


import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


UpperCAmelCase					:  Union[str, Any]			=						os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f"""{bindir}/../../examples/pytorch/translation"""):
		from run_translation import main  # noqa


set_seed(42)
UpperCAmelCase					:  Union[str, Any]			=						"sshleifer/student_marian_en_ro_6_1"
UpperCAmelCase					:  Union[str, Any]			=						"sshleifer/tiny-mbart"







@require_torch
class     __lowercase    (				SCREAMING_SNAKE_CASE_   ):



		"""simple docstring"""

		def  __A					(      self						,	A=False						,	A=None						,	A=True						,	A=True						,	A=True						,	A=True						,	)   ->      Tuple:







					'''simple docstring'''







					lowerCamelCase  						=   self.run_trainer(
					    eval_steps=1						,	max_len=12						,	model_name=__UpperCAmelCase						,	num_train_epochs=1						,	distributed=__UpperCAmelCase						,	extra_args_str=__UpperCAmelCase						,	predict_with_generate=__UpperCAmelCase						,	do_train=__UpperCAmelCase						,	do_eval=__UpperCAmelCase						,	do_predict=__UpperCAmelCase						,	)
					lowerCamelCase  						=   TrainerState.load_from_json(os.path.join(__UpperCAmelCase						,	"""trainer_state.json"""     )     ).log_history

					if not do_eval:
								return

					lowerCamelCase  						=   [log for log in logs if """eval_loss""" in log.keys()]

					lowerCamelCase  						=   eval_metrics[0]
					if predict_with_generate:
								assert "eval_bleu" in first_step_stats

								lowerCamelCase  						=   eval_metrics[-1]
								assert isinstance(last_step_stats["""eval_bleu"""]						,	__UpperCAmelCase     )
								assert not math.isnan(float(last_step_stats["""eval_loss"""]     )     ), "eval_loss must not be `nan`"







		@require_torch_non_multi_gpu
		def  __A					(      self     )   ->      Optional[int]:







					'''simple docstring'''







					self.run_seqaseq_quick()







		@require_torch_multi_gpu
		def  __A					(      self     )   ->      List[Any]:







					'''simple docstring'''







					self.run_seqaseq_quick(distributed=__UpperCAmelCase     )







		@require_torch_multi_gpu
		def  __A					(      self     )   ->      Tuple:







					'''simple docstring'''







					self.run_seqaseq_quick(distributed=__UpperCAmelCase     )







		@unittest.skip("""Requires an update of the env running those tests"""     )
		@require_torch_multi_gpu
		@require_fairscale
		def  __A					(      self     )   ->      Any:







					'''simple docstring'''







					self.run_seqaseq_quick(distributed=__UpperCAmelCase						,	extra_args_str="""--sharded_ddp simple"""     )







		@unittest.skip("""Requires an update of the env running those tests"""     )
		@require_torch_multi_gpu
		@require_fairscale
		def  __A					(      self     )   ->      Dict:







					'''simple docstring'''







					self.run_seqaseq_quick(distributed=__UpperCAmelCase						,	extra_args_str="""--sharded_ddp simple --fp16"""     )







		@unittest.skip("""Requires an update of the env running those tests"""     )
		@require_torch_multi_gpu
		@require_fairscale
		def  __A					(      self     )   ->      Dict:







					'''simple docstring'''







					self.run_seqaseq_quick(distributed=__UpperCAmelCase						,	extra_args_str="""--sharded_ddp zero_dp_2"""						,	predict_with_generate=__UpperCAmelCase     )







		@unittest.skip("""Requires an update of the env running those tests"""     )
		@require_torch_multi_gpu
		@require_fairscale
		def  __A					(      self     )   ->      List[Any]:







					'''simple docstring'''







					self.run_seqaseq_quick(
					    distributed=__UpperCAmelCase						,	extra_args_str="""--sharded_ddp zero_dp_2 --fp16"""						,	predict_with_generate=__UpperCAmelCase     )







