


'''simple docstring'''

import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


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

def 							__magic_name__(						lowerCamelCase):
						__lowerCAmelCase							       =				MobileNetVaConfig(layer_norm_eps=0.0_01)

						if "_quant" in model_name:
												raise ValueError('''Quantized models are not supported.''')

						__lowerCAmelCase							       =				re.match(r'''^mobilenet_v1_([^_]*)_([^_]*)$''',				lowerCamelCase)
						if matches:
												__lowerCAmelCase							       =				float(matches[1])
												__lowerCAmelCase							       =				int(matches[2])

						# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
						# the usual 1000. The first class (index 0) is "background".
						__lowerCAmelCase							       =				1_0_0_1
						__lowerCAmelCase							       =				'''imagenet-1k-id2label.json'''
						__lowerCAmelCase							       =				'''huggingface/label-files'''
						__lowerCAmelCase							       =				json.load(open(hf_hub_download(lowerCamelCase,				lowerCamelCase,				repo_type='''dataset'''),				'''r'''))
						__lowerCAmelCase							       =				{int(lowerCamelCase) + 1: v for k, v in idalabel.items()}
						__lowerCAmelCase							       =				'''background'''
						__lowerCAmelCase							       =				idalabel
						__lowerCAmelCase							       =				{v: k for k, v in idalabel.items()}

						return config

def 							__magic_name__(						):
						__lowerCAmelCase							       =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
						__lowerCAmelCase							       =				Image.open(requests.get(lowerCamelCase,				stream=lowerCamelCase).raw)
						return im

@torch.no_grad()
def 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase,				lowerCamelCase=False):
						__lowerCAmelCase							       =				get_mobilenet_va_config(lowerCamelCase)

						# Load 🤗 model
						__lowerCAmelCase							       =				MobileNetVaForImageClassification(lowerCamelCase).eval()

						# Load weights from TensorFlow checkpoint
						load_tf_weights_in_mobilenet_va(lowerCamelCase,				lowerCamelCase,				lowerCamelCase)

						# Check outputs on an image, prepared by MobileNetV1ImageProcessor
						__lowerCAmelCase							       =				MobileNetVaImageProcessor(
						    crop_size={'''width''': config.image_size, '''height''': config.image_size},				size={'''shortest_edge''': config.image_size + 3_2},				)
						__lowerCAmelCase							       =				image_processor(images=prepare_img(),				return_tensors='''pt''')
						__lowerCAmelCase							       =				model(**lowerCamelCase)
						__lowerCAmelCase							       =				outputs.logits

						assert logits.shape == (1, 1_0_0_1)

						if model_name == "mobilenet_v1_1.0_224":
												__lowerCAmelCase							       =				torch.tensor([-4.17_39, -1.12_33, 3.12_05])
						elif model_name == "mobilenet_v1_0.75_192":
												__lowerCAmelCase							       =				torch.tensor([-3.94_40, -2.31_41, -0.33_33])
						else:
												__lowerCAmelCase							       =				None

						if expected_logits is not None:
												assert torch.allclose(logits[0, :3],				lowerCamelCase,				atol=1E-4)

						Path(lowerCamelCase).mkdir(exist_ok=lowerCamelCase)
						print(F"""Saving model {model_name} to {pytorch_dump_folder_path}""")
						model.save_pretrained(lowerCamelCase)
						print(F"""Saving image processor to {pytorch_dump_folder_path}""")
						image_processor.save_pretrained(lowerCamelCase)

						if push_to_hub:
												print('''Pushing to the hub...''')
												__lowerCAmelCase							       =				'''google/''' + model_name
												image_processor.push_to_hub(lowerCamelCase)
												model.push_to_hub(lowerCamelCase)


if __name__ == "__main__":
	_UpperCAmelCase				: int							    =				argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--model_name""",
	    default="""mobilenet_v1_1.0_224""",
	    type=str,
	    help="""Name of the MobileNetV1 model you'd like to convert. Should in the form 'mobilenet_v1_<depth>_<size>'.""",
	)
	parser.add_argument(
	    """--checkpoint_path""", required=True, type=str, help="""Path to the original TensorFlow checkpoint (.ckpt file)."""
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
	)
	parser.add_argument(
	    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
	)

	_UpperCAmelCase				: Tuple							    =				parser.parse_args()
	convert_movilevit_checkpoint(
	    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
	)





