



"""simple docstring"""


import argparse

import torch

from diffusers import UNetaDConditionModel

def 					_lowerCAmelCase (  lowercase_						,      lowercase_						,      lowercase_						,      lowercase_=None						,      lowercase_=None						,      lowercase_=None		):
						assert isinstance(_UpperCAmelCase						,      _UpperCAmelCase		), "Paths should be a list of dicts containing 'old' and 'new' keys."

						# Splits the attention layers into three variables.
						if attention_paths_to_split is not None:
												for path, path_map in attention_paths_to_split.items():
																		UpperCAmelCase			 =					old_checkpoint[path]
																		UpperCAmelCase			 =					old_tensor.shape[0] // 3

																		UpperCAmelCase			 =					(-1, channels) if len(old_tensor.shape		) == 3 else (-1)

																		UpperCAmelCase			 =					old_tensor.shape[0] // config['num_head_channels'] // 3

																		UpperCAmelCase			 =					old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]		)
																		UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase			 =					old_tensor.split(channels // num_heads						,      dim=1		)

																		UpperCAmelCase			 =					query.reshape(_UpperCAmelCase		)
																		UpperCAmelCase			 =					key.reshape(_UpperCAmelCase		)
																		UpperCAmelCase			 =					value.reshape(_UpperCAmelCase		)

						for path in paths:
												UpperCAmelCase			 =					path['new']

												# These have already been assigned
												if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																		continue

												if additional_replacements is not None:
																		for replacement in additional_replacements:
																								UpperCAmelCase			 =					new_path.replace(replacement['old']						,      replacement['new']		)

        # proj_attn.weight has to be converted from conv 1D to linear
												UpperCAmelCase			 =					old_checkpoint[path['old']]
												UpperCAmelCase			 =					['proj_attn.weight']
												UpperCAmelCase			 =					['proj_out.weight', 'proj_in.weight']
												if any(k in new_path for k in names		):
																		UpperCAmelCase			 =					weight[:, :, 0]
												elif any(k in new_path for k in names_a		) and len(weight.shape		) > 2 and ".attentions." not in new_path:
																		UpperCAmelCase			 =					weight[:, :, 0]
												else:
																		UpperCAmelCase			 =					weight

def 					_lowerCAmelCase (  lowercase_						,      lowercase_=0		):
						UpperCAmelCase			 =					[]
						for old_item in old_list:
												UpperCAmelCase			 =					old_item

												#         new_item = new_item.replace('norm.weight', 'group_norm.weight')
												#         new_item = new_item.replace('norm.bias', 'group_norm.bias')

												#         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
												#         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')

												#         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

												mapping.append({'old': old_item, 'new': new_item}		)

						return mapping

def 					_lowerCAmelCase (  lowercase_						,      lowercase_=1		):
						if n_shave_prefix_segments >= 0:
												return ".".join(path.split('.'		)[n_shave_prefix_segments:]		)
						else:
												return ".".join(path.split('.'		)[:n_shave_prefix_segments]		)

def 					_lowerCAmelCase (  lowercase_						,      lowercase_=0		):
						UpperCAmelCase			 =					[]
						for old_item in old_list:
												mapping.append({'old': old_item, 'new': old_item}		)

						return mapping

def 					_lowerCAmelCase (  lowercase_						,      lowercase_=0		):
						UpperCAmelCase			 =					[]
						for old_item in old_list:
												UpperCAmelCase			 =					old_item.replace('in_layers.0'						,      'norm1'		)
												UpperCAmelCase			 =					new_item.replace('in_layers.2'						,      'conv1'		)

												UpperCAmelCase			 =					new_item.replace('out_layers.0'						,      'norm2'		)
												UpperCAmelCase			 =					new_item.replace('out_layers.3'						,      'conv2'		)

												UpperCAmelCase			 =					new_item.replace('emb_layers.1'						,      'time_emb_proj'		)
												UpperCAmelCase			 =					new_item.replace('skip_connection'						,      'conv_shortcut'		)

												UpperCAmelCase			 =					shave_segments(_UpperCAmelCase						,      n_shave_prefix_segments=_UpperCAmelCase		)

												if "temopral_conv" not in old_item:
																		mapping.append({'old': old_item, 'new': new_item}		)

						return mapping

def 					_lowerCAmelCase (  lowercase_						,      lowercase_						,      lowercase_=None						,      lowercase_=False		):
						UpperCAmelCase			 =					{}
						UpperCAmelCase			 =					list(checkpoint.keys()		)

						UpperCAmelCase			 =					'model.diffusion_model.'

