





'''simple docstring'''

import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase				:      Any						      =logging.get_logger(__name__)


__lowerCAmelCase				:      int						      ="https://openaipublic.azureedge.net/jukebox/models/"
__lowerCAmelCase				:      Any						      ={
    "jukebox-1b-lyrics": [
        "5b/vqvae.pth.tar",
        "5b/prior_level_0.pth.tar",
        "5b/prior_level_1.pth.tar",
        "1b_lyrics/prior_level_2.pth.tar",
    ],
    "jukebox-5b-lyrics": [
        "5b/vqvae.pth.tar",
        "5b/prior_level_0.pth.tar",
        "5b/prior_level_1.pth.tar",
        "5b_lyrics/prior_level_2.pth.tar",
    ],
}





def 		UpperCamelCase							(				_lowerCamelCase   :   str			):
    if key.endswith(".model.1.bias"			) and len(key.split("."			)			) > 10:
        A__							=     key.replace(".model.1.bias"		,			".conv1d_1.bias"			)
    elif key.endswith(".model.1.weight"			) and len(key.split("."			)			) > 10:
        A__							=     key.replace(".model.1.weight"		,			".conv1d_1.weight"			)
    elif key.endswith(".model.3.bias"			) and len(key.split("."			)			) > 10:
        A__							=     key.replace(".model.3.bias"		,			".conv1d_2.bias"			)
    elif key.endswith(".model.3.weight"			) and len(key.split("."			)			) > 10:
        A__							=     key.replace(".model.3.weight"		,			".conv1d_2.weight"			)

    if "conditioner_blocks.0." in key:
        A__							=     key.replace("conditioner_blocks.0"		,			"conditioner_blocks"			)

    if "prime_prior" in key:
        A__							=     key.replace("prime_prior"		,			"encoder"			)

    if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
        A__							=     key.replace(".emb."		,			"."			)

    if key.endswith("k"			):  # replace vqvae.X.k with vqvae.X.codebook
        return key.replace(".k"		,			".codebook"			)
    if "y_emb." in key:
        return key.replace("y_emb."		,			"metadata_embedding."			)

    if "x_emb.emb." in key:
        A__							=     key.replace("0.x_emb.emb"		,			"embed_tokens"			)

    if "prime_state_ln" in key:
        return key.replace("prime_state_ln"		,			"encoder.final_layer_norm"			)
    if ".ln" in key:
        return key.replace(".ln"		,			".layer_norm"			)
    if "_ln" in key:
        return key.replace("_ln"		,			"_layer_norm"			)

    if "prime_state_proj" in key:
        return key.replace("prime_state_proj"		,			"encoder.proj_in"			)
    if "prime_x_out" in key:
        return key.replace("prime_x_out"		,			"encoder.lm_head"			)
    if "prior.x_out" in key:
        return key.replace("x_out"		,			"fc_proj_out"			)
    if "x_emb" in key:
        return key.replace("x_emb"		,			"embed_tokens"			)

    return key





def 		UpperCamelCase							(				_lowerCamelCase   :   Optional[Any]		,			_lowerCamelCase   :   Optional[int]		,			_lowerCamelCase   :   Dict		,			_lowerCamelCase   :   str			):
    A__							=     {}
    import re

    A__							=     re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"			)
    A__							=     re.compile(
        r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"			)
    A__							=     re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"			)

    A__							=     re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"			)
    A__							=     re.compile(
        r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"			)
    A__							=     re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"			)

