


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






def 						a__			(				A__				):
			SCREAMING_SNAKE_CASE_		:     int					=					MobileNetVaConfig(layer_norm_eps=0.0_01				)

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

			SCREAMING_SNAKE_CASE_		:     List[Any]					=					re.match(r'^mobilenet_v1_([^_]*)_([^_]*)$',      A__				)
			if matches:
						SCREAMING_SNAKE_CASE_		:     Any					=					float(matches[1]				)
						SCREAMING_SNAKE_CASE_		:     Tuple					=					int(matches[2]				)

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

			return config






def 						a__			(				):
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					'http://images.cocodataset.org/val2017/000000039769.jpg'
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					Image.open(requests.get(A__,      stream=A__				).raw				)
			return im






@torch.no_grad()
def 						a__			(				A__,      A__,      A__,      A__=False				):
			SCREAMING_SNAKE_CASE_		:     str					=					get_mobilenet_va_config(A__				)

			# Load 🤗 model
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					MobileNetVaForImageClassification(A__				).eval()

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

			# Check outputs on an image, prepared by MobileNetV1ImageProcessor
			SCREAMING_SNAKE_CASE_		:     Any					=					MobileNetVaImageProcessor(
			    crop_size={'width': config.image_size, 'height': config.image_size},      size={'shortest_edge': config.image_size + 3_2},      )
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					image_processor(images=prepare_img(),      return_tensors='pt'				)
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					model(**A__				)
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					outputs.logits

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

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

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

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

			if push_to_hub:
						print('Pushing to the hub...'				)
						SCREAMING_SNAKE_CASE_		:     Any					=					'google/' + model_name
						image_processor.push_to_hub(A__				)
						model.push_to_hub(A__				)


if __name__ == "__main__":
				lowerCAmelCase__			:      Any								=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.'
				)

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









import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
				from transformers.models.roberta.modeling_flax_roberta import (
				    FlaxRobertaForCausalLM,
				    FlaxRobertaForMaskedLM,
				    FlaxRobertaForMultipleChoice,
				    FlaxRobertaForQuestionAnswering,
				    FlaxRobertaForSequenceClassification,
				    FlaxRobertaForTokenClassification,
				    FlaxRobertaModel,
				)







class 	__lowercase    (unittest.TestCase  ):





							"""simple docstring"""







							def __init__(      self			,						lowerCAmelCase__			,						lowerCAmelCase__=1_3			,						lowerCAmelCase__=7			,						lowerCAmelCase__=True			,						lowerCAmelCase__=True			,						lowerCAmelCase__=True			,						lowerCAmelCase__=True			,						lowerCAmelCase__=9_9			,						lowerCAmelCase__=3_2			,						lowerCAmelCase__=5			,						lowerCAmelCase__=4			,						lowerCAmelCase__=3_7			,						lowerCAmelCase__="gelu"			,						lowerCAmelCase__=0.1			,						lowerCAmelCase__=0.1			,						lowerCAmelCase__=5_1_2			,						lowerCAmelCase__=1_6			,						lowerCAmelCase__=2			,						lowerCAmelCase__=0.02			,						lowerCAmelCase__=4			,						):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					parent
										SCREAMING_SNAKE_CASE_		:     List[str]					=					batch_size
										SCREAMING_SNAKE_CASE_		:     Dict					=					seq_length
										SCREAMING_SNAKE_CASE_		:     Any					=					is_training
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					use_attention_mask
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					use_token_type_ids
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					use_labels
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					vocab_size
										SCREAMING_SNAKE_CASE_		:     Dict					=					hidden_size
										SCREAMING_SNAKE_CASE_		:     Any					=					num_hidden_layers
										SCREAMING_SNAKE_CASE_		:     List[str]					=					num_attention_heads
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					intermediate_size
										SCREAMING_SNAKE_CASE_		:     int					=					hidden_act
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					hidden_dropout_prob
										SCREAMING_SNAKE_CASE_		:     str					=					attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE_		:     Tuple					=					max_position_embeddings
										SCREAMING_SNAKE_CASE_		:     Any					=					type_vocab_size
										SCREAMING_SNAKE_CASE_		:     str					=					type_sequence_label_size
										SCREAMING_SNAKE_CASE_		:     str					=					initializer_range
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					num_choices






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					ids_tensor([self.batch_size, self.seq_length]			,						self.vocab_size       )

										SCREAMING_SNAKE_CASE_		:     str					=					None
										if self.use_attention_mask:
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					random_attention_mask([self.batch_size, self.seq_length]       )

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					None
										if self.use_token_type_ids:
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					ids_tensor([self.batch_size, self.seq_length]			,						self.type_vocab_size       )

										SCREAMING_SNAKE_CASE_		:     Any					=					RobertaConfig(
										    vocab_size=self.vocab_size			,						hidden_size=self.hidden_size			,						num_hidden_layers=self.num_hidden_layers			,						num_attention_heads=self.num_attention_heads			,						intermediate_size=self.intermediate_size			,						hidden_act=self.hidden_act			,						hidden_dropout_prob=self.hidden_dropout_prob			,						attention_probs_dropout_prob=self.attention_probs_dropout_prob			,						max_position_embeddings=self.max_position_embeddings			,						type_vocab_size=self.type_vocab_size			,						is_decoder=lowerCAmelCase__			,						initializer_range=self.initializer_range			,						)

										return config, input_ids, token_type_ids, attention_mask






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     str					=					self.prepare_config_and_inputs()
										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     str					=					config_and_inputs
										SCREAMING_SNAKE_CASE_		:     List[str]					=					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
										return config, inputs_dict






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[str]					=					self.prepare_config_and_inputs()
										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					config_and_inputs

										SCREAMING_SNAKE_CASE_		:     str					=					True
										SCREAMING_SNAKE_CASE_		:     Dict					=					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
										SCREAMING_SNAKE_CASE_		:     int					=					ids_tensor([self.batch_size, self.seq_length]			,						vocab_size=2       )

										return (
										    config,
										    input_ids,
										    token_type_ids,
										    encoder_hidden_states,
										    encoder_attention_mask,
										)







@require_flax
class 	__lowercase    (__SCREAMING_SNAKE_CASE	,       unittest.TestCase  ):





							"""simple docstring"""







							_UpperCAmelCase      =						True

							_UpperCAmelCase      =						(
							    (
							        FlaxRobertaModel,
							        FlaxRobertaForCausalLM,
							        FlaxRobertaForMaskedLM,
							        FlaxRobertaForSequenceClassification,
							        FlaxRobertaForTokenClassification,
							        FlaxRobertaForMultipleChoice,
							        FlaxRobertaForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Any					=					FlaxRobertaModelTester(self       )






							@slow
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										for model_class_name in self.all_model_classes:
													SCREAMING_SNAKE_CASE_		:     List[str]					=					model_class_name.from_pretrained('roberta-base'			,						from_pt=lowerCAmelCase__       )
													SCREAMING_SNAKE_CASE_		:     str					=					model(np.ones((1, 1)       )       )
													self.assertIsNotNone(lowerCAmelCase__       )