						# at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
						if sum(k.startswith('model_ema'		) for k in keys		) > 100 and extract_ema:
												print(F"""Checkpoint {path} has both EMA and non-EMA weights."""		)
												print(
												    'In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA'
												    ' weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag.'		)
												for key in keys:
																		if key.startswith('model.diffusion_model'		):
																								UpperCAmelCase			 =					'model_ema.' + ''.join(key.split('.'		)[1:]		)
																								UpperCAmelCase			 =					checkpoint.pop(_UpperCAmelCase		)
						else:
												if sum(k.startswith('model_ema'		) for k in keys		) > 100:
																		print(
																		    'In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA'
																		    ' weights (usually better for inference), please make sure to add the `--extract_ema` flag.'		)

												for key in keys:
																		UpperCAmelCase			 =					checkpoint.pop(_UpperCAmelCase		)

						UpperCAmelCase			 =					{}

						UpperCAmelCase			 =					unet_state_dict['time_embed.0.weight']
						UpperCAmelCase			 =					unet_state_dict['time_embed.0.bias']
						UpperCAmelCase			 =					unet_state_dict['time_embed.2.weight']
						UpperCAmelCase			 =					unet_state_dict['time_embed.2.bias']

						if config["class_embed_type"] is None:
												# No parameters to port
												...
						elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
												UpperCAmelCase			 =					unet_state_dict['label_emb.0.0.weight']
												UpperCAmelCase			 =					unet_state_dict['label_emb.0.0.bias']
												UpperCAmelCase			 =					unet_state_dict['label_emb.0.2.weight']
												UpperCAmelCase			 =					unet_state_dict['label_emb.0.2.bias']
						else:
												raise NotImplementedError(F"""Not implemented `class_embed_type`: {config['class_embed_type']}"""		)

						UpperCAmelCase			 =					unet_state_dict['input_blocks.0.0.weight']
						UpperCAmelCase			 =					unet_state_dict['input_blocks.0.0.bias']

						UpperCAmelCase			 =					[v for v in unet_state_dict if v.startswith('input_blocks.0.1'		)]
						UpperCAmelCase			 =					renew_attention_paths(_UpperCAmelCase		)
						UpperCAmelCase			 =					{'old': 'input_blocks.0.1', 'new': 'transformer_in'}
						assign_to_checkpoint(_UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      additional_replacements=[meta_path]						,      config=_UpperCAmelCase		)

						UpperCAmelCase			 =					unet_state_dict['out.0.weight']
						UpperCAmelCase			 =					unet_state_dict['out.0.bias']
						UpperCAmelCase			 =					unet_state_dict['out.2.weight']
						UpperCAmelCase			 =					unet_state_dict['out.2.bias']

						# Retrieves the keys for the input blocks only
						UpperCAmelCase			 =					len({'.'.join(layer.split('.'		)[:2]		) for layer in unet_state_dict if 'input_blocks' in layer}		)
						UpperCAmelCase			 =					{
						    layer_id: [key for key in unet_state_dict if F"""input_blocks.{layer_id}""" in key]
						    for layer_id in range(_UpperCAmelCase		)
						}

						# Retrieves the keys for the middle blocks only
						UpperCAmelCase			 =					len({'.'.join(layer.split('.'		)[:2]		) for layer in unet_state_dict if 'middle_block' in layer}		)
						UpperCAmelCase			 =					{
						    layer_id: [key for key in unet_state_dict if F"""middle_block.{layer_id}""" in key]
						    for layer_id in range(_UpperCAmelCase		)
						}

						# Retrieves the keys for the output blocks only
						UpperCAmelCase			 =					len({'.'.join(layer.split('.'		)[:2]		) for layer in unet_state_dict if 'output_blocks' in layer}		)
						UpperCAmelCase			 =					{
						    layer_id: [key for key in unet_state_dict if F"""output_blocks.{layer_id}""" in key]
						    for layer_id in range(_UpperCAmelCase		)
						}

						for i in range(1						,      _UpperCAmelCase		):
												UpperCAmelCase			 =					(i - 1) // (config['layers_per_block'] + 1)
												UpperCAmelCase			 =					(i - 1) % (config['layers_per_block'] + 1)

