






import itertools
import os
import subprocess
from os.path import dirname

from parameterized import parameterized

from tests.trainer.test_trainer import TrainerIntegrationCommon  # noqa
from transformers import is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_tests_dir,
    require_deepspeed,
    require_torch_gpu,
    slow,
)
from transformers.trainer_utils import set_seed


if is_torch_available():
       from tests.trainer.test_trainer import (  # noqa
           RegressionModelConfig,
           RegressionPreTrainedModel,
           get_regression_trainer,
       )


set_seed(42)

a	: List[str]      =				get_tests_dir("fixtures")
a	: Dict      =				os.path.join(dirname(get_tests_dir()))
a	: Any      =				dirname(os.path.abspath(__file__))

# default torch.distributed port
a	: List[str]      =				"10999"

a	: str      =				"t5-small"

# *** Working Models ***
a	: str      =				"hf-internal-testing/tiny-albert"
a	: int      =				"sshleifer/bart-tiny-random"
a	: Union[str, Any]      =				"hf-internal-testing/tiny-bert"
a	: List[str]      =				"hf-internal-testing/tiny-random-bigbird_pegasus"
a	: List[Any]      =				"hf-internal-testing/tiny-random-big_bird"
a	: Union[str, Any]      =				"hf-internal-testing/tiny-random-blenderbot"
a	: Tuple      =				"bigscience/bigscience-small-testing"
a	: int      =				"hf-internal-testing/tiny-random-deberta"
a	: str      =				"hf-internal-testing/tiny-random-deberta-v2"
a	: Tuple      =				"sshleifer/tiny-distilbert-base-cased"
a	: Optional[int]      =				"hf-internal-testing/tiny-electra"
a	: List[Any]      =				"hf-internal-testing/tiny-random-flaubert"
a	: Optional[int]      =				"stas/tiny-wmt19-en-de"
a	: Optional[int]      =				"hf-internal-testing/tiny-random-funnel"
a	: Any      =				"sshleifer/tiny-gpt2"
a	: int      =				"hf-internal-testing/tiny-random-gptj"
a	: Tuple      =				"hf-internal-testing/tiny-random-gpt_neo"
a	: Union[str, Any]      =				"hf-internal-testing/tiny-layoutlm"
a	: Dict      =				"hf-internal-testing/tiny-random-led"
a	: Dict      =				"hf-internal-testing/tiny-random-longformer"
a	: str      =				"stas/tiny-m2m_100"  # hf tiny model is unsuitable
a	: Dict      =				"sshleifer/tiny-marian-en-de"
a	: str      =				"sshleifer/tiny-mbart"
a	: Tuple      =				"hf-internal-testing/tiny-random-mobilebert"
a	: Any      =				"hf-internal-testing/tiny-random-mpnet"
a	: Any      =				"stas/pegasus-cnn_dailymail-tiny-random"
a	: Optional[int]      =				"hf-internal-testing/tiny-random-prophetnet"
a	: List[str]      =				"sshleifer/tiny-distilroberta-base"
a	: List[Any]      =				"hf-internal-testing/tiny-random-squeezebert"
a	: List[Any]      =				"patrickvonplaten/t5-tiny-random"
a	: Any      =				"hf-internal-testing/tiny-random-t5-v1.1"
a	: Any      =				"hf-internal-testing/tiny-random-vit"
a	: int      =				"hf-internal-testing/tiny-xlm-roberta"
a	: Union[str, Any]      =				"sshleifer/tiny-xlnet-base-cased"


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
a	: Optional[Any]      =				"hf-internal-testing/tiny-random-mt5"
a	: List[str]      =				"hf-internal-testing/tiny-random-camembert"
a	: Union[str, Any]      =				"hf-internal-testing/tiny-random-openai-gpt"

# missing tokenizer files
a	: Union[str, Any]      =				"hf-internal-testing/tiny-random-convbert"
a	: Optional[int]      =				"hf-internal-testing/tiny-random-layoutlmv2"
a	: Union[str, Any]      =				"hf-internal-testing/tiny-random-hubert"

