



import argparse
import json
import os

import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image

from transformers import (
    EfficientNetConfig,
    EfficientNetForImageClassification,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase   :							Optional[int]							 =logging.get_logger(__name__)

UpperCAmelCase   :							int							 ={
    """b0""": efficientnet.EfficientNetBa,
    """b1""": efficientnet.EfficientNetBa,
    """b2""": efficientnet.EfficientNetBa,
    """b3""": efficientnet.EfficientNetBa,
    """b4""": efficientnet.EfficientNetBa,
    """b5""": efficientnet.EfficientNetBa,
    """b6""": efficientnet.EfficientNetBa,
    """b7""": efficientnet.EfficientNetBa,
}

UpperCAmelCase   :							List[str]							 ={
    """b0""": {
        """hidden_dim""": 1280,
        """width_coef""": 1.0,
        """depth_coef""": 1.0,
        """image_size""": 224,
        """dropout_rate""": 0.2,
        """dw_padding""": [],
    },
    """b1""": {
        """hidden_dim""": 1280,
        """width_coef""": 1.0,
        """depth_coef""": 1.1,
        """image_size""": 240,
        """dropout_rate""": 0.2,
        """dw_padding""": [16],
    },
    """b2""": {
        """hidden_dim""": 1408,
        """width_coef""": 1.1,
        """depth_coef""": 1.2,
        """image_size""": 260,
        """dropout_rate""": 0.3,
        """dw_padding""": [5, 8, 16],
    },
    """b3""": {
        """hidden_dim""": 1536,
        """width_coef""": 1.2,
        """depth_coef""": 1.4,
        """image_size""": 300,
        """dropout_rate""": 0.3,
        """dw_padding""": [5, 18],
    },
    """b4""": {
        """hidden_dim""": 1792,
        """width_coef""": 1.4,
        """depth_coef""": 1.8,
        """image_size""": 380,
        """dropout_rate""": 0.4,
        """dw_padding""": [6],
    },
    """b5""": {
        """hidden_dim""": 2048,
        """width_coef""": 1.6,
        """depth_coef""": 2.2,
        """image_size""": 456,
        """dropout_rate""": 0.4,
        """dw_padding""": [13, 27],
    },
    """b6""": {
        """hidden_dim""": 2304,
        """width_coef""": 1.8,
        """depth_coef""": 2.6,
        """image_size""": 528,
        """dropout_rate""": 0.5,
        """dw_padding""": [31],
    },
    """b7""": {
        """hidden_dim""": 2560,
        """width_coef""": 2.0,
        """depth_coef""": 3.1,
        """image_size""": 600,
        """dropout_rate""": 0.5,
        """dw_padding""": [18],
    },
}



def    _lowerCAmelCase    (_lowerCAmelCase):
					UpperCamelCase_		  =   EfficientNetConfig()
					UpperCamelCase_		  =   CONFIG_MAP[model_name]["hidden_dim"]
					UpperCamelCase_		  =   CONFIG_MAP[model_name]["width_coef"]
					UpperCamelCase_		  =   CONFIG_MAP[model_name]["depth_coef"]
					UpperCamelCase_		  =   CONFIG_MAP[model_name]["image_size"]
					UpperCamelCase_		  =   CONFIG_MAP[model_name]["dropout_rate"]
					UpperCamelCase_		  =   CONFIG_MAP[model_name]["dw_padding"]

					UpperCamelCase_		  =   "huggingface/label-files"
					UpperCamelCase_		  =   "imagenet-1k-id2label.json"
					UpperCamelCase_		  =   10_00
					UpperCamelCase_		  =   json.load(open(hf_hub_download(_lowerCAmelCase   ,	_lowerCAmelCase   ,	repo_type="dataset")   ,	"r"))
					UpperCamelCase_		  =   {int(_lowerCAmelCase): v for k, v in idalabel.items()}

					UpperCamelCase_		  =   idalabel
					UpperCamelCase_		  =   {v: k for k, v in idalabel.items()}
					return config



def    _lowerCAmelCase    ():
					UpperCamelCase_		  =   "http://images.cocodataset.org/val2017/000000039769.jpg"
					UpperCamelCase_		  =   Image.open(requests.get(_lowerCAmelCase   ,	stream=_lowerCAmelCase).raw)
					return im



def    _lowerCAmelCase    (_lowerCAmelCase):
					UpperCamelCase_		  =   CONFIG_MAP[model_name]["image_size"]
					UpperCamelCase_		  =   EfficientNetImageProcessor(
					    size={"height": size, "width": size}   ,	image_mean=[0.485, 0.456, 0.406]   ,	image_std=[0.4785_3944, 0.473_2864, 0.4743_4163]   ,	do_center_crop=_lowerCAmelCase   ,	)
					return preprocessor



def    _lowerCAmelCase    (_lowerCAmelCase):
					UpperCamelCase_		  =   [v.split("_")[0].split("block")[1] for v in original_param_names if v.startswith("block")]
					UpperCamelCase_		  =   sorted(set(_lowerCAmelCase))
					UpperCamelCase_		  =   len(_lowerCAmelCase)
					UpperCamelCase_		  =   {b: str(_lowerCAmelCase) for b, i in zip(_lowerCAmelCase   ,	range(_lowerCAmelCase))}

