



"""simple docstring"""
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()
A:     int										=	logging.get_logger(__name__)

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

A:     Tuple										=	{
    "b0": {
        "hidden_dim": 1_2_8_0,
        "width_coef": 1.0,
        "depth_coef": 1.0,
        "image_size": 2_2_4,
        "dropout_rate": 0.2,
        "dw_padding": [],
    },
    "b1": {
        "hidden_dim": 1_2_8_0,
        "width_coef": 1.0,
        "depth_coef": 1.1,
        "image_size": 2_4_0,
        "dropout_rate": 0.2,
        "dw_padding": [1_6],
    },
    "b2": {
        "hidden_dim": 1_4_0_8,
        "width_coef": 1.1,
        "depth_coef": 1.2,
        "image_size": 2_6_0,
        "dropout_rate": 0.3,
        "dw_padding": [5, 8, 1_6],
    },
    "b3": {
        "hidden_dim": 1_5_3_6,
        "width_coef": 1.2,
        "depth_coef": 1.4,
        "image_size": 3_0_0,
        "dropout_rate": 0.3,
        "dw_padding": [5, 1_8],
    },
    "b4": {
        "hidden_dim": 1_7_9_2,
        "width_coef": 1.4,
        "depth_coef": 1.8,
        "image_size": 3_8_0,
        "dropout_rate": 0.4,
        "dw_padding": [6],
    },
    "b5": {
        "hidden_dim": 2_0_4_8,
        "width_coef": 1.6,
        "depth_coef": 2.2,
        "image_size": 4_5_6,
        "dropout_rate": 0.4,
        "dw_padding": [1_3, 2_7],
    },
    "b6": {
        "hidden_dim": 2_3_0_4,
        "width_coef": 1.8,
        "depth_coef": 2.6,
        "image_size": 5_2_8,
        "dropout_rate": 0.5,
        "dw_padding": [3_1],
    },
    "b7": {
        "hidden_dim": 2_5_6_0,
        "width_coef": 2.0,
        "depth_coef": 3.1,
        "image_size": 6_0_0,
        "dropout_rate": 0.5,
        "dw_padding": [1_8],
    },
}

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

 UpperCAmelCase			:      int			=			"""huggingface/label-files"""
 UpperCAmelCase			:      List[str]			=			"""imagenet-1k-id2label.json"""
 UpperCAmelCase			:      str			=			1000
 UpperCAmelCase			:      Optional[int]			=			json.load(open(hf_hub_download(UpperCamelCase    ,	UpperCamelCase    ,	repo_type="""dataset"""		)    ,	"""r"""		)		)
 UpperCAmelCase			:      List[Any]			=			{int(UpperCamelCase		): v for k, v in idalabel.items()}

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

def    _snake_case		(		)     ->   str:
 UpperCAmelCase			:      str			=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
 UpperCAmelCase			:      Union[str, Any]			=			Image.open(requests.get(UpperCamelCase    ,	stream=UpperCamelCase		).raw		)
 return im

def    _snake_case		(		UpperCamelCase		:	Optional[int]		)     ->   List[Any]:
 UpperCAmelCase			:      Dict			=			CONFIG_MAP[model_name]["""image_size"""]
 UpperCAmelCase			:      Dict			=			EfficientNetImageProcessor(
     size={"""height""": size, """width""": size}    ,	image_mean=[0.485, 0.456, 0.406]    ,	image_std=[0.47853944, 0.4732864, 0.47434163]    ,	do_center_crop=UpperCamelCase    ,	)
 return preprocessor

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

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

 UpperCAmelCase			:      List[str]			=			"""classifier.weight"""
 UpperCAmelCase			:      List[Any]			=			"""classifier.bias"""
 return key_mapping

def    _snake_case		(		UpperCamelCase		:	Tuple    ,	UpperCamelCase		:	List[str]    ,	UpperCamelCase		:	Dict		)     ->   int:
 for key, value in tf_params.items():
  if "normalization" in key:
   continue

  UpperCAmelCase			:      Optional[int]			=			key_mapping[key]
  if "_conv" in key and "kernel" in key:
   UpperCAmelCase			:      Dict			=			torch.from_numpy(UpperCamelCase		).permute(3    ,	2    ,	0    ,	1		)
  elif "depthwise_kernel" in key:
   UpperCAmelCase			:      Optional[Any]			=			torch.from_numpy(UpperCamelCase		).permute(2    ,	3    ,	0    ,	1		)
  elif "kernel" in key:
   UpperCAmelCase			:      int			=			torch.from_numpy(np.transpose(UpperCamelCase		)		)
  else:
   UpperCAmelCase			:      str			=			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    _snake_case		(		UpperCamelCase		:	Tuple    ,	UpperCamelCase		:	str    ,	UpperCamelCase		:	Any    ,	UpperCamelCase		:	Union[str, Any]		)     ->   Any:
 UpperCAmelCase			:      Optional[int]			=			model_classes[model_name](
     include_top=UpperCamelCase    ,	weights="""imagenet"""    ,	input_tensor=UpperCamelCase    ,	input_shape=UpperCamelCase    ,	pooling=UpperCamelCase    ,	classes=1000    ,	classifier_activation="""softmax"""    ,	)

