
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)

lowercase__		:Optional[int]   =    logging.getLogger()



def        UpperCamelCase    (							):
					'''simple docstring'''
					lowercase							=						argparse.ArgumentParser()
					parser.add_argument('''-f'''		)
					lowercase							=						parser.parse_args()
					return args.f



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						{}
					lowercase							=						os.path.join(lowerCAmelCase__ ,			'''all_results.json'''		)
					if os.path.exists(lowerCAmelCase__		):
										with open(lowerCAmelCase__ ,			'''r'''		) as f:
															lowercase							=						json.load(lowerCAmelCase__		)
					else:
										raise ValueError(f'can\'t find {path}'		)
					return results



def        UpperCamelCase    (							):
					'''simple docstring'''
					lowercase							=						torch.cuda.is_available() and torch_device == '''cuda'''
					return is_using_cuda and is_apex_available()


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







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):




							@classmethod
							def  A__      (				cls):
												# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
												lowercase							=						tempfile.mkdtemp()
												lowercase							=						os.path.join(cls.tmpdir   ,'''default_config.yml''')
												write_basic_config(save_location=cls.configPath)
												lowercase							=						['''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):
												lowercase							=						self.get_auto_remove_tmp_dir()
												lowercase							=						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)
												lowercase							=						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  A__      (				self):
												lowercase							=						self.get_auto_remove_tmp_dir()
												lowercase							=						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)
												lowercase							=						get_results(A__)
												self.assertLess(result['''perplexity''']   ,1_0_0)
												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  A__      (				self):
												lowercase							=						self.get_auto_remove_tmp_dir()
												lowercase							=						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)
												lowercase							=						get_results(A__)
												self.assertLess(result['''perplexity''']   ,4_2)
												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  A__      (				self):
												# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
												lowercase							=						7 if get_gpu_count() > 1 else 2

												lowercase							=						self.get_auto_remove_tmp_dir()
												lowercase							=						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)
												lowercase							=						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  A__      (				self):
												lowercase							=						self.get_auto_remove_tmp_dir()
												lowercase							=						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)
												lowercase							=						get_results(A__)
												# 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(A__   ,'''epoch_0''')))
												self.assertTrue(os.path.exists(os.path.join(A__   ,'''qa_no_trainer''')))




							@mock.patch.dict(os.environ   ,{'''WANDB_MODE''': '''offline'''})
							def  A__      (				self):
												lowercase							=						self.get_auto_remove_tmp_dir()
												lowercase							=						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)
												lowercase							=						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  A__      (				self):
												lowercase							=						self.get_auto_remove_tmp_dir()
												lowercase							=						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)
												lowercase							=						get_results(A__)
												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(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  A__      (				self):
												lowercase							=						self.get_auto_remove_tmp_dir()
												lowercase							=						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)
												lowercase							=						get_results(A__)
												self.assertGreaterEqual(result['''eval_bleu''']   ,3_0)
												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  A__      (				self):
												lowercase							=						logging.StreamHandler(sys.stdout)
												logger.addHandler(A__)

												lowercase							=						self.get_auto_remove_tmp_dir()
												lowercase							=						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)
												lowercase							=						get_results(A__)
												self.assertGreaterEqual(result['''eval_overall_accuracy''']   ,0.10)





							@mock.patch.dict(os.environ   ,{'''WANDB_MODE''': '''offline'''})
							def  A__      (				self):
												lowercase							=						self.get_auto_remove_tmp_dir()
												lowercase							=						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)
												lowercase							=						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
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__		:str   =    logging.get_logger(__name__)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						'''huggingface/label-files'''
					lowercase							=						'''imagenet-1k-id2label.json'''
					lowercase							=						json.load(open(hf_hub_download(lowerCAmelCase__ ,			lowerCAmelCase__ ,			repo_type='''dataset'''		) ,			'''r'''		)		)
					lowercase							=						{int(lowerCAmelCase__		): v for k, v in idalabel.items()}
					lowercase							=						{v: k for k, v in idalabel.items()}

					lowercase							=						'''std_conv''' if '''bit''' in model_name else False

					# note that when using BiT as backbone for ViT-hybrid checkpoints,
					# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
					# config.conv_layer = "std_conv_same"
					lowercase							=						BitConfig(
					    conv_layer=lowerCAmelCase__ ,			num_labels=1000 ,			idalabel=lowerCAmelCase__ ,			labelaid=lowerCAmelCase__ ,			)

