

'''simple docstring'''

import glob
import os
from typing import Dict, List, Union

import torch

from diffusers.utils import is_safetensors_available


if is_safetensors_available():
						import safetensors.torch

from huggingface_hub import snapshot_download

from diffusers import DiffusionPipeline, __version__
from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
from diffusers.utils import CONFIG_NAME, DIFFUSERS_CACHE, ONNX_WEIGHTS_NAME, WEIGHTS_NAME







class lowerCamelCase   (    __lowerCAmelCase				):

				def __init__(       self )    ->							List[str]:
					self.register_to_config()
					super().__init__()

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_ )    ->							List[str]:
					if dicta == dicta:
						return True
					else:
						snake_case			,			snake_case							=					self._remove_meta_keys(lowercase_ )
						snake_case			,			snake_case							=					self._remove_meta_keys(lowercase_ )
						if configa == configa:
							print(F'''Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}.''' )
							return True
					return False

				def 					_lowerCamelCase    (       self,						lowercase_ )    ->							str:
					snake_case							=					[]
					snake_case							=					config_dict.copy()
					for key in config_dict.keys():
						if key.startswith('_' ):
							temp_dict.pop(lowercase_ )
							meta_keys.append(lowercase_ )
					return (temp_dict, meta_keys)

				@torch.no_grad()
				def 					_lowerCamelCase    (       self,						lowercase_,						**lowercase_ )    ->							Any:
					snake_case							=					kwargs.pop('cache_dir',						lowercase_ )
					snake_case							=					kwargs.pop('resume_download',						lowercase_ )
					snake_case							=					kwargs.pop('force_download',						lowercase_ )
					snake_case							=					kwargs.pop('proxies',						lowercase_ )
					snake_case							=					kwargs.pop('local_files_only',						lowercase_ )
					snake_case							=					kwargs.pop('use_auth_token',						lowercase_ )
					snake_case							=					kwargs.pop('revision',						lowercase_ )
					snake_case							=					kwargs.pop('torch_dtype',						lowercase_ )
					snake_case							=					kwargs.pop('device_map',						lowercase_ )

					snake_case							=					kwargs.pop('alpha',						0.5 )
					snake_case							=					kwargs.pop('interp',						lowercase_ )

					print('Received list',						lowercase_ )
					print(F'''Combining with alpha={alpha}, interpolation mode={interp}''' )

					snake_case							=					len(lowercase_ )
					# Ignore result from model_index_json comparision of the two checkpoints
					snake_case							=					kwargs.pop('force',						lowercase_ )

					# If less than 2 checkpoints, nothing to merge. If more than 3, not supported for now.
					if checkpoint_count > 3 or checkpoint_count < 2:
						raise ValueError(
						    'Received incorrect number of checkpoints to merge. Ensure that either 2 or 3 checkpoints are being'
						    ' passed.' )

					print('Received the right number of checkpoints' )
					# chkpt0, chkpt1 = pretrained_model_name_or_path_list[0:2]
					# chkpt2 = pretrained_model_name_or_path_list[2] if checkpoint_count == 3 else None

					# Validate that the checkpoints can be merged
					# Step 1: Load the model config and compare the checkpoints. We'll compare the model_index.json first while ignoring the keys starting with '_'
					snake_case							=					[]
					for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
						snake_case							=					DiffusionPipeline.load_config(
						    lowercase_,						cache_dir=lowercase_,						resume_download=lowercase_,						force_download=lowercase_,						proxies=lowercase_,						local_files_only=lowercase_,						use_auth_token=lowercase_,						revision=lowercase_,						)
						config_dicts.append(lowercase_ )

					snake_case							=					True
					for idx in range(1,						len(lowercase_ ) ):
						comparison_result &= self._compare_model_configs(config_dicts[idx - 1],						config_dicts[idx] )
						if not force and comparison_result is False:
							raise ValueError('Incompatible checkpoints. Please check model_index.json for the models.' )
							print(config_dicts[0],						config_dicts[1] )
					print('Compatible model_index.json files found' )
					# Step 2: Basic Validation has succeeded. Let's download the models and save them into our local files.
					snake_case							=					[]
					for pretrained_model_name_or_path, config_dict in zip(lowercase_,						lowercase_ ):
						snake_case							=					[k for k in config_dict.keys() if not k.startswith('_' )]
						snake_case							=					[os.path.join(lowercase_,						'*' ) for k in folder_names]
						allow_patterns += [
						    WEIGHTS_NAME,
						    SCHEDULER_CONFIG_NAME,
						    CONFIG_NAME,
						    ONNX_WEIGHTS_NAME,
						    DiffusionPipeline.config_name,
						]
						snake_case							=					config_dict.get('_class_name' )
						snake_case							=					{'diffusers': __version__, 'pipeline_class': requested_pipeline_class}

