
import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel


def 		UpperCAmelCase    (			a_	,     a_=1		)	->			str:







				"""simple docstring"""



				if n_shave_prefix_segments >= 0:
								return ".".join(path.split("."		)[n_shave_prefix_segments:]		)
				else:
								return ".".join(path.split("."		)[:n_shave_prefix_segments]		)


def 		UpperCAmelCase    (			a_	,     a_=0		)	->			Union[str, Any]:







				"""simple docstring"""



				__A         =				[]
				for old_item in old_list:
								__A         =				old_item
								__A         =				new_item.replace("block."	,     "resnets."		)
								__A         =				new_item.replace("conv_shorcut"	,     "conv1"		)
								__A         =				new_item.replace("in_shortcut"	,     "conv_shortcut"		)
								__A         =				new_item.replace("temb_proj"	,     "time_emb_proj"		)

								__A         =				shave_segments(a_	,     n_shave_prefix_segments=a_		)

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

				return mapping


def 		UpperCAmelCase    (			a_	,     a_=0	,     a_=False		)	->			Optional[Any]:







				"""simple docstring"""



				__A         =				[]
				for old_item in old_list:
								__A         =				old_item

								# In `model.mid`, the layer is called `attn`.
								if not in_mid:
												__A         =				new_item.replace("attn"	,     "attentions"		)
								__A         =				new_item.replace(".k."	,     ".key."		)
								__A         =				new_item.replace(".v."	,     ".value."		)
								__A         =				new_item.replace(".q."	,     ".query."		)

								__A         =				new_item.replace("proj_out"	,     "proj_attn"		)
								__A         =				new_item.replace("norm"	,     "group_norm"		)

								__A         =				shave_segments(a_	,     n_shave_prefix_segments=a_		)
								mapping.append({"old": old_item, "new": new_item}		)

				return mapping


def 		UpperCAmelCase    (			a_	,     a_	,     a_	,     a_=None	,     a_=None	,     a_=None		)	->			Dict:







				"""simple docstring"""



				assert isinstance(a_	,     a_		), "Paths should be a list of dicts containing 'old' and 'new' keys."

				if attention_paths_to_split is not None:
								if config is None:
												raise ValueError("Please specify the config if setting 'attention_paths_to_split' to 'True'."		)

								for path, path_map in attention_paths_to_split.items():
												__A         =				old_checkpoint[path]
												__A         =				old_tensor.shape[0] // 3

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

												__A         =				old_tensor.shape[0] // config.get("num_head_channels"	,     1		) // 3

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

												__A         =				query.reshape(a_		).squeeze()
												__A         =				key.reshape(a_		).squeeze()
												__A         =				value.reshape(a_		).squeeze()

				for path in paths:
								__A         =				path["new"]

								if attention_paths_to_split is not None and new_path in attention_paths_to_split:
												continue

								__A         =				new_path.replace("down."	,     "down_blocks."		)
								__A         =				new_path.replace("up."	,     "up_blocks."		)

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

								if "attentions" in new_path:
												__A         =				old_checkpoint[path["old"]].squeeze()
								else:
												__A         =				old_checkpoint[path["old"]]


def 		UpperCAmelCase    (			a_	,     a_		)	->			Optional[int]:







				"""simple docstring"""



				__A         =				{}

				__A         =				checkpoint["temb.dense.0.weight"]
				__A         =				checkpoint["temb.dense.0.bias"]
				__A         =				checkpoint["temb.dense.1.weight"]
				__A         =				checkpoint["temb.dense.1.bias"]

				__A         =				checkpoint["norm_out.weight"]
				__A         =				checkpoint["norm_out.bias"]

				__A         =				checkpoint["conv_in.weight"]
				__A         =				checkpoint["conv_in.bias"]
				__A         =				checkpoint["conv_out.weight"]
				__A         =				checkpoint["conv_out.bias"]

				__A         =				len({".".join(layer.split("."		)[:2]		) for layer in checkpoint if "down" in layer}		)
				__A         =				{
				    layer_id: [key for key in checkpoint if F'''down.{layer_id}''' in key] for layer_id in range(a_		)
				}

