




'''simple docstring'''

# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import ast
import difflib
from collections.abc import Mapping
from typing import Any, Callable, Dict





class __magic_name__(						__lowerCAmelCase      ):
						pass







def    snake_case__     (  a       ,      a       ,      a=None       ,      a=False	)			->       Union[str, Any]:





			'''simple docstring'''






			try:
						snake_case__          =						ast.parse(a	)
			except SyntaxError as e:
						print("""The code generated by the agent is not valid.\n"""       ,      a	)
						return
			if state is None:
						snake_case__          =						{}
			snake_case__          =						None
			for idx, node in enumerate(expression.body	):
						try:
									snake_case__          =						evaluate_ast(a       ,      a       ,      a	)
						except InterpretorError as e:
									snake_case__          =						F"""Evaluation of the code stopped at line {idx} before the end because of the following error"""
									if chat_mode:
												msg += (
												    F""". Copy paste the following error message and send it back to the agent:\nI get an error: '{e}'"""
												)
									else:
												msg += F""":\n{e}"""
									print(a	)
									break
						if line_result is not None:
									snake_case__          =						line_result

			return result




def    snake_case__     (  a       ,      a       ,      a	)			->       int:





			'''simple docstring'''






			if isinstance(a       ,      ast.Assign	):
						# Assignement -> we evaluate the assignement which should update the state
						# We return the variable assigned as it may be used to determine the final result.
						return evaluate_assign(a       ,      a       ,      a	)
			elif isinstance(a       ,      ast.Call	):
						# Function call -> we return the value of the function call
						return evaluate_call(a       ,      a       ,      a	)
			elif isinstance(a       ,      ast.Constant	):
						# Constant -> just return the value
						return expression.value
			elif isinstance(a       ,      ast.Dict	):
						# Dict -> evaluate all keys and values
						snake_case__          =						[evaluate_ast(a       ,      a       ,      a	) for k in expression.keys]
						snake_case__          =						[evaluate_ast(a       ,      a       ,      a	) for v in expression.values]
						return dict(zip(a       ,      a	)	)
			elif isinstance(a       ,      ast.Expr	):
						# Expression -> evaluate the content
						return evaluate_ast(expression.value       ,      a       ,      a	)
			elif isinstance(a       ,      ast.For	):
						# For loop -> execute the loop
						return evaluate_for(a       ,      a       ,      a	)
			elif isinstance(a       ,      ast.FormattedValue	):
						# Formatted value (part of f-string) -> evaluate the content and return
						return evaluate_ast(expression.value       ,      a       ,      a	)
			elif isinstance(a       ,      ast.If	):
						# If -> execute the right branch
						return evaluate_if(a       ,      a       ,      a	)
			elif hasattr(a       ,      """Index"""	) and isinstance(a       ,      ast.Index	):
						return evaluate_ast(expression.value       ,      a       ,      a	)
			elif isinstance(a       ,      ast.JoinedStr	):
						return "".join([str(evaluate_ast(a       ,      a       ,      a	)	) for v in expression.values]	)
			elif isinstance(a       ,      ast.List	):
						# List -> evaluate all elements
						return [evaluate_ast(a       ,      a       ,      a	) for elt in expression.elts]
			elif isinstance(a       ,      ast.Name	):
						# Name -> pick up the value in the state
						return evaluate_name(a       ,      a       ,      a	)
			elif isinstance(a       ,      ast.Subscript	):
						# Subscript -> return the value of the indexing
						return evaluate_subscript(a       ,      a       ,      a	)
			else:
						# For now we refuse anything else. Let's add things as we need them.
						raise InterpretorError(F"""{expression.__class__.__name__} is not supported."""	)




def    snake_case__     (  a       ,      a       ,      a	)			->       Union[str, Any]:





