												a__					   =		"""(""" + """, """.join([x.__name__ for x in model_classes]		) + """)"""
								else:
												# A single model class
												a__					   =		model_classes.__name__
								texts.append(F'''"{task}": {value}'''		)
				a__					   =		"""{""" + """, """.join(__a		) + """}"""
				a__					   =		F'''pipeline_model_mapping = {text} if is_{framework}_available() else {default_value}'''

				return text

def       A_   (						__a  :					Dict		):

				"""simple docstring"""
				a__					   =		{"""ModelTesterMixin""", """TFModelTesterMixin""", """FlaxModelTesterMixin"""}
				if not issubclass(__a			,		unittest.TestCase		):
								return False
				return len(base_class_names.intersection([x.__name__ for x in test_class.__bases__]		)		) > 0

def       A_   (						__a  :					List[Any]		):

				"""simple docstring"""
				a__					   =		[x for x in get_test_classes(__a		) if is_valid_test_class(__a		)]

				a__					   =		None
				for test_class in test_classes:
								# If a test class has defined `pipeline_model_mapping`, let's take it
								if getattr(__a			,		"""pipeline_model_mapping"""			,		__a		) is not None:
												a__					   =		test_class
												break
    # Take the test class with the shortest name (just a heuristic)
				if target_test_class is None and len(__a		) > 0:
								a__					   =		sorted(__a			,		key=lambda __a		: (len(x.__name__		), x.__name__)		)[0]

				return target_test_class

def       A_   (						__a  :					List[Any]			,		__a  :					int			,		__a  :					int		):

				"""simple docstring"""
				a__					   =		start_idx
				for idx, line in enumerate(lines[start_idx:]		):
								a__					   =		len(__a		) - len(line.lstrip()		)
								if idx == 0 or indent > indent_level or (indent == indent_level and line.strip() == ")"):
												a__					   =		start_idx + idx
								elif idx > 0 and indent <= indent_level:
												# Outside the definition block of `pipeline_model_mapping`
												break

				return end_idx

def       A_   (						__a  :					Tuple			,		__a  :					str=False		):

				"""simple docstring"""
				if getattr(__a			,		"""pipeline_model_mapping"""			,		__a		) is not None:
								if not overwrite:
												return "", -1

				a__					   =		get_pipeline_model_mapping_string(__a		)
				if len(__a		) == 0:
								return "", -1
				a__					   =		line_to_add + """\n"""

				# The code defined the class `test_class`
				a__   ,      a__					   =		inspect.getsourcelines(__a		)
				# `inspect` gives the code for an object, including decorator(s) if any.
				# We (only) need the exact line of the class definition.
				for idx, line in enumerate(__a		):
								if line.lstrip().startswith("""class """		):
												a__					   =		class_lines[idx:]
												class_start_line_no += idx
												break
				a__					   =		class_start_line_no + len(__a		) - 1

				# The index in `class_lines` that starts the definition of `all_model_classes`, `all_generative_model_classes` or
				# `pipeline_model_mapping`. This assumes they are defined in such order, and we take the start index of the last
				# block that appears in a `test_class`.
				a__					   =		None
				# The indent level of the line at `class_lines[start_idx]` (if defined)
				a__					   =		0
				# To record if `pipeline_model_mapping` is found in `test_class`.
				a__					   =		None
				for idx, line in enumerate(__a		):
								if line.strip().startswith("""all_model_classes = """		):
												a__					   =		len(__a		) - len(line.lstrip()		)
												a__					   =		idx
								elif line.strip().startswith("""all_generative_model_classes = """		):
												a__					   =		len(__a		) - len(line.lstrip()		)
												a__					   =		idx
								elif line.strip().startswith("""pipeline_model_mapping = """		):
												a__					   =		len(__a		) - len(line.lstrip()		)
												a__					   =		idx
												a__					   =		line
												break

				if start_idx is None:
								return "", -1
				# Find the ending index (inclusive) of the above found block.
				a__					   =		find_block_ending(__a			,		__a			,		__a		)

