





'''simple docstring'''

import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
  from sagemaker import Session, TrainingJobAnalytics
  from sagemaker.huggingface import HuggingFace






@pytest.mark.skipif(
    literal_eval(os.getenv("""TEST_SAGEMAKER"""			,       """False"""				)				) is not True			,       reason="""Skipping test because should only be run when releasing minor transformers version"""			,       )
@pytest.mark.usefixtures("""sm_env"""				)
@parameterized_class(
    [
        {
            """framework""": """pytorch""",
            """script""": """run_glue_model_parallelism.py""",
            """model_name_or_path""": """roberta-large""",
            """instance_type""": """ml.p3dn.24xlarge""",
            """results""": {"""train_runtime""": 1600, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
        {
            """framework""": """pytorch""",
            """script""": """run_glue.py""",
            """model_name_or_path""": """roberta-large""",
            """instance_type""": """ml.p3dn.24xlarge""",
            """results""": {"""train_runtime""": 1600, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
    ]				)
class    UpperCAmelCase	(       unittest.TestCase				):
 def      UpperCAmelCase_							(				self      :Tuple							)->							str:
     if self.framework == "pytorch":
         subprocess.run(
             F"cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py".split()						,       encoding="utf-8"						,       check=lowercase_						,       )
     assert hasattr(self						,       "env"							)
 def      UpperCAmelCase_							(				self      :List[Any]						,       lowercase_      :str							)->							Any:
     # configuration for running training on smdistributed Model Parallel
     A__				=			{
         "enabled": True,
         "processes_per_host": 8,
     }
     A__				=			{
         "enabled": True,
         "parameters": {
             "microbatches": 4,
             "placement_strategy": "spread",
             "pipeline": "interleaved",
             "optimize": "speed",
             "partitions": 4,
             "ddp": True,
         },
     }

     A__				=			{"smdistributed": {"modelparallel": smp_options}, "mpi": mpi_options}

     A__				=			"trainer" if self.script == "run_glue.py" else "smtrainer"
     # creates estimator
     return HuggingFace(
         entry_point=self.script						,       source_dir=self.env.test_path						,       role=self.env.role						,       image_uri=self.env.image_uri						,       base_job_name=F"{self.env.base_job_name}-{instance_count}-smp-{name_extension}"						,       instance_count=lowercase_						,       instance_type=self.instance_type						,       debugger_hook_config=lowercase_						,       hyperparameters={
             **self.env.hyperparameters,
             "model_name_or_path": self.model_name_or_path,
             "max_steps": 5_00,
         }						,       metric_definitions=self.env.metric_definitions						,       distribution=lowercase_						,       py_version="py36"						,       )
 def      UpperCAmelCase_							(				self      :Optional[int]						,       lowercase_      :Dict							)->							Union[str, Any]:
     TrainingJobAnalytics(lowercase_							).export_csv(F"{self.env.test_path}/{job_name}_metrics.csv"							)



 @parameterized.expand([(1,)]							)
 def      UpperCAmelCase_							(				self      :Union[str, Any]						,       lowercase_      :int							)->							Dict:
     # create estimator
     A__				=			self.create_estimator(lowercase_							)

     # run training
     estimator.fit()

     # result dataframe
     A__				=			TrainingJobAnalytics(estimator.latest_training_job.name							).dataframe()

     # extract kpis
     A__				=			list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]							)
     A__				=			list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]							)
     # get train time from SageMaker job, this includes starting, preprocessing, stopping
     A__				=			(
         Session().describe_training_job(estimator.latest_training_job.name							).get("TrainingTimeInSeconds"						,       99_99_99							)
     )

     # assert kpis
     assert train_runtime <= self.results["train_runtime"]
     assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy							)
     assert all(t <= self.results["eval_loss"] for t in eval_loss							)

     # dump tests result into json file to share in PR
     with open(F"{estimator.latest_training_job.name}.json"						,       "w"							) as outfile:
         json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss}						,       lowercase_							)










'''simple docstring'''

import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__lowerCAmelCase							:	List[str]			={
    "sample_size": 32,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": 1000,
    "block_out_channels": [32, 64],
    "attention_head_dim": 8,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "scale_shift",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

__lowerCAmelCase							:	Dict			={
    "sample_size": 64,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 3,
    "num_class_embeds": 1000,
    "block_out_channels": [192, 192 * 2, 192 * 3, 192 * 4],
    "attention_head_dim": 64,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "scale_shift",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

__lowerCAmelCase							:	Union[str, Any]			={
    "sample_size": 256,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": None,
    "block_out_channels": [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    "attention_head_dim": 64,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "ResnetDownsampleBlock2D",
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
        "ResnetUpsampleBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "default",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

