






'''simple docstring'''







import argparse
import collections

import jax
import jax.numpy as jnp
import torch
import torch.nn as nn
from clip.model import CLIP
from flax.training import checkpoints
from huggingface_hub import Repository

from transformers import (
    CLIPTokenizer,
    OwlViTConfig,
    OwlViTForObjectDetection,
    OwlViTImageProcessor,
    OwlViTModel,
    OwlViTProcessor,
)


__lowerCAmelCase					   =       {
    "vit_b32": {
        "embed_dim": 5_12,
        "image_resolution": 7_68,
        "context_length": 16,
        "vocab_size": 4_94_08,
        "vision_layers": 12,
        "vision_width": 7_68,
        "vision_patch_size": 32,
        "transformer_width": 5_12,
        "transformer_heads": 8,
        "transformer_layers": 12,
    },
    "vit_b16": {
        "embed_dim": 5_12,
        "image_resolution": 7_68,
        "context_length": 16,
        "vocab_size": 4_94_08,
        "vision_layers": 12,
        "vision_width": 7_68,
        "vision_patch_size": 16,
        "transformer_width": 5_12,
        "transformer_heads": 8,
        "transformer_layers": 12,
    },
    "vit_l14": {
        "embed_dim": 7_68,
        "image_resolution": 8_40,
        "context_length": 16,
        "vocab_size": 4_94_08,
        "vision_layers": 24,
        "vision_width": 10_24,
        "vision_patch_size": 14,
        "transformer_width": 7_68,
        "transformer_heads": 12,
        "transformer_layers": 12,
    },
}


def 			__UpperCamelCase				(			lowercase_					:     str      ,					lowercase_					:     Union[str, Any]=""      ,					lowercase_					:     List[Any]="/"      ):
       """simple docstring"""




       a_	    = []

       for k, v in params.items():
              a_	    = parent_key + sep + k if parent_key else k

              if isinstance(lowercase_      ,					collections.MutableMapping      ):
                     items.extend(flatten_nested_dict(lowercase_      ,					lowercase_      ,					sep=lowercase_      ).items()      )
              else:
                     items.append((new_key, v)      )
       return dict(lowercase_      )


def 			__UpperCamelCase				(			lowercase_					:     int      ):
       """simple docstring"""




       return jax.tree_util.tree_map(lambda lowercase_      : x.astype(jnp.floataa      ) if x.dtype == jnp.bfloataa else x      ,					lowercase_      )


def 			__UpperCamelCase				(			lowercase_					:     int      ,					lowercase_					:     List[Any]      ):
       """simple docstring"""




       a_    ,  a_    ,  a_	    = pt_attn_layer.in_proj_weight.chunk(3      ,					dim=0      )
       a_    ,  a_    ,  a_	    = pt_attn_layer.in_proj_bias.chunk(3      ,					dim=0      )

       a_	    = pt_attn_layer.out_proj.weight
       a_	    = pt_attn_layer.out_proj.bias

       a_	    = q_proj
       a_	    = q_proj_bias

       a_	    = k_proj
       a_	    = k_proj_bias

       a_	    = v_proj
       a_	    = v_proj_bias

       a_	    = out_proj_weights
       a_	    = out_proj_bias


def 			__UpperCamelCase				(			lowercase_					:     List[Any]      ,					lowercase_					:     Any      ):
       """simple docstring"""




       copy_linear(hf_mlp.fca      ,					pt_mlp.c_fc      )
       copy_linear(hf_mlp.fca      ,					pt_mlp.c_proj      )


def 			__UpperCamelCase				(			lowercase_					:     str      ,					lowercase_					:     Optional[int]      ):
       """simple docstring"""




       a_	    = pt_linear.weight
       a_	    = pt_linear.bias


def 			__UpperCamelCase				(			lowercase_					:     Dict      ,					lowercase_					:     str      ):
       """simple docstring"""




       copy_linear(hf_layer.layer_norma      ,					pt_layer.ln_a      )
       copy_linear(hf_layer.layer_norma      ,					pt_layer.ln_a      )