    A__							=     re.compile(r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)"			)
    A__							=     re.compile(
        r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"			)
    A__							=     re.compile(r"conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)"			)

    for original_key, value in state_dict.items():
        # rename vqvae.encoder keys
        if re_encoder_block_conv_in.fullmatch(_lowerCamelCase			):
            A__							=     re_encoder_block_conv_in.match(_lowerCamelCase			)
            A__							=     regex_match.groups()
            A__							=     int(groups[2]			) * 2 + int(groups[3]			)
            A__							=     F"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"
            A__							=     re_encoder_block_conv_in.sub(_lowerCamelCase		,			_lowerCamelCase			)

        elif re_encoder_block_resnet.fullmatch(_lowerCamelCase			):
            A__							=     re_encoder_block_resnet.match(_lowerCamelCase			)
            A__							=     regex_match.groups()
            A__							=     int(groups[2]			) * 2 + int(groups[3]			)
            A__							=     {"1": 1, "3": 2}[groups[-2]]
            A__							=     F"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."
            A__							=     F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
            A__							=     prefix + resnet_block
            A__							=     re_encoder_block_resnet.sub(_lowerCamelCase		,			_lowerCamelCase			)

        elif re_encoder_block_proj_out.fullmatch(_lowerCamelCase			):
            A__							=     re_encoder_block_proj_out.match(_lowerCamelCase			)
            A__							=     regex_match.groups()
            A__							=     F"encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"
            A__							=     re_encoder_block_proj_out.sub(_lowerCamelCase		,			_lowerCamelCase			)

        # rename vqvae.decoder keys
        elif re_decoder_block_conv_out.fullmatch(_lowerCamelCase			):
            A__							=     re_decoder_block_conv_out.match(_lowerCamelCase			)
            A__							=     regex_match.groups()
            A__							=     int(groups[2]			) * 2 + int(groups[3]			) - 2
            A__							=     F"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"
            A__							=     re_decoder_block_conv_out.sub(_lowerCamelCase		,			_lowerCamelCase			)

        elif re_decoder_block_resnet.fullmatch(_lowerCamelCase			):
            A__							=     re_decoder_block_resnet.match(_lowerCamelCase			)
            A__							=     regex_match.groups()
            A__							=     int(groups[2]			) * 2 + int(groups[3]			) - 2
            A__							=     {"1": 1, "3": 2}[groups[-2]]
            A__							=     F"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."
            A__							=     F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
            A__							=     prefix + resnet_block
            A__							=     re_decoder_block_resnet.sub(_lowerCamelCase		,			_lowerCamelCase			)

        elif re_decoder_block_proj_in.fullmatch(_lowerCamelCase			):
            A__							=     re_decoder_block_proj_in.match(_lowerCamelCase			)
            A__							=     regex_match.groups()
            A__							=     F"decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"
            A__							=     re_decoder_block_proj_in.sub(_lowerCamelCase		,			_lowerCamelCase			)

        # rename prior cond.model to upsampler.upsample_block and resnet
        elif re_prior_cond_conv_out.fullmatch(_lowerCamelCase			):
            A__							=     re_prior_cond_conv_out.match(_lowerCamelCase			)
            A__							=     regex_match.groups()
            A__							=     int(groups[1]			) * 2 + int(groups[2]			) - 2
            A__							=     F"conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"
            A__							=     re_prior_cond_conv_out.sub(_lowerCamelCase		,			_lowerCamelCase			)

        elif re_prior_cond_resnet.fullmatch(_lowerCamelCase			):
            A__							=     re_prior_cond_resnet.match(_lowerCamelCase			)
            A__							=     regex_match.groups()
            A__							=     int(groups[1]			) * 2 + int(groups[2]			) - 2
            A__							=     {"1": 1, "3": 2}[groups[-2]]
            A__							=     F"conditioner_blocks.upsampler.upsample_block.{block_index}."
            A__							=     F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
            A__							=     prefix + resnet_block
            A__							=     re_prior_cond_resnet.sub(_lowerCamelCase		,			_lowerCamelCase			)

        elif re_prior_cond_proj_in.fullmatch(_lowerCamelCase			):
            A__							=     re_prior_cond_proj_in.match(_lowerCamelCase			)
            A__							=     regex_match.groups()
            A__							=     F"conditioner_blocks.upsampler.proj_in.{groups[-1]}"
            A__							=     re_prior_cond_proj_in.sub(_lowerCamelCase		,			_lowerCamelCase			)