# issues with tokenizer
a	: Dict      =				"hf-internal-testing/tiny-random-ctrl"
a	: Union[str, Any]      =				"hf-internal-testing/tiny-random-transfo-xl"  # same as ctrl

# other issues with tiny models
a	: List[Any]      =				"hf-internal-testing/tiny-random-ibert"  # multiple issues with either mlm/qa/clas
a	: Optional[int]      =				"hf-internal-testing/tiny-random-reformer"  # multiple issues with either mlm/qa/clas

# *** Lacking official examples to test with ***
# or not working with examples
a	: Dict      =				"hf-internal-testing/tiny-random-dpr"
# - "dpr"  examples/research_projects/rag-end2end-retriever/
a	: Dict      =				"hf-internal-testing/tiny-random-rag"
# - "rag" research_projects
a	: int      =				""
# - "luke" Entities classes - no plan to make such example
a	: Union[str, Any]      =				"hf-internal-testing/tiny-random-lxmert"
# - "lxmert" doesn't work with run_qa.py
a	: Optional[Any]      =				"hf-internal-testing/tiny-random-clip"
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
a	: Any      =				"hf-internal-testing/tiny-random-speech_to_text"
# - "speech_to_text", nothing under pytorch examples


# *** Reactive mode ***
# models with low usage, unstable API, things about to change - do nothing about the following until someone runs into a problem
a	: Dict      =				"hf-internal-testing/tiny-random-tapas"






def        lowerCamelCase__   (					__lowerCamelCase    :  Dict=False	):
  __UpperCAmelCase							:			Optional[Any]     =						min(2						,	get_gpu_count()	) if distributed else 1
  __UpperCAmelCase							:			Union[str, Any]     =						os.environ.get("""DS_TEST_PORT"""						,	UpperCamelCase__	)
  return f"""deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}""".split()






def        lowerCamelCase__   (					):
  __UpperCAmelCase							:			Dict     =						f"""{FIXTURE_DIRECTORY}/tests_samples"""
  __UpperCAmelCase							:			int     =						f"""{data_dir_samples}/wmt_en_ro"""
  __UpperCAmelCase							:			Optional[int]     =						f"""{data_dir_samples}/xsum"""
  __UpperCAmelCase							:			Dict     =						"""\n        --do_train\n        --max_train_samples 4\n        --per_device_train_batch_size 2\n        --num_train_epochs 1\n        --fp16\n        --report_to none\n        --overwrite_output_dir\n        """.split()

  # try to cover as many models as possible once (it's enough to run on one task per model)
  # but need a tiny model for each
  #
  # should have "{model_type.upper()}_TINY" corresponding vars defined, e.g., T5_TINY, etc.
  __UpperCAmelCase							:			Dict     =						{
      """trans""": [
          """bart""",
          """fsmt""",
          """m2m_100""",
          """marian""",
          """mbart""",
          """t5""",
          """t5_v1""",
          # "mt5", missing model files
      ],
      """sum""": [
          """pegasus""",
      ],
      """clm""": [
          """big_bird""",
          """bigbird_pegasus""",
          """blenderbot""",
          """bloom""",
          """gpt2""",
          """gpt_neo""",
          """gptj""",
          """xlm-roberta""",
          """prophetnet""",
          # "camembert", missing model files
      ],
      """mlm""": [
          """albert""",
          """deberta""",
          """deberta-v2""",
          """distilbert""",
          """electra""",
          """flaubert""",
          """funnel""",
          """layoutlm""",
          # "reformer", # multiple issues with either mlm/qa/clas
      ],
      """qa""": [
          """led""",
          """longformer""",
          """mobilebert""",
          """mpnet""",
          """roberta""",
          """squeezebert""",
          # "convbert", # missing tokenizer files
          # "layoutlmv2", missing model files
      ],
      """clas""": [
          """bert""",
          """xlnet""",
          # "hubert", # missing tokenizer files
          # "ibert", # multiple issues with either mlm/qa/clas
          # "transfo-xl", # tokenizer issues as ctrl
          # "ctrl", # tokenizer issues
          # "openai-gpt", missing model files
          # "tapas", multiple issues
      ],
      """img_clas""": [
          """vit""",
      ],
  }