					UpperCamelCase_		  =   []
					rename_keys.append(("stem_conv/kernel:0", "embeddings.convolution.weight"))
					rename_keys.append(("stem_bn/gamma:0", "embeddings.batchnorm.weight"))
					rename_keys.append(("stem_bn/beta:0", "embeddings.batchnorm.bias"))
					rename_keys.append(("stem_bn/moving_mean:0", "embeddings.batchnorm.running_mean"))
					rename_keys.append(("stem_bn/moving_variance:0", "embeddings.batchnorm.running_var"))

					for b in block_names:
										UpperCamelCase_		  =   block_name_mapping[b]
										rename_keys.append((f"""block{b}_expand_conv/kernel:0""", f"""encoder.blocks.{hf_b}.expansion.expand_conv.weight"""))
										rename_keys.append((f"""block{b}_expand_bn/gamma:0""", f"""encoder.blocks.{hf_b}.expansion.expand_bn.weight"""))
										rename_keys.append((f"""block{b}_expand_bn/beta:0""", f"""encoder.blocks.{hf_b}.expansion.expand_bn.bias"""))
										rename_keys.append(
										    (f"""block{b}_expand_bn/moving_mean:0""", f"""encoder.blocks.{hf_b}.expansion.expand_bn.running_mean"""))
										rename_keys.append(
										    (f"""block{b}_expand_bn/moving_variance:0""", f"""encoder.blocks.{hf_b}.expansion.expand_bn.running_var"""))
										rename_keys.append(
										    (f"""block{b}_dwconv/depthwise_kernel:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight"""))
										rename_keys.append((f"""block{b}_bn/gamma:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight"""))
										rename_keys.append((f"""block{b}_bn/beta:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias"""))
										rename_keys.append(
										    (f"""block{b}_bn/moving_mean:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean"""))
										rename_keys.append(
										    (f"""block{b}_bn/moving_variance:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var"""))

										rename_keys.append((f"""block{b}_se_reduce/kernel:0""", f"""encoder.blocks.{hf_b}.squeeze_excite.reduce.weight"""))
										rename_keys.append((f"""block{b}_se_reduce/bias:0""", f"""encoder.blocks.{hf_b}.squeeze_excite.reduce.bias"""))
										rename_keys.append((f"""block{b}_se_expand/kernel:0""", f"""encoder.blocks.{hf_b}.squeeze_excite.expand.weight"""))
										rename_keys.append((f"""block{b}_se_expand/bias:0""", f"""encoder.blocks.{hf_b}.squeeze_excite.expand.bias"""))
										rename_keys.append(
										    (f"""block{b}_project_conv/kernel:0""", f"""encoder.blocks.{hf_b}.projection.project_conv.weight"""))
										rename_keys.append((f"""block{b}_project_bn/gamma:0""", f"""encoder.blocks.{hf_b}.projection.project_bn.weight"""))
										rename_keys.append((f"""block{b}_project_bn/beta:0""", f"""encoder.blocks.{hf_b}.projection.project_bn.bias"""))
										rename_keys.append(
										    (f"""block{b}_project_bn/moving_mean:0""", f"""encoder.blocks.{hf_b}.projection.project_bn.running_mean"""))
										rename_keys.append(
										    (f"""block{b}_project_bn/moving_variance:0""", f"""encoder.blocks.{hf_b}.projection.project_bn.running_var"""))

					rename_keys.append(("top_conv/kernel:0", "encoder.top_conv.weight"))
					rename_keys.append(("top_bn/gamma:0", "encoder.top_bn.weight"))
					rename_keys.append(("top_bn/beta:0", "encoder.top_bn.bias"))
					rename_keys.append(("top_bn/moving_mean:0", "encoder.top_bn.running_mean"))
					rename_keys.append(("top_bn/moving_variance:0", "encoder.top_bn.running_var"))

					UpperCamelCase_		  =   {}
					for item in rename_keys:
										if item[0] in original_param_names:
															UpperCamelCase_		  =   "efficientnet." + item[1]

					UpperCamelCase_		  =   "classifier.weight"
					UpperCamelCase_		  =   "classifier.bias"
					return key_mapping



def    _lowerCAmelCase    (_lowerCAmelCase   ,	_lowerCAmelCase   ,	_lowerCAmelCase):
					for key, value in tf_params.items():
										if "normalization" in key:
															continue

