


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)

snake_case__				:	Tuple					       =					logging.getLogger()


def    __lowerCamelCase   (      )     ->    int:
   lowerCamelCase_						:   Optional[int]		= argparse.ArgumentParser()
   parser.add_argument("""-f"""					)
   lowerCamelCase_						:   Any		= parser.parse_args()
   return args.f


def    __lowerCamelCase   (      A__						: Union[str, Any]					)     ->    Tuple:
   lowerCamelCase_						:   Optional[int]		= {}
   lowerCamelCase_						:   Tuple		= os.path.join(A__						,						"""all_results.json"""					)
   if os.path.exists(A__					):
      with open(A__						,						"""r"""					) as f:
         lowerCamelCase_						:   Tuple		= json.load(A__					)
   else:
      raise ValueError(f'''can\'t find {path}'''					)
   return results


def    __lowerCamelCase   (      )     ->    Tuple:
   lowerCamelCase_						:   int		= torch.cuda.is_available() and torch_device == """cuda"""
   return is_using_cuda and is_apex_available()


snake_case__				:	int					       =					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       @classmethod
       def 				_lowerCAmelCase							(						cls				:				Union[str, Any]							)					->Union[str, Any]:
          # Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
          lowerCamelCase_						:   str		= tempfile.mkdtemp()
          lowerCamelCase_						:   Union[str, Any]		= os.path.join(cls.tmpdir		,				"""default_config.yml"""							)
          write_basic_config(save_location=cls.configPath							)
          lowerCamelCase_						:   str		= ["""accelerate""", """launch""", """--config_file""", cls.configPath]



       @classmethod
       def 				_lowerCAmelCase							(						cls				:				Optional[int]							)					->str:
          shutil.rmtree(cls.tmpdir							)



       @mock.patch.dict(os.environ		,				{"""WANDB_MODE""": """offline"""}							)
       def 				_lowerCAmelCase							(						self				:				Union[str, Any]							)					->Optional[Any]:
          lowerCamelCase_						:   List[Any]		= self.get_auto_remove_tmp_dir()
          lowerCamelCase_						:   Optional[int]		= 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							)
          lowerCamelCase_						:   Dict		= get_results(__a							)
          self.assertGreaterEqual(result["""eval_accuracy"""]		,				0.75							)
          self.assertTrue(os.path.exists(os.path.join(__a		,				"""epoch_0"""							)							)							)
          self.assertTrue(os.path.exists(os.path.join(__a		,				"""glue_no_trainer"""							)							)							)



       @mock.patch.dict(os.environ		,				{"""WANDB_MODE""": """offline"""}							)
       def 				_lowerCAmelCase							(						self				:				Any							)					->str:
          lowerCamelCase_						:   int		= self.get_auto_remove_tmp_dir()
          lowerCamelCase_						:   Dict		= 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							)
          lowerCamelCase_						:   Optional[Any]		= get_results(__a							)
          self.assertLess(result["""perplexity"""]		,				100							)
          self.assertTrue(os.path.exists(os.path.join(__a		,				"""epoch_0"""							)							)							)
          self.assertTrue(os.path.exists(os.path.join(__a		,				"""clm_no_trainer"""							)							)							)



       @mock.patch.dict(os.environ		,				{"""WANDB_MODE""": """offline"""}							)
       def 				_lowerCAmelCase							(						self				:				Optional[Any]							)					->Optional[int]:
          lowerCamelCase_						:   int		= self.get_auto_remove_tmp_dir()
          lowerCamelCase_						:   str		= 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							)
          lowerCamelCase_						:   List[Any]		= get_results(__a							)
          self.assertLess(result["""perplexity"""]		,				42							)
          self.assertTrue(os.path.exists(os.path.join(__a		,				"""epoch_0"""							)							)							)
          self.assertTrue(os.path.exists(os.path.join(__a		,				"""mlm_no_trainer"""							)							)							)



       @mock.patch.dict(os.environ		,				{"""WANDB_MODE""": """offline"""}							)
       def 				_lowerCAmelCase							(						self				:				List[Any]							)					->List[Any]:
          # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
          lowerCamelCase_						:   Optional[int]		= 7 if get_gpu_count() > 1 else 2

          lowerCamelCase_						:   Optional[Any]		= self.get_auto_remove_tmp_dir()
          lowerCamelCase_						:   Optional[Any]		= 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							)
          lowerCamelCase_						:   Optional[Any]		= get_results(__a							)
          self.assertGreaterEqual(result["""eval_accuracy"""]		,				0.75							)
          self.assertLess(result["""train_loss"""]		,				0.5							)
          self.assertTrue(os.path.exists(os.path.join(__a		,				"""epoch_0"""							)							)							)
          self.assertTrue(os.path.exists(os.path.join(__a		,				"""ner_no_trainer"""							)							)							)