												UpperCAmelCase			 =					[
												    key for key in input_blocks[i] if F"""input_blocks.{i}.0""" in key and F"""input_blocks.{i}.0.op""" not in key
												]
												UpperCAmelCase			 =					[key for key in input_blocks[i] if F"""input_blocks.{i}.1""" in key]
												UpperCAmelCase			 =					[key for key in input_blocks[i] if F"""input_blocks.{i}.2""" in key]

												if F"""input_blocks.{i}.op.weight""" in unet_state_dict:
																		UpperCAmelCase			 =					unet_state_dict.pop(
																		    F"""input_blocks.{i}.op.weight"""		)
																		UpperCAmelCase			 =					unet_state_dict.pop(
																		    F"""input_blocks.{i}.op.bias"""		)

												UpperCAmelCase			 =					renew_resnet_paths(_UpperCAmelCase		)
												UpperCAmelCase			 =					{'old': F"""input_blocks.{i}.0""", 'new': F"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
												assign_to_checkpoint(
												    _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      additional_replacements=[meta_path]						,      config=_UpperCAmelCase		)

												UpperCAmelCase			 =					[key for key in resnets if 'temopral_conv' in key]
												UpperCAmelCase			 =					renew_temp_conv_paths(_UpperCAmelCase		)
												UpperCAmelCase			 =					{
												    'old': F"""input_blocks.{i}.0.temopral_conv""",
												    'new': F"""down_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
												}
												assign_to_checkpoint(
												    _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      additional_replacements=[meta_path]						,      config=_UpperCAmelCase		)

												if len(_UpperCAmelCase		):
																		UpperCAmelCase			 =					renew_attention_paths(_UpperCAmelCase		)
																		UpperCAmelCase			 =					{'old': F"""input_blocks.{i}.1""", 'new': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}"""}
																		assign_to_checkpoint(
																		    _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      additional_replacements=[meta_path]						,      config=_UpperCAmelCase		)

												if len(_UpperCAmelCase		):
																		UpperCAmelCase			 =					renew_attention_paths(_UpperCAmelCase		)
																		UpperCAmelCase			 =					{
																		    'old': F"""input_blocks.{i}.2""",
																		    'new': F"""down_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
																		}
																		assign_to_checkpoint(
																		    _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      additional_replacements=[meta_path]						,      config=_UpperCAmelCase		)

						UpperCAmelCase			 =					middle_blocks[0]
						UpperCAmelCase			 =					[key for key in resnet_a if 'temopral_conv' in key]
						UpperCAmelCase			 =					middle_blocks[1]
						UpperCAmelCase			 =					middle_blocks[2]
						UpperCAmelCase			 =					middle_blocks[3]
						UpperCAmelCase			 =					[key for key in resnet_a if 'temopral_conv' in key]

						UpperCAmelCase			 =					renew_resnet_paths(_UpperCAmelCase		)
						UpperCAmelCase			 =					{'old': 'middle_block.0', 'new': 'mid_block.resnets.0'}
						assign_to_checkpoint(
						    _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      config=_UpperCAmelCase						,      additional_replacements=[meta_path]		)

						UpperCAmelCase			 =					renew_temp_conv_paths(_UpperCAmelCase		)
						UpperCAmelCase			 =					{'old': 'middle_block.0.temopral_conv', 'new': 'mid_block.temp_convs.0'}
						assign_to_checkpoint(
						    _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      config=_UpperCAmelCase						,      additional_replacements=[meta_path]		)

						UpperCAmelCase			 =					renew_resnet_paths(_UpperCAmelCase		)
						UpperCAmelCase			 =					{'old': 'middle_block.3', 'new': 'mid_block.resnets.1'}
						assign_to_checkpoint(
						    _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      config=_UpperCAmelCase						,      additional_replacements=[meta_path]		)

						UpperCAmelCase			 =					renew_temp_conv_paths(_UpperCAmelCase		)
						UpperCAmelCase			 =					{'old': 'middle_block.3.temopral_conv', 'new': 'mid_block.temp_convs.1'}
						assign_to_checkpoint(
						    _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      config=_UpperCAmelCase						,      additional_replacements=[meta_path]		)

