"""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)

SCREAMING_SNAKE_CASE__:List[Any]			=	logging.getLogger()



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



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



def 					_lowerCamelCase(					):
       __a     				=  torch.cuda.is_available() and torch_device == "cuda"
       return is_using_cuda and is_apex_available()


SCREAMING_SNAKE_CASE__:List[str]			=	logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class 				snake_case__     (   snake_case_   ):


 @classmethod
 def 		a__ (				cls		):
        # 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 		a__ (				cls		):
        shutil.rmtree(cls.tmpdir		)


 @mock.patch.dict(os.environ  ,						{"WANDB_MODE": "offline"}		)
 def 		a__ (				self		):
        __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(lowerCamelCase		)
        self.assertGreaterEqual(result["eval_accuracy"]  ,						0.75		)
        self.assertTrue(os.path.exists(os.path.join(lowerCamelCase  ,						"epoch_0"		)		)		)
        self.assertTrue(os.path.exists(os.path.join(lowerCamelCase  ,						"glue_no_trainer"		)		)		)


 @mock.patch.dict(os.environ  ,						{"WANDB_MODE": "offline"}		)
 def 		a__ (				self		):
        __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(lowerCamelCase		)
        self.assertLess(result["perplexity"]  ,						100		)
        self.assertTrue(os.path.exists(os.path.join(lowerCamelCase  ,						"epoch_0"		)		)		)
        self.assertTrue(os.path.exists(os.path.join(lowerCamelCase  ,						"clm_no_trainer"		)		)		)


 @mock.patch.dict(os.environ  ,						{"WANDB_MODE": "offline"}		)
 def 		a__ (				self		):
        __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(lowerCamelCase		)
        self.assertLess(result["perplexity"]  ,						42		)
        self.assertTrue(os.path.exists(os.path.join(lowerCamelCase  ,						"epoch_0"		)		)		)
        self.assertTrue(os.path.exists(os.path.join(lowerCamelCase  ,						"mlm_no_trainer"		)		)		)


 @mock.patch.dict(os.environ  ,						{"WANDB_MODE": "offline"}		)
 def 		a__ (				self		):
        # 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(lowerCamelCase		)
        self.assertGreaterEqual(result["eval_accuracy"]  ,						0.75		)
        self.assertLess(result["train_loss"]  ,						0.5		)
        self.assertTrue(os.path.exists(os.path.join(lowerCamelCase  ,						"epoch_0"		)		)		)
        self.assertTrue(os.path.exists(os.path.join(lowerCamelCase  ,						"ner_no_trainer"		)		)		)


 @unittest.skip(reason="Fix me @muellerzr"		)
 @mock.patch.dict(os.environ  ,						{"WANDB_MODE": "offline"}		)
 def 		a__ (				self		):
        __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(lowerCamelCase		)
        # 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(lowerCamelCase  ,						"epoch_0"		)		)		)
        self.assertTrue(os.path.exists(os.path.join(lowerCamelCase  ,						"qa_no_trainer"		)		)		)


 @mock.patch.dict(os.environ  ,						{"WANDB_MODE": "offline"}		)
 def 		a__ (				self		):
        __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(lowerCamelCase		)
        self.assertGreaterEqual(result["eval_accuracy"]  ,						0.8		)
        self.assertTrue(os.path.exists(os.path.join(lowerCamelCase  ,						"swag_no_trainer"		)		)		)


 @slow
 @mock.patch.dict(os.environ  ,						{"WANDB_MODE": "offline"}		)
 def 		a__ (				self		):
        __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(lowerCamelCase		)
        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(lowerCamelCase  ,						"epoch_0"		)		)		)
        self.assertTrue(os.path.exists(os.path.join(lowerCamelCase  ,						"summarization_no_trainer"		)		)		)


 @slow
 @mock.patch.dict(os.environ  ,						{"WANDB_MODE": "offline"}		)
 def 		a__ (				self		):
        __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(lowerCamelCase		)
        self.assertGreaterEqual(result["eval_bleu"]  ,						30		)
        self.assertTrue(os.path.exists(os.path.join(lowerCamelCase  ,						"epoch_0"		)		)		)
        self.assertTrue(os.path.exists(os.path.join(lowerCamelCase  ,						"translation_no_trainer"		)		)		)


 @slow
 def 		a__ (				self		):
        __a     				=  logging.StreamHandler(sys.stdout		)
        logger.addHandler(lowerCamelCase		)

        __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(lowerCamelCase		)
        self.assertGreaterEqual(result["eval_overall_accuracy"]  ,						0.10		)






 @mock.patch.dict(os.environ  ,						{"WANDB_MODE": "offline"}		)
 def 		a__ (				self		):
        __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(lowerCamelCase		)
        # The base model scores a 25%
        self.assertGreaterEqual(result["eval_accuracy"]  ,						0.6		)
        self.assertTrue(os.path.exists(os.path.join(lowerCamelCase  ,						"step_1"		)		)		)
        self.assertTrue(os.path.exists(os.path.join(lowerCamelCase  ,						"image_classification_no_trainer"		)		)		)