										UpperCamelCase_		  =   key_mapping[key]
										if "_conv" in key and "kernel" in key:
															UpperCamelCase_		  =   torch.from_numpy(_lowerCAmelCase).permute(3   ,	2   ,	0   ,	1)
										elif "depthwise_kernel" in key:
															UpperCamelCase_		  =   torch.from_numpy(_lowerCAmelCase).permute(2   ,	3   ,	0   ,	1)
										elif "kernel" in key:
															UpperCamelCase_		  =   torch.from_numpy(np.transpose(_lowerCAmelCase))
										else:
															UpperCamelCase_		  =   torch.from_numpy(_lowerCAmelCase)

										# Replace HF parameters with original TF model parameters
										assert hf_params[hf_key].shape == new_hf_value.shape
										hf_params[hf_key].copy_(_lowerCAmelCase)



@torch.no_grad()
def    _lowerCAmelCase    (_lowerCAmelCase   ,	_lowerCAmelCase   ,	_lowerCAmelCase   ,	_lowerCAmelCase):
					UpperCamelCase_		  =   model_classes[model_name](
					    include_top=_lowerCAmelCase   ,	weights="imagenet"   ,	input_tensor=_lowerCAmelCase   ,	input_shape=_lowerCAmelCase   ,	pooling=_lowerCAmelCase   ,	classes=10_00   ,	classifier_activation="softmax"   ,	)

					UpperCamelCase_		  =   original_model.trainable_variables
					UpperCamelCase_		  =   original_model.non_trainable_variables
					UpperCamelCase_		  =   {param.name: param.numpy() for param in tf_params}
					for param in tf_non_train_params:
										UpperCamelCase_		  =   param.numpy()
					UpperCamelCase_		  =   list(tf_params.keys())

					# Load HuggingFace model
					UpperCamelCase_		  =   get_efficientnet_config(_lowerCAmelCase)
					UpperCamelCase_		  =   EfficientNetForImageClassification(_lowerCAmelCase).eval()
					UpperCamelCase_		  =   hf_model.state_dict()

					# Create src-to-dst parameter name mapping dictionary
					print("Converting parameters...")
					UpperCamelCase_		  =   rename_keys(_lowerCAmelCase)
					replace_params(_lowerCAmelCase   ,	_lowerCAmelCase   ,	_lowerCAmelCase)

					# Initialize preprocessor and preprocess input image
					UpperCamelCase_		  =   convert_image_processor(_lowerCAmelCase)
					UpperCamelCase_		  =   preprocessor(images=prepare_img()   ,	return_tensors="pt")

					# HF model inference
					hf_model.eval()
					with torch.no_grad():
										UpperCamelCase_		  =   hf_model(**_lowerCAmelCase)
					UpperCamelCase_		  =   outputs.logits.detach().numpy()

					# Original model inference
					UpperCamelCase_		  =   False
					UpperCamelCase_		  =   CONFIG_MAP[model_name]["image_size"]
					UpperCamelCase_		  =   prepare_img().resize((image_size, image_size)   ,	resample=PIL.Image.NEAREST)
					UpperCamelCase_		  =   image.img_to_array(_lowerCAmelCase)
					UpperCamelCase_		  =   np.expand_dims(_lowerCAmelCase   ,	axis=0)
					UpperCamelCase_		  =   original_model.predict(_lowerCAmelCase)

					# Check whether original and HF model outputs match  -> np.allclose
					assert np.allclose(_lowerCAmelCase   ,	_lowerCAmelCase   ,	atol=1e-3), "The predicted logits are not the same."
					print("Model outputs match!")

					if save_model:
										# Create folder to save model
										if not os.path.isdir(_lowerCAmelCase):
															os.mkdir(_lowerCAmelCase)
										# Save converted model and image processor
										hf_model.save_pretrained(_lowerCAmelCase)
										preprocessor.save_pretrained(_lowerCAmelCase)

					if push_to_hub:
										# Push model and image processor to hub
										print(f"""Pushing converted {model_name} to the hub...""")
										UpperCamelCase_		  =   f"""efficientnet-{model_name}"""
										preprocessor.push_to_hub(_lowerCAmelCase)
										hf_model.push_to_hub(_lowerCAmelCase)


if __name__ == "__main__":
	UpperCAmelCase   :							Union[str, Any]							 =argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--model_name""",
	    default="""b0""",
	    type=str,
	    help="""Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""",
	    default="""hf_model""",
	    type=str,
	    help="""Path to the output PyTorch model directory.""",
	)
	parser.add_argument("""--save_model""", action="""store_true""", help="""Save model to local""")
	parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")

	UpperCAmelCase   :							Union[str, Any]							 =parser.parse_args()
	convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)





# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


UpperCAmelCase   :							List[str]							 =Path(__file__).resolve().parents[3] / """src"""
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