						UpperCAmelCase			 =					renew_attention_paths(_UpperCAmelCase		)
						UpperCAmelCase			 =					{'old': 'middle_block.1', 'new': 'mid_block.attentions.0'}
						assign_to_checkpoint(
						    _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      additional_replacements=[meta_path]						,      config=_UpperCAmelCase		)

						UpperCAmelCase			 =					renew_attention_paths(_UpperCAmelCase		)
						UpperCAmelCase			 =					{'old': 'middle_block.2', 'new': 'mid_block.temp_attentions.0'}
						assign_to_checkpoint(
						    _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      additional_replacements=[meta_path]						,      config=_UpperCAmelCase		)

						for i in range(_UpperCAmelCase		):
												UpperCAmelCase			 =					i // (config['layers_per_block'] + 1)
												UpperCAmelCase			 =					i % (config['layers_per_block'] + 1)
												UpperCAmelCase			 =					[shave_segments(_UpperCAmelCase						,      2		) for name in output_blocks[i]]
												UpperCAmelCase			 =					{}

												for layer in output_block_layers:
																		UpperCAmelCase ,       UpperCAmelCase			 =					layer.split('.'		)[0], shave_segments(_UpperCAmelCase						,      1		)
																		if layer_id in output_block_list:
																								output_block_list[layer_id].append(_UpperCAmelCase		)
																		else:
																								UpperCAmelCase			 =					[layer_name]

												if len(_UpperCAmelCase		) > 1:
																		UpperCAmelCase			 =					[key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
																		UpperCAmelCase			 =					[key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]
																		UpperCAmelCase			 =					[key for key in output_blocks[i] if F"""output_blocks.{i}.2""" in key]

																		UpperCAmelCase			 =					renew_resnet_paths(_UpperCAmelCase		)
																		UpperCAmelCase			 =					renew_resnet_paths(_UpperCAmelCase		)

																		UpperCAmelCase			 =					{'old': F"""output_blocks.{i}.0""", 'new': F"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
																		assign_to_checkpoint(
																		    _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      additional_replacements=[meta_path]						,      config=_UpperCAmelCase		)

																		UpperCAmelCase			 =					[key for key in resnets if 'temopral_conv' in key]
																		UpperCAmelCase			 =					renew_temp_conv_paths(_UpperCAmelCase		)
																		UpperCAmelCase			 =					{
																		    'old': F"""output_blocks.{i}.0.temopral_conv""",
																		    'new': F"""up_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
																		}
																		assign_to_checkpoint(
																		    _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      additional_replacements=[meta_path]						,      config=_UpperCAmelCase		)

																		UpperCAmelCase			 =					{k: sorted(_UpperCAmelCase		) for k, v in output_block_list.items()}
																		if ["conv.bias", "conv.weight"] in output_block_list.values():
																								UpperCAmelCase			 =					list(output_block_list.values()		).index(['conv.bias', 'conv.weight']		)
																								UpperCAmelCase			 =					unet_state_dict[
																								    F"""output_blocks.{i}.{index}.conv.weight"""
																								]
																								UpperCAmelCase			 =					unet_state_dict[
																								    F"""output_blocks.{i}.{index}.conv.bias"""
																								]

																								# Clear attentions as they have been attributed above.
																								if len(_UpperCAmelCase		) == 2:
																														UpperCAmelCase			 =					[]

																		if len(_UpperCAmelCase		):
																								UpperCAmelCase			 =					renew_attention_paths(_UpperCAmelCase		)
																								UpperCAmelCase			 =					{
																								    'old': F"""output_blocks.{i}.1""",
																								    'new': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
																								}
																								assign_to_checkpoint(
																								    _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      additional_replacements=[meta_path]						,      config=_UpperCAmelCase		)

																		if len(_UpperCAmelCase		):
																								UpperCAmelCase			 =					renew_attention_paths(_UpperCAmelCase		)
																								UpperCAmelCase			 =					{
																								    'old': F"""output_blocks.{i}.2""",
																								    'new': F"""up_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
																								}
																								assign_to_checkpoint(
																								    _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      additional_replacements=[meta_path]						,      config=_UpperCAmelCase		)
												else:
																		UpperCAmelCase			 =					renew_resnet_paths(_UpperCAmelCase						,      n_shave_prefix_segments=1		)
																		for path in resnet_0_paths:
																								UpperCAmelCase			 =					'.'.join(['output_blocks', str(_UpperCAmelCase		), path['old']]		)
																								UpperCAmelCase			 =					'.'.join(['up_blocks', str(_UpperCAmelCase		), 'resnets', str(_UpperCAmelCase		), path['new']]		)
																								UpperCAmelCase			 =					unet_state_dict[old_path]