			'''simple docstring'''






			snake_case__          =						assign.targets
			snake_case__          =						evaluate_ast(assign.value       ,      a       ,      a	)

			if len(a	) == 1:
						snake_case__          =						result
			else:
						if len(a	) != len(a	):
									raise InterpretorError(F"""Expected {len(a	)} values but got {len(a	)}."""	)
						for var_name, r in zip(a       ,      a	):
									snake_case__          =						r
			return result




def    snake_case__     (  a       ,      a       ,      a	)			->       int:





			'''simple docstring'''






			if not isinstance(call.func       ,      ast.Name	):
						raise InterpretorError(
						    F"""It is not permitted to evaluate other functions than the provided tools (tried to execute {call.func} of """
						    F"""type {type(call.func	)}."""	)
			snake_case__          =						call.func.id
			if func_name not in tools:
						raise InterpretorError(
						    F"""It is not permitted to evaluate other functions than the provided tools (tried to execute {call.func.id})."""	)

			snake_case__          =						tools[func_name]
			# Todo deal with args
			snake_case__          =						[evaluate_ast(a       ,      a       ,      a	) for arg in call.args]
			snake_case__          =						{keyword.arg: evaluate_ast(keyword.value       ,      a       ,      a	) for keyword in call.keywords}
			return func(*a       ,      **a	)




def    snake_case__     (  a       ,      a       ,      a	)			->       Tuple:





			'''simple docstring'''






			snake_case__          =						evaluate_ast(subscript.slice       ,      a       ,      a	)
			snake_case__          =						evaluate_ast(subscript.value       ,      a       ,      a	)
			if isinstance(a       ,      (list, tuple)	):
						return value[int(a	)]
			if index in value:
						return value[index]
			if isinstance(a       ,      a	) and isinstance(a       ,      a	):
						snake_case__          =						difflib.get_close_matches(a       ,      list(value.keys()	)	)
						if len(a	) > 0:
									return value[close_matches[0]]

			raise InterpretorError(F"""Could not index {value} with '{index}'."""	)




def    snake_case__     (  a       ,      a       ,      a	)			->       Any:





			'''simple docstring'''






			if name.id in state:
						return state[name.id]
			snake_case__          =						difflib.get_close_matches(name.id       ,      list(state.keys()	)	)
			if len(a	) > 0:
						return state[close_matches[0]]
			raise InterpretorError(F"""The variable `{name.id}` is not defined."""	)




def    snake_case__     (  a       ,      a       ,      a	)			->       str:





			'''simple docstring'''






			if len(condition.ops	) > 1:
						raise InterpretorError("""Cannot evaluate conditions with multiple operators"""	)

			snake_case__          =						evaluate_ast(condition.left       ,      a       ,      a	)
			snake_case__          =						condition.ops[0]
			snake_case__          =						evaluate_ast(condition.comparators[0]       ,      a       ,      a	)

			if isinstance(a       ,      ast.Eq	):
						return left == right
			elif isinstance(a       ,      ast.NotEq	):
						return left != right
			elif isinstance(a       ,      ast.Lt	):
						return left < right
			elif isinstance(a       ,      ast.LtE	):
						return left <= right
			elif isinstance(a       ,      ast.Gt	):
						return left > right
			elif isinstance(a       ,      ast.GtE	):
						return left >= right
			elif isinstance(a       ,      ast.Is	):
						return left is right
			elif isinstance(a       ,      ast.IsNot	):
						return left is not right
			elif isinstance(a       ,      ast.In	):
						return left in right
			elif isinstance(a       ,      ast.NotIn	):
						return left not in right
			else:
						raise InterpretorError(F"""Operator not supported: {comparator}"""	)




def    snake_case__     (  a       ,      a       ,      a	)			->       Optional[int]:





			'''simple docstring'''






			snake_case__          =						None
			if evaluate_condition(if_statement.test       ,      a       ,      a	):
						for line in if_statement.body:
									snake_case__          =						evaluate_ast(a       ,      a       ,      a	)
									if line_result is not None:
												snake_case__          =						line_result
			else:
						for line in if_statement.orelse:
									snake_case__          =						evaluate_ast(a       ,      a       ,      a	)
									if line_result is not None:
												snake_case__          =						line_result
			return result




def    snake_case__     (  a       ,      a       ,      a	)			->       Any:





			'''simple docstring'''






			snake_case__          =						None
			snake_case__          =						evaluate_ast(for_loop.iter       ,      a       ,      a	)
			for counter in iterator:
						snake_case__          =						counter
						for expression in for_loop.body:
									snake_case__          =						evaluate_ast(a       ,      a       ,      a	)
									if line_result is not None:
												snake_case__          =						line_result
			return result





'''simple docstring'''

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()


a__         =     '''./tests/fixtures/elise_format0.mid'''





@unittest.skip("""The note-seq package currently throws an error on import"""      )
class __magic_name__(						__lowerCAmelCase    ,		unittest.TestCase      ):
						UpperCAmelCase_		:					Tuple    =  SpectrogramDiffusionPipeline
						UpperCAmelCase_		:					List[str]    =  PipelineTesterMixin.required_optional_params - {
						    """callback""",
						    """latents""",
						    """callback_steps""",
						    """output_type""",
						    """num_images_per_prompt""",
						}
						UpperCAmelCase_		:					Dict    =  False
						UpperCAmelCase_		:					Dict    =  False
						UpperCAmelCase_		:					List[str]    =  TOKENS_TO_AUDIO_GENERATION_PARAMS
						UpperCAmelCase_		:					Union[str, Any]    =  TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS






						def       __lowerCAmelCase(   self						:						Tuple       ):



									'''simple docstring'''






									torch.manual_seed(0       )
									snake_case__          =						SpectrogramNotesEncoder(
									    max_length=2_0_4_8					,   vocab_size=1_5_3_6					,   d_model=7_6_8					,   dropout_rate=0.1					,   num_layers=1					,   num_heads=1					,   d_kv=4					,   d_ff=2_0_4_8					,   feed_forward_proj="""gated-gelu"""					,   )

									snake_case__          =						SpectrogramContEncoder(
									    input_dims=1_2_8					,   targets_context_length=2_5_6					,   d_model=7_6_8					,   dropout_rate=0.1					,   num_layers=1					,   num_heads=1					,   d_kv=4					,   d_ff=2_0_4_8					,   feed_forward_proj="""gated-gelu"""					,   )

									snake_case__          =						TaFilmDecoder(
									    input_dims=1_2_8					,   targets_length=2_5_6					,   max_decoder_noise_time=2_00_00.0					,   d_model=7_6_8					,   num_layers=1					,   num_heads=1					,   d_kv=4					,   d_ff=2_0_4_8					,   dropout_rate=0.1					,   )

									snake_case__          =						DDPMScheduler()

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






						def       __lowerCAmelCase(   self						:						Tuple					,   __UpperCamelCase						:						Any					,   __UpperCamelCase						:						str=0       ):



									'''simple docstring'''






									if str(__UpperCamelCase       ).startswith("""mps"""       ):
												snake_case__          =						torch.manual_seed(__UpperCamelCase       )
									else:
												snake_case__          =						torch.Generator(device=__UpperCamelCase       ).manual_seed(__UpperCamelCase       )
									snake_case__          =						{
									    """input_tokens""": [
									        [1_1_3_4, 9_0, 1_1_3_5, 1_1_3_3, 1_0_8_0, 1_1_2, 1_1_3_2, 1_0_8_0, 1_1_3_3, 1_0_7_9, 1_3_3, 1_1_3_2, 1_0_7_9, 1_1_3_3, 1] + [0] * 2_0_3_3
									    ],
									    """generator""": generator,
									    """num_inference_steps""": 4,
									    """output_type""": """mel""",
									}
									return inputs






						def       __lowerCAmelCase(   self						:						Optional[Any]       ):