				# Extract `is_xxx_available()` from existing blocks: some models require specific libraries like `timm` and use
				# `is_timm_available()` instead of `is_torch_available()`.
				# Keep leading and trailing whitespaces
				a__					   =		re.compile(r"""\s(is_\S+?_available\(\))\s"""		)
				for line in class_lines[start_idx : end_idx + 1]:
								a__					   =		r.search(__a		)
								if backend_condition is not None:
												# replace the leading and trailing whitespaces to the space character " ".
												a__					   =		""" """ + backend_condition[0][1:-1] + """ """
												a__					   =		r.sub(__a			,		__a		)
												break

				if def_line is None:
								# `pipeline_model_mapping` is not defined. The target index is set to the ending index (inclusive) of
								# `all_model_classes` or `all_generative_model_classes`.
								a__					   =		end_idx
				else:
								# `pipeline_model_mapping` is defined. The target index is set to be one **BEFORE** its start index.
								a__					   =		start_idx - 1
								# mark the lines of the currently existing `pipeline_model_mapping` to be removed.
								for idx in range(__a			,		end_idx + 1		):
												# These lines are going to be removed before writing to the test file.
												a__					   =		None  # noqa

    # Make sure the test class is a subclass of `PipelineTesterMixin`.
				a__					   =		[x.__name__ for x in test_class.__bases__]
				if "PipelineTesterMixin" not in parent_classes:
								# Put `PipelineTesterMixin` just before `unittest.TestCase`
								a__					   =		[x for x in parent_classes if x != """TestCase"""] + ["""PipelineTesterMixin"""]
								if "TestCase" in parent_classes:
												# Here we **assume** the original string is always with `unittest.TestCase`.
												_parent_classes.append("""unittest.TestCase"""		)
								a__					   =		""", """.join(_parent_classes		)
								for idx, line in enumerate(__a		):
												# Find the ending of the declaration of `test_class`
												if line.strip().endswith("""):"""		):
																# mark the lines of the declaration of `test_class` to be removed
																for _idx in range(idx + 1		):
																				a__					   =		None  # noqa
																break
        # Add the new, one-line, class declaration for `test_class`
								a__					   =		F'''class {test_class.__name__}({parent_classes}):\n'''

				# Add indentation
				a__					   =		""" """ * indent_level + line_to_add
				# Insert `pipeline_model_mapping` to `class_lines`.
				# (The line at `target_idx` should be kept by definition!)
				a__					   =		class_lines[: target_idx + 1] + [line_to_add] + class_lines[target_idx + 1 :]
				# Remove the lines that are marked to be removed
				a__					   =		[x for x in class_lines if x is not None]

				# Move from test class to module (in order to write to the test file)
				a__					   =		inspect.getsourcelines(inspect.getmodule(__a		)		)[0]
				# Be careful with the 1-off between line numbers and array indices
				a__					   =		module_lines[: class_start_line_no - 1] + class_lines + module_lines[class_end_line_no:]
				a__					   =		"""""".join(__a		)

				a__					   =		inspect.getsourcefile(__a		)
				with open(__a			,		"""w"""			,		encoding="""UTF-8"""			,		newline="""\n"""		) as fp:
								fp.write(__a		)

				return line_to_add

def       A_   (						__a  :					Optional[Any]			,		__a  :					List[str]=False		):

				"""simple docstring"""
				a__					   =		find_test_class(__a		)
				if test_class:
								add_pipeline_model_mapping(__a			,		overwrite=__a		)


if __name__ == "__main__":
	UpperCAmelCase							=       argparse.ArgumentParser()
	parser.add_argument(
	    """--test_file""", type=str, help="""A path to the test file, starting with the repository's `tests` directory."""
	)
	parser.add_argument(
	    """--all""",
	    action="""store_true""",
	    help="""If to check and modify all test files.""",
	)
	parser.add_argument(
	    """--overwrite""",
	    action="""store_true""",
	    help="""If to overwrite a test class if it has already defined `pipeline_model_mapping`.""",
	)
	UpperCAmelCase							=       parser.parse_args()

	if not args.all and not args.test_file:
		raise ValueError("""Please specify either `test_file` or pass `--all` to check/modify all test files.""")
	elif args.all and args.test_file:
		raise ValueError("""Only one of `--test_file` and `--all` could be specified.""")

