

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						:  Union[str, Any]									=			logging.get_logger(__name__)





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       MobileNetVaConfig(layer_norm_eps=0.0_01 )

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

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

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

							return config





def    __lowerCamelCase      (					):







							'''simple docstring'''






							snake_case_     							=       'http://images.cocodataset.org/val2017/000000039769.jpg'
							snake_case_     							=       Image.open(requests.get(UpperCamelCase__  ,				stream=UpperCamelCase__ ).raw )
							return im





@torch.no_grad()
def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__=False ):







							'''simple docstring'''






							snake_case_     							=       get_mobilenet_va_config(UpperCamelCase__ )

							# Load 🤗 model
							snake_case_     							=       MobileNetVaForImageClassification(UpperCamelCase__ ).eval()

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

							# Check outputs on an image, prepared by MobileNetV1ImageProcessor
							snake_case_     							=       MobileNetVaImageProcessor(
							    crop_size={'width': config.image_size, 'height': config.image_size}  ,				size={'shortest_edge': config.image_size + 32}  ,				)
							snake_case_     							=       image_processor(images=prepare_img()  ,				return_tensors='pt' )
							snake_case_     							=       model(**UpperCamelCase__ )
							snake_case_     							=       outputs.logits

							assert logits.shape == (1, 1001)

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

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

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

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


if __name__ == "__main__":
					_UpperCAmelCase						:  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."""
					)

					_UpperCAmelCase						:  List[Any]									=			parser.parse_args()
					convert_movilevit_checkpoint(
					    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
					)







import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
					import faiss







@require_faiss
class 					lowercase							(			lowercase_     ):




				def        a				(   self			):
											snake_case_     							=       tempfile.mkdtemp()
											snake_case_     							=       8

											# DPR tok
											snake_case_     							=       [
											    '[UNK]',
											    '[CLS]',
											    '[SEP]',
											    '[PAD]',
											    '[MASK]',
											    'want',
											    '##want',
											    '##ed',
											    'wa',
											    'un',
											    'runn',
											    '##ing',
											    ',',
											    'low',
											    'lowest',
											]
											snake_case_     							=       os.path.join(self.tmpdirname ,  'dpr_tokenizer'			)
											os.makedirs(snake_case ,  exist_ok=snake_case			)
											snake_case_     							=       os.path.join(snake_case ,  DPR_VOCAB_FILES_NAMES['vocab_file']			)
											with open(self.vocab_file ,  'w' ,  encoding='utf-8'			) as vocab_writer:
																		vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]			)			)

											# BART tok
											snake_case_     							=       [
											    'l',
											    'o',
											    'w',
											    'e',
											    'r',
											    's',
											    't',
											    'i',
											    'd',
											    'n',
											    '\u0120',
											    '\u0120l',
											    '\u0120n',
											    '\u0120lo',
											    '\u0120low',
											    'er',
											    '\u0120lowest',
											    '\u0120newer',
											    '\u0120wider',
											    '<unk>',
											]
											snake_case_     							=       dict(zip(snake_case ,  range(len(snake_case			)			)			)			)
											snake_case_     							=       ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
											snake_case_     							=       {'unk_token': '<unk>'}

											snake_case_     							=       os.path.join(self.tmpdirname ,  'bart_tokenizer'			)
											os.makedirs(snake_case ,  exist_ok=snake_case			)
											snake_case_     							=       os.path.join(snake_case ,  BART_VOCAB_FILES_NAMES['vocab_file']			)
											snake_case_     							=       os.path.join(snake_case ,  BART_VOCAB_FILES_NAMES['merges_file']			)
											with open(self.vocab_file ,  'w' ,  encoding='utf-8'			) as fp:
																		fp.write(json.dumps(snake_case			) + '\n'			)
											with open(self.merges_file ,  'w' ,  encoding='utf-8'			) as fp:
																		fp.write('\n'.join(snake_case			)			)




