





from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


__lowerCAmelCase :Optional[Any]     =    logging.get_logger(__name__)  # pylint: disable=invalid-name







def    A				(							UpperCAmelCase				):
					if not path:
										return "pipe"

					for ext in PipelineDataFormat.SUPPORTED_FORMATS:
										if path.endswith(UpperCAmelCase__				):
															return ext

					raise Exception(
					    F"""Unable to determine file format from file extension {path}. """
					    F"""Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}"""				)







def    A				(							UpperCAmelCase				):
					_snake_case       :					int				       =   pipeline(
					    task=args.task  ,  model=args.model if args.model else None  ,  config=args.config  ,  tokenizer=args.tokenizer  ,  device=args.device  ,  )
					_snake_case       :					str				       =   try_infer_format_from_ext(args.input				) if args.format == "infer" else args.format
					_snake_case       :					Union[str, Any]				       =   PipelineDataFormat.from_str(
					    format=UpperCAmelCase__  ,  output_path=args.output  ,  input_path=args.input  ,  column=args.column if args.column else nlp.default_input_names  ,  overwrite=args.overwrite  ,  )
					return RunCommand(UpperCAmelCase__  ,  UpperCAmelCase__				)






class        _a(    __A    ):







		def __init__(    self ,						__snake_case ,						__snake_case				)      ->      Dict:


							'''simple docstring'''

							_snake_case       :					List[str]				       =   nlp
							_snake_case       :					Tuple				       =   reader







		@staticmethod
		def        lowercase       (    __snake_case				)      ->      Any:


							'''simple docstring'''

							_snake_case       :					str				       =   parser.add_parser("run" ,						help="Run a pipeline through the CLI"				)
							run_parser.add_argument("--task" ,						choices=get_supported_tasks() ,						help="Task to run"				)
							run_parser.add_argument("--input" ,						type=__UpperCamelCase ,						help="Path to the file to use for inference"				)
							run_parser.add_argument("--output" ,						type=__UpperCamelCase ,						help="Path to the file that will be used post to write results."				)
							run_parser.add_argument("--model" ,						type=__UpperCamelCase ,						help="Name or path to the model to instantiate."				)
							run_parser.add_argument("--config" ,						type=__UpperCamelCase ,						help="Name or path to the model's config to instantiate."				)
							run_parser.add_argument(
							    "--tokenizer" ,						type=__UpperCamelCase ,						help="Name of the tokenizer to use. (default: same as the model name)"				)
							run_parser.add_argument(
							    "--column" ,						type=__UpperCamelCase ,						help="Name of the column to use as input. (For multi columns input as QA use column1,columns2)" ,						)
							run_parser.add_argument(
							    "--format" ,						type=__UpperCamelCase ,						default="infer" ,						choices=PipelineDataFormat.SUPPORTED_FORMATS ,						help="Input format to read from" ,						)
							run_parser.add_argument(
							    "--device" ,						type=__UpperCamelCase ,						default=-1 ,						help="Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)" ,						)
							run_parser.add_argument("--overwrite" ,						action="store_true" ,						help="Allow overwriting the output file."				)
							run_parser.set_defaults(func=__UpperCamelCase				)







		def        lowercase       (    self				)      ->      Optional[int]:


							'''simple docstring'''

							_snake_case       , _snake_case       :					int				       =   self._nlp, []

							for entry in self._reader:
												_snake_case       :					str				       =   nlp(**__UpperCamelCase				) if self._reader.is_multi_columns else nlp(__UpperCamelCase				)
												if isinstance(__UpperCamelCase ,						__UpperCamelCase				):
																	outputs.append(__UpperCamelCase				)
												else:
																	outputs += output

        # Saving data
							if self._nlp.binary_output:
												_snake_case       :					Optional[int]				       =   self._reader.save_binary(__UpperCamelCase				)
												logger.warning(f"""Current pipeline requires output to be in binary format, saving at {binary_path}"""				)
							else:
												self._reader.save(__UpperCamelCase				)






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()
__lowerCAmelCase :str     =    logging.get_logger(__name__)