       # copy MLP
       copy_mlp(hf_layer.mlp      ,					pt_layer.mlp      )

       # copy attn
       copy_attn_layer(hf_layer.self_attn      ,					pt_layer.attn      )


def 			__UpperCamelCase				(			lowercase_					:     Optional[int]      ,					lowercase_					:     Optional[Any]      ):
       """simple docstring"""




       for hf_layer, pt_layer in zip(lowercase_      ,					lowercase_      ):
              copy_layer(lowercase_      ,					lowercase_      )


def 			__UpperCamelCase				(			lowercase_					:     Union[str, Any]      ,					lowercase_					:     Dict      ):
       """simple docstring"""




       a_	    = pt_model.token_embedding.weight
       a_	    = pt_model.positional_embedding

       # copy layer norm
       copy_linear(hf_encoder.final_layer_norm      ,					pt_model.ln_final      )

       # copy hidden layers
       copy_layers(hf_encoder.encoder.layers      ,					pt_model.transformer.resblocks      )


def 			__UpperCamelCase				(			lowercase_					:     str      ,					lowercase_					:     Tuple      ):
       """simple docstring"""




       a_	    = pt_model.text_projection.data.T

       # copy text encoder
       copy_encoder(hf_model.text_model      ,					lowercase_      )


def 			__UpperCamelCase				(			lowercase_					:     Union[str, Any]      ,					lowercase_					:     Union[str, Any]      ):
       """simple docstring"""




       a_	    = pt_model.visual.proj.data.T

       # copy layer norms
       copy_linear(hf_model.vision_model.pre_layernorm      ,					pt_model.visual.ln_pre      )
       copy_linear(hf_model.vision_model.post_layernorm      ,					pt_model.visual.ln_post      )

       # copy embeds
       a_	    = pt_model.visual.conva.weight.data
       a_	    = pt_model.visual.class_embedding
       a_	    = pt_model.visual.positional_embedding.data

       # copy encoder
       copy_layers(hf_model.vision_model.encoder.layers      ,					pt_model.visual.transformer.resblocks      )


def 			__UpperCamelCase				(			lowercase_					:     Union[str, Any]      ,					lowercase_					:     Union[str, Any]      ):
       """simple docstring"""




       a_	    = flatten_nested_dict(flax_params['backbone']['merged_class_token']      )

       a_	    = torch.from_numpy(flax_class_token_params['scale']      )
       a_	    = torch.from_numpy(flax_class_token_params['bias']      )
       a_	    = nn.Parameter(lowercase_      )
       a_	    = nn.Parameter(lowercase_      )


def 			__UpperCamelCase				(			lowercase_					:     Optional[int]      ,					lowercase_					:     Optional[int]      ):
       """simple docstring"""




       a_	    = hf_model.state_dict()
       a_	    = {}

       # Rename class prediction head flax params to pytorch HF
       a_	    = flatten_nested_dict(flax_params['class_head']      )

       for flax_key, v in flax_class_params.items():
              a_	    = flax_key.replace('/'      ,					'.'      )
              a_	    = torch_key.replace('.kernel'      ,					'.weight'      )
              a_	    = torch_key.replace('Dense_0'      ,					'dense0'      )
              a_	    = 'class_head.' + torch_key

              if "weight" in torch_key and v.ndim == 2:
                     a_	    = v.T

              a_	    = nn.Parameter(torch.from_numpy(lowercase_      )      )

       # Rename box prediction box flax params to pytorch HF
       a_	    = flatten_nested_dict(flax_params['obj_box_head']      )

       for flax_key, v in flax_box_params.items():
              a_	    = flax_key.replace('/'      ,					'.'      )
              a_	    = torch_key.replace('.kernel'      ,					'.weight'      )
              a_	    = torch_key.replace('_'      ,					''      ).lower()
              a_	    = 'box_head.' + torch_key

              if "weight" in torch_key and v.ndim == 2:
                     a_	    = v.T

              a_	    = nn.Parameter(torch.from_numpy(lowercase_      )      )