  __UpperCAmelCase							:			List[Any]     =						f"""{ROOT_DIRECTORY}/examples/pytorch"""

  __UpperCAmelCase							:			Tuple     =						{
      """trans""": f"""\n        {scripts_dir}/translation/run_translation.py\n        --train_file {data_dir_wmt}/train.json\n        --source_lang en\n        --target_lang ro\n        """,
      """sum""": f"""\n        {scripts_dir}/summarization/run_summarization.py\n        --train_file {data_dir_xsum}/sample.json\n        --max_source_length 12\n        --max_target_length 12\n        --lang en\n        """,
      """clm""": f"""\n        {scripts_dir}/language-modeling/run_clm.py\n        --train_file {FIXTURE_DIRECTORY}/sample_text.txt\n        --block_size 8\n        """,
      """mlm""": f"""\n        {scripts_dir}/language-modeling/run_mlm.py\n        --train_file {FIXTURE_DIRECTORY}/sample_text.txt\n        """,
      """qa""": f"""\n        {scripts_dir}/question-answering/run_qa.py\n        --train_file {data_dir_samples}/SQUAD/sample.json\n        """,
      """clas""": f"""\n        {scripts_dir}/text-classification/run_glue.py\n        --train_file {data_dir_samples}/MRPC/train.csv\n        --max_seq_length 12\n        --task_name MRPC\n        """,
      """img_clas""": f"""\n        {scripts_dir}/image-classification/run_image_classification.py\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --remove_unused_columns False\n            --max_steps 10\n            --image_processor_name {DS_TESTS_DIRECTORY}/vit_feature_extractor.json\n        """,
  }

  __UpperCAmelCase							:			List[Any]     =						get_launcher(distributed=UpperCamelCase__	)

  __UpperCAmelCase							:			int     =						{}
  for task, args in tasks.items():
    __UpperCAmelCase							:			Optional[int]     =						args.split()
    for model in tasksamodels[task]:
      __UpperCAmelCase							:			Any     =						globals()[f"""{model.upper().replace("-"						,	"_"	)}_TINY"""]
      __UpperCAmelCase							:			Union[str, Any]     =						f"""--model_name_or_path {model_name}""".split()
      __UpperCAmelCase							:			Any     =						launcher + args + args_model + args_main

      # # generation special case
      # if task == "gen":
      #     launcher = f"deepspeed --num_nodes 1 --num_gpus 1".split()
      #     args_model += f"--model_type {model}".split()
      #     cmds[f"{task}_{model}"] = launcher + args + args_model
      # else:

  return cmds


a	: Any      =				make_task_cmds()

a	: List[str]      =				"zero2"
a	: Optional[Any]      =				"zero3"

a	: List[Any]      =				[ZEROa, ZEROa]






def        lowerCamelCase__   (					__lowerCamelCase    :  List[str]						,	__lowerCamelCase    :  int						,	__lowerCamelCase    :  Optional[Any]	):
  __UpperCAmelCase							:			Union[str, Any]     =						parameterized.to_safe_name("""_""".join(str(UpperCamelCase__	) for x in param.args	)	)
  return f"""{func.__name__}_{param_based_name}"""


# Cartesian-product of zero stages with models to test
a	: List[Any]      =				list(itertools.product(stages, task_cmds.keys()))







@slow
@require_deepspeed
@require_torch_gpu
class 					a					(				lowercase__     ):



      """simple docstring"""


      def 			UpperCAmelCase   (   self      :    str	,   __lowercase      :    Optional[int]	,   __lowercase      :    Dict      )							->   Tuple:
        # return a ready to run train cmd
        if task not in task_cmds:
          raise ValueError(f"""don't know of task {task}, have {task_cmds.keys()}"""      )

