
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      =logging.getLogger()


def 							SCREAMING_SNAKE_CASE_      (			):
      UpperCamelCase__    :   Union[str, Any]				=				argparse.ArgumentParser()
      parser.add_argument('''-f'''		)
      UpperCamelCase__    :   List[str]				=				parser.parse_args()
      return args.f


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      UpperCamelCase__    :   str				=				{}
      UpperCamelCase__    :   Optional[Any]				=				os.path.join(UpperCamelCase__ ,		'''all_results.json'''		)
      if os.path.exists(UpperCamelCase__		):
            with open(UpperCamelCase__ ,		'''r'''		) as f:
                  UpperCamelCase__    :   Optional[Any]				=				json.load(UpperCamelCase__		)
      else:
            raise ValueError(f'''can\'t find {path}'''		)
      return results


def 							SCREAMING_SNAKE_CASE_      (			):
      UpperCamelCase__    :   Any				=				torch.cuda.is_available() and torch_device == '''cuda'''
      return is_using_cuda and is_apex_available()


lowerCamelCase      =logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)




class 				_lowerCamelCase (      UpperCamelCase__ ):





     """simple docstring"""







     @classmethod
     def    __SCREAMING_SNAKE_CASE			(							cls  )  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   Tuple				=				tempfile.mkdtemp()
           UpperCamelCase__    :   List[Any]				=				os.path.join(cls.tmpdir						,						'''default_config.yml'''  )
           write_basic_config(save_location=cls.configPath  )
           UpperCamelCase__    :   List[Any]				=				['''accelerate''', '''launch''', '''--config_file''', cls.configPath]






     @classmethod
     def    __SCREAMING_SNAKE_CASE			(							cls  )  ->   List[Any]:




           """simple docstring"""







           shutil.rmtree(cls.tmpdir  )






     @mock.patch.dict(os.environ						,						{'''WANDB_MODE''': '''offline'''}  )
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   str				=				self.get_auto_remove_tmp_dir()
           UpperCamelCase__    :   List[Any]				=				F'''
            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --seed=42
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

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

           run_command(self._launch_args + testargs  )
           UpperCamelCase__    :   Optional[int]				=				get_results(__SCREAMING_SNAKE_CASE  )
           self.assertGreaterEqual(result['''eval_accuracy''']						,						0.75  )
           self.assertTrue(os.path.exists(os.path.join(__SCREAMING_SNAKE_CASE						,						'''epoch_0'''  )  )  )
           self.assertTrue(os.path.exists(os.path.join(__SCREAMING_SNAKE_CASE						,						'''glue_no_trainer'''  )  )  )






     @mock.patch.dict(os.environ						,						{'''WANDB_MODE''': '''offline'''}  )
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   List[Any]				=				self.get_auto_remove_tmp_dir()
           UpperCamelCase__    :   Union[str, Any]				=				F'''
            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --block_size 128
            --per_device_train_batch_size 5
            --per_device_eval_batch_size 5
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --checkpointing_steps epoch
            --with_tracking
        '''.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  )
           UpperCamelCase__    :   str				=				get_results(__SCREAMING_SNAKE_CASE  )
           self.assertLess(result['''perplexity''']						,						1_0_0  )
           self.assertTrue(os.path.exists(os.path.join(__SCREAMING_SNAKE_CASE						,						'''epoch_0'''  )  )  )
           self.assertTrue(os.path.exists(os.path.join(__SCREAMING_SNAKE_CASE						,						'''clm_no_trainer'''  )  )  )






     @mock.patch.dict(os.environ						,						{'''WANDB_MODE''': '''offline'''}  )
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[Any]:




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]				=				self.get_auto_remove_tmp_dir()
           UpperCamelCase__    :   Dict				=				F'''
            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --num_train_epochs=1
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

           run_command(self._launch_args + testargs  )
           UpperCamelCase__    :   List[Any]				=				get_results(__SCREAMING_SNAKE_CASE  )
           self.assertLess(result['''perplexity''']						,						4_2  )
           self.assertTrue(os.path.exists(os.path.join(__SCREAMING_SNAKE_CASE						,						'''epoch_0'''  )  )  )
           self.assertTrue(os.path.exists(os.path.join(__SCREAMING_SNAKE_CASE						,						'''mlm_no_trainer'''  )  )  )