		@require_apex
		@require_torch_gpu
		def  __A					(      self     )   ->      Tuple:







					'''simple docstring'''







					self.run_seqaseq_quick(distributed=__UpperCAmelCase						,	extra_args_str="""--fp16 --fp16_backend=apex"""     )
					# test 2nd time - was getting eval_loss': nan'
					# to reproduce the problem set distributed=False
					self.run_seqaseq_quick(distributed=__UpperCAmelCase						,	extra_args_str="""--fp16 --fp16_backend=apex"""     )







		@parameterized.expand(["""base""", """low""", """high""", """mixed"""]     )
		@require_torch_multi_gpu
		def  __A					(      self						,	A     )   ->      Any:







					'''simple docstring'''







					lowerCamelCase  						=   {
					    # test with the default log_level - should be info and thus log info once
					    """base""": {"""extra_args_str""": """""", """n_matches""": 1},
					    # test with low log_level and log_level_replica - should be noisy on all processes
					    # now the info string should appear twice on 2 processes
					    """low""": {"""extra_args_str""": """--log_level debug --log_level_replica debug""", """n_matches""": 2},
					    # test with high log_level and low log_level_replica
					    # now the info string should appear once only on the replica
					    """high""": {"""extra_args_str""": """--log_level error --log_level_replica debug""", """n_matches""": 1},
					    # test with high log_level and log_level_replica - should be quiet on all processes
					    """mixed""": {"""extra_args_str""": """--log_level error --log_level_replica error""", """n_matches""": 0},
					}

					lowerCamelCase  						=   experiments[experiment_id]
					lowerCamelCase  						=   {"""distributed""": True, """predict_with_generate""": False, """do_eval""": False, """do_predict""": False}
					lowerCamelCase  						=   """Running training"""
					with CaptureStderr() as cl:
								self.run_seqaseq_quick(**__UpperCAmelCase						,	extra_args_str=data["""extra_args_str"""]     )
					lowerCamelCase  						=   len(re.findall(__UpperCAmelCase						,	cl.err     )     )
					self.assertEqual(__UpperCAmelCase						,	data["""n_matches"""]     )







		@slow
		def  __A					(      self     )   ->      Optional[Any]:







					'''simple docstring'''







					lowerCamelCase  						=   self.run_trainer(
					    eval_steps=2						,	max_len=1_28						,	model_name=__UpperCAmelCase						,	learning_rate=3e-4						,	num_train_epochs=10						,	distributed=__UpperCAmelCase						,	)

					# Check metrics
					lowerCamelCase  						=   TrainerState.load_from_json(os.path.join(__UpperCAmelCase						,	"""trainer_state.json"""     )     ).log_history
					lowerCamelCase  						=   [log for log in logs if """eval_loss""" in log.keys()]
					lowerCamelCase  						=   eval_metrics[0]
					lowerCamelCase  						=   eval_metrics[-1]

					assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
					assert isinstance(last_step_stats["""eval_bleu"""]						,	__UpperCAmelCase     )

					# test if do_predict saves generations and metrics
					lowerCamelCase  						=   os.listdir(__UpperCAmelCase     )
					lowerCamelCase  						=   {os.path.basename(__UpperCAmelCase     ) for p in contents}
					assert "generated_predictions.txt" in contents
					assert "predict_results.json" in contents







		@slow
		@require_bitsandbytes
		def  __A					(      self     )   ->      Union[str, Any]:







					'''simple docstring'''







					from transformers.training_args import OptimizerNames

					def train_and_return_metrics(A     ) -> Tuple[int, float]:
								lowerCamelCase  						=   """--skip_memory_metrics 0"""

								lowerCamelCase  						=   self.run_trainer(
								    max_len=1_28						,	model_name=__UpperCAmelCase						,	learning_rate=3e-4						,	num_train_epochs=1						,	optim=__UpperCAmelCase						,	distributed=__UpperCAmelCase						,	extra_args_str=__UpperCAmelCase						,	do_eval=__UpperCAmelCase						,	do_predict=__UpperCAmelCase						,	n_gpus_to_use=1						,	)