        __UpperCAmelCase							:			Union[str, Any]     =						task_cmds[task]
        __UpperCAmelCase							:			str     =						f"""--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json""".split()

        __UpperCAmelCase							:			int     =						self.get_auto_remove_tmp_dir()
        __UpperCAmelCase							:			Tuple     =						f"""--output_dir {output_dir}""".split()

        cmd += args_ds + args_out

        return cmd, output_dir




      @parameterized.expand(a_	,   name_func=a_      )
      def 			UpperCAmelCase   (   self      :    List[Any]	,   __lowercase      :    Optional[int]	,   __lowercase      :    Any      )							->   int:
        # testing the ability to do a run followed by recovery of full fp32 weights

        __UpperCAmelCase   ,				__UpperCAmelCase							:			Optional[Any]     =						self.get_task_cmd(a_	,   a_      )

        # 1. generate the checkpoint
        cmd += "--save_steps 1".split()
        # keep for quick debug
        # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] + cmd)); die
        execute_subprocess_async(a_	,   env=self.get_env()      )

        # 2. test that the fp32 weights get reconsolidated
        __UpperCAmelCase							:			int     =						f"""{output_dir}/checkpoint-1"""
        __UpperCAmelCase							:			Any     =						f"""{chkpt_dir}/out.bin"""
        __UpperCAmelCase							:			Any     =						f"""{chkpt_dir}/zero_to_fp32.py {chkpt_dir} {recovered_model_path}"""
        # keep for quick debug
        # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
        subprocess.check_call(a_	,   shell=a_      )
        assert os.path.exists(a_      ), f"""{recovered_model_path} was not found"""

        # possibly could also test that the resulting saved model is usable but given that we use
        # random models we won't know if it's any good










import argparse

import requests
import torch
from PIL import Image

from transformers import (
    CLIPSegConfig,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPSegTextConfig,
    CLIPSegVisionConfig,
    CLIPTokenizer,
    ViTImageProcessor,
)






def        lowerCamelCase__   (					__lowerCamelCase    :  int	):
  __UpperCAmelCase							:			Any     =						CLIPSegTextConfig()
  __UpperCAmelCase							:			Optional[int]     =						CLIPSegVisionConfig(patch_size=16	)

  __UpperCAmelCase							:			List[str]     =						True if """refined""" in model_name else False
  __UpperCAmelCase							:			List[Any]     =						16 if """rd16""" in model_name else 64

  __UpperCAmelCase							:			Tuple     =						CLIPSegConfig.from_text_vision_configs(
      __lowerCamelCase						,	__lowerCamelCase						,	use_complex_transposed_convolution=__lowerCamelCase						,	reduce_dim=__lowerCamelCase						,	)
  return config