						snake_case							=					(
						    pretrained_model_name_or_path
						    if os.path.isdir(lowercase_ )
						    else snapshot_download(
						        lowercase_,						cache_dir=lowercase_,						resume_download=lowercase_,						proxies=lowercase_,						local_files_only=lowercase_,						use_auth_token=lowercase_,						revision=lowercase_,						allow_patterns=lowercase_,						user_agent=lowercase_,						)
						)
						print('Cached Folder',						lowercase_ )
						cached_folders.append(lowercase_ )

					# Step 3:-
					# Load the first checkpoint as a diffusion pipeline and modify its module state_dict in place
					snake_case							=					DiffusionPipeline.from_pretrained(
					    cached_folders[0],						torch_dtype=lowercase_,						device_map=lowercase_ )
					final_pipe.to(self.device )

					snake_case							=					None
					if len(lowercase_ ) > 2:
						snake_case							=					os.path.join(cached_folders[2] )

					if interp == "sigmoid":
						snake_case							=					CheckpointMergerPipeline.sigmoid
					elif interp == "inv_sigmoid":
						snake_case							=					CheckpointMergerPipeline.inv_sigmoid
					elif interp == "add_diff":
						snake_case							=					CheckpointMergerPipeline.add_difference
					else:
						snake_case							=					CheckpointMergerPipeline.weighted_sum

					# Find each module's state dict.
					for attr in final_pipe.config.keys():
						if not attr.startswith('_' ):
							snake_case							=					os.path.join(cached_folders[1],						lowercase_ )
							if os.path.exists(lowercase_ ):
								snake_case							=					[
								    *glob.glob(os.path.join(lowercase_,						'*.safetensors' ) ),
								    *glob.glob(os.path.join(lowercase_,						'*.bin' ) ),
								]
								snake_case							=					files[0] if len(lowercase_ ) > 0 else None
							if len(lowercase_ ) < 3:
								snake_case							=					None
							else:
								snake_case							=					os.path.join(cached_folders[2],						lowercase_ )
								if os.path.exists(lowercase_ ):
									snake_case							=					[
									    *glob.glob(os.path.join(lowercase_,						'*.safetensors' ) ),
									    *glob.glob(os.path.join(lowercase_,						'*.bin' ) ),
									]
									snake_case							=					files[0] if len(lowercase_ ) > 0 else None
                # For an attr if both checkpoint_path_1 and 2 are None, ignore.
                # If atleast one is present, deal with it according to interp method, of course only if the state_dict keys match.
							if checkpoint_path_a is None and checkpoint_path_a is None:
								print(F'''Skipping {attr}: not present in 2nd or 3d model''' )
								continue
							try:
								snake_case							=					getattr(lowercase_,						lowercase_ )
								if isinstance(lowercase_,						lowercase_ ):  # ignore requires_safety_checker boolean
									continue
								snake_case							=					getattr(lowercase_,						'state_dict' )
								snake_case							=					theta_a()

								snake_case							=					getattr(lowercase_,						'load_state_dict' )
								snake_case							=					(
								    safetensors.torch.load_file(lowercase_ )
								    if (is_safetensors_available() and checkpoint_path_a.endswith('.safetensors' ))
								    else torch.load(lowercase_,						map_location='cpu' )
								)
								snake_case							=					None
								if checkpoint_path_a:
									snake_case							=					(
									    safetensors.torch.load_file(lowercase_ )
									    if (is_safetensors_available() and checkpoint_path_a.endswith('.safetensors' ))
									    else torch.load(lowercase_,						map_location='cpu' )
									)

								if not theta_a.keys() == theta_a.keys():
									print(F'''Skipping {attr}: key mismatch''' )
									continue
								if theta_a and not theta_a.keys() == theta_a.keys():
									print(F'''Skipping {attr}:y mismatch''' )
							except Exception as e:
								print(F'''Skipping {attr} do to an unexpected error: {str(lowercase_ )}''' )
								continue
							print(F'''MERGING {attr}''' )

							for key in theta_a.keys():
								if theta_a:
									snake_case							=					theta_func(theta_a[key],						theta_a[key],						theta_a[key],						lowercase_ )
								else:
									snake_case							=					theta_func(theta_a[key],						theta_a[key],						lowercase_,						lowercase_ )

							del theta_a
							del theta_a
							update_theta_a(lowercase_ )

							del theta_a
					return final_pipe

				@staticmethod
				def 					_lowerCamelCase    (       lowercase_,						lowercase_,						lowercase_,						lowercase_ )    ->							Dict:
					return ((1 - alpha) * thetaa) + (alpha * thetaa)