	UpperCAmelCase							=       []
	if args.test_file:
		UpperCAmelCase							=       [args.test_file]
	else:
		UpperCAmelCase							=       os.path.join("""tests""", """models""", """**""", """test_modeling_*.py""")
		for test_file in glob.glob(pattern):
			# `Flax` is not concerned at this moment
			if not test_file.startswith("""test_modeling_flax_"""):
				test_files.append(test_file)

	for test_file in test_files:
		if test_file in TEST_FILE_TO_IGNORE:
			print(f"""[SKIPPED] {test_file} is skipped as it is in `TEST_FILE_TO_IGNORE` in the file {__file__}.""")
			continue
		add_pipeline_model_mapping_to_test_file(test_file, overwrite=args.overwrite)







import gc
import unittest

import numpy as np
import torch

from diffusers import DDPMScheduler, MidiProcessor, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder
from diffusers.utils import require_torch_gpu, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_note_seq, require_onnxruntime

from ..pipeline_params import TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS, TOKENS_TO_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


__lowerCAmelCase						:							Optional[int]			     =						'./tests/fixtures/elise_format0.mid'




@unittest.skip("""The note-seq package currently throws an error on import"""  )
class        snake_case__				(_UpperCamelCase       ,			unittest.TestCase  ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				Tuple									=   SpectrogramDiffusionPipeline
				SCREAMING_SNAKE_CASE_			:				Dict									=   PipelineTesterMixin.required_optional_params - {
				    """callback""",
				    """latents""",
				    """callback_steps""",
				    """output_type""",
				    """num_images_per_prompt""",
				}
				SCREAMING_SNAKE_CASE_			:				int									=   False
				SCREAMING_SNAKE_CASE_			:				Tuple									=   False
				SCREAMING_SNAKE_CASE_			:				str									=   TOKENS_TO_AUDIO_GENERATION_PARAMS
				SCREAMING_SNAKE_CASE_			:				List[str]									=   TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS





				def 				__UpperCAmelCase				( self							:	Dict				)   ->	str:
							torch.manual_seed(0				)
							a       =     SpectrogramNotesEncoder(
							    max_length=20_48   , vocab_size=15_36   , d_model=7_68   , dropout_rate=0.1   , num_layers=1   , num_heads=1   , d_kv=4   , d_ff=20_48   , feed_forward_proj="gated-gelu"   , )

							a       =     SpectrogramContEncoder(
							    input_dims=1_28   , targets_context_length=2_56   , d_model=7_68   , dropout_rate=0.1   , num_layers=1   , num_heads=1   , d_kv=4   , d_ff=20_48   , feed_forward_proj="gated-gelu"   , )

							a       =     TaFilmDecoder(
							    input_dims=1_28   , targets_length=2_56   , max_decoder_noise_time=20_000.0   , d_model=7_68   , num_layers=1   , num_heads=1   , d_kv=4   , d_ff=20_48   , dropout_rate=0.1   , )

							a       =     DDPMScheduler()

							a       =     {
							    "notes_encoder": notes_encoder.eval(),
							    "continuous_encoder": continuous_encoder.eval(),
							    "decoder": decoder.eval(),
							    "scheduler": scheduler,
							    "melgan": None,
							}
							return components





				def 				__UpperCAmelCase				( self							:	Any   , __lowerCamelCase							:	Optional[Any]   , __lowerCamelCase							:	Union[str, Any]=0				)   ->	Dict:
							if str(__lowerCamelCase				).startswith("mps"				):
										a       =     torch.manual_seed(__lowerCamelCase				)
							else:
										a       =     torch.Generator(device=__lowerCamelCase				).manual_seed(__lowerCamelCase				)
							a       =     {
							    "input_tokens": [
							        [11_34, 90, 11_35, 11_33, 10_80, 1_12, 11_32, 10_80, 11_33, 10_79, 1_33, 11_32, 10_79, 11_33, 1] + [0] * 20_33
							    ],
							    "generator": generator,
							    "num_inference_steps": 4,
							    "output_type": "mel",
							}
							return inputs





				def 				__UpperCAmelCase				( self							:	Dict				)   ->	Union[str, Any]:
							a       =     "cpu"  # ensure determinism for the device-dependent torch.Generator
							a       =     self.get_dummy_components()
							a       =     SpectrogramDiffusionPipeline(**__lowerCamelCase				)
							a       =     pipe.to(__lowerCamelCase				)
							pipe.set_progress_bar_config(disable=__lowerCamelCase				)

							a       =     self.get_dummy_inputs(__lowerCamelCase				)
							a       =     pipe(**__lowerCamelCase				)
							a       =     output.audios

							a       =     mel[0, -3:, -3:]

							assert mel_slice.shape == (3, 3)
							a       =     np.array(
							    [-11.512_925, -4.788_215, -0.46_172_905, -2.051_715, -10.539_147, -10.970_963, -9.091_634, 4.0, 4.0]				)
							assert np.abs(mel_slice.flatten() - expected_slice				).max() < 1e-2