"""simple docstring"""



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
   import PIL


SCREAMING_SNAKE_CASE__:Tuple			=	logging.get_logger(__name__)






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			Optional[Any]           =		["""pixel_values"""]


 def __init__(				self  ,						lowerCamelCase = True  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = PILImageResampling.BILINEAR  ,						lowerCamelCase = True  ,						lowerCamelCase = 1 / 255  ,						lowerCamelCase = True  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
        super().__init__(**lowerCamelCase		)
        __a     				=  size if size is not None else {"shortest_edge": 384}
        __a     				=  get_size_dict(lowerCamelCase  ,						default_to_square=lowerCamelCase		)

        __a     				=  do_resize
        __a     				=  size
        # Default value set here for backwards compatibility where the value in config is None
        __a     				=  crop_pct if crop_pct is not None else 224 / 256
        __a     				=  resample
        __a     				=  do_rescale
        __a     				=  rescale_factor
        __a     				=  do_normalize
        __a     				=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        __a     				=  image_std if image_std is not None else IMAGENET_STANDARD_STD


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase = PILImageResampling.BICUBIC  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
        __a     				=  get_size_dict(lowerCamelCase  ,						default_to_square=lowerCamelCase		)
        if "shortest_edge" not in size:
               raise ValueError(F"Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"		)
        __a     				=  size["shortest_edge"]

        if shortest_edge < 384:
               # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
               __a     				=  int(shortest_edge / crop_pct		)
               __a     				=  get_resize_output_image_size(lowerCamelCase  ,						size=lowerCamelCase  ,						default_to_square=lowerCamelCase		)
               __a     				=  resize(image=lowerCamelCase  ,						size=lowerCamelCase  ,						resample=lowerCamelCase  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)
               # then crop to (shortest_edge, shortest_edge)
               return center_crop(image=lowerCamelCase  ,						size=(shortest_edge, shortest_edge)  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)
        else:
               # warping (no cropping) when evaluated at 384 or larger
               return resize(
                   lowerCamelCase  ,						size=(shortest_edge, shortest_edge)  ,						resample=lowerCamelCase  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
        return rescale(lowerCamelCase  ,						scale=lowerCamelCase  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
        return normalize(lowerCamelCase  ,						mean=lowerCamelCase  ,						std=lowerCamelCase  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = ChannelDimension.FIRST  ,						**lowerCamelCase  ,						):
        __a     				=  do_resize if do_resize is not None else self.do_resize
        __a     				=  crop_pct if crop_pct is not None else self.crop_pct
        __a     				=  resample if resample is not None else self.resample
        __a     				=  do_rescale if do_rescale is not None else self.do_rescale
        __a     				=  rescale_factor if rescale_factor is not None else self.rescale_factor
        __a     				=  do_normalize if do_normalize is not None else self.do_normalize
        __a     				=  image_mean if image_mean is not None else self.image_mean
        __a     				=  image_std if image_std is not None else self.image_std

        __a     				=  size if size is not None else self.size
        __a     				=  get_size_dict(lowerCamelCase  ,						default_to_square=lowerCamelCase		)

        __a     				=  make_list_of_images(lowerCamelCase		)

        if not valid_images(lowerCamelCase		):
               raise ValueError(
                   "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                   "torch.Tensor, tf.Tensor or jax.ndarray."		)

        if do_resize and size is None or resample is None:
               raise ValueError("Size and resample must be specified if do_resize is True."		)

        if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
               raise ValueError("crop_pct must be specified if size < 384."		)

        if do_rescale and rescale_factor is None:
               raise ValueError("Rescale factor must be specified if do_rescale is True."		)

        if do_normalize and (image_mean is None or image_std is None):
               raise ValueError("Image mean and std must be specified if do_normalize is True."		)

        # All transformations expect numpy arrays.
        __a     				=  [to_numpy_array(lowerCamelCase		) for image in images]

        if do_resize:
               __a     				=  [self.resize(image=lowerCamelCase  ,						size=lowerCamelCase  ,						crop_pct=lowerCamelCase  ,						resample=lowerCamelCase		) for image in images]

        if do_rescale:
               __a     				=  [self.rescale(image=lowerCamelCase  ,						scale=lowerCamelCase		) for image in images]

        if do_normalize:
               __a     				=  [self.normalize(image=lowerCamelCase  ,						mean=lowerCamelCase  ,						std=lowerCamelCase		) for image in images]

        __a     				=  [to_channel_dimension_format(lowerCamelCase  ,						lowerCamelCase		) for image in images]

        __a     				=  {"pixel_values": images}
        return BatchFeature(data=lowerCamelCase  ,						tensor_type=lowerCamelCase		)