								# Check metrics
								lowerCamelCase  						=   TrainerState.load_from_json(Path(__UpperCAmelCase						,	"""trainer_state.json"""     )     ).log_history
								lowerCamelCase  						=   int(logs[0]["""train_mem_gpu_peaked_delta"""] / 2**20     )
								lowerCamelCase  						=   int(logs[0]["""train_mem_gpu_alloc_delta"""] / 2**20     )

								lowerCamelCase  						=   logs[0]["""train_loss"""]
								return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

					lowerCamelCase  						=   train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value     )
					lowerCamelCase  						=   train_and_return_metrics(OptimizerNames.ADAMW_BNB.value     )

					lowerCamelCase  						=   gpu_alloc_mem_orig - gpu_alloc_mem_bnb

					lowerCamelCase  						=   gpu_peak_mem_orig + gpu_alloc_mem_orig
					lowerCamelCase  						=   gpu_peak_mem_bnb + gpu_alloc_mem_bnb
					lowerCamelCase  						=   gpu_total_mem_orig - gpu_total_mem_bnb

					# sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
					# doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
					# in 2 bytes and the diff in optim memory usage is derived as so:
					#
					# - normal 25*8=~200MB (8 bytes per param)
					# - bnb    25*2= ~50MB (2 bytes per param)
					#
					# Thus we should expect ~150MB total memory saved.
					#
					# Peak memory should be the same - the total should be different by about that same margin
					#
					# After leaving a small margin to accommodate for differences between gpus let's check
					# that we have at least 120MB in savings
					lowerCamelCase  						=   1_20

					# uncomment the following if this test starts failing - requires py38 for a new print feature
					# gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
					# print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
					# print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
					# print(f"{gpu_alloc_mem_diff=}MB")
					# print(f"{gpu_peak_mem_diff=}MB")
					# print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
					# print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

					self.assertGreater(
					    __UpperCAmelCase						,	__UpperCAmelCase						,	"""should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got"""
					    F' a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and'
					    F' gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB'						,	)

					self.assertGreater(
					    __UpperCAmelCase						,	__UpperCAmelCase						,	"""should use ~150MB less total gpu memory with BNB, compared to without it for this model but got"""
					    F' a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and'
					    F' gpu_total_mem_bnb={gpu_total_mem_bnb}MB'						,	)

					self.assertEqual(
					    __UpperCAmelCase						,	__UpperCAmelCase						,	F'loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'     )







		def  __A					(      self						,	A						,	A						,	A						,	A = 3e-3						,	A = "adafactor"						,	A = False						,	A = None						,	A = 0						,	A = True						,	A = True						,	A = True						,	A = True						,	A = None						,	)   ->      List[str]:







					'''simple docstring'''







					lowerCamelCase  						=   self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
					lowerCamelCase  						=   self.get_auto_remove_tmp_dir()
					lowerCamelCase  						=   F'\n            --model_name_or_path {model_name}\n            --train_file {data_dir}/train.json\n            --validation_file {data_dir}/val.json\n            --test_file {data_dir}/test.json\n            --output_dir {output_dir}\n            --overwrite_output_dir\n            --max_train_samples 8\n            --max_source_length {max_len}\n            --max_target_length {max_len}\n            --do_train\n            --num_train_epochs {str(__UpperCAmelCase     )}\n            --per_device_train_batch_size 4\n            --learning_rate {learning_rate}\n            --warmup_steps 8\n            --logging_steps 0\n            --logging_strategy no\n            --save_steps {str(__UpperCAmelCase     )}\n            --group_by_length\n            --label_smoothing_factor 0.1\n            --target_lang ro_RO\n            --source_lang en_XX\n        '.split()

					lowerCamelCase  						=   F'\n            --do_eval\n            --per_device_eval_batch_size 4\n            --max_eval_samples 8\n            --val_max_target_length {max_len}\n            --evaluation_strategy steps\n            --eval_steps {str(__UpperCAmelCase     )}\n        '.split()