       # Copy flax params to PyTorch params
       for name, param in new_params.items():
              if name in pt_params.keys():
                     pt_params[name].copy_(lowercase_      )


def 			__UpperCamelCase				(			lowercase_					:     Any      ,					lowercase_					:     int      ):
       """simple docstring"""




       for k, v in flax_attn_params.items():
              if k.startswith('transformer'      ):
                     a_	    = k.replace('transformer.resblocks'      ,					'text_model.encoder.layers'      )
              else:
                     a_	    = k.replace('visual.transformer.resblocks'      ,					'vision_model.encoder.layers'      )

              a_	    = torch_key.replace('attn'      ,					'self_attn'      )
              a_	    = torch_key.replace('key'      ,					'k_proj'      )
              a_	    = torch_key.replace('value'      ,					'v_proj'      )
              a_	    = torch_key.replace('query'      ,					'q_proj'      )
              a_	    = torch_key.replace('out'      ,					'out_proj'      )

              if "bias" in torch_key and v.ndim == 2:
                     a_	    = v.shape[0] * v.shape[1]
                     a_	    = v.reshape(lowercase_      )

              if "weight" in torch_key and "out" in torch_key:
                     a_	    = (v.shape[0] * v.shape[1], v.shape[2])
                     a_	    = v.reshape(lowercase_      ).T

              if "weight" in torch_key and "out" not in torch_key:
                     a_	    = (v.shape[0], v.shape[1] * v.shape[2])
                     a_	    = v.reshape(lowercase_      ).T

              # Copy flax CLIP attn params to HF PyTorch params
              a_	    = torch.from_numpy(lowercase_      )
              hf_backbone.state_dict()[torch_key].copy_(lowercase_      )


def 			__UpperCamelCase				(			lowercase_					:     Optional[Any]      ):
       """simple docstring"""




       a_	    = {}
       a_	    = []

       for k, v in params.items():
              if "attn." in k:
                     a_	    = k[: k.rindex('attn.'      ) + 5]
                     if base in processed_attn_layers:
                            continue

                     processed_attn_layers.append(lowercase_      )
                     a_	    = params[base + 'out.weight'].shape[-1]
                     a_	    = params[base + 'out.weight'].reshape(lowercase_      ,					lowercase_      ).T
                     a_	    = params[base + 'out.bias']
              else:
                     a_	    = v
       return new_params


def 			__UpperCamelCase				(			lowercase_					:     Tuple      ,					lowercase_					:     Tuple      ):
       """simple docstring"""




       a_	    = CLIP(**lowercase_      )
       torch_model.eval()
       a_	    = torch_model.state_dict()

       a_	    = flatten_nested_dict(flax_params['backbone']['clip']      )
       a_	    = {}

       for flax_key, v in flax_clip_params.items():
              a_	    = flax_key.replace('/'      ,					'.'      )
              a_	    = torch_key.replace('text.token_embedding.embedding'      ,					'token_embedding.kernel'      )

              if (
                  torch_key.startswith('text.transformer'      )
                  or torch_key.startswith('text.text_projection'      )
                  or torch_key.startswith('text.ln_final'      )
                  or torch_key.startswith('text.positional_embedding'      )
              ):
                     a_	    = torch_key[5:]

              a_	    = torch_key.replace('text_projection.kernel'      ,					'text_projection'      )
              a_	    = torch_key.replace('visual.proj.kernel'      ,					'visual.proj'      )
              a_	    = torch_key.replace('.scale'      ,					'.weight'      )
              a_	    = torch_key.replace('.kernel'      ,					'.weight'      )

              if "conv" in torch_key or "downsample.0.weight" in torch_key:
                     a_	    = v.transpose(3      ,					2      ,					0      ,					1      )

              elif "weight" in torch_key and v.ndim == 2 and "embedding" not in torch_key:
                     # Fully connected layers are transposed, embeddings are not
                     a_	    = v.T

              a_	    = v

       a_	    = _convert_attn_layers(lowercase_      )
       new_torch_params.update(lowercase_      )
       a_	    = {}