        # keep original key
        else:
            A__							=     original_key

        A__							=     replace_key(_lowerCamelCase			)

        if F"{key_prefix}.{key}" not in model_state_dict or key is None:
            print(F"failed converting {original_key} to {key}, does not match"			)

        # handle missmatched shape
        elif value.shape != model_state_dict[F"{key_prefix}.{key}"].shape:
            A__							=     model_state_dict[F"{key_prefix}.{key}"]
            print(F"{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match"			)
            A__							=     original_key

        A__							=     original_key
        A__							=     value

    return new_dict





@torch.no_grad()
def 		UpperCamelCase							(				_lowerCamelCase   :   str=None		,			_lowerCamelCase   :   Dict=None			):
    for file in MODEL_MAPPING[model_name]:
        if not os.path.isfile(F"{pytorch_dump_folder_path}/{file.split('/'			)[-1]}"			):
            A__							=     requests.get(F"{PREFIX}{file}"		,			allow_redirects=_lowerCamelCase			)
            os.makedirs(F"{pytorch_dump_folder_path}/"		,			exist_ok=_lowerCamelCase			)
            open(F"{pytorch_dump_folder_path}/{file.split('/'			)[-1]}"		,			"wb"			).write(r.content			)

    A__							=     MODEL_MAPPING[model_name.split("/"			)[-1]]

    A__							=     JukeboxConfig.from_pretrained(_lowerCamelCase			)
    A__							=     JukeboxModel(_lowerCamelCase			)

    A__							=     []
    A__							=     {}
    for i, dict_name in enumerate(_lowerCamelCase			):
        A__							=     torch.load(F"{pytorch_dump_folder_path}/{dict_name.split('/'			)[-1]}"			)["model"]

        A__							=     {}
        for k in old_dic.keys():
            if k.endswith(".b"			):
                A__							=     old_dic[k]
            elif k.endswith(".w"			):
                A__							=     old_dic[k]
            elif "level_2" not in dict_name and "cond.model." in k:
                A__							=     old_dic[k]
            else:
                A__							=     old_dic[k]

        A__							=     "vqvae" if i == 0 else F"priors.{3 - i}"
        A__							=     fix_jukebox_keys(_lowerCamelCase		,			model.state_dict()		,			_lowerCamelCase		,			_lowerCamelCase			)
        weight_dict.append(_lowerCamelCase			)

    A__							=     weight_dict.pop(0			)
    model.vqvae.load_state_dict(_lowerCamelCase			)
    for i in range(len(_lowerCamelCase			)			):
        model.priors[i].load_state_dict(weight_dict[2 - i]			)

    Path(_lowerCamelCase			).mkdir(exist_ok=_lowerCamelCase			)
    with open(F"{pytorch_dump_folder_path}/mapping.json"		,			"w"			) as txtfile:
        json.dump(_lowerCamelCase		,			_lowerCamelCase			)

    print(F"Saving model {model_name} to {pytorch_dump_folder_path}"			)
    model.save_pretrained(_lowerCamelCase			)

    return weight_dict


if __name__ == "__main__":
  __lowerCAmelCase				:      Dict						      =argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--model_name",
      default="jukebox-5b-lyrics",
      type=str,
      help="Name of the model you'd like to convert.",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path",
      default="jukebox-5b-lyrics-converted",
      type=str,
      help="Path to the output PyTorch model directory.",
  )
  __lowerCAmelCase				:      int						      =parser.parse_args()
  convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)