				__A         =				len({".".join(layer.split("."		)[:2]		) for layer in checkpoint if "up" in layer}		)
				__A         =				{layer_id: [key for key in checkpoint if F'''up.{layer_id}''' in key] for layer_id in range(a_		)}

				for i in range(a_		):
								__A         =				(i - 1) // (config["layers_per_block"] + 1)

								if any("downsample" in layer for layer in down_blocks[i]		):
												__A         =				checkpoint[
												    F'''down.{i}.downsample.op.weight'''
												]
												__A         =				checkpoint[F'''down.{i}.downsample.op.bias''']
								#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
								#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

								if any("block" in layer for layer in down_blocks[i]		):
												__A         =				len(
												    {".".join(shave_segments(a_	,     2		).split("."		)[:2]		) for layer in down_blocks[i] if "block" in layer}		)
												__A         =				{
												    layer_id: [key for key in down_blocks[i] if F'''block.{layer_id}''' in key]
												    for layer_id in range(a_		)
												}

												if num_blocks > 0:
																for j in range(config["layers_per_block"]		):
																				__A         =				renew_resnet_paths(blocks[j]		)
																				assign_to_checkpoint(a_	,     a_	,     a_		)

								if any("attn" in layer for layer in down_blocks[i]		):
												__A         =				len(
												    {".".join(shave_segments(a_	,     2		).split("."		)[:2]		) for layer in down_blocks[i] if "attn" in layer}		)
												__A         =				{
												    layer_id: [key for key in down_blocks[i] if F'''attn.{layer_id}''' in key]
												    for layer_id in range(a_		)
												}

												if num_attn > 0:
																for j in range(config["layers_per_block"]		):
																				__A         =				renew_attention_paths(attns[j]		)
																				assign_to_checkpoint(a_	,     a_	,     a_	,     config=a_		)

				__A         =				[key for key in checkpoint if "mid.block_1" in key]
				__A         =				[key for key in checkpoint if "mid.block_2" in key]
				__A         =				[key for key in checkpoint if "mid.attn_1" in key]

				# Mid new 2
				__A         =				renew_resnet_paths(a_		)
				assign_to_checkpoint(
				    a_	,     a_	,     a_	,     additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]	,     )

				__A         =				renew_resnet_paths(a_		)
				assign_to_checkpoint(
				    a_	,     a_	,     a_	,     additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]	,     )

				__A         =				renew_attention_paths(a_	,     in_mid=a_		)
				assign_to_checkpoint(
				    a_	,     a_	,     a_	,     additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]	,     )

				for i in range(a_		):
								__A         =				num_up_blocks - 1 - i

								if any("upsample" in layer for layer in up_blocks[i]		):
												__A         =				checkpoint[
												    F'''up.{i}.upsample.conv.weight'''
												]
												__A         =				checkpoint[F'''up.{i}.upsample.conv.bias''']

								if any("block" in layer for layer in up_blocks[i]		):
												__A         =				len(
												    {".".join(shave_segments(a_	,     2		).split("."		)[:2]		) for layer in up_blocks[i] if "block" in layer}		)
												__A         =				{
												    layer_id: [key for key in up_blocks[i] if F'''block.{layer_id}''' in key] for layer_id in range(a_		)
												}

												if num_blocks > 0:
																for j in range(config["layers_per_block"] + 1		):
																				__A         =				{"old": F'''up_blocks.{i}''', "new": F'''up_blocks.{block_id}'''}
																				__A         =				renew_resnet_paths(blocks[j]		)
																				assign_to_checkpoint(a_	,     a_	,     a_	,     additional_replacements=[replace_indices]		)

								if any("attn" in layer for layer in up_blocks[i]		):
												__A         =				len(
												    {".".join(shave_segments(a_	,     2		).split("."		)[:2]		) for layer in up_blocks[i] if "attn" in layer}		)
												__A         =				{
												    layer_id: [key for key in up_blocks[i] if F'''attn.{layer_id}''' in key] for layer_id in range(a_		)
												}

												if num_attn > 0:
																for j in range(config["layers_per_block"] + 1		):
																				__A         =				{"old": F'''up_blocks.{i}''', "new": F'''up_blocks.{block_id}'''}
																				__A         =				renew_attention_paths(attns[j]		)
																				assign_to_checkpoint(a_	,     a_	,     a_	,     additional_replacements=[replace_indices]		)

				__A         =				{k.replace("mid_new_2"	,     "mid_block"		): v for k, v in new_checkpoint.items()}
				return new_checkpoint


def 		UpperCAmelCase    (			a_	,     a_		)	->			Union[str, Any]:







				"""simple docstring"""



				__A         =				{}

				__A         =				checkpoint["encoder.norm_out.weight"]
				__A         =				checkpoint["encoder.norm_out.bias"]

				__A         =				checkpoint["encoder.conv_in.weight"]
				__A         =				checkpoint["encoder.conv_in.bias"]
				__A         =				checkpoint["encoder.conv_out.weight"]
				__A         =				checkpoint["encoder.conv_out.bias"]