__lowerCAmelCase							:	str			={
    "num_train_timesteps": 40,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

__lowerCAmelCase							:	Tuple			={
    "num_train_timesteps": 201,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

__lowerCAmelCase							:	Dict			={
    "num_train_timesteps": 151,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}





def 						UpperCamelCase				(							_lowerCamelCase					:					Tuple       ):
    if isinstance(_lowerCamelCase						,       _lowerCamelCase       ):
        return v
    if v.lower() in ("yes", "true", "t", "y", "1"):
        return True
    elif v.lower() in ("no", "false", "f", "n", "0"):
        return False
    else:
        raise argparse.ArgumentTypeError("boolean value expected"       )





def 						UpperCamelCase				(							_lowerCamelCase					:					List[str]						,       _lowerCamelCase					:					List[str]						,       _lowerCamelCase					:					Optional[int]						,       _lowerCamelCase					:					Dict						,       _lowerCamelCase					:					Optional[int]=False       ):
    A__				=			checkpoint[F"{old_prefix}.in_layers.0.weight"]
    A__				=			checkpoint[F"{old_prefix}.in_layers.0.bias"]
    A__				=			checkpoint[F"{old_prefix}.in_layers.2.weight"]
    A__				=			checkpoint[F"{old_prefix}.in_layers.2.bias"]
    A__				=			checkpoint[F"{old_prefix}.emb_layers.1.weight"]
    A__				=			checkpoint[F"{old_prefix}.emb_layers.1.bias"]
    A__				=			checkpoint[F"{old_prefix}.out_layers.0.weight"]
    A__				=			checkpoint[F"{old_prefix}.out_layers.0.bias"]
    A__				=			checkpoint[F"{old_prefix}.out_layers.3.weight"]
    A__				=			checkpoint[F"{old_prefix}.out_layers.3.bias"]

    if has_skip:
        A__				=			checkpoint[F"{old_prefix}.skip_connection.weight"]
        A__				=			checkpoint[F"{old_prefix}.skip_connection.bias"]

    return new_checkpoint





def 						UpperCamelCase				(							_lowerCamelCase					:					str						,       _lowerCamelCase					:					Any						,       _lowerCamelCase					:					Tuple						,       _lowerCamelCase					:					Optional[int]						,       _lowerCamelCase					:					List[Any]=None       ):
    A__,	A__,	A__				=			checkpoint[F"{old_prefix}.qkv.weight"].chunk(3						,       dim=0       )
    A__,	A__,	A__				=			checkpoint[F"{old_prefix}.qkv.bias"].chunk(3						,       dim=0       )

    A__				=			checkpoint[F"{old_prefix}.norm.weight"]
    A__				=			checkpoint[F"{old_prefix}.norm.bias"]

    A__				=			weight_q.squeeze(-1       ).squeeze(-1       )
    A__				=			bias_q.squeeze(-1       ).squeeze(-1       )
    A__				=			weight_k.squeeze(-1       ).squeeze(-1       )
    A__				=			bias_k.squeeze(-1       ).squeeze(-1       )
    A__				=			weight_v.squeeze(-1       ).squeeze(-1       )
    A__				=			bias_v.squeeze(-1       ).squeeze(-1       )

    A__				=			(
        checkpoint[F"{old_prefix}.proj_out.weight"].squeeze(-1       ).squeeze(-1       )
    )
    A__				=			checkpoint[F"{old_prefix}.proj_out.bias"].squeeze(-1       ).squeeze(-1       )

    return new_checkpoint





def 						UpperCamelCase				(							_lowerCamelCase					:					str						,       _lowerCamelCase					:					List[str]       ):
    A__				=			torch.load(_lowerCamelCase						,       map_location="cpu"       )
    A__				=			{}

    A__				=			checkpoint["time_embed.0.weight"]
    A__				=			checkpoint["time_embed.0.bias"]
    A__				=			checkpoint["time_embed.2.weight"]
    A__				=			checkpoint["time_embed.2.bias"]

    if unet_config["num_class_embeds"] is not None:
        A__				=			checkpoint["label_emb.weight"]

    A__				=			checkpoint["input_blocks.0.0.weight"]
    A__				=			checkpoint["input_blocks.0.0.bias"]

    A__				=			unet_config["down_block_types"]
    A__				=			unet_config["layers_per_block"]
    A__				=			unet_config["attention_head_dim"]
    A__				=			unet_config["block_out_channels"]
    A__				=			1
    A__				=			channels_list[0]

    for i, layer_type in enumerate(_lowerCamelCase       ):
        A__				=			channels_list[i]
        A__				=			current_channels != prev_channels
        if layer_type == "ResnetDownsampleBlock2D":
            for j in range(_lowerCamelCase       ):
                A__				=			F"down_blocks.{i}.resnets.{j}"
                A__				=			F"input_blocks.{current_layer}.0"
                A__				=			True if j == 0 and downsample_block_has_skip else False
                A__				=			convert_resnet(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       has_skip=_lowerCamelCase       )
                current_layer += 1

        elif layer_type == "AttnDownBlock2D":
            for j in range(_lowerCamelCase       ):
                A__				=			F"down_blocks.{i}.resnets.{j}"
                A__				=			F"input_blocks.{current_layer}.0"
                A__				=			True if j == 0 and downsample_block_has_skip else False
                A__				=			convert_resnet(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       has_skip=_lowerCamelCase       )
                A__				=			F"down_blocks.{i}.attentions.{j}"
                A__				=			F"input_blocks.{current_layer}.1"
                A__				=			convert_attention(
                    _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       )
                current_layer += 1

        if i != len(_lowerCamelCase       ) - 1:
            A__				=			F"down_blocks.{i}.downsamplers.0"
            A__				=			F"input_blocks.{current_layer}.0"
            A__				=			convert_resnet(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       )
            current_layer += 1