'''simple docstring'''

import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

__lowerCAmelCase				:      Dict						      =logging.getLogger()





def 		UpperCamelCase							(				):
    A__							=     argparse.ArgumentParser()
    parser.add_argument("-f"			)
    A__							=     parser.parse_args()
    return args.f





def 		UpperCamelCase							(				_lowerCamelCase   :   List[str]			):
    A__							=     {}
    A__							=     os.path.join(_lowerCamelCase		,			"all_results.json"			)
    if os.path.exists(_lowerCamelCase			):
        with open(_lowerCamelCase		,			"r"			) as f:
            A__							=     json.load(_lowerCamelCase			)
    else:
        raise ValueError(F"can't find {path}"			)
    return results





def 		UpperCamelCase							(				):
    A__							=     torch.cuda.is_available() and torch_device == "cuda"
    return is_using_cuda and is_apex_available()


__lowerCAmelCase				:      Union[str, Any]						      =logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class   UpperCAmelCase      (			UpperCamelCase__							):
 @classmethod
 def 	UpperCAmelCase_				(							cls :List[Any]				)->				Union[str, Any]:
     # Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
     A__							=     tempfile.mkdtemp()
     A__							=     os.path.join(cls.tmpdir		,			"default_config.yml"				)
     write_basic_config(save_location=cls.configPath				)
     A__							=     ["accelerate", "launch", "--config_file", cls.configPath]
 @classmethod
 def 	UpperCAmelCase_				(							cls :Tuple				)->				List[Any]:
     shutil.rmtree(cls.tmpdir				)
 @mock.patch.dict(os.environ		,			{"WANDB_MODE": "offline"}				)
 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				Any:
     A__							=     self.get_auto_remove_tmp_dir()
     A__							=     F"\n            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --seed=42\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

     if is_cuda_and_apex_available():
         testargs.append("--fp16"				)

     run_command(self._launch_args + testargs				)
     A__							=     get_results(lowercase_				)
     self.assertGreaterEqual(result["eval_accuracy"]		,			0.7_5				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"epoch_0"				)				)				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"glue_no_trainer"				)				)				)
 @mock.patch.dict(os.environ		,			{"WANDB_MODE": "offline"}				)
 def 	UpperCAmelCase_				(							self :List[Any]				)->				Tuple:
     A__							=     self.get_auto_remove_tmp_dir()
     A__							=     F"\n            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --block_size 128\n            --per_device_train_batch_size 5\n            --per_device_eval_batch_size 5\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

     if torch.cuda.device_count() > 1:
         # Skipping because there are not enough batches to train the model + would need a drop_last to work.
         return

     run_command(self._launch_args + testargs				)
     A__							=     get_results(lowercase_				)
     self.assertLess(result["perplexity"]		,			1_00				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"epoch_0"				)				)				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"clm_no_trainer"				)				)				)
 @mock.patch.dict(os.environ		,			{"WANDB_MODE": "offline"}				)
 def 	UpperCAmelCase_				(							self :Any				)->				str:
     A__							=     self.get_auto_remove_tmp_dir()
     A__							=     F"\n            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --num_train_epochs=1\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

     run_command(self._launch_args + testargs				)
     A__							=     get_results(lowercase_				)
     self.assertLess(result["perplexity"]		,			42				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"epoch_0"				)				)				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"mlm_no_trainer"				)				)				)
 @mock.patch.dict(os.environ		,			{"WANDB_MODE": "offline"}				)
 def 	UpperCAmelCase_				(							self :Optional[Any]				)->				List[str]:
     # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
     A__							=     7 if get_gpu_count() > 1 else 2