				__A         =				checkpoint["decoder.norm_out.weight"]
				__A         =				checkpoint["decoder.norm_out.bias"]

				__A         =				checkpoint["decoder.conv_in.weight"]
				__A         =				checkpoint["decoder.conv_in.bias"]
				__A         =				checkpoint["decoder.conv_out.weight"]
				__A         =				checkpoint["decoder.conv_out.bias"]

				__A         =				len({".".join(layer.split("."		)[:3]		) for layer in checkpoint if "down" in layer}		)
				__A         =				{
				    layer_id: [key for key in checkpoint if F'''down.{layer_id}''' in key] for layer_id in range(a_		)
				}

				__A         =				len({".".join(layer.split("."		)[:3]		) for layer in checkpoint if "up" in layer}		)
				__A         =				{layer_id: [key for key in checkpoint if F'''up.{layer_id}''' in key] for layer_id in range(a_		)}

				for i in range(a_		):
								__A         =				(i - 1) // (config["layers_per_block"] + 1)

								if any("downsample" in layer for layer in down_blocks[i]		):
												__A         =				checkpoint[
												    F'''encoder.down.{i}.downsample.conv.weight'''
												]
												__A         =				checkpoint[
												    F'''encoder.down.{i}.downsample.conv.bias'''
												]

								if any("block" in layer for layer in down_blocks[i]		):
												__A         =				len(
												    {".".join(shave_segments(a_	,     3		).split("."		)[:3]		) for layer in down_blocks[i] if "block" in layer}		)
												__A         =				{
												    layer_id: [key for key in down_blocks[i] if F'''block.{layer_id}''' in key]
												    for layer_id in range(a_		)
												}

												if num_blocks > 0:
																for j in range(config["layers_per_block"]		):
																				__A         =				renew_resnet_paths(blocks[j]		)
																				assign_to_checkpoint(a_	,     a_	,     a_		)

								if any("attn" in layer for layer in down_blocks[i]		):
												__A         =				len(
												    {".".join(shave_segments(a_	,     3		).split("."		)[:3]		) for layer in down_blocks[i] if "attn" in layer}		)
												__A         =				{
												    layer_id: [key for key in down_blocks[i] if F'''attn.{layer_id}''' in key]
												    for layer_id in range(a_		)
												}

												if num_attn > 0:
																for j in range(config["layers_per_block"]		):
																				__A         =				renew_attention_paths(attns[j]		)
																				assign_to_checkpoint(a_	,     a_	,     a_	,     config=a_		)

				__A         =				[key for key in checkpoint if "mid.block_1" in key]
				__A         =				[key for key in checkpoint if "mid.block_2" in key]
				__A         =				[key for key in checkpoint if "mid.attn_1" in key]

				# Mid new 2
				__A         =				renew_resnet_paths(a_		)
				assign_to_checkpoint(
				    a_	,     a_	,     a_	,     additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]	,     )

				__A         =				renew_resnet_paths(a_		)
				assign_to_checkpoint(
				    a_	,     a_	,     a_	,     additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]	,     )

				__A         =				renew_attention_paths(a_	,     in_mid=a_		)
				assign_to_checkpoint(
				    a_	,     a_	,     a_	,     additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]	,     )

				for i in range(a_		):
								__A         =				num_up_blocks - 1 - i

								if any("upsample" in layer for layer in up_blocks[i]		):
												__A         =				checkpoint[
												    F'''decoder.up.{i}.upsample.conv.weight'''
												]
												__A         =				checkpoint[
												    F'''decoder.up.{i}.upsample.conv.bias'''
												]

								if any("block" in layer for layer in up_blocks[i]		):
												__A         =				len(
												    {".".join(shave_segments(a_	,     3		).split("."		)[:3]		) for layer in up_blocks[i] if "block" in layer}		)
												__A         =				{
												    layer_id: [key for key in up_blocks[i] if F'''block.{layer_id}''' in key] for layer_id in range(a_		)
												}

												if num_blocks > 0:
																for j in range(config["layers_per_block"] + 1		):
																				__A         =				{"old": F'''up_blocks.{i}''', "new": F'''up_blocks.{block_id}'''}
																				__A         =				renew_resnet_paths(blocks[j]		)
																				assign_to_checkpoint(a_	,     a_	,     a_	,     additional_replacements=[replace_indices]		)