					return config



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					if "stem.conv" in name:
										lowercase							=						name.replace('''stem.conv''' ,			'''bit.embedder.convolution'''		)
					if "blocks" in name:
										lowercase							=						name.replace('''blocks''' ,			'''layers'''		)
					if "head.fc" in name:
										lowercase							=						name.replace('''head.fc''' ,			'''classifier.1'''		)
					if name.startswith('''norm'''		):
										lowercase							=						'''bit.''' + name
					if "bit" not in name and "classifier" not in name:
										lowercase							=						'''bit.encoder.''' + name

					return name



def        UpperCamelCase    (							):
					'''simple docstring'''
					lowercase							=						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					lowercase							=						Image.open(requests.get(lowerCAmelCase__ ,			stream=lowerCAmelCase__		).raw		)
					return im



@torch.no_grad()
def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__=False		):
					'''simple docstring'''
					lowercase							=						get_config(lowerCAmelCase__		)

					# load original model from timm
					lowercase							=						create_model(lowerCAmelCase__ ,			pretrained=lowerCAmelCase__		)
					timm_model.eval()

					# load state_dict of original model
					lowercase							=						timm_model.state_dict()
					for key in state_dict.copy().keys():
										lowercase							=						state_dict.pop(lowerCAmelCase__		)
										lowercase							=						val.squeeze() if '''head''' in key else val

					# load HuggingFace model
					lowercase							=						BitForImageClassification(lowerCAmelCase__		)
					model.eval()
					model.load_state_dict(lowerCAmelCase__		)

					# create image processor
					lowercase							=						create_transform(**resolve_data_config({} ,			model=lowerCAmelCase__		)		)
					lowercase							=						transform.transforms

					lowercase							=						{
					    '''bilinear''': PILImageResampling.BILINEAR,
					    '''bicubic''': PILImageResampling.BICUBIC,
					    '''nearest''': PILImageResampling.NEAREST,
					}

					lowercase							=						BitImageProcessor(
					    do_resize=lowerCAmelCase__ ,			size={'''shortest_edge''': timm_transforms[0].size} ,			resample=pillow_resamplings[timm_transforms[0].interpolation.value] ,			do_center_crop=lowerCAmelCase__ ,			crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]} ,			do_normalize=lowerCAmelCase__ ,			image_mean=timm_transforms[-1].mean.tolist() ,			image_std=timm_transforms[-1].std.tolist() ,			)

					lowercase							=						prepare_img()
					lowercase							=						transform(lowerCAmelCase__		).unsqueeze(0		)
					lowercase							=						processor(lowerCAmelCase__ ,			return_tensors='''pt'''		).pixel_values

					# verify pixel values
					assert torch.allclose(lowerCAmelCase__ ,			lowerCAmelCase__		)

					# verify logits
					with torch.no_grad():
										lowercase							=						model(lowerCAmelCase__		)
										lowercase							=						outputs.logits

					print('''Logits:''' ,			logits[0, :3]		)
					print('''Predicted class:''' ,			model.config.idalabel[logits.argmax(-1		).item()]		)
					lowercase							=						timm_model(lowerCAmelCase__		)
					assert timm_logits.shape == outputs.logits.shape
					assert torch.allclose(lowerCAmelCase__ ,			outputs.logits ,			atol=1E-3		)
					print('''Looks ok!'''		)

					if pytorch_dump_folder_path is not None:
										Path(lowerCAmelCase__		).mkdir(exist_ok=lowerCAmelCase__		)
										print(f'Saving model {model_name} and processor to {pytorch_dump_folder_path}'		)
										model.save_pretrained(lowerCAmelCase__		)
										processor.save_pretrained(lowerCAmelCase__		)

					if push_to_hub:
										print(f'Pushing model {model_name} and processor to the hub'		)
										model.push_to_hub(f'ybelkada/{model_name}'		)
										processor.push_to_hub(f'ybelkada/{model_name}'		)


if __name__ == "__main__":
							lowercase__		:List[Any]   =    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--model_name",
							    default="resnetv2_50x1_bitm",
							    type=str,
							    help="Name of the BiT timm model you'd like to convert.",
							)
							parser.add_argument(
							    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
							)
							parser.add_argument(
							    "--push_to_hub",
							    action="store_true",
							    help="Whether to push the model to the hub.",
							)

							lowercase__		:List[str]   =    parser.parse_args()
							convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