       # Copy flax CLIP backbone params to PyTorch params
       for name, param in new_torch_params.items():
              if name in torch_clip_params.keys():
                     a_	    = torch.from_numpy(new_torch_params[name]      )
                     torch_clip_params[name].copy_(lowercase_      )
              else:
                     a_	    = param

       return torch_clip_params, torch_model, attn_params


@torch.no_grad()
def 			__UpperCamelCase				(			lowercase_					:     List[Any]      ,					lowercase_					:     Any      ,					lowercase_					:     List[str]      ,					lowercase_					:     str      ,					lowercase_					:     List[Any]=None      ):
       """simple docstring"""




       a_	    = Repository(lowercase_      ,					clone_from=F'google/{pytorch_dump_folder_path}'      )
       repo.git_pull()

       if config_path is not None:
              a_	    = OwlViTConfig.from_pretrained(lowercase_      )
       else:
              a_	    = OwlViTConfig()

       a_	    = OwlViTModel(lowercase_      ).eval()
       a_	    = OwlViTForObjectDetection(lowercase_      ).eval()

       copy_text_model_and_projection(lowercase_      ,					lowercase_      )
       copy_vision_model_and_projection(lowercase_      ,					lowercase_      )
       a_	    = pt_backbone.logit_scale
       copy_flax_attn_params(lowercase_      ,					lowercase_      )

       a_	    = hf_backbone
       copy_class_merge_token(lowercase_      ,					lowercase_      )
       copy_class_box_heads(lowercase_      ,					lowercase_      )

       # Save HF model
       hf_model.save_pretrained(repo.local_dir      )

       # Initialize image processor
       a_	    = OwlViTImageProcessor(
           size=config.vision_config.image_size      ,					crop_size=config.vision_config.image_size      )
       # Initialize tokenizer
       a_	    = CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'      ,					pad_token='!'      ,					model_max_length=16      )

       # Initialize processor
       a_	    = OwlViTProcessor(image_processor=lowercase_      ,					tokenizer=lowercase_      )
       image_processor.save_pretrained(repo.local_dir      )
       processor.save_pretrained(repo.local_dir      )

       repo.git_add()
       repo.git_commit('Upload model and processor'      )
       repo.git_push()


if __name__ == "__main__":
  __lowerCAmelCase					   =       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--owlvit_version",
      default=None,
      type=str,
      required=True,
      help="OWL-ViT model name [clip_b16, clip_b32, clip_l14].",
  )
  parser.add_argument(
      "--owlvit_checkpoint", default=None, type=str, required=True, help="Path to flax model checkpoint."
  )
  parser.add_argument("--hf_config", default=None, type=str, required=True, help="Path to HF model config.")
  parser.add_argument(
      "--pytorch_dump_folder_path", default="hf_model", type=str, help="Path to the output PyTorch model."
  )
  __lowerCAmelCase					   =       parser.parse_args()

  # Initialize PyToch clip model
  __lowerCAmelCase					   =       args.owlvit_version
  if model_name == "clip_b16":
    __lowerCAmelCase					   =       CONFIGS["vit_b16"]
  elif model_name == "clip_b32":
    __lowerCAmelCase					   =       CONFIGS["vit_b32"]
  elif model_name == "clip_l14":
    __lowerCAmelCase					   =       CONFIGS["vit_l14"]

  # Load from checkpoint and convert params to float-32
  __lowerCAmelCase					   =       checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)["optimizer"]["target"]
  __lowerCAmelCase					   =       jax.tree_util.tree_map(lambda x: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x, variables)
  del variables

  # Convert CLIP backbone
  __lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase					   =       convert_clip_backbone(flax_params, torch_config)

  convert_owlvit_checkpoint(clip_pt, flax_params, attn_params, args.pytorch_dump_folder_path, args.hf_config)