																		UpperCAmelCase			 =					[l for l in output_block_layers if 'temopral_conv' in l]
																		for path in temopral_conv_paths:
																								UpperCAmelCase			 =					path.split('temopral_conv.'		)[-1]
																								UpperCAmelCase			 =					'.'.join(['output_blocks', str(_UpperCAmelCase		), str(_UpperCAmelCase		), 'temopral_conv', pruned_path]		)
																								UpperCAmelCase			 =					'.'.join(['up_blocks', str(_UpperCAmelCase		), 'temp_convs', str(_UpperCAmelCase		), pruned_path]		)
																								UpperCAmelCase			 =					unet_state_dict[old_path]

						return new_checkpoint


if __name__ == "__main__":
				snake_case_								=			argparse.ArgumentParser()

				parser.add_argument(
				    """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
				)
				parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
				snake_case_								=			parser.parse_args()

				snake_case_								=			torch.load(args.checkpoint_path, map_location="""cpu""")
				snake_case_								=			UNetaDConditionModel()

				snake_case_								=			convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

				snake_case_								=			set(unet.state_dict().keys()) - set(converted_ckpt.keys())
				snake_case_								=			set(converted_ckpt.keys()) - set(unet.state_dict().keys())

				assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"

				# load state_dict
				unet.load_state_dict(converted_ckpt)

				unet.save_pretrained(args.dump_path)

				# -- finish converting the unet --










import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
 import torch


__snake_case		:Any 						=	random.Random()





def 	__snake_case      (     _UpperCAmelCase  ,					_UpperCAmelCase=1.0  ,					_UpperCAmelCase=None  ,					_UpperCAmelCase=None							):
    if rng is None:
        __a					=							global_rng

    __a					=							[]
    for batch_idx in range(shape[0]							):
        values.append([]							)
        for _ in range(shape[1]							):
            values[-1].append(rng.random() * scale							)

    return values

@require_torch
class        _A			(      unittest.TestCase       ):



       def __init__(						self				:	Dict	,						__SCREAMING_SNAKE_CASE				:	str	,						__SCREAMING_SNAKE_CASE				:	Tuple=7	,						__SCREAMING_SNAKE_CASE				:	Any=400	,						__SCREAMING_SNAKE_CASE				:	Any=2_000	,						__SCREAMING_SNAKE_CASE				:	int=1	,						__SCREAMING_SNAKE_CASE				:	Dict=0.0	,						__SCREAMING_SNAKE_CASE				:	Optional[int]=24_000	,						__SCREAMING_SNAKE_CASE				:	Dict=True	,						):


           '''simple docstring'''

           __a					=							parent
           __a					=							batch_size
           __a					=							min_seq_length
           __a					=							max_seq_length
           __a					=							(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
           __a					=							feature_size
           __a					=							padding_value
           __a					=							sampling_rate
           __a					=							return_attention_mask



       def 						_lowerCamelCase     (						self				:	List[str]):


           '''simple docstring'''

           return {
               "feature_size": self.feature_size,
               "padding_value": self.padding_value,
               "sampling_rate": self.sampling_rate,
               "return_attention_mask": self.return_attention_mask,
           }





       def 						_lowerCamelCase     (						self				:	List[Any]	,						__SCREAMING_SNAKE_CASE				:	Union[str, Any]=False	,						__SCREAMING_SNAKE_CASE				:	Optional[Any]=False):