				def        a				(   self			):
											return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname ,  'dpr_tokenizer'			)			)




				def        a				(   self			):
											return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname ,  'dpr_tokenizer'			)			)




				def        a				(   self			):
											return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname ,  'bart_tokenizer'			)			)




				def        a				(   self			):
											shutil.rmtree(self.tmpdirname			)




				def        a				(   self			):
											snake_case_     							=       Dataset.from_dict(
											    {
											        'id': ['0', '1'],
											        'text': ['foo', 'bar'],
											        'title': ['Foo', 'Bar'],
											        'embeddings': [np.ones(self.retrieval_vector_size			), 2 * np.ones(self.retrieval_vector_size			)],
											    }			)
											dataset.add_faiss_index('embeddings' ,  string_factory='Flat' ,  metric_type=faiss.METRIC_INNER_PRODUCT			)
											return dataset




				def        a				(   self			):
											snake_case_     							=       self.get_dummy_dataset()
											snake_case_     							=       RagConfig(
											    retrieval_vector_size=self.retrieval_vector_size ,  question_encoder=DPRConfig().to_dict() ,  generator=BartConfig().to_dict() ,  )
											with patch('transformers.models.rag.retrieval_rag.load_dataset'			) as mock_load_dataset:
																		snake_case_     							=       dataset
																		snake_case_     							=       RagRetriever(
																		    snake_case ,  question_encoder_tokenizer=self.get_dpr_tokenizer() ,  generator_tokenizer=self.get_bart_tokenizer() ,  )
											return retriever




				def        a				(   self ,  snake_case			):
											snake_case_     							=       self.get_dummy_dataset()
											snake_case_     							=       RagConfig(
											    retrieval_vector_size=self.retrieval_vector_size ,  question_encoder=DPRConfig().to_dict() ,  generator=BartConfig().to_dict() ,  index_name='custom' ,  )
											if from_disk:
																		snake_case_     							=       os.path.join(self.tmpdirname ,  'dataset'			)
																		snake_case_     							=       os.path.join(self.tmpdirname ,  'index.faiss'			)
																		dataset.get_index('embeddings'			).save(os.path.join(self.tmpdirname ,  'index.faiss'			)			)
																		dataset.drop_index('embeddings'			)
																		dataset.save_to_disk(os.path.join(self.tmpdirname ,  'dataset'			)			)
																		del dataset
																		snake_case_     							=       RagRetriever(
																		    snake_case ,  question_encoder_tokenizer=self.get_dpr_tokenizer() ,  generator_tokenizer=self.get_bart_tokenizer() ,  )
											else:
																		snake_case_     							=       RagRetriever(
																		    snake_case ,  question_encoder_tokenizer=self.get_dpr_tokenizer() ,  generator_tokenizer=self.get_bart_tokenizer() ,  index=CustomHFIndex(config.retrieval_vector_size ,  snake_case			) ,  )
											return retriever




				def        a				(   self			):
											snake_case_     							=       Dataset.from_dict(
											    {
											        'id': ['0', '1'],
											        'text': ['foo', 'bar'],
											        'title': ['Foo', 'Bar'],
											        'embeddings': [np.ones(self.retrieval_vector_size + 1			), 2 * np.ones(self.retrieval_vector_size + 1			)],
											    }			)
											dataset.add_faiss_index('embeddings' ,  string_factory='Flat' ,  metric_type=faiss.METRIC_INNER_PRODUCT			)

											snake_case_     							=       os.path.join(self.tmpdirname ,  'hf_bert_base.hnswSQ8_correct_phi_128.c_index'			)
											dataset.save_faiss_index('embeddings' ,  index_file_name + '.index.dpr'			)
											pickle.dump(dataset['id'] ,  open(index_file_name + '.index_meta.dpr' ,  'wb'			)			)

