





from jiwer import compute_measures

import datasets


__lowerCAmelCase :str     =    '\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n'

__lowerCAmelCase :List[str]     =    '\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n'

__lowerCAmelCase :Any     =    '\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word error rate\n\nExamples:\n\n    >>> predictions = ["this is the prediction", "there is an other sample"]\n    >>> references = ["this is the reference", "there is another one"]\n    >>> wer = datasets.load_metric("wer")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n'






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







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


							'''simple docstring'''

							return datasets.MetricInfo(
							    description=_DESCRIPTION ,						citation=_CITATION ,						inputs_description=_KWARGS_DESCRIPTION ,						features=datasets.Features(
							        {
							            "predictions": datasets.Value("string" ,						id="sequence"				),
							            "references": datasets.Value("string" ,						id="sequence"				),
							        }				) ,						codebase_urls=["https://github.com/jitsi/jiwer/"] ,						reference_urls=[
							        "https://en.wikipedia.org/wiki/Word_error_rate",
							    ] ,						)







		def        lowercase       (    self ,						__snake_case=None ,						__snake_case=None ,						__snake_case=False				)      ->      Optional[int]:


							'''simple docstring'''

							if concatenate_texts:
												return compute_measures(__snake_case ,						__snake_case				)["wer"]
							else:
												_snake_case       :					Optional[Any]				       =   0
												_snake_case       :					List[Any]				       =   0
												for prediction, reference in zip(__snake_case ,						__snake_case				):
																	_snake_case       :					List[Any]				       =   compute_measures(__snake_case ,						__snake_case				)
																	incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
																	total += measures["substitutions"] + measures["deletions"] + measures["hits"]
												return incorrect / total






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)