           '''simple docstring'''

           def _flatten(__SCREAMING_SNAKE_CASE				:	List[str]):
               return list(itertools.chain(*__SCREAMING_SNAKE_CASE))

           if equal_length:
               __a					=							floats_list((self.batch_size, self.max_seq_length))
           else:
               # make sure that inputs increase in size
               __a					=							[
                   _flatten(floats_list((x, self.feature_size)))
                   for x in range(self.min_seq_length	,						self.max_seq_length	,						self.seq_length_diff)
               ]

           if numpify:
               __a					=							[np.asarray(__SCREAMING_SNAKE_CASE) for x in audio_inputs]

           return audio_inputs


@require_torch
class        _A			(      __UpperCAmelCase ,unittest.TestCase       ):
       UpperCamelCase__    :       str		   =		EncodecFeatureExtractor



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


           '''simple docstring'''

           __a					=							EnCodecFeatureExtractionTester(self)



       def 						_lowerCamelCase     (						self				:	Optional[int]):


           '''simple docstring'''

           __a					=							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
           # create three inputs of length 800, 1000, and 1200
           __a					=							[floats_list((1, x))[0] for x in range(800	,						1_400	,						200)]
           __a					=							[np.asarray(__SCREAMING_SNAKE_CASE) for audio_input in audio_inputs]

           # Test not batched input
           __a					=							feat_extract(audio_inputs[0]	,						return_tensors='''np''').input_values
           __a					=							feat_extract(np_audio_inputs[0]	,						return_tensors='''np''').input_values
           self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						atol=1E-3))

           # Test batched
           __a					=							feat_extract(__SCREAMING_SNAKE_CASE	,						padding=__SCREAMING_SNAKE_CASE	,						return_tensors='''np''').input_values
           __a					=							feat_extract(__SCREAMING_SNAKE_CASE	,						padding=__SCREAMING_SNAKE_CASE	,						return_tensors='''np''').input_values
           for enc_seq_a, enc_seq_a in zip(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE):
               self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						atol=1E-3))



       def 						_lowerCamelCase     (						self				:	Optional[Any]):


           '''simple docstring'''

           __a					=							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
           __a					=							np.random.rand(100).astype(np.floataa)
           __a					=							np_audio_inputs.tolist()

           for inputs in [py_audio_inputs, np_audio_inputs]:
               __a					=							feature_extractor.pad([{'''input_values''': inputs}]	,						return_tensors='''np''')
               self.assertTrue(np_processed.input_values.dtype == np.floataa)
               __a					=							feature_extractor.pad([{'''input_values''': inputs}]	,						return_tensors='''pt''')
               self.assertTrue(pt_processed.input_values.dtype == torch.floataa)



       def 						_lowerCamelCase     (						self				:	Tuple	,						__SCREAMING_SNAKE_CASE				:	str):


           '''simple docstring'''

           from datasets import load_dataset

           __a					=							load_dataset('''hf-internal-testing/librispeech_asr_dummy'''	,						'''clean'''	,						split='''validation''')
           # automatic decoding with librispeech
           __a					=							ds.sort('''id''').select(range(__SCREAMING_SNAKE_CASE))[:num_samples]['''audio''']

           return [x["array"] for x in audio_samples]



       def 						_lowerCamelCase     (						self				:	int):


           '''simple docstring'''

           __a					=							torch.tensor(
               [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
                3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
                2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
                4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
                7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
                4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03])
           # fmt: on
           __a					=							self._load_datasamples(1)
           __a					=							EncodecFeatureExtractor()
           __a					=							feature_extractor(__SCREAMING_SNAKE_CASE	,						return_tensors='''pt''').input_values
           self.assertEquals(input_values.shape	,						(1, 1, 93_680))
           self.assertTrue(torch.allclose(input_values[0, 0, :30]	,						__SCREAMING_SNAKE_CASE	,						atol=1E-6))



       def 						_lowerCamelCase     (						self				:	Optional[int]):


           '''simple docstring'''

           __a					=							torch.tensor(
               [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
                3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
                2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
                4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
                7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
                4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03])
           # fmt: on
           __a					=							self._load_datasamples(1)
           __a					=							[np.tile(input_audio[0][None]	,						reps=(2, 1))]
           input_audio[0][1] *= 0.5
           __a					=							EncodecFeatureExtractor(feature_size=2)
           __a					=							feature_extractor(__SCREAMING_SNAKE_CASE	,						return_tensors='''pt''').input_values
           self.assertEquals(input_values.shape	,						(1, 2, 93_680))
           self.assertTrue(torch.allclose(input_values[0, 0, :30]	,						__SCREAMING_SNAKE_CASE	,						atol=1E-6))
           self.assertTrue(torch.allclose(input_values[0, 1, :30]	,						EXPECTED_INPUT_VALUES * 0.5	,						atol=1E-6))





       def 						_lowerCamelCase     (						self				:	Any):