     @mock.patch.dict(os.environ						,						{'''WANDB_MODE''': '''offline'''}  )
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   Dict				=				7 if get_gpu_count() > 1 else 2

           UpperCamelCase__    :   Any				=				self.get_auto_remove_tmp_dir()
           UpperCamelCase__    :   Optional[int]				=				F'''
            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

           run_command(self._launch_args + testargs  )
           UpperCamelCase__    :   Any				=				get_results(__SCREAMING_SNAKE_CASE  )
           self.assertGreaterEqual(result['''eval_accuracy''']						,						0.75  )
           self.assertLess(result['''train_loss''']						,						0.5  )
           self.assertTrue(os.path.exists(os.path.join(__SCREAMING_SNAKE_CASE						,						'''epoch_0'''  )  )  )
           self.assertTrue(os.path.exists(os.path.join(__SCREAMING_SNAKE_CASE						,						'''ner_no_trainer'''  )  )  )






     @unittest.skip(reason='''Fix me @muellerzr'''  )
     @mock.patch.dict(os.environ						,						{'''WANDB_MODE''': '''offline'''}  )
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Union[str, Any]:




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]				=				self.get_auto_remove_tmp_dir()
           UpperCamelCase__    :   Tuple				=				F'''
            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --seed=42
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

           run_command(self._launch_args + testargs  )
           UpperCamelCase__    :   Optional[int]				=				get_results(__SCREAMING_SNAKE_CASE  )
           # Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
           self.assertGreaterEqual(result['''eval_f1''']						,						2_8  )
           self.assertGreaterEqual(result['''eval_exact''']						,						2_8  )
           self.assertTrue(os.path.exists(os.path.join(__SCREAMING_SNAKE_CASE						,						'''epoch_0'''  )  )  )
           self.assertTrue(os.path.exists(os.path.join(__SCREAMING_SNAKE_CASE						,						'''qa_no_trainer'''  )  )  )






     @mock.patch.dict(os.environ						,						{'''WANDB_MODE''': '''offline'''}  )
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




           """simple docstring"""







           UpperCamelCase__    :   Optional[int]				=				self.get_auto_remove_tmp_dir()
           UpperCamelCase__    :   Union[str, Any]				=				F'''
            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=20
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --with_tracking
        '''.split()

           run_command(self._launch_args + testargs  )
           UpperCamelCase__    :   Optional[Any]				=				get_results(__SCREAMING_SNAKE_CASE  )
           self.assertGreaterEqual(result['''eval_accuracy''']						,						0.8  )
           self.assertTrue(os.path.exists(os.path.join(__SCREAMING_SNAKE_CASE						,						'''swag_no_trainer'''  )  )  )






     @slow
     @mock.patch.dict(os.environ						,						{'''WANDB_MODE''': '''offline'''}  )
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Union[str, Any]:




           """simple docstring"""







           UpperCamelCase__    :   int				=				self.get_auto_remove_tmp_dir()
           UpperCamelCase__    :   int				=				F'''
            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

           run_command(self._launch_args + testargs  )
           UpperCamelCase__    :   List[str]				=				get_results(__SCREAMING_SNAKE_CASE  )
           self.assertGreaterEqual(result['''eval_rouge1''']						,						1_0  )
           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(__SCREAMING_SNAKE_CASE						,						'''epoch_0'''  )  )  )
           self.assertTrue(os.path.exists(os.path.join(__SCREAMING_SNAKE_CASE						,						'''summarization_no_trainer'''  )  )  )






     @slow
     @mock.patch.dict(os.environ						,						{'''WANDB_MODE''': '''offline'''}  )
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[Any]:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]				=				self.get_auto_remove_tmp_dir()
           UpperCamelCase__    :   List[str]				=				F'''
            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
            --model_name_or_path sshleifer/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --num_beams=6
            --learning_rate=3e-3
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

           run_command(self._launch_args + testargs  )
           UpperCamelCase__    :   Optional[int]				=				get_results(__SCREAMING_SNAKE_CASE  )
           self.assertGreaterEqual(result['''eval_bleu''']						,						3_0  )
           self.assertTrue(os.path.exists(os.path.join(__SCREAMING_SNAKE_CASE						,						'''epoch_0'''  )  )  )
           self.assertTrue(os.path.exists(os.path.join(__SCREAMING_SNAKE_CASE						,						'''translation_no_trainer'''  )  )  )