       @unittest.skip(reason="""Fix me @muellerzr"""							)
       @mock.patch.dict(os.environ		,				{"""WANDB_MODE""": """offline"""}							)
       def 				_lowerCAmelCase							(						self				:				int							)					->Optional[Any]:
          lowerCamelCase_						:   Optional[Any]		= self.get_auto_remove_tmp_dir()
          lowerCamelCase_						:   List[Any]		= 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							)
          lowerCamelCase_						:   str		= get_results(__a							)
          # 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(__a		,				"""epoch_0"""							)							)							)
          self.assertTrue(os.path.exists(os.path.join(__a		,				"""qa_no_trainer"""							)							)							)



       @mock.patch.dict(os.environ		,				{"""WANDB_MODE""": """offline"""}							)
       def 				_lowerCAmelCase							(						self				:				Union[str, Any]							)					->List[str]:
          lowerCamelCase_						:   Union[str, Any]		= self.get_auto_remove_tmp_dir()
          lowerCamelCase_						:   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							)
          lowerCamelCase_						:   Tuple		= get_results(__a							)
          self.assertGreaterEqual(result["""eval_accuracy"""]		,				0.8							)
          self.assertTrue(os.path.exists(os.path.join(__a		,				"""swag_no_trainer"""							)							)							)



       @slow
       @mock.patch.dict(os.environ		,				{"""WANDB_MODE""": """offline"""}							)
       def 				_lowerCAmelCase							(						self				:				str							)					->Tuple:
          lowerCamelCase_						:   List[Any]		= self.get_auto_remove_tmp_dir()
          lowerCamelCase_						:   List[str]		= 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							)
          lowerCamelCase_						:   Tuple		= get_results(__a							)
          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(__a		,				"""epoch_0"""							)							)							)
          self.assertTrue(os.path.exists(os.path.join(__a		,				"""summarization_no_trainer"""							)							)							)



       @slow
       @mock.patch.dict(os.environ		,				{"""WANDB_MODE""": """offline"""}							)
       def 				_lowerCAmelCase							(						self				:				Union[str, Any]							)					->Any:
          lowerCamelCase_						:   Optional[int]		= self.get_auto_remove_tmp_dir()
          lowerCamelCase_						:   Optional[Any]		= 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							)
          lowerCamelCase_						:   Tuple		= get_results(__a							)
          self.assertGreaterEqual(result["""eval_bleu"""]		,				30							)
          self.assertTrue(os.path.exists(os.path.join(__a		,				"""epoch_0"""							)							)							)
          self.assertTrue(os.path.exists(os.path.join(__a		,				"""translation_no_trainer"""							)							)							)



       @slow
       def 				_lowerCAmelCase							(						self				:				Tuple							)					->Optional[Any]:
          lowerCamelCase_						:   Tuple		= logging.StreamHandler(sys.stdout							)
          logger.addHandler(__a							)

          lowerCamelCase_						:   Any		= self.get_auto_remove_tmp_dir()
          lowerCamelCase_						:   Tuple		= 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							)
          lowerCamelCase_						:   Optional[Any]		= get_results(__a							)
          self.assertGreaterEqual(result["""eval_overall_accuracy"""]		,				0.10							)




       @mock.patch.dict(os.environ		,				{"""WANDB_MODE""": """offline"""}							)
       def 				_lowerCAmelCase							(						self				:				Optional[int]							)					->List[str]:
          lowerCamelCase_						:   Dict		= self.get_auto_remove_tmp_dir()
          lowerCamelCase_						:   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							)
          lowerCamelCase_						:   List[str]		= get_results(__a							)
          # The base model scores a 25%
          self.assertGreaterEqual(result["""eval_accuracy"""]		,				0.6							)
          self.assertTrue(os.path.exists(os.path.join(__a		,				"""step_1"""							)							)							)
          self.assertTrue(os.path.exists(os.path.join(__a		,				"""image_classification_no_trainer"""							)							)							)









import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
      snake_case__				:	Optional[int]					       =					argparse.ArgumentParser()

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

      parser.add_argument(
          '--txt2img_unclip',
          default='kakaobrain/karlo-v1-alpha',
          type=str,
          required=False,
          help='The pretrained txt2img unclip.',
      )

      snake_case__				:	Tuple					       =					parser.parse_args()

      snake_case__				:	Dict					       =					UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

      snake_case__				:	Optional[int]					       =					CLIPImageProcessor()
      snake_case__				:	Optional[Any]					       =					CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

      snake_case__				:	Dict					       =					UnCLIPImageVariationPipeline(
          decoder=txtaimg.decoder,
          text_encoder=txtaimg.text_encoder,
          tokenizer=txtaimg.tokenizer,
          text_proj=txtaimg.text_proj,
          feature_extractor=feature_extractor,
          image_encoder=image_encoder,
          super_res_first=txtaimg.super_res_first,
          super_res_last=txtaimg.super_res_last,
          decoder_scheduler=txtaimg.decoder_scheduler,
          super_res_scheduler=txtaimg.super_res_scheduler,
      )

      imgaimg.save_pretrained(args.dump_path)