def        lowerCamelCase__   (					__lowerCamelCase    :  str	):
  # update prefixes
  if "clip_model" in name:
    __UpperCAmelCase							:			int     =						name.replace("""clip_model"""						,	"""clip"""	)
  if "transformer" in name:
    if "visual" in name:
      __UpperCAmelCase							:			List[Any]     =						name.replace("""visual.transformer"""						,	"""vision_model"""	)
    else:
      __UpperCAmelCase							:			str     =						name.replace("""transformer"""						,	"""text_model"""	)
  if "resblocks" in name:
    __UpperCAmelCase							:			Union[str, Any]     =						name.replace("""resblocks"""						,	"""encoder.layers"""	)
  if "ln_1" in name:
    __UpperCAmelCase							:			List[str]     =						name.replace("""ln_1"""						,	"""layer_norm1"""	)
  if "ln_2" in name:
    __UpperCAmelCase							:			str     =						name.replace("""ln_2"""						,	"""layer_norm2"""	)
  if "c_fc" in name:
    __UpperCAmelCase							:			Tuple     =						name.replace("""c_fc"""						,	"""fc1"""	)
  if "c_proj" in name:
    __UpperCAmelCase							:			Union[str, Any]     =						name.replace("""c_proj"""						,	"""fc2"""	)
  if "attn" in name and "self" not in name:
    __UpperCAmelCase							:			Tuple     =						name.replace("""attn"""						,	"""self_attn"""	)
  # text encoder
  if "token_embedding" in name:
    __UpperCAmelCase							:			Any     =						name.replace("""token_embedding"""						,	"""text_model.embeddings.token_embedding"""	)
  if "positional_embedding" in name and "visual" not in name:
    __UpperCAmelCase							:			Dict     =						name.replace("""positional_embedding"""						,	"""text_model.embeddings.position_embedding.weight"""	)
  if "ln_final" in name:
    __UpperCAmelCase							:			List[str]     =						name.replace("""ln_final"""						,	"""text_model.final_layer_norm"""	)
  # vision encoder
  if "visual.class_embedding" in name:
    __UpperCAmelCase							:			Any     =						name.replace("""visual.class_embedding"""						,	"""vision_model.embeddings.class_embedding"""	)
  if "visual.conv1" in name:
    __UpperCAmelCase							:			str     =						name.replace("""visual.conv1"""						,	"""vision_model.embeddings.patch_embedding"""	)
  if "visual.positional_embedding" in name:
    __UpperCAmelCase							:			Any     =						name.replace("""visual.positional_embedding"""						,	"""vision_model.embeddings.position_embedding.weight"""	)
  if "visual.ln_pre" in name:
    __UpperCAmelCase							:			List[Any]     =						name.replace("""visual.ln_pre"""						,	"""vision_model.pre_layrnorm"""	)
  if "visual.ln_post" in name:
    __UpperCAmelCase							:			Any     =						name.replace("""visual.ln_post"""						,	"""vision_model.post_layernorm"""	)
  # projection layers
  if "visual.proj" in name:
    __UpperCAmelCase							:			Any     =						name.replace("""visual.proj"""						,	"""visual_projection.weight"""	)
  if "text_projection" in name:
    __UpperCAmelCase							:			List[str]     =						name.replace("""text_projection"""						,	"""text_projection.weight"""	)
  # decoder
  if "trans_conv" in name:
    __UpperCAmelCase							:			str     =						name.replace("""trans_conv"""						,	"""transposed_convolution"""	)
  if "film_mul" in name or "film_add" in name or "reduce" in name or "transposed_convolution" in name:
    __UpperCAmelCase							:			int     =						"""decoder.""" + name
  if "blocks" in name:
    __UpperCAmelCase							:			List[str]     =						name.replace("""blocks"""						,	"""decoder.layers"""	)
  if "linear1" in name:
    __UpperCAmelCase							:			str     =						name.replace("""linear1"""						,	"""mlp.fc1"""	)
  if "linear2" in name:
    __UpperCAmelCase							:			str     =						name.replace("""linear2"""						,	"""mlp.fc2"""	)
  if "norm1" in name and "layer_" not in name:
    __UpperCAmelCase							:			int     =						name.replace("""norm1"""						,	"""layer_norm1"""	)
  if "norm2" in name and "layer_" not in name:
    __UpperCAmelCase							:			Union[str, Any]     =						name.replace("""norm2"""						,	"""layer_norm2"""	)

  return name






def        lowerCamelCase__   (					__lowerCamelCase    :  Union[str, Any]						,	__lowerCamelCase    :  int	):
  for key in orig_state_dict.copy().keys():
    __UpperCAmelCase							:			Optional[Any]     =						orig_state_dict.pop(__lowerCamelCase	)

    if key.startswith("""clip_model"""	) and "attn.in_proj" in key:
      __UpperCAmelCase							:			Dict     =						key.split("""."""	)
      if "visual" in key:
        __UpperCAmelCase							:			List[str]     =						int(key_split[4]	)
        __UpperCAmelCase							:			Union[str, Any]     =						config.vision_config.hidden_size
        __UpperCAmelCase							:			List[Any]     =						"""vision_model"""
      else:
        __UpperCAmelCase							:			Optional[Any]     =						int(key_split[3]	)
        __UpperCAmelCase							:			str     =						config.text_config.hidden_size
        __UpperCAmelCase							:			Optional[Any]     =						"""text_model"""