'''simple docstring'''







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)

__lowerCAmelCase					   =       get_tests_dir("fixtures")
__lowerCAmelCase					   =       os.path.join(dirname(get_tests_dir()))
__lowerCAmelCase					   =       dirname(os.path.abspath(__file__))

# default torch.distributed port
__lowerCAmelCase					   =       "10999"

__lowerCAmelCase					   =       "t5-small"

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


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-mt5"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-camembert"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-openai-gpt"

# missing tokenizer files
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-convbert"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-layoutlmv2"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-hubert"

# issues with tokenizer
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-ctrl"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-transfo-xl"  # same as ctrl

# other issues with tiny models
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-ibert"  # multiple issues with either mlm/qa/clas
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-reformer"  # multiple issues with either mlm/qa/clas

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


def 			__UpperCamelCase				(			lowercase_					:     Optional[int]=False      ):
       """simple docstring"""




       a_	    = min(2      ,					get_gpu_count()      ) if distributed else 1
       a_	    = os.environ.get('DS_TEST_PORT'      ,					lowercase_      )
       return F'deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'.split()


def 			__UpperCamelCase				(			):
       """simple docstring"""




       a_	    = F'{FIXTURE_DIRECTORY}/tests_samples'
       a_	    = F'{data_dir_samples}/wmt_en_ro'
       a_	    = F'{data_dir_samples}/xsum'
       a_	    = '\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.
       a_	    = {
           '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',
           ],
       }

       a_	    = F'{ROOT_DIRECTORY}/examples/pytorch'

       a_	    = {
           '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        ',
       }

       a_	    = get_launcher(distributed=lowercase_      )

       a_	    = {}
       for task, args in tasks.items():
              a_	    = args.split()
              for model in tasksamodels[task]:
                     a_	    = globals()[F'{model.upper().replace("-"      ,					"_"      )}_TINY']
                     a_	    = F'--model_name_or_path {model_name}'.split()
                     a_	    = 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


__lowerCAmelCase					   =       make_task_cmds()

__lowerCAmelCase					   =       "zero2"
__lowerCAmelCase					   =       "zero3"

__lowerCAmelCase					   =       [ZEROa, ZEROa]


def 			__UpperCamelCase				(			lowercase_					:     str      ,					lowercase_					:     Optional[int]      ,					lowercase_					:     Dict      ):
       """simple docstring"""




       a_	    = parameterized.to_safe_name('_'.join(str(lowercase_      ) for x in param.args      )      )
       return F'{func.__name__}_{param_based_name}'


# Cartesian-product of zero stages with models to test
__lowerCAmelCase					   =       list(itertools.product(stages, task_cmds.keys()))




@slow
@require_deepspeed
@require_torch_gpu
class      __SCREAMING_SNAKE_CASE					(__A					):


  """simple docstring"""


  def       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__  ):






         """simple docstring"""


         if task not in task_cmds:
                raise ValueError(f'don\'t know of task {task}, have {task_cmds.keys()}'  )

         a_	    = task_cmds[task]
         a_	    = f'--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'.split()

         a_	    = self.get_auto_remove_tmp_dir()
         a_	    = f'--output_dir {output_dir}'.split()

         cmd += args_ds + args_out

         return cmd, output_dir







  @parameterized.expand(UpperCamelCase__      ,  name_func=UpperCamelCase__  )
  def       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__  ):






         """simple docstring"""


         a_    ,  a_	    = self.get_task_cmd(UpperCamelCase__      ,  UpperCamelCase__  )

         # 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(UpperCamelCase__      ,  env=self.get_env()  )

         # 2. test that the fp32 weights get reconsolidated
         a_	    = f'{output_dir}/checkpoint-1'
         a_	    = f'{chkpt_dir}/out.bin'
         a_	    = 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(UpperCamelCase__      ,  shell=UpperCamelCase__  )
         assert os.path.exists(UpperCamelCase__  ), 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