     @slow
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]				=				logging.StreamHandler(sys.stdout  )
           logger.addHandler(__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   List[Any]				=				self.get_auto_remove_tmp_dir()
           UpperCamelCase__    :   str				=				F'''
            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
            --dataset_name huggingface/semantic-segmentation-test-sample
            --output_dir {tmp_dir}
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
        '''.split()

           run_command(self._launch_args + testargs  )
           UpperCamelCase__    :   Union[str, Any]				=				get_results(__SCREAMING_SNAKE_CASE  )
           self.assertGreaterEqual(result['''eval_overall_accuracy''']						,						0.10  )






     @mock.patch.dict(os.environ						,						{'''WANDB_MODE''': '''offline'''}  )
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[Any]:




           """simple docstring"""







           UpperCamelCase__    :   Dict				=				self.get_auto_remove_tmp_dir()
           UpperCamelCase__    :   Union[str, Any]				=				F'''
            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
            --model_name_or_path google/vit-base-patch16-224-in21k
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --max_train_steps 2
            --train_val_split 0.1
            --seed 42
            --output_dir {tmp_dir}
            --with_tracking
            --checkpointing_steps 1
        '''.split()

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

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




import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowerCamelCase      =False
lowerCamelCase      =True
lowerCamelCase      =False


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

    parser.add_argument(
        "--repo_path",
        default=None,
        type=str,
        required=True,
        help="The config json file corresponding to the architecture.",
    )

    parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

    lowerCamelCase      =parser.parse_args()

    lowerCamelCase      ={
        "image_size": "sample_size",
        "num_res_blocks": "layers_per_block",
        "block_channels": "block_out_channels",
        "down_blocks": "down_block_types",
        "up_blocks": "up_block_types",
        "downscale_freq_shift": "freq_shift",
        "resnet_num_groups": "norm_num_groups",
        "resnet_act_fn": "act_fn",
        "resnet_eps": "norm_eps",
        "num_head_channels": "attention_head_dim",
    }

    lowerCamelCase      ={
        "time_steps": "time_proj",
        "mid": "mid_block",
        "downsample_blocks": "down_blocks",
        "upsample_blocks": "up_blocks",
    }

    lowerCamelCase      ="" if has_file(args.repo_path, "config.json") else "unet"

    with open(os.path.join(args.repo_path, subfolder, "config.json"), "r", encoding="utf-8") as reader:
        lowerCamelCase      =reader.read()
        lowerCamelCase      =json.loads(text)

    if do_only_config:
        for key in config_parameters_to_change.keys():
            config.pop(key, None)

    if has_file(args.repo_path, "config.json"):
        lowerCamelCase      =UNetaDModel(**config)
    else:
        lowerCamelCase      =UNetaDConditionModel if "ldm-text2im-large-256" in args.repo_path else UNetaDModel
        lowerCamelCase      =class_name(**config)

    if do_only_config:
        model.save_config(os.path.join(args.repo_path, subfolder))

    lowerCamelCase      =dict(model.config)

    if do_only_renaming:
        for key, value in config_parameters_to_change.items():
            if key in config:
                lowerCamelCase      =config[key]
                del config[key]

        lowerCamelCase      =[k.replace("UNetRes", "") for k in config["down_block_types"]]
        lowerCamelCase      =[k.replace("UNetRes", "") for k in config["up_block_types"]]

    if do_only_weights:
        lowerCamelCase      =torch.load(os.path.join(args.repo_path, subfolder, "diffusion_pytorch_model.bin"))

        lowerCamelCase      ={}
        for param_key, param_value in state_dict.items():
            if param_key.endswith(".op.bias") or param_key.endswith(".op.weight"):
                continue
            lowerCamelCase      =False
            for key, new_key in key_parameters_to_change.items():
                if not has_changed and param_key.split(".")[0] == key:
                    lowerCamelCase      =param_value
                    lowerCamelCase      =True
            if not has_changed:
                lowerCamelCase      =param_value

        model.load_state_dict(new_state_dict)
        model.save_pretrained(os.path.join(args.repo_path, subfolder))