      if "weight" in key:
        __UpperCAmelCase							:			Optional[Any]     =						val[:dim, :]
        __UpperCAmelCase							:			Any     =						val[
            dim : dim * 2, :
        ]
        __UpperCAmelCase							:			List[str]     =						val[-dim:, :]
      else:
        __UpperCAmelCase							:			List[str]     =						val[:dim]
        __UpperCAmelCase							:			Optional[int]     =						val[dim : dim * 2]
        __UpperCAmelCase							:			List[str]     =						val[-dim:]
    elif "self_attn" in key and "out_proj" not in key:
      __UpperCAmelCase							:			Union[str, Any]     =						key.split("""."""	)
      __UpperCAmelCase							:			List[str]     =						int(key_split[1]	)
      __UpperCAmelCase							:			List[str]     =						config.reduce_dim
      if "weight" in key:
        __UpperCAmelCase							:			Optional[int]     =						val[:dim, :]
        __UpperCAmelCase							:			List[str]     =						val[dim : dim * 2, :]
        __UpperCAmelCase							:			Union[str, Any]     =						val[-dim:, :]
      else:
        __UpperCAmelCase							:			Tuple     =						val[:dim]
        __UpperCAmelCase							:			Any     =						val[dim : dim * 2]
        __UpperCAmelCase							:			int     =						val[-dim:]
    else:
      __UpperCAmelCase							:			List[Any]     =						rename_key(__lowerCamelCase	)
      if "visual_projection" in new_name or "text_projection" in new_name:
        __UpperCAmelCase							:			List[Any]     =						val.T
      __UpperCAmelCase							:			Any     =						val

  return orig_state_dict






def        lowerCamelCase__   (					):
  __UpperCAmelCase							:			Optional[Any]     =						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
  __UpperCAmelCase							:			List[Any]     =						Image.open(requests.get(__lowerCamelCase						,	stream=__lowerCamelCase	).raw	)
  return image






def        lowerCamelCase__   (					__lowerCamelCase    :  Optional[int]						,	__lowerCamelCase    :  Optional[Any]						,	__lowerCamelCase    :  str						,	__lowerCamelCase    :  List[str]	):
  __UpperCAmelCase							:			Optional[Any]     =						get_clipseg_config(__lowerCamelCase	)
  __UpperCAmelCase							:			Tuple     =						CLIPSegForImageSegmentation(__lowerCamelCase	)
  model.eval()

  __UpperCAmelCase							:			Optional[int]     =						torch.load(__lowerCamelCase						,	map_location="""cpu"""	)

  # remove some keys
  for key in state_dict.copy().keys():
    if key.startswith("""model"""	):
      state_dict.pop(__lowerCamelCase						,	__lowerCamelCase	)

    # rename some keys
  __UpperCAmelCase							:			Optional[Any]     =						convert_state_dict(__lowerCamelCase						,	__lowerCamelCase	)
  __UpperCAmelCase   ,				__UpperCAmelCase							:			Optional[Any]     =						model.load_state_dict(__lowerCamelCase						,	strict=__lowerCamelCase	)

  if missing_keys != ["clip.text_model.embeddings.position_ids", "clip.vision_model.embeddings.position_ids"]:
    raise ValueError("""Missing keys that are not expected: {}""".format(__lowerCamelCase	)	)
  if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]:
    raise ValueError(f"""Unexpected keys: {unexpected_keys}"""	)

  __UpperCAmelCase							:			Optional[Any]     =						ViTImageProcessor(size=352	)
  __UpperCAmelCase							:			Tuple     =						CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""	)
  __UpperCAmelCase							:			List[Any]     =						CLIPSegProcessor(image_processor=__lowerCamelCase						,	tokenizer=__lowerCamelCase	)