					lowerCamelCase  						=   """
            --do_predict
        """.split()

					lowerCamelCase  						=   []
					if do_train:
								args += args_train

					if do_eval:
								args += args_eval

					if do_predict:
								args += args_predict

					if predict_with_generate:
								args += "--predict_with_generate".split()

					if do_train:
								if optim == "adafactor":
											args += "--adafactor".split()
								else:
											args += F'--optim {optim}'.split()

					if extra_args_str is not None:
								args += extra_args_str.split()

					if distributed:
								if n_gpus_to_use is None:
											lowerCamelCase  						=   get_gpu_count()
								lowerCamelCase  						=   get_torch_dist_unique_port()
								lowerCamelCase  						=   F'\n                -m torch.distributed.run\n                --nproc_per_node={n_gpus_to_use}\n                --master_port={master_port}\n                {self.examples_dir_str}/pytorch/translation/run_translation.py\n            '.split()
								lowerCamelCase  						=   [sys.executable] + distributed_args + args
								# keep for quick debug
								# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
								execute_subprocess_async(__UpperCAmelCase						,	env=self.get_env()     )
					else:
								lowerCamelCase  						=   ["""run_translation.py"""] + args
								with patch.object(__UpperCAmelCase						,	"""argv"""						,	__UpperCAmelCase     ):
											main()

					return output_dir






'''simple docstring'''





import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

_lowerCAmelCase      						=	[
    # (stable-diffusion, HF Diffusers)
    ('''time_embed.0.weight''', '''time_embedding.linear_1.weight'''),
    ('''time_embed.0.bias''', '''time_embedding.linear_1.bias'''),
    ('''time_embed.2.weight''', '''time_embedding.linear_2.weight'''),
    ('''time_embed.2.bias''', '''time_embedding.linear_2.bias'''),
    ('''input_blocks.0.0.weight''', '''conv_in.weight'''),
    ('''input_blocks.0.0.bias''', '''conv_in.bias'''),
    ('''out.0.weight''', '''conv_norm_out.weight'''),
    ('''out.0.bias''', '''conv_norm_out.bias'''),
    ('''out.2.weight''', '''conv_out.weight'''),
    ('''out.2.bias''', '''conv_out.bias'''),
]

_lowerCAmelCase      						=	[
    # (stable-diffusion, HF Diffusers)
    ('''in_layers.0''', '''norm1'''),
    ('''in_layers.2''', '''conv1'''),
    ('''out_layers.0''', '''norm2'''),
    ('''out_layers.3''', '''conv2'''),
    ('''emb_layers.1''', '''time_emb_proj'''),
    ('''skip_connection''', '''conv_shortcut'''),
]

_lowerCAmelCase      						=	[]
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
 # loop over downblocks/upblocks

 for j in range(2):
  # loop over resnets/attentions for downblocks
  _lowerCAmelCase      						=	F"""down_blocks.{i}.resnets.{j}."""
  _lowerCAmelCase      						=	F"""input_blocks.{3*i + j + 1}.0."""
  unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

  if i < 3:
   # no attention layers in down_blocks.3
   _lowerCAmelCase      						=	F"""down_blocks.{i}.attentions.{j}."""
   _lowerCAmelCase      						=	F"""input_blocks.{3*i + j + 1}.1."""
   unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

 for j in range(3):
  # loop over resnets/attentions for upblocks
  _lowerCAmelCase      						=	F"""up_blocks.{i}.resnets.{j}."""
  _lowerCAmelCase      						=	F"""output_blocks.{3*i + j}.0."""
  unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

  if i > 0:
   # no attention layers in up_blocks.0
   _lowerCAmelCase      						=	F"""up_blocks.{i}.attentions.{j}."""
   _lowerCAmelCase      						=	F"""output_blocks.{3*i + j}.1."""
   unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

 if i < 3:
  # no downsample in down_blocks.3
  _lowerCAmelCase      						=	F"""down_blocks.{i}.downsamplers.0.conv."""
  _lowerCAmelCase      						=	F"""input_blocks.{3*(i+1)}.0.op."""
  unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