				@staticmethod
				def 					_lowerCamelCase    (       lowercase_,						lowercase_,						lowercase_,						lowercase_ )    ->							int:
					snake_case							=					alpha * alpha * (3 - (2 * alpha))
					return thetaa + ((thetaa - thetaa) * alpha)

				@staticmethod
				def 					_lowerCamelCase    (       lowercase_,						lowercase_,						lowercase_,						lowercase_ )    ->							Tuple:
					import math

					snake_case							=					0.5 - math.sin(math.asin(1.0 - 2.0 * alpha ) / 3.0 )
					return thetaa + ((thetaa - thetaa) * alpha)






				@staticmethod
				def 					_lowerCamelCase    (       lowercase_,						lowercase_,						lowercase_,						lowercase_ )    ->							Optional[int]:
					return thetaa + (thetaa - thetaa) * (1.0 - alpha)










import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
				from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

				from .test_feature_extraction_speechta import floats_list


__A       =get_tests_dir("fixtures/test_sentencepiece_bpe_char.model")

@require_torch
class 						UpperCAmelCase__     (     unittest.TestCase  ):




			'''simple docstring'''






			def 					snake_case__	(  self				:					List[str]					):
					'''simple docstring'''





					__UpperCAmelCase       :	str						=      tempfile.mkdtemp()

					__UpperCAmelCase       :	Optional[int]						=      SpeechTaTokenizer(a_					)
					tokenizer.save_pretrained(self.tmpdirname					)

					__UpperCAmelCase       :	Union[str, Any]						=      {
					    '''feature_size''': 1,
					    '''padding_value''': 0.0,
					    '''sampling_rate''': 1_60_00,
					    '''do_normalize''': False,
					    '''num_mel_bins''': 80,
					    '''hop_length''': 16,
					    '''win_length''': 64,
					    '''win_function''': '''hann_window''',
					    '''fmin''': 80,
					    '''fmax''': 76_00,
					    '''mel_floor''': 1e-10,
					    '''reduction_factor''': 2,
					    '''return_attention_mask''': True,
					}

					__UpperCAmelCase       :	Tuple						=      os.path.join(self.tmpdirname			,			a_					)
					with open(self.feature_extraction_file			,			'''w'''			,			encoding='''utf-8'''					) as fp:
							fp.write(json.dumps(a_					) + '''\n'''					)




			def 					snake_case__	(  self				:					Any			,			**a_				:					Tuple					):
					'''simple docstring'''





					return SpeechTaTokenizer.from_pretrained(self.tmpdirname			,			**a_					)




			def 					snake_case__	(  self				:					Dict			,			**a_				:					str					):
					'''simple docstring'''





					return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname			,			**a_					)




			def 					snake_case__	(  self				:					List[str]					):
					'''simple docstring'''





					shutil.rmtree(self.tmpdirname					)




			def 					snake_case__	(  self				:					Dict					):
					'''simple docstring'''





					__UpperCAmelCase       :	int						=      self.get_tokenizer()
					__UpperCAmelCase       :	str						=      self.get_feature_extractor()

					__UpperCAmelCase       :	List[Any]						=      SpeechTaProcessor(tokenizer=a_			,			feature_extractor=a_					)

					processor.save_pretrained(self.tmpdirname					)
					__UpperCAmelCase       :	List[str]						=      SpeechTaProcessor.from_pretrained(self.tmpdirname					)

					self.assertEqual(processor.tokenizer.get_vocab()			,			tokenizer.get_vocab()					)
					self.assertIsInstance(processor.tokenizer			,			a_					)

					self.assertEqual(processor.feature_extractor.to_json_string()			,			feature_extractor.to_json_string()					)
					self.assertIsInstance(processor.feature_extractor			,			a_					)




			def 					snake_case__	(  self				:					int					):
					'''simple docstring'''





					__UpperCAmelCase       :	Optional[int]						=      SpeechTaProcessor(tokenizer=self.get_tokenizer()			,			feature_extractor=self.get_feature_extractor()					)
					processor.save_pretrained(self.tmpdirname					)

					__UpperCAmelCase       :	Optional[Any]						=      self.get_tokenizer(bos_token='''(BOS)'''			,			eos_token='''(EOS)'''					)
					__UpperCAmelCase       :	List[Any]						=      self.get_feature_extractor(do_normalize=a_			,			padding_value=1.0					)