											snake_case_     							=       os.path.join(self.tmpdirname ,  'psgs_w100.tsv.pkl'			)
											snake_case_     							=       {sample['id']: [sample['text'], sample['title']] for sample in dataset}
											pickle.dump(snake_case ,  open(snake_case ,  'wb'			)			)

											snake_case_     							=       RagConfig(
											    retrieval_vector_size=self.retrieval_vector_size ,  question_encoder=DPRConfig().to_dict() ,  generator=BartConfig().to_dict() ,  index_name='legacy' ,  index_path=self.tmpdirname ,  )
											snake_case_     							=       RagRetriever(
											    snake_case ,  question_encoder_tokenizer=self.get_dpr_tokenizer() ,  generator_tokenizer=self.get_bart_tokenizer()			)
											return retriever




				def        a				(   self			):
											snake_case_     							=       1
											snake_case_     							=       self.get_dummy_canonical_hf_index_retriever()
											snake_case_     							=       np.array(
											    [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)] ,  dtype=np.floataa			)
											snake_case_							,       snake_case_							,       snake_case_     							=       retriever.retrieve(snake_case ,  n_docs=snake_case			)
											self.assertEqual(retrieved_doc_embeds.shape ,  (2, n_docs, self.retrieval_vector_size)			)
											self.assertEqual(len(snake_case			) ,  2			)
											self.assertEqual(sorted(doc_dicts[0]			) ,  ['embeddings', 'id', 'text', 'title']			)
											self.assertEqual(len(doc_dicts[0]['id']			) ,  snake_case			)
											self.assertEqual(doc_dicts[0]['id'][0] ,  '1'			)  # max inner product is reached with second doc
											self.assertEqual(doc_dicts[1]['id'][0] ,  '0'			)  # max inner product is reached with first doc
											self.assertListEqual(doc_ids.tolist() ,  [[1], [0]]			)




				def        a				(   self			):
											snake_case_     							=       self.get_dummy_canonical_hf_index_retriever()
											with tempfile.TemporaryDirectory() as tmp_dirname:
																		with patch('transformers.models.rag.retrieval_rag.load_dataset'			) as mock_load_dataset:
																									snake_case_     							=       self.get_dummy_dataset()
																									retriever.save_pretrained(snake_case			)
																									snake_case_     							=       RagRetriever.from_pretrained(snake_case			)
																									self.assertIsInstance(snake_case ,  snake_case			)
																									snake_case_     							=       np.array(
																									    [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)] ,  dtype=np.floataa			)
																									snake_case_     							=       retriever.retrieve(snake_case ,  n_docs=1			)
																									self.assertTrue(out is not None			)




				def        a				(   self			):
											snake_case_     							=       1
											snake_case_     							=       self.get_dummy_custom_hf_index_retriever(from_disk=snake_case			)
											snake_case_     							=       np.array(
											    [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)] ,  dtype=np.floataa			)
											snake_case_							,       snake_case_							,       snake_case_     							=       retriever.retrieve(snake_case ,  n_docs=snake_case			)
											self.assertEqual(retrieved_doc_embeds.shape ,  (2, n_docs, self.retrieval_vector_size)			)
											self.assertEqual(len(snake_case			) ,  2			)
											self.assertEqual(sorted(doc_dicts[0]			) ,  ['embeddings', 'id', 'text', 'title']			)
											self.assertEqual(len(doc_dicts[0]['id']			) ,  snake_case			)
											self.assertEqual(doc_dicts[0]['id'][0] ,  '1'			)  # max inner product is reached with second doc
											self.assertEqual(doc_dicts[1]['id'][0] ,  '0'			)  # max inner product is reached with first doc
											self.assertListEqual(doc_ids.tolist() ,  [[1], [0]]			)




				def        a				(   self			):
											snake_case_     							=       self.get_dummy_custom_hf_index_retriever(from_disk=snake_case			)
											with tempfile.TemporaryDirectory() as tmp_dirname:
																		retriever