  # no upsample in up_blocks.3
  _lowerCAmelCase      						=	F"""up_blocks.{i}.upsamplers.0."""
  _lowerCAmelCase      						=	F"""output_blocks.{3*i + 2}.{1 if i == 0 else 2}."""
  unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

_lowerCAmelCase      						=	'''mid_block.attentions.0.'''
_lowerCAmelCase      						=	'''middle_block.1.'''
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
 _lowerCAmelCase      						=	F"""mid_block.resnets.{j}."""
 _lowerCAmelCase      						=	F"""middle_block.{2*j}."""
 unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Any							      =					{k: k for k in unet_state_dict.keys()}
    for sd_name, hf_name in unet_conversion_map:
        lowerCAmelCase__ :  Optional[int]							      =					sd_name
    for k, v in mapping.items():
        if "resnets" in k:
            for sd_part, hf_part in unet_conversion_map_resnet:
                lowerCAmelCase__ :  Any							      =					v.replace(UpperCamelCase							,    UpperCamelCase			)
            lowerCAmelCase__ :  List[Any]							      =					v
    for k, v in mapping.items():
        for sd_part, hf_part in unet_conversion_map_layer:
            lowerCAmelCase__ :  List[Any]							      =					v.replace(UpperCamelCase							,    UpperCamelCase			)
        lowerCAmelCase__ :  Optional[Any]							      =					v
    lowerCAmelCase__ :  Tuple							      =					{v: unet_state_dict[k] for k, v in mapping.items()}
    return new_state_dict


# ================#
# VAE Conversion #
# ================#

_lowerCAmelCase      						=	[
    # (stable-diffusion, HF Diffusers)
    ('''nin_shortcut''', '''conv_shortcut'''),
    ('''norm_out''', '''conv_norm_out'''),
    ('''mid.attn_1.''', '''mid_block.attentions.0.'''),
]

for i in range(4):
 # down_blocks have two resnets
 for j in range(2):
  _lowerCAmelCase      						=	F"""encoder.down_blocks.{i}.resnets.{j}."""
  _lowerCAmelCase      						=	F"""encoder.down.{i}.block.{j}."""
  vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

 if i < 3:
  _lowerCAmelCase      						=	F"""down_blocks.{i}.downsamplers.0."""
  _lowerCAmelCase      						=	F"""down.{i}.downsample."""
  vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

  _lowerCAmelCase      						=	F"""up_blocks.{i}.upsamplers.0."""
  _lowerCAmelCase      						=	F"""up.{3-i}.upsample."""
  vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

 # up_blocks have three resnets
 # also, up blocks in hf are numbered in reverse from sd
 for j in range(3):
  _lowerCAmelCase      						=	F"""decoder.up_blocks.{i}.resnets.{j}."""
  _lowerCAmelCase      						=	F"""decoder.up.{3-i}.block.{j}."""
  vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
 _lowerCAmelCase      						=	F"""mid_block.resnets.{i}."""
 _lowerCAmelCase      						=	F"""mid.block_{i+1}."""
 vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