  __UpperCAmelCase							:			int     =						prepare_img()
  __UpperCAmelCase							:			Optional[Any]     =						["""a glass""", """something to fill""", """wood""", """a jar"""]

  __UpperCAmelCase							:			Union[str, Any]     =						processor(text=__lowerCamelCase						,	images=[image] * len(__lowerCamelCase	)						,	padding="""max_length"""						,	return_tensors="""pt"""	)

  with torch.no_grad():
    __UpperCAmelCase							:			int     =						model(**__lowerCamelCase	)

  # verify values
  __UpperCAmelCase							:			Dict     =						torch.tensor([0.1_1_1_0, -0.1_8_8_2, 0.1_6_4_5]	)
  __UpperCAmelCase							:			Union[str, Any]     =						torch.tensor([0.2_6_9_2, -0.7_1_9_7, -0.1_3_2_8]	)
  if model_name == "clipseg-rd64-refined":
    __UpperCAmelCase							:			str     =						torch.tensor(
        [[-1_0.0_4_0_7, -9.9_4_3_1, -1_0.2_6_4_6], [-9.9_7_5_1, -9.7_0_6_4, -9.9_5_8_6], [-9.6_8_9_1, -9.5_6_4_5, -9.9_6_1_8]]	)
  elif model_name == "clipseg-rd64":
    __UpperCAmelCase							:			List[Any]     =						torch.tensor(
        [[-7.2_8_7_7, -7.2_7_1_1, -7.2_4_6_3], [-7.2_6_5_2, -7.2_7_8_0, -7.2_5_2_0], [-7.2_2_3_9, -7.2_2_0_4, -7.2_0_0_1]]	)
  elif model_name == "clipseg-rd16":
    __UpperCAmelCase							:			str     =						torch.tensor(
        [[-6.3_9_5_5, -6.4_0_5_5, -6.4_1_5_1], [-6.3_9_1_1, -6.4_0_3_3, -6.4_1_0_0], [-6.3_4_7_4, -6.3_7_0_2, -6.3_7_6_2]]	)
  else:
    raise ValueError(f"""Model name {model_name} not supported."""	)

  assert torch.allclose(outputs.logits[0, :3, :3]						,	__lowerCamelCase						,	atol=1E-3	)
  assert torch.allclose(outputs.conditional_embeddings[0, :3]						,	__lowerCamelCase						,	atol=1E-3	)
  assert torch.allclose(outputs.pooled_output[0, :3]						,	__lowerCamelCase						,	atol=1E-3	)
  print("""Looks ok!"""	)

  if pytorch_dump_folder_path is not None:
    print(f"""Saving model and processor to {pytorch_dump_folder_path}"""	)
    model.save_pretrained(__lowerCamelCase	)
    processor.save_pretrained(__lowerCamelCase	)

  if push_to_hub:
    print(f"""Pushing model and processor for {model_name} to the hub"""	)
    model.push_to_hub(f"""CIDAS/{model_name}"""	)
    processor.push_to_hub(f"""CIDAS/{model_name}"""	)


if __name__ == "__main__":
       a	: Optional[Any]      =				argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--model_name",
           default="clipseg-rd64",
           type=str,
           choices=["clipseg-rd16", "clipseg-rd64", "clipseg-rd64-refined"],
           help=(
               "Name of the model. Supported models are: clipseg-rd64, clipseg-rd16 and clipseg-rd64-refined (rd meaning"
               " reduce dimension)"
           ),
       )
       parser.add_argument(
           "--checkpoint_path",
           default="/Users/nielsrogge/Documents/CLIPSeg/clip_plus_rd64-uni.pth",
           type=str,
           help=(
               "Path to the original checkpoint. Note that the script assumes that the checkpoint includes both CLIP and"
               " the decoder weights."
           ),
       )
       parser.add_argument(
           "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
       )
       parser.add_argument(
           "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
       )

       a	: int      =				parser.parse_args()
       convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)