__lowerCAmelCase :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,
}

__lowerCAmelCase :List[str]     =    {
    'b0': {
        'hidden_dim': 12_80,
        'width_coef': 1.0,
        'depth_coef': 1.0,
        'image_size': 2_24,
        'dropout_rate': 0.2,
        'dw_padding': [],
    },
    'b1': {
        'hidden_dim': 12_80,
        'width_coef': 1.0,
        'depth_coef': 1.1,
        'image_size': 2_40,
        'dropout_rate': 0.2,
        'dw_padding': [16],
    },
    'b2': {
        'hidden_dim': 14_08,
        'width_coef': 1.1,
        'depth_coef': 1.2,
        'image_size': 2_60,
        'dropout_rate': 0.3,
        'dw_padding': [5, 8, 16],
    },
    'b3': {
        'hidden_dim': 15_36,
        'width_coef': 1.2,
        'depth_coef': 1.4,
        'image_size': 3_00,
        'dropout_rate': 0.3,
        'dw_padding': [5, 18],
    },
    'b4': {
        'hidden_dim': 17_92,
        'width_coef': 1.4,
        'depth_coef': 1.8,
        'image_size': 3_80,
        'dropout_rate': 0.4,
        'dw_padding': [6],
    },
    'b5': {
        'hidden_dim': 20_48,
        'width_coef': 1.6,
        'depth_coef': 2.2,
        'image_size': 4_56,
        'dropout_rate': 0.4,
        'dw_padding': [13, 27],
    },
    'b6': {
        'hidden_dim': 23_04,
        'width_coef': 1.8,
        'depth_coef': 2.6,
        'image_size': 5_28,
        'dropout_rate': 0.5,
        'dw_padding': [31],
    },
    'b7': {
        'hidden_dim': 25_60,
        'width_coef': 2.0,
        'depth_coef': 3.1,
        'image_size': 6_00,
        'dropout_rate': 0.5,
        'dw_padding': [18],
    },
}







def    A				(							UpperCAmelCase				):
					_snake_case       :					str				       =   EfficientNetConfig()
					_snake_case       :					Optional[int]				       =   CONFIG_MAP[model_name]["hidden_dim"]
					_snake_case       :					Tuple				       =   CONFIG_MAP[model_name]["width_coef"]
					_snake_case       :					Dict				       =   CONFIG_MAP[model_name]["depth_coef"]
					_snake_case       :					List[Any]				       =   CONFIG_MAP[model_name]["image_size"]
					_snake_case       :					Tuple				       =   CONFIG_MAP[model_name]["dropout_rate"]
					_snake_case       :					str				       =   CONFIG_MAP[model_name]["dw_padding"]

					_snake_case       :					Union[str, Any]				       =   "huggingface/label-files"
					_snake_case       :					Any				       =   "imagenet-1k-id2label.json"
					_snake_case       :					Optional[Any]				       =   1_000
					_snake_case       :					Optional[Any]				       =   json.load(open(hf_hub_download(UpperCAmelCase  ,  UpperCAmelCase  ,  repo_type="dataset"				)  ,  "r"				)				)
					_snake_case       :					int				       =   {int(UpperCAmelCase				): v for k, v in idalabel.items()}

					_snake_case       :					Optional[int]				       =   idalabel
					_snake_case       :					List[Any]				       =   {v: k for k, v in idalabel.items()}
					return config







def    A				(							):
					_snake_case       :					List[Any]				       =   "http://images.cocodataset.org/val2017/000000039769.jpg"
					_snake_case       :					Optional[int]				       =   Image.open(requests.get(UpperCAmelCase  ,  stream=UpperCAmelCase				).raw				)
					return im







def    A				(							UpperCAmelCase				):
					_snake_case       :					Optional[Any]				       =   CONFIG_MAP[model_name]["image_size"]
					_snake_case       :					int				       =   EfficientNetImageProcessor(
					    size={"height": size, "width": size}  ,  image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]  ,  image_std=[0.4_7_8_5_3_9_4_4, 0.4_7_3_2_8_6_4, 0.4_7_4_3_4_1_6_3]  ,  do_center_crop=UpperCAmelCase  ,  )
					return preprocessor







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

					_snake_case       :					Optional[int]				       =   []
					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:
										_snake_case       :					Optional[int]				       =   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")				)

					_snake_case       :					Union[str, Any]				       =   {}
					for item in rename_keys:
										if item[0] in original_param_names:
															_snake_case       :					str				       =   "efficientnet." + item[1]