'''simple docstring'''

import logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEncoder,
    BertModel,
    BertPreTrainedModel,
)


_UpperCAmelCase				: Optional[Any]							    =				logging.getLogger(__name__)


class       a__				( __A      ):







				"""simple docstring"""



				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase       ,			__lowercase=None       ,			__lowercase=None		):
										__lowerCAmelCase							       =				self.layer[current_layer](__lowercase       ,			__lowercase       ,			head_mask[current_layer]		)

										__lowerCAmelCase							       =				layer_outputs[0]

										return hidden_states


@add_start_docstrings(
    'The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top.'							,						__A							,						)
class       a__				( __A      ):







				"""simple docstring"""



				def __init__(self       ,			__lowercase		):
										super().__init__(__lowercase		)

										__lowerCAmelCase							       =				BertEncoderWithPabee(__lowercase		)

										self.init_weights()
										__lowerCAmelCase							       =				0
										__lowerCAmelCase							       =				0
										__lowerCAmelCase							       =				0

										__lowerCAmelCase							       =				0




				def 					_snake_case  (self       ,			__lowercase		):
										__lowerCAmelCase							       =				threshold




				def 					_snake_case  (self       ,			__lowercase		):
										__lowerCAmelCase							       =				patience




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				0
										__lowerCAmelCase							       =				0




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				self.inference_layers_num / self.inference_instances_num
										__lowerCAmelCase							       =				(
										    F"""*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ="""
										    F""" {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***"""
										)
										print(__lowercase		)




				@add_start_docstrings_to_model_forward(__lowercase		)
				def 					_snake_case  (self       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=False       ,			):

										if input_ids is not None and inputs_embeds is not None:
																raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time'''		)
										elif input_ids is not None:
																__lowerCAmelCase							       =				input_ids.size()
										elif inputs_embeds is not None:
																__lowerCAmelCase							       =				inputs_embeds.size()[:-1]
										else:
																raise ValueError('''You have to specify either input_ids or inputs_embeds'''		)

										__lowerCAmelCase							       =				input_ids.device if input_ids is not None else inputs_embeds.device

										if attention_mask is None:
																__lowerCAmelCase							       =				torch.ones(__lowercase       ,			device=__lowercase		)
										if token_type_ids is None:
																__lowerCAmelCase							       =				torch.zeros(__lowercase       ,			dtype=torch.long       ,			device=__lowercase		)

										# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
										# ourselves in which case we just need to make it broadcastable to all heads.
										__lowerCAmelCase							       =				self.get_extended_attention_mask(__lowercase       ,			__lowercase       ,			__lowercase		)

										# If a 2D ou 3D attention mask is provided for the cross-attention
										# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
										if self.config.is_decoder and encoder_hidden_states is not None:
																__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase							       =				encoder_hidden_states.size()
																__lowerCAmelCase							       =				(encoder_batch_size, encoder_sequence_length)
																if encoder_attention_mask is None:
																						__lowerCAmelCase							       =				torch.ones(__lowercase       ,			device=__lowercase		)
																__lowerCAmelCase							       =				self.invert_attention_mask(__lowercase		)
										else:
																__lowerCAmelCase							       =				None

										# Prepare head mask if needed
										# 1.0 in head_mask indicate we keep the head
										# attention_probs has shape bsz x n_heads x N x N
										# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
										# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
										__lowerCAmelCase							       =				self.get_head_mask(__lowercase       ,			self.config.num_hidden_layers		)

										__lowerCAmelCase							       =				self.embeddings(
										    input_ids=__lowercase       ,			position_ids=__lowercase       ,			token_type_ids=__lowercase       ,			inputs_embeds=__lowercase		)
										__lowerCAmelCase							       =				embedding_output

										if self.training:
																__lowerCAmelCase							       =				[]
																for i in range(self.config.num_hidden_layers		):
																						__lowerCAmelCase							       =				self.encoder.adaptive_forward(
																						    __lowercase       ,			current_layer=__lowercase       ,			attention_mask=__lowercase       ,			head_mask=__lowercase		)