        A__				=			current_channels

    # hardcoded the mid-block for now
    A__				=			"mid_block.resnets.0"
    A__				=			"middle_block.0"
    A__				=			convert_resnet(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       )
    A__				=			"mid_block.attentions.0"
    A__				=			"middle_block.1"
    A__				=			convert_attention(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       )
    A__				=			"mid_block.resnets.1"
    A__				=			"middle_block.2"
    A__				=			convert_resnet(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       )

    A__				=			0
    A__				=			unet_config["up_block_types"]

    for i, layer_type in enumerate(_lowerCamelCase       ):
        if layer_type == "ResnetUpsampleBlock2D":
            for j in range(layers_per_block + 1       ):
                A__				=			F"up_blocks.{i}.resnets.{j}"
                A__				=			F"output_blocks.{current_layer}.0"
                A__				=			convert_resnet(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       has_skip=_lowerCamelCase       )
                current_layer += 1

            if i != len(_lowerCamelCase       ) - 1:
                A__				=			F"up_blocks.{i}.upsamplers.0"
                A__				=			F"output_blocks.{current_layer-1}.1"
                A__				=			convert_resnet(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       )
        elif layer_type == "AttnUpBlock2D":
            for j in range(layers_per_block + 1       ):
                A__				=			F"up_blocks.{i}.resnets.{j}"
                A__				=			F"output_blocks.{current_layer}.0"
                A__				=			convert_resnet(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       has_skip=_lowerCamelCase       )
                A__				=			F"up_blocks.{i}.attentions.{j}"
                A__				=			F"output_blocks.{current_layer}.1"
                A__				=			convert_attention(
                    _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       )
                current_layer += 1

            if i != len(_lowerCamelCase       ) - 1:
                A__				=			F"up_blocks.{i}.upsamplers.0"
                A__				=			F"output_blocks.{current_layer-1}.2"
                A__				=			convert_resnet(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       )

    A__				=			checkpoint["out.0.weight"]
    A__				=			checkpoint["out.0.bias"]
    A__				=			checkpoint["out.2.weight"]
    A__				=			checkpoint["out.2.bias"]

    return new_checkpoint


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

  parser.add_argument("--unet_path", default=None, type=str, required=True, help="Path to the unet.pt to convert.")
  parser.add_argument(
      "--dump_path", default=None, type=str, required=True, help="Path to output the converted UNet model."
  )
  parser.add_argument("--class_cond", default=True, type=str, help="Whether the model is class-conditional.")

  __lowerCAmelCase							:	Optional[Any]			=parser.parse_args()
  __lowerCAmelCase							:	List[Any]			=strabool(args.class_cond)

  __lowerCAmelCase							:	List[str]			=os.path.basename(args.unet_path)
  print(f"""Checkpoint: {ckpt_name}""")

  # Get U-Net config
  if "imagenet64" in ckpt_name:
    __lowerCAmelCase							:	List[str]			=IMAGENET_64_UNET_CONFIG
  elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
    __lowerCAmelCase							:	List[str]			=LSUN_256_UNET_CONFIG
  elif "test" in ckpt_name:
    __lowerCAmelCase							:	Any			=TEST_UNET_CONFIG
  else:
    raise ValueError(f"""Checkpoint type {ckpt_name} is not currently supported.""")

  if not args.class_cond:
    __lowerCAmelCase							:	Dict			=None

  __lowerCAmelCase							:	Optional[int]			=con_pt_to_diffuser(args.unet_path, unet_config)

  __lowerCAmelCase							:	Dict			=UNetaDModel(**unet_config)
  image_unet.load_state_dict(converted_unet_ckpt)

  # Get scheduler config
  if "cd" in ckpt_name or "test" in ckpt_name:
    __lowerCAmelCase							:	List[str]			=CD_SCHEDULER_CONFIG
  elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
    __lowerCAmelCase							:	Dict			=CT_IMAGENET_64_SCHEDULER_CONFIG
  elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
    __lowerCAmelCase							:	Dict			=CT_LSUN_256_SCHEDULER_CONFIG
  else:
    raise ValueError(f"""Checkpoint type {ckpt_name} is not currently supported.""")

  __lowerCAmelCase							:	Dict			=CMStochasticIterativeScheduler(**scheduler_config)

  __lowerCAmelCase							:	str			=ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
  consistency_model.save_pretrained(args.dump_path)