					__UpperCAmelCase       :	List[str]						=      SpeechTaProcessor.from_pretrained(
					    self.tmpdirname			,			bos_token='''(BOS)'''			,			eos_token='''(EOS)'''			,			do_normalize=a_			,			padding_value=1.0					)

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

					self.assertEqual(processor.feature_extractor.to_json_string()			,			feature_extractor_add_kwargs.to_json_string()					)
					self.assertIsInstance(processor.feature_extractor			,			a_					)




			def 					snake_case__	(  self				:					Union[str, Any]					):
					'''simple docstring'''





					__UpperCAmelCase       :	Optional[Any]						=      self.get_feature_extractor()
					__UpperCAmelCase       :	Union[str, Any]						=      self.get_tokenizer()

					__UpperCAmelCase       :	List[Any]						=      SpeechTaProcessor(tokenizer=a_			,			feature_extractor=a_					)

					__UpperCAmelCase       :	List[Any]						=      floats_list((3, 10_00)					)

					__UpperCAmelCase       :	Tuple						=      feature_extractor(audio=a_			,			return_tensors='''np'''					)
					__UpperCAmelCase       :	List[str]						=      processor(audio=a_			,			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 					snake_case__	(  self				:					Optional[Any]					):
					'''simple docstring'''





					__UpperCAmelCase       :	List[Any]						=      self.get_feature_extractor()
					__UpperCAmelCase       :	Dict						=      self.get_tokenizer()

					__UpperCAmelCase       :	Optional[Any]						=      SpeechTaProcessor(tokenizer=a_			,			feature_extractor=a_					)

					__UpperCAmelCase       :	Union[str, Any]						=      floats_list((3, 10_00)					)

					__UpperCAmelCase       :	Dict						=      feature_extractor(audio_target=a_			,			return_tensors='''np'''					)
					__UpperCAmelCase       :	str						=      processor(audio_target=a_			,			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 					snake_case__	(  self				:					Union[str, Any]					):
					'''simple docstring'''





					__UpperCAmelCase       :	Union[str, Any]						=      self.get_feature_extractor()
					__UpperCAmelCase       :	Optional[int]						=      self.get_tokenizer()

					__UpperCAmelCase       :	Optional[Any]						=      SpeechTaProcessor(tokenizer=a_			,			feature_extractor=a_					)

					__UpperCAmelCase       :	List[str]						=      '''This is a test string'''

					__UpperCAmelCase       :	List[Any]						=      processor(text=a_					)
					__UpperCAmelCase       :	Optional[int]						=      tokenizer(a_					)

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




			def 					snake_case__	(  self				:					List[str]					):
					'''simple docstring'''





					__UpperCAmelCase       :	Optional[Any]						=      self.get_feature_extractor()
					__UpperCAmelCase       :	Optional[Any]						=      self.get_tokenizer()

					__UpperCAmelCase       :	List[Any]						=      SpeechTaProcessor(tokenizer=a_			,			feature_extractor=a_					)

					__UpperCAmelCase       :	List[str]						=      '''This is a test string'''

					__UpperCAmelCase       :	Optional[Any]						=      processor(text_target=a_					)
					__UpperCAmelCase       :	Optional[int]						=      tokenizer(a_					)

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




			def 					snake_case__	(  self				:					List[str]					):
					'''simple docstring'''





					__UpperCAmelCase       :	int						=      self.get_feature_extractor()
					__UpperCAmelCase       :	Union[str, Any]						=      self.get_tokenizer()

					__UpperCAmelCase       :	int						=      SpeechTaProcessor(tokenizer=a_			,			feature_extractor=a_					)

					__UpperCAmelCase       :	Optional[Any]						=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					__UpperCAmelCase       :	Optional[Any]						=      processor.batch_decode(a_					)
					__UpperCAmelCase       :	Any						=      tokenizer.batch_decode(a_					)

					self.assertListEqual(a_			,			a_					)




			def 					snake_case__	(  self				:					str					):
					'''simple docstring'''





					__UpperCAmelCase       :	str						=      self.get_feature_extractor()
					__UpperCAmelCase       :	str						=      self.get_tokenizer()

					__UpperCAmelCase       :	Optional[Any]						=      SpeechTaProcessor(tokenizer=a_			,			feature_extractor=a_					)

					self.assertListEqual(
					    processor.model_input_names			,			feature_extractor.model_input_names			,			msg='''`processor` and `feature_extractor` model input names do not match'''			,			)