								if any("attn" in layer for layer in up_blocks[i]		):
												__A         =				len(
												    {".".join(shave_segments(a_	,     3		).split("."		)[:3]		) for layer in up_blocks[i] if "attn" in layer}		)
												__A         =				{
												    layer_id: [key for key in up_blocks[i] if F'''attn.{layer_id}''' in key] for layer_id in range(a_		)
												}

												if num_attn > 0:
																for j in range(config["layers_per_block"] + 1		):
																				__A         =				{"old": F'''up_blocks.{i}''', "new": F'''up_blocks.{block_id}'''}
																				__A         =				renew_attention_paths(attns[j]		)
																				assign_to_checkpoint(a_	,     a_	,     a_	,     additional_replacements=[replace_indices]		)

				__A         =				{k.replace("mid_new_2"	,     "mid_block"		): v for k, v in new_checkpoint.items()}
				__A         =				checkpoint["quant_conv.weight"]
				__A         =				checkpoint["quant_conv.bias"]
				if "quantize.embedding.weight" in checkpoint:
								__A         =				checkpoint["quantize.embedding.weight"]
				__A         =				checkpoint["post_quant_conv.weight"]
				__A         =				checkpoint["post_quant_conv.bias"]

				return new_checkpoint


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:List[Any]											=       argparse.ArgumentParser()

					parser.add_argument(
					    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
					)

					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The config json file corresponding to the architecture.',
					)

					parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

					SCREAMING_SNAKE_CASE							:List[Any]											=       parser.parse_args()
					SCREAMING_SNAKE_CASE							:Optional[int]											=       torch.load(args.checkpoint_path)

					with open(args.config_file) as f:
										SCREAMING_SNAKE_CASE							:str											=       json.loads(f.read())

					# unet case
					SCREAMING_SNAKE_CASE							:int											=       {key.split('.')[0] for key in checkpoint.keys()}
					if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
										SCREAMING_SNAKE_CASE							:int											=       convert_vq_autoenc_checkpoint(checkpoint, config)
					else:
										SCREAMING_SNAKE_CASE							:Dict											=       convert_ddpm_checkpoint(checkpoint, config)

					if "ddpm" in config:
										del config["ddpm"]

					if config["_class_name"] == "VQModel":
										SCREAMING_SNAKE_CASE							:Union[str, Any]											=       VQModel(**config)
										model.load_state_dict(converted_checkpoint)
										model.save_pretrained(args.dump_path)
					elif config["_class_name"] == "AutoencoderKL":
										SCREAMING_SNAKE_CASE							:Union[str, Any]											=       AutoencoderKL(**config)
										model.load_state_dict(converted_checkpoint)
										model.save_pretrained(args.dump_path)
					else:
										SCREAMING_SNAKE_CASE							:Optional[int]											=       UNetaDModel(**config)
										model.load_state_dict(converted_checkpoint)

										SCREAMING_SNAKE_CASE							:Tuple											=       DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))

										SCREAMING_SNAKE_CASE							:Optional[int]											=       DDPMPipeline(unet=model, scheduler=scheduler)
										pipe.save_pretrained(args.dump_path)


import unittest

from transformers import SpeechaTextaConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers.models.speech_to_text_a.modeling_speech_to_text_a import (
					    SpeechaTextaDecoder,
					    SpeechaTextaForCausalLM,
					)





@require_torch
class 							UpperCAmelCase   :




	'''simple docstring'''





	def __init__( self      :				Dict							,A      :				Tuple							,A      :				Union[str, Any]=99							,A      :				Dict=13							,A      :				int=16							,A      :				int=7							,A      :				Optional[int]=True							,A      :				str=True							,A      :				Optional[Any]=True							,A      :				Optional[int]=False							,A      :				Dict=True							,A      :				Any=2							,A      :				List[Any]=32							,A      :				Optional[Any]=4							,A      :				Tuple=4							,A      :				List[Any]=30							,A      :				Optional[Any]=0							,A      :				str=1							,A      :				List[Any]=2							,A      :				List[Any]=None							,):
					__A         =				parent
					__A         =				batch_size
					__A         =				decoder_seq_length
					# For common tests
					__A         =				self.decoder_seq_length
					__A         =				is_training
					__A         =				use_attention_mask
					__A         =				use_labels