				@skip_mps
				def 				__UpperCAmelCase				( self							:	List[Any]				)   ->	Tuple:
							return super().test_save_load_local()





				@skip_mps
				def 				__UpperCAmelCase				( self							:	str				)   ->	List[Any]:
							return super().test_dict_tuple_outputs_equivalent()





				@skip_mps
				def 				__UpperCAmelCase				( self							:	int				)   ->	Any:
							return super().test_save_load_optional_components()





				@skip_mps
				def 				__UpperCAmelCase				( self							:	List[Any]				)   ->	List[str]:
							return super().test_attention_slicing_forward_pass()





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





				def 				__UpperCAmelCase				( self							:	Tuple				)   ->	Tuple:
							pass





				@skip_mps
				def 				__UpperCAmelCase				( self							:	int				)   ->	Optional[Any]:
							return super().test_progress_bar()




@slow
@require_torch_gpu
@require_onnxruntime
@require_note_seq
class        snake_case__				(unittest.TestCase  ):
				"""simple docstring"""



				def 				__UpperCAmelCase				( self							:	Union[str, Any]				)   ->	Dict:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





				def 				__UpperCAmelCase				( self							:	Union[str, Any]				)   ->	Any:
							# TODO - test that pipeline can decode tokens in a callback
							# so that music can be played live
							a       =     torch_device

							a       =     SpectrogramDiffusionPipeline.from_pretrained("google/music-spectrogram-diffusion"				)
							a       =     pipe.melgan
							a       =     None

							a       =     pipe.to(__lowerCamelCase				)
							pipe.set_progress_bar_config(disable=__lowerCamelCase				)

							def callback(__lowerCamelCase							:	List[str]   , __lowerCamelCase							:	Union[str, Any]				):
										# decode mel to audio
										a       =     melgan(input_features=mel_output.astype(np.floataa				)				)[0]
										assert len(audio[0]				) == 8_19_20 * (step + 1)
										# simulate that audio is played
										return audio

							a       =     MidiProcessor()
							a       =     processor(__lowerCamelCase				)

							a       =     input_tokens[:3]
							a       =     torch.manual_seed(0				)
							pipe(__lowerCamelCase   , num_inference_steps=5   , generator=__lowerCamelCase   , callback=__lowerCamelCase   , output_type="mel"				)





				def 				__UpperCAmelCase				( self							:	Any				)   ->	Union[str, Any]:
							a       =     torch_device

							a       =     SpectrogramDiffusionPipeline.from_pretrained("google/music-spectrogram-diffusion"				)
							a       =     pipe.to(__lowerCamelCase				)
							pipe.set_progress_bar_config(disable=__lowerCamelCase				)
							a       =     MidiProcessor()

							a       =     processor(__lowerCamelCase				)
							# just run two denoising loops
							a       =     input_tokens[:2]

							a       =     torch.manual_seed(0				)
							a       =     pipe(__lowerCamelCase   , num_inference_steps=2   , generator=__lowerCamelCase				)

							a       =     output.audios[0]

							assert abs(np.abs(__lowerCamelCase				).sum() - 3_612.841				) < 1e-1





				def 				__UpperCAmelCase				( self							:	List[str]				)   ->	str:
							a       =     torch_device

							a       =     SpectrogramDiffusionPipeline.from_pretrained("google/music-spectrogram-diffusion"				)
							a       =     pipe.to(__lowerCamelCase				)
							pipe.set_progress_bar_config(disable=__lowerCamelCase				)

							a       =     MidiProcessor()

							a       =     processor(__lowerCamelCase				)

							# just run 4 denoising loops
							a       =     input_tokens[:4]

							a       =     torch.manual_seed(0				)
							a       =     pipe(__lowerCamelCase   , num_inference_steps=1_00   , generator=__lowerCamelCase				)

							a       =     output.audios[0]
							assert abs(np.abs(__lowerCamelCase				).sum() - 9_389.1_111				) < 5e-2