UpperCAmelCase   :							List[str]							 ={"""base""": """patrickvonplaten/wav2vec2_tiny_random""", """robust""": """patrickvonplaten/wav2vec2_tiny_random_robust"""}

UpperCAmelCase   :							str							 ="""zero2"""
UpperCAmelCase   :							int							 ="""zero3"""
UpperCAmelCase   :							Optional[int]							 =[ZEROa, ZEROa]



def    _lowerCAmelCase    (_lowerCAmelCase   ,	_lowerCAmelCase   ,	_lowerCAmelCase):
					# customize the test name generator function as we want both params to appear in the sub-test
					# name, as by default it shows only the first param
					UpperCamelCase_		  =   parameterized.to_safe_name("_".join(str(_lowerCAmelCase) for x in param.args))
					return f"""{func.__name__}_{param_based_name}"""


# Cartesian-product of zero stages with models to test
UpperCAmelCase   :							Dict							 =list(itertools.product(stages, models.keys()))






@slow
@require_deepspeed
@require_torch_gpu
class       _lowercase   (a_      ):
				'''simple docstring'''







				@parameterized.expand(snake_case__     , name_func=snake_case__    )
				def       _lowerCamelCase					( self     , snake_case__     , snake_case__    ):

									'''simple docstring'''


									self.run_and_check(
									    stage=snake_case__     , model=snake_case__     , distributed=snake_case__     , fpaa=snake_case__     , )

				@require_torch_multi_gpu
				@parameterized.expand(snake_case__     , name_func=snake_case__    )
				def       _lowerCamelCase					( self     , snake_case__     , snake_case__    ):

									'''simple docstring'''


									self.run_and_check(
									    stage=snake_case__     , model=snake_case__     , distributed=snake_case__     , fpaa=snake_case__     , )

				@parameterized.expand(snake_case__     , name_func=snake_case__    )
				def       _lowerCamelCase					( self     , snake_case__     , snake_case__    ):

									'''simple docstring'''


									self.run_and_check(
									    stage=snake_case__     , model=snake_case__     , distributed=snake_case__     , fpaa=snake_case__     , )

				@require_torch_multi_gpu
				@parameterized.expand(snake_case__     , name_func=snake_case__    )
				def       _lowerCamelCase					( self     , snake_case__     , snake_case__    ):

									'''simple docstring'''


									self.run_and_check(
									    stage=snake_case__     , model=snake_case__     , distributed=snake_case__     , fpaa=snake_case__     , )

				def       _lowerCamelCase					( self     , snake_case__    ):

									'''simple docstring'''


									pass

				def       _lowerCamelCase					( self     , snake_case__     , snake_case__     , snake_case__ = 10     , snake_case__ = True     , snake_case__ = True     , snake_case__ = True     , ):

									'''simple docstring'''


									UpperCamelCase_		  =   models[model]

									UpperCamelCase_		  =   self.run_trainer(
									    stage=snake_case__     , model_name=snake_case__     , eval_steps=snake_case__     , num_train_epochs=1     , distributed=snake_case__     , fpaa=snake_case__     , )

									self.do_checks(snake_case__    )

									return output_dir

				def       _lowerCamelCase					( self     , snake_case__     , snake_case__     , snake_case__ = 10     , snake_case__ = 1     , snake_case__ = True     , snake_case__ = True     , ):

									'''simple docstring'''


									UpperCamelCase_		  =   self.get_auto_remove_tmp_dir("./xxx"     , after=snake_case__    )
									UpperCamelCase_		  =   F"""
            --model_name_or_path {model_name}
            --dataset_name hf-internal-testing/librispeech_asr_dummy
            --dataset_config_name clean
            --train_split_name validation
            --validation_split_name validation
            --output_dir {output_dir}
            --num_train_epochs {str(snake_case__    )}
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
            --evaluation_strategy steps
            --learning_rate 5e-4
            --warmup_steps 8
            --orthography timit
            --preprocessing_num_workers 1
            --group_by_length
            --freeze_feature_extractor
            --report_to none
            --save_steps 0
            --eval_steps {eval_steps}
            --report_to none
        """.split()

									if fpaa:
														args.extend(["--fp16"]    )

									# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
									# hence the separate config files
									UpperCamelCase_		  =   F"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
									UpperCamelCase_		  =   [F"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
									UpperCamelCase_		  =   self.get_launcher(snake_case__    )

									UpperCamelCase_		  =   launcher + script + args + ds_args
									# keep for quick debug
									# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
									execute_subprocess_async(snake_case__     , env=self.get_env()    )

									return output_dir

				def       _lowerCamelCase					( self     , snake_case__=False    ):

									'''simple docstring'''


									UpperCamelCase_		  =   min(2     , get_gpu_count()    ) if distributed else 1
									return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()