_lowerCAmelCase      						=	[
    # (stable-diffusion, HF Diffusers)
    ('''norm.''', '''group_norm.'''),
    ('''q.''', '''query.'''),
    ('''k.''', '''key.'''),
    ('''v.''', '''value.'''),
    ('''proj_out.''', '''proj_attn.'''),
]
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    return w.reshape(*w.shape							,    1							,    1			)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Optional[int]							      =					{k: k for k in vae_state_dict.keys()}
    for k, v in mapping.items():
        for sd_part, hf_part in vae_conversion_map:
            lowerCAmelCase__ :  str							      =					v.replace(UpperCamelCase							,    UpperCamelCase			)
        lowerCAmelCase__ :  Optional[Any]							      =					v
    for k, v in mapping.items():
        if "attentions" in k:
            for sd_part, hf_part in vae_conversion_map_attn:
                lowerCAmelCase__ :  Dict							      =					v.replace(UpperCamelCase							,    UpperCamelCase			)
            lowerCAmelCase__ :  List[Any]							      =					v
    lowerCAmelCase__ :  Union[str, Any]							      =					{v: vae_state_dict[k] for k, v in mapping.items()}
    lowerCAmelCase__ :  Tuple							      =					["""q""", """k""", """v""", """proj_out"""]
    for k, v in new_state_dict.items():
        for weight_name in weights_to_convert:
            if f"""mid.attn_1.{weight_name}.weight""" in k:
                print(f"""Reshaping {k} for SD format"""			)
                lowerCAmelCase__ :  Optional[int]							      =					reshape_weight_for_sd(UpperCamelCase			)
    return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


_lowerCAmelCase      						=	[
    # (stable-diffusion, HF Diffusers)
    ('''resblocks.''', '''text_model.encoder.layers.'''),
    ('''ln_1''', '''layer_norm1'''),
    ('''ln_2''', '''layer_norm2'''),
    ('''.c_fc.''', '''.fc1.'''),
    ('''.c_proj.''', '''.fc2.'''),
    ('''.attn''', '''.self_attn'''),
    ('''ln_final.''', '''transformer.text_model.final_layer_norm.'''),
    ('''token_embedding.weight''', '''transformer.text_model.embeddings.token_embedding.weight'''),
    ('''positional_embedding''', '''transformer.text_model.embeddings.position_embedding.weight'''),
]
_lowerCAmelCase      						=	{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
_lowerCAmelCase      						=	re.compile('''|'''.join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
_lowerCAmelCase      						=	{'''q''': 0, '''k''': 1, '''v''': 2}
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Optional[Any]							      =					{}
    lowerCAmelCase__ :  int							      =					{}
    lowerCAmelCase__ :  List[Any]							      =					{}
    for k, v in text_enc_dict.items():
        if (
            k.endswith(""".self_attn.q_proj.weight"""			)
            or k.endswith(""".self_attn.k_proj.weight"""			)
            or k.endswith(""".self_attn.v_proj.weight"""			)
        ):
            lowerCAmelCase__ :  Optional[int]							      =					k[: -len(""".q_proj.weight"""			)]
            lowerCAmelCase__ :  Tuple							      =					k[-len("""q_proj.weight"""			)]
            if k_pre not in capture_qkv_weight:
                lowerCAmelCase__ :  List[Any]							      =					[None, None, None]
            lowerCAmelCase__ :  Dict							      =					v
            continue

        if (
            k.endswith(""".self_attn.q_proj.bias"""			)
            or k.endswith(""".self_attn.k_proj.bias"""			)
            or k.endswith(""".self_attn.v_proj.bias"""			)
        ):
            lowerCAmelCase__ :  str							      =					k[: -len(""".q_proj.bias"""			)]
            lowerCAmelCase__ :  List[str]							      =					k[-len("""q_proj.bias"""			)]
            if k_pre not in capture_qkv_bias:
                lowerCAmelCase__ :  Union[str, Any]							      =					[None, None, None]
            lowerCAmelCase__ :  Any							      =					v
            continue

        lowerCAmelCase__ :  Dict							      =					textenc_pattern.sub(lambda UpperCamelCase			: protected[re.escape(m.group(0			)			)]							,    UpperCamelCase			)
        lowerCAmelCase__ :  Union[str, Any]							      =					v

    for k_pre, tensors in capture_qkv_weight.items():
        if None in tensors:
            raise Exception("""CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"""			)
        lowerCAmelCase__ :  Any							      =					textenc_pattern.sub(lambda UpperCamelCase			: protected[re.escape(m.group(0			)			)]							,    UpperCamelCase			)
        lowerCAmelCase__ :  Tuple							      =					torch.cat(UpperCamelCase			)

    for k_pre, tensors in capture_qkv_bias.items():
        if None in tensors:
            raise Exception("""CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"""			)
        lowerCAmelCase__ :  str							      =					textenc_pattern.sub(lambda UpperCamelCase			: protected[re.escape(m.group(0			)			)]							,    UpperCamelCase			)
        lowerCAmelCase__ :  List[Any]							      =					torch.cat(UpperCamelCase			)

    return new_state_dict
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    return text_enc_dict