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

 # Load HuggingFace model
 UpperCAmelCase			:      int			=			get_efficientnet_config(UpperCamelCase		)
 UpperCAmelCase			:      int			=			EfficientNetForImageClassification(UpperCamelCase		).eval()
 UpperCAmelCase			:      int			=			hf_model.state_dict()

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

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

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

 # Original model inference
 UpperCAmelCase			:      Union[str, Any]			=			False
 UpperCAmelCase			:      List[Any]			=			CONFIG_MAP[model_name]["""image_size"""]
 UpperCAmelCase			:      Dict			=			prepare_img().resize((image_size, image_size)    ,	resample=PIL.Image.NEAREST		)
 UpperCAmelCase			:      int			=			image.img_to_array(UpperCamelCase		)
 UpperCAmelCase			:      Tuple			=			np.expand_dims(UpperCamelCase    ,	axis=0		)
 UpperCAmelCase			:      Optional[Any]			=			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..."		)
  UpperCAmelCase			:      Optional[Any]			=			F"efficientnet-{model_name}"
  preprocessor.push_to_hub(UpperCamelCase		)
  hf_model.push_to_hub(UpperCamelCase		)


if __name__ == "__main__":
      A:     str										=	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")

      A:     Any										=	parser.parse_args()
      convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)





"""simple docstring"""
import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


A:     Optional[Any]										=	"\\n@INPROCEEDINGS{Papineni02bleu:a,\n    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n    booktitle = {},\n    year = {2002},\n    pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n    title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",\n    author = \"Lin, Chin-Yew  and\n      Och, Franz Josef\",\n    booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",\n    month = \"aug 23{--}aug 27\",\n    year = \"2004\",\n    address = \"Geneva, Switzerland\",\n    publisher = \"COLING\",\n    url = \"https://www.aclweb.org/anthology/C04-1072\",\n    pages = \"501--507\",\n}\n"

A:     Optional[int]										=	"\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine's output and that of a human: \"the closer a machine translation is to a professional human translation,\nthe better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU's output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n"

A:     Optional[int]										=	"\nComputes BLEU score of translated segments against one or more references.\nArgs:\n    predictions: list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    max_order: Maximum n-gram order to use when computing BLEU score.\n    smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n    'bleu': bleu score,\n    'precisions': geometric mean of n-gram precisions,\n    'brevity_penalty': brevity penalty,\n    'length_ratio': ratio of lengths,\n    'translation_length': translation_length,\n    'reference_length': reference_length\nExamples:\n\n    >>> predictions = [\n    ...     [\"hello\", \"there\", \"general\", \"kenobi\"],                             # tokenized prediction of the first sample\n    ...     [\"foo\", \"bar\", \"foobar\"]                                             # tokenized prediction of the second sample\n    ... ]\n    >>> references = [\n    ...     [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]],  # tokenized references for the first sample (2 references)\n    ...     [[\"foo\", \"bar\", \"foobar\"]]                                           # tokenized references for the second sample (1 reference)\n    ... ]\n    >>> bleu = datasets.load_metric(\"bleu\")\n    >>> results = bleu.compute(predictions=predictions, references=references)\n    >>> print(results[\"bleu\"])\n    1.0\n"



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,    _KWARGS_DESCRIPTION				)
class    SCREAMING_SNAKE_CASE__						(      datasets.Metric				):

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Union[str, Any]:





    '''simple docstring'''




    return datasets.MetricInfo(
        description=_DESCRIPTION   ,  citation=_CITATION   ,  inputs_description=_KWARGS_DESCRIPTION   ,  features=datasets.Features(
            {
                """predictions""": datasets.Sequence(datasets.Value("""string"""   ,  id="""token"""		)   ,  id="""sequence"""		),
                """references""": datasets.Sequence(
                    datasets.Sequence(datasets.Value("""string"""   ,  id="""token"""		)   ,  id="""sequence"""		)   ,  id="""references"""		),
            }		)   ,  codebase_urls=["""https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py"""]   ,  reference_urls=[
            """https://en.wikipedia.org/wiki/BLEU""",
            """https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""",
        ]   ,  )




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=4   ,  _SCREAMING_SNAKE_CASE=False		)						->   Optional[int]:





    '''simple docstring'''




    UpperCAmelCase			:      Dict			=			compute_bleu(
        reference_corpus=_SCREAMING_SNAKE_CASE   ,  translation_corpus=_SCREAMING_SNAKE_CASE   ,  max_order=_SCREAMING_SNAKE_CASE   ,  smooth=_SCREAMING_SNAKE_CASE		)
    ((UpperCAmelCase)			,	(UpperCAmelCase)			,	(UpperCAmelCase)			,	(UpperCAmelCase)			,	(UpperCAmelCase)			,	(UpperCAmelCase))			:      Dict			=			score
    return {
        "bleu": bleu,
        "precisions": precisions,
        "brevity_penalty": bp,
        "length_ratio": ratio,
        "translation_length": translation_length,
        "reference_length": reference_length,
    }