					__A         =				vocab_size
					__A         =				d_model
					__A         =				d_model
					__A         =				decoder_layers
					__A         =				decoder_layers
					__A         =				decoder_ffn_dim
					__A         =				decoder_attention_heads
					__A         =				decoder_attention_heads
					__A         =				eos_token_id
					__A         =				bos_token_id
					__A         =				pad_token_id
					__A         =				decoder_start_token_id
					__A         =				use_cache
					__A         =				max_position_embeddings

					__A         =				None
					__A         =				decoder_seq_length
					__A         =				2
					__A         =				1






	def        UpperCamelCase_						( self      :				Tuple				):
					__A         =				ids_tensor([self.batch_size, self.decoder_seq_length]							,self.vocab_size				)

					__A         =				None
					if self.use_attention_mask:
									__A         =				ids_tensor([self.batch_size, self.decoder_seq_length]							,vocab_size=2				)

					__A         =				None
					if self.use_labels:
									__A         =				ids_tensor([self.batch_size, self.decoder_seq_length]							,self.vocab_size				)

					__A         =				SpeechaTextaConfig(
					    vocab_size=self.vocab_size							,d_model=self.d_model							,decoder_layers=self.decoder_layers							,decoder_ffn_dim=self.decoder_ffn_dim							,decoder_attention_heads=self.decoder_attention_heads							,eos_token_id=self.eos_token_id							,bos_token_id=self.bos_token_id							,use_cache=self.use_cache							,pad_token_id=self.pad_token_id							,decoder_start_token_id=self.decoder_start_token_id							,max_position_embeddings=self.max_position_embeddings							,)

					return (
					    config,
					    input_ids,
					    attention_mask,
					    lm_labels,
					)






	def        UpperCamelCase_						( self      :				Tuple							,A      :				List[str]							,A      :				Tuple							,A      :				Optional[int]							,A      :				List[str]							,):
					__A         =				True
					__A         =				SpeechaTextaDecoder(config=A				).to(A				).eval()
					__A         =				input_ids[:2]

					input_ids[input_ids == 0] += 1
					# first forward pass
					__A         =				model(A							,use_cache=A				)
					__A         =				model(A				)
					__A         =				model(A							,use_cache=A				)

					self.parent.assertTrue(len(A				) == len(A				)				)
					self.parent.assertTrue(len(A				) == len(A				) + 1				)

					__A         =				outputs["past_key_values"]

					# create hypothetical next token and extent to next_input_ids
					__A         =				ids_tensor((2, 1)							,config.vocab_size - 1				) + 1

					# append to next input_ids and
					__A         =				torch.cat([input_ids, next_tokens]							,dim=-1				)

					print(A				)

					__A         =				model(A				)["last_hidden_state"]
					__A         =				model(A							,past_key_values=A				)["last_hidden_state"]

					# select random slice
					__A         =				ids_tensor((1,)							,output_from_past.shape[-1]				).item()
					__A         =				output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
					__A         =				output_from_past[:, 0, random_slice_idx].detach()

					# test that outputs are equal for slice
					assert torch.allclose(A							,A							,atol=1E-3				)







	def        UpperCamelCase_						( self      :				Any				):
					__A         =				self.prepare_config_and_inputs()
					(
					    (
					    __A
					)    ,							(
					    __A
					)    ,							(
					    __A
					)    ,							(
					    __A
					)    ,							
					)         =				config_and_inputs

					__A         =				{
					    "input_ids": input_ids,
					    "attention_mask": attention_mask,
					}
					return config, inputs_dict





@require_torch
class 							UpperCAmelCase   (						__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    unittest.TestCase						):




	'''simple docstring'''





	snake_case_      					=  (SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
	snake_case_      					=  (SpeechaTextaForCausalLM,) if is_torch_available() else ()
	snake_case_      					=  {"text-generation": SpeechaTextaForCausalLM} if is_torch_available() else {}
	snake_case_      					=  True
	snake_case_      					=  False






	def        UpperCamelCase_						( self      :				Any							,):
					__A         =				SpeechaTextaStandaloneDecoderModelTester(self							,is_training=A				)
					__A         =				ConfigTester(self							,config_class=A				)






	def        UpperCamelCase_						( self      :				Optional[Any]				):
					pass






	def        UpperCamelCase_						( self      :				int				):
					pass






	def        UpperCamelCase_						( self      :				Tuple				):
					pass






	def        UpperCamelCase_						( self      :				str				):
					self.config_tester.run_common_tests()






	def        UpperCamelCase_						( self      :				Dict				):
					__A         =				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_decoder_model_past(*A				)







	def        UpperCamelCase_						( self      :				str				):
					return