     A__							=     self.get_auto_remove_tmp_dir()
     A__							=     F"\n            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

     run_command(self._launch_args + testargs				)
     A__							=     get_results(lowercase_				)
     self.assertGreaterEqual(result["eval_accuracy"]		,			0.7_5				)
     self.assertLess(result["train_loss"]		,			0.5				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"epoch_0"				)				)				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"ner_no_trainer"				)				)				)
 @unittest.skip(reason="Fix me @muellerzr"				)
 @mock.patch.dict(os.environ		,			{"WANDB_MODE": "offline"}				)
 def 	UpperCAmelCase_				(							self :Any				)->				Optional[int]:
     A__							=     self.get_auto_remove_tmp_dir()
     A__							=     F"\n            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --seed=42\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

     run_command(self._launch_args + testargs				)
     A__							=     get_results(lowercase_				)
     # Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
     self.assertGreaterEqual(result["eval_f1"]		,			28				)
     self.assertGreaterEqual(result["eval_exact"]		,			28				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"epoch_0"				)				)				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"qa_no_trainer"				)				)				)
 @mock.patch.dict(os.environ		,			{"WANDB_MODE": "offline"}				)
 def 	UpperCAmelCase_				(							self :Optional[int]				)->				List[str]:
     A__							=     self.get_auto_remove_tmp_dir()
     A__							=     F"\n            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=20\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --with_tracking\n        ".split()

     run_command(self._launch_args + testargs				)
     A__							=     get_results(lowercase_				)
     self.assertGreaterEqual(result["eval_accuracy"]		,			0.8				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"swag_no_trainer"				)				)				)
 @slow
 @mock.patch.dict(os.environ		,			{"WANDB_MODE": "offline"}				)
 def 	UpperCAmelCase_				(							self :Tuple				)->				Any:
     A__							=     self.get_auto_remove_tmp_dir()
     A__							=     F"\n            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

     run_command(self._launch_args + testargs				)
     A__							=     get_results(lowercase_				)
     self.assertGreaterEqual(result["eval_rouge1"]		,			10				)
     self.assertGreaterEqual(result["eval_rouge2"]		,			2				)
     self.assertGreaterEqual(result["eval_rougeL"]		,			7				)
     self.assertGreaterEqual(result["eval_rougeLsum"]		,			7				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"epoch_0"				)				)				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"summarization_no_trainer"				)				)				)
 @slow
 @mock.patch.dict(os.environ		,			{"WANDB_MODE": "offline"}				)
 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				Optional[int]:
     A__							=     self.get_auto_remove_tmp_dir()
     A__							=     F"\n            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py\n            --model_name_or_path sshleifer/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --num_beams=6\n            --learning_rate=3e-3\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

     run_command(self._launch_args + testargs				)
     A__							=     get_results(lowercase_				)
     self.assertGreaterEqual(result["eval_bleu"]		,			30				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"epoch_0"				)				)				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"translation_no_trainer"				)				)				)
 @slow
 def 	UpperCAmelCase_				(							self :Optional[int]				)->				List[Any]:
     A__							=     logging.StreamHandler(sys.stdout				)
     logger.addHandler(lowercase_				)

     A__							=     self.get_auto_remove_tmp_dir()
     A__							=     F"\n            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py\n            --dataset_name huggingface/semantic-segmentation-test-sample\n            --output_dir {tmp_dir}\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n        ".split()

     run_command(self._launch_args + testargs				)
     A__							=     get_results(lowercase_				)
     self.assertGreaterEqual(result["eval_overall_accuracy"]		,			0.1_0				)



 @mock.patch.dict(os.environ		,			{"WANDB_MODE": "offline"}				)
 def 	UpperCAmelCase_				(							self :str				)->				Dict:
     A__							=     self.get_auto_remove_tmp_dir()
     A__							=     F"\n            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py\n            --model_name_or_path google/vit-base-patch16-224-in21k\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --max_train_steps 2\n            --train_val_split 0.1\n            --seed 42\n            --output_dir {tmp_dir}\n            --with_tracking\n            --checkpointing_steps 1\n        ".split()

     if is_cuda_and_apex_available():
         testargs.append("--fp16"				)

     run_command(self._launch_args + testargs				)
     A__							=     get_results(lowercase_				)
     # The base model scores a 25%
     self.assertGreaterEqual(result["eval_accuracy"]		,			0.6				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"step_1"				)				)				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"image_classification_no_trainer"				)				)				)