           '''simple docstring'''

           __a					=							self._load_datasamples(2)
           # would be easier if the stride was like
           __a					=							EncodecFeatureExtractor(feature_size=1	,						chunk_length_s=1	,						overlap=0.01)

           # pad and trunc raise an error ?
           with self.assertRaisesRegex(
               __SCREAMING_SNAKE_CASE	,						'''^Both padding and truncation were set. Make sure you only set one.$'''	,						):
               __a					=							feature_extractor(
                   __SCREAMING_SNAKE_CASE	,						padding='''max_length'''	,						truncation=__SCREAMING_SNAKE_CASE	,						return_tensors='''pt''').input_values

           # truncate to chunk
           __a					=							feature_extractor(__SCREAMING_SNAKE_CASE	,						truncation=__SCREAMING_SNAKE_CASE	,						return_tensors='''pt''').input_values
           self.assertEquals(truncated_outputs.shape	,						(2, 1, 71_520))  # 2 chunks

           # force truncate to max_length
           __a					=							feature_extractor(
               __SCREAMING_SNAKE_CASE	,						truncation=__SCREAMING_SNAKE_CASE	,						max_length=48_000	,						return_tensors='''pt''').input_values
           self.assertEquals(truncated_outputs.shape	,						(2, 1, 48_000))

           # pad to chunk
           __a					=							feature_extractor(__SCREAMING_SNAKE_CASE	,						padding=__SCREAMING_SNAKE_CASE	,						return_tensors='''pt''').input_values
           self.assertEquals(padded_outputs.shape	,						(2, 1, 95_280))

           # pad to chunk
           __a					=							feature_extractor(__SCREAMING_SNAKE_CASE	,						return_tensors='''pt''').input_values
           self.assertEquals(truncated_outputs.shape	,						(2, 1, 95_280))

           # force pad to max length
           __a					=							feature_extractor(
               __SCREAMING_SNAKE_CASE	,						padding='''max_length'''	,						max_length=100_000	,						return_tensors='''pt''').input_values
           self.assertEquals(truncated_outputs.shape	,						(2, 1, 100_000))

           # force no pad
           with self.assertRaisesRegex(
               __SCREAMING_SNAKE_CASE	,						'''^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'''	,						):
               __a					=							feature_extractor(__SCREAMING_SNAKE_CASE	,						padding=__SCREAMING_SNAKE_CASE	,						return_tensors='''pt''').input_values

           __a					=							feature_extractor(input_audio[0]	,						padding=__SCREAMING_SNAKE_CASE	,						return_tensors='''pt''').input_values
           self.assertEquals(truncated_outputs.shape	,						(1, 1, 93_680))

           # no pad if no chunk_length_s
           __a					=							None
           with self.assertRaisesRegex(
               __SCREAMING_SNAKE_CASE	,						'''^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'''	,						):
               __a					=							feature_extractor(__SCREAMING_SNAKE_CASE	,						padding=__SCREAMING_SNAKE_CASE	,						return_tensors='''pt''').input_values

           __a					=							feature_extractor(input_audio[0]	,						padding=__SCREAMING_SNAKE_CASE	,						return_tensors='''pt''').input_values
           self.assertEquals(truncated_outputs.shape	,						(1, 1, 93_680))

           # no pad if no overlap
           __a					=							2
           __a					=							None
           with self.assertRaisesRegex(
               __SCREAMING_SNAKE_CASE	,						'''^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'''	,						):
               __a					=							feature_extractor(__SCREAMING_SNAKE_CASE	,						padding=__SCREAMING_SNAKE_CASE	,						return_tensors='''pt''').input_values

           __a					=							feature_extractor(input_audio[0]	,						padding=__SCREAMING_SNAKE_CASE	,						return_tensors='''pt''').input_values
           self.assertEquals(truncated_outputs.shape	,						(1, 1, 93_680))