					_snake_case       :					List[str]				       =   "classifier.weight"
					_snake_case       :					int				       =   "classifier.bias"
					return key_mapping







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

										_snake_case       :					Union[str, Any]				       =   key_mapping[key]
										if "_conv" in key and "kernel" in key:
															_snake_case       :					Union[str, Any]				       =   torch.from_numpy(UpperCAmelCase				).permute(3  ,  2  ,  0  ,  1				)
										elif "depthwise_kernel" in key:
															_snake_case       :					str				       =   torch.from_numpy(UpperCAmelCase				).permute(2  ,  3  ,  0  ,  1				)
										elif "kernel" in key:
															_snake_case       :					Optional[Any]				       =   torch.from_numpy(np.transpose(UpperCAmelCase				)				)
										else:
															_snake_case       :					int				       =   torch.from_numpy(UpperCAmelCase				)

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







@torch.no_grad()
def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					Optional[int]				       =   model_classes[model_name](
					    include_top=UpperCAmelCase  ,  weights="imagenet"  ,  input_tensor=UpperCAmelCase  ,  input_shape=UpperCAmelCase  ,  pooling=UpperCAmelCase  ,  classes=1_000  ,  classifier_activation="softmax"  ,  )

					_snake_case       :					int				       =   original_model.trainable_variables
					_snake_case       :					str				       =   original_model.non_trainable_variables
					_snake_case       :					List[Any]				       =   {param.name: param.numpy() for param in tf_params}
					for param in tf_non_train_params:
										_snake_case       :					str				       =   param.numpy()
					_snake_case       :					int				       =   list(tf_params.keys()				)

					# Load HuggingFace model
					_snake_case       :					Optional[int]				       =   get_efficientnet_config(UpperCAmelCase				)
					_snake_case       :					Dict				       =   EfficientNetForImageClassification(UpperCAmelCase				).eval()
					_snake_case       :					int				       =   hf_model.state_dict()

					# Create src-to-dst parameter name mapping dictionary
					print("Converting parameters..."				)
					_snake_case       :					Optional[int]				       =   rename_keys(UpperCAmelCase				)
					replace_params(UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				)

					# Initialize preprocessor and preprocess input image
					_snake_case       :					Optional[Any]				       =   convert_image_processor(UpperCAmelCase				)
					_snake_case       :					List[str]				       =   preprocessor(images=prepare_img()  ,  return_tensors="pt"				)

					# HF model inference
					hf_model.eval()
					with torch.no_grad():
										_snake_case       :					Tuple				       =   hf_model(**UpperCAmelCase				)
					_snake_case       :					str				       =   outputs.logits.detach().numpy()

					# Original model inference
					_snake_case       :					List[Any]				       =   False
					_snake_case       :					Optional[Any]				       =   CONFIG_MAP[model_name]["image_size"]
					_snake_case       :					Optional[int]				       =   prepare_img().resize((image_size, image_size)  ,  resample=PIL.Image.NEAREST				)
					_snake_case       :					List[Any]				       =   image.img_to_array(UpperCAmelCase				)
					_snake_case       :					Optional[int]				       =   np.expand_dims(UpperCAmelCase  ,  axis=0				)
					_snake_case       :					int				       =   original_model.predict(UpperCAmelCase				)

					# Check whether original and HF model outputs match  -> np.allclose
					assert np.allclose(UpperCAmelCase  ,  UpperCAmelCase  ,  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(UpperCAmelCase				):
															os.mkdir(UpperCAmelCase				)
										# Save converted model and image processor
										hf_model.save_pretrained(UpperCAmelCase				)
										preprocessor.save_pretrained(UpperCAmelCase				)

					if push_to_hub:
										# Push model and image processor to hub
										print(F"""Pushing converted {model_name} to the hub..."""				)
										_snake_case       :					Optional[Any]				       =   F"""efficientnet-{model_name}"""
										preprocessor.push_to_hub(UpperCAmelCase				)
										hf_model.push_to_hub(UpperCAmelCase				)


if __name__ == "__main__":
							__lowerCAmelCase :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')

							__lowerCAmelCase :List[Any]     =    parser.parse_args()
							convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)