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

 parser.add_argument('''--model_path''', default=None, type=str, required=True, help='''Path to the model to convert.''')
 parser.add_argument('''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the output model.''')
 parser.add_argument('''--half''', action='''store_true''', help='''Save weights in half precision.''')
 parser.add_argument(
     '''--use_safetensors''', action='''store_true''', help='''Save weights use safetensors, default is ckpt.'''
 )

 _lowerCAmelCase      						=	parser.parse_args()

 assert args.model_path is not None, "Must provide a model path!"

 assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

 # Path for safetensors
 _lowerCAmelCase      						=	osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.safetensors''')
 _lowerCAmelCase      						=	osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.safetensors''')
 _lowerCAmelCase      						=	osp.join(args.model_path, '''text_encoder''', '''model.safetensors''')

 # Load models from safetensors if it exists, if it doesn't pytorch
 if osp.exists(unet_path):
  _lowerCAmelCase      						=	load_file(unet_path, device='''cpu''')
 else:
  _lowerCAmelCase      						=	osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.bin''')
  _lowerCAmelCase      						=	torch.load(unet_path, map_location='''cpu''')

 if osp.exists(vae_path):
  _lowerCAmelCase      						=	load_file(vae_path, device='''cpu''')
 else:
  _lowerCAmelCase      						=	osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.bin''')
  _lowerCAmelCase      						=	torch.load(vae_path, map_location='''cpu''')

 if osp.exists(text_enc_path):
  _lowerCAmelCase      						=	load_file(text_enc_path, device='''cpu''')
 else:
  _lowerCAmelCase      						=	osp.join(args.model_path, '''text_encoder''', '''pytorch_model.bin''')
  _lowerCAmelCase      						=	torch.load(text_enc_path, map_location='''cpu''')

 # Convert the UNet model
 _lowerCAmelCase      						=	convert_unet_state_dict(unet_state_dict)
 _lowerCAmelCase      						=	{'''model.diffusion_model.''' + k: v for k, v in unet_state_dict.items()}

 # Convert the VAE model
 _lowerCAmelCase      						=	convert_vae_state_dict(vae_state_dict)
 _lowerCAmelCase      						=	{'''first_stage_model.''' + k: v for k, v in vae_state_dict.items()}

 # Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
 _lowerCAmelCase      						=	'''text_model.encoder.layers.22.layer_norm2.bias''' in text_enc_dict

 if is_vaa_model:
  # Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
  _lowerCAmelCase      						=	{'''transformer.''' + k: v for k, v in text_enc_dict.items()}
  _lowerCAmelCase      						=	convert_text_enc_state_dict_vaa(text_enc_dict)
  _lowerCAmelCase      						=	{'''cond_stage_model.model.''' + k: v for k, v in text_enc_dict.items()}
 else:
  _lowerCAmelCase      						=	convert_text_enc_state_dict(text_enc_dict)
  _lowerCAmelCase      						=	{'''cond_stage_model.transformer.''' + k: v for k, v in text_enc_dict.items()}

 # Put together new checkpoint
 _lowerCAmelCase      						=	{**unet_state_dict, **vae_state_dict, **text_enc_dict}
 if args.half:
  _lowerCAmelCase      						=	{k: v.half() for k, v in state_dict.items()}

 if args.use_safetensors:
  save_file(state_dict, args.checkpoint_path)
 else:
  _lowerCAmelCase      						=	{'''state_dict''': state_dict}
  torch.save(state_dict, args.checkpoint_path)