																						__lowerCAmelCase							       =				self.pooler(__lowercase		)
																						__lowerCAmelCase							       =				output_layers[i](output_dropout(__lowercase		)		)
																						res.append(__lowercase		)
										elif self.patience == 0:  # Use all layers for inference
																__lowerCAmelCase							       =				self.encoder(
																    __lowercase       ,			attention_mask=__lowercase       ,			head_mask=__lowercase       ,			encoder_hidden_states=__lowercase       ,			encoder_attention_mask=__lowercase       ,			)
																__lowerCAmelCase							       =				self.pooler(encoder_outputs[0]		)
																__lowerCAmelCase							       =				[output_layers[self.config.num_hidden_layers - 1](__lowercase		)]
										else:
																__lowerCAmelCase							       =				0
																__lowerCAmelCase							       =				None
																__lowerCAmelCase							       =				0
																for i in range(self.config.num_hidden_layers		):
																						calculated_layer_num += 1
																						__lowerCAmelCase							       =				self.encoder.adaptive_forward(
																						    __lowercase       ,			current_layer=__lowercase       ,			attention_mask=__lowercase       ,			head_mask=__lowercase		)

																						__lowerCAmelCase							       =				self.pooler(__lowercase		)
																						__lowerCAmelCase							       =				output_layers[i](__lowercase		)
																						if regression:
																												__lowerCAmelCase							       =				logits.detach()
																												if patient_result is not None:
																																		__lowerCAmelCase							       =				patient_result.detach()
																												if (patient_result is not None) and torch.abs(patient_result - labels		) < self.regression_threshold:
																																		patient_counter += 1
																												else:
																																		__lowerCAmelCase							       =				0
																						else:
																												__lowerCAmelCase							       =				logits.detach().argmax(dim=1		)
																												if patient_result is not None:
																																		__lowerCAmelCase							       =				patient_result.detach().argmax(dim=1		)
																												if (patient_result is not None) and torch.all(labels.eq(__lowercase		)		):
																																		patient_counter += 1
																												else:
																																		__lowerCAmelCase							       =				0

																						__lowerCAmelCase							       =				logits
																						if patient_counter == self.patience:
																												break
																__lowerCAmelCase							       =				[patient_result]
																self.inference_layers_num += calculated_layer_num
																self.inference_instances_num += 1

										return res


@add_start_docstrings(
    'Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of\n    the pooled output) e.g. for GLUE tasks. '							,						__A							,						)
class       a__				( __A      ):







				"""simple docstring"""



				def __init__(self       ,			__lowercase		):
										super().__init__(__lowercase		)
										__lowerCAmelCase							       =				config.num_labels

										__lowerCAmelCase							       =				BertModelWithPabee(__lowercase		)
										__lowerCAmelCase							       =				nn.Dropout(config.hidden_dropout_prob		)
										__lowerCAmelCase							       =				nn.ModuleList(
										    [nn.Linear(config.hidden_size       ,			self.config.num_labels		) for _ in range(config.num_hidden_layers		)]		)

										self.init_weights()




				@add_start_docstrings_to_model_forward(__lowercase		)
				def 					_snake_case  (self       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			):
										__lowerCAmelCase							       =				self.bert(
										    input_ids=__lowercase       ,			attention_mask=__lowercase       ,			token_type_ids=__lowercase       ,			position_ids=__lowercase       ,			head_mask=__lowercase       ,			inputs_embeds=__lowercase       ,			output_dropout=self.dropout       ,			output_layers=self.classifiers       ,			regression=self.num_labels == 1       ,			)

										__lowerCAmelCase							       =				(logits[-1],)

										if labels is not None:
																__lowerCAmelCase							       =				None
																__lowerCAmelCase							       =				0
																for ix, logits_item in enumerate(__lowercase		):
																						if self.num_labels == 1:
																												#  We are doing regression
																												__lowerCAmelCase							       =				MSELoss()
																												__lowerCAmelCase							       =				loss_fct(logits_item.view(-1		)       ,			labels.view(-1		)		)
																						else:
																												__lowerCAmelCase							       =				CrossEntropyLoss()
																												__lowerCAmelCase							       =				loss_fct(logits_item.view(-1       ,			self.num_labels		)       ,			labels.view(-1		)		)
																						if total_loss is None:
																												__lowerCAmelCase							       =				loss
																						else:
																												total_loss += loss * (ix + 1)
																						total_weights += ix + 1
																__lowerCAmelCase							       =				(total_loss / total_weights,) + outputs

										return outputs