									'''simple docstring'''






									snake_case__          =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
									snake_case__          =						self.get_dummy_components()
									snake_case__          =						SpectrogramDiffusionPipeline(**__UpperCamelCase       )
									snake_case__          =						pipe.to(__UpperCamelCase       )
									pipe.set_progress_bar_config(disable=__UpperCamelCase       )

									snake_case__          =						self.get_dummy_inputs(__UpperCamelCase       )
									snake_case__          =						pipe(**__UpperCamelCase       )
									snake_case__          =						output.audios

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

									assert mel_slice.shape == (3, 3)
									snake_case__          =						np.array(
									    [-11.51_29_25, -4.78_82_15, -0.46_17_29_05, -2.05_17_15, -10.53_91_47, -10.97_09_63, -9.09_16_34, 4.0, 4.0]       )
									assert np.abs(mel_slice.flatten() - expected_slice       ).max() < 1E-2






						@skip_mps
						def       __lowerCAmelCase(   self						:						Tuple       ):



									'''simple docstring'''






									return super().test_save_load_local()






						@skip_mps
						def       __lowerCAmelCase(   self						:						List[Any]       ):



									'''simple docstring'''






									return super().test_dict_tuple_outputs_equivalent()






						@skip_mps
						def       __lowerCAmelCase(   self						:						Optional[int]       ):



									'''simple docstring'''






									return super().test_save_load_optional_components()






						@skip_mps
						def       __lowerCAmelCase(   self						:						Union[str, Any]       ):



									'''simple docstring'''






									return super().test_attention_slicing_forward_pass()






						def       __lowerCAmelCase(   self						:						Dict       ):



									'''simple docstring'''






									pass






						def       __lowerCAmelCase(   self						:						int       ):



									'''simple docstring'''






									pass







						@skip_mps
						def       __lowerCAmelCase(   self						:						Dict       ):



									'''simple docstring'''






									return super().test_progress_bar()







@slow
@require_torch_gpu
@require_onnxruntime
@require_note_seq
class __magic_name__(						unittest.TestCase      ):






						def       __lowerCAmelCase(   self						:						List[Any]       ):



									'''simple docstring'''






									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()






						def       __lowerCAmelCase(   self						:						int       ):



									'''simple docstring'''






									snake_case__          =						torch_device

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

									snake_case__          =						pipe.to(__UpperCamelCase       )
									pipe.set_progress_bar_config(disable=__UpperCamelCase       )

									def callback(__UpperCamelCase						:						List[str]					,   __UpperCamelCase						:						List[str]       ):
												# decode mel to audio
												snake_case__          =						melgan(input_features=mel_output.astype(np.floataa       )       )[0]
												assert len(audio[0]       ) == 8_1_9_2_0 * (step + 1)
												# simulate that audio is played
												return audio

									snake_case__          =						MidiProcessor()
									snake_case__          =						processor(__UpperCamelCase       )

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






						def       __lowerCAmelCase(   self						:						List[Any]       ):



									'''simple docstring'''






									snake_case__          =						torch_device

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

									snake_case__          =						processor(__UpperCamelCase       )
									# just run two denoising loops
									snake_case__          =						input_tokens[:2]

									snake_case__          =						torch.manual_seed(0       )
									snake_case__          =						pipe(__UpperCamelCase					,   num_inference_steps=2					,   generator=__UpperCamelCase       )

									snake_case__          =						output.audios[0]

									assert abs(np.abs(__UpperCamelCase       ).sum() - 36_12.8_41       ) < 1E-1







						def       __lowerCAmelCase(   self						:						Union[str, Any]       ):



									'''simple docstring'''






									snake_case__          =						torch_device

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

									snake_case__          =						MidiProcessor()

									snake_case__          =						processor(__UpperCamelCase       )

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

									snake_case__          =						torch.manual_seed(0       )
									snake_case__          =						pipe(__UpperCamelCase					,   num_inference_steps=1_0_0					,   generator=__UpperCamelCase       )

									snake_case__          =						output.audios[0]
									assert abs(np.abs(__UpperCamelCase       ).sum() - 93_89.11_11       ) < 5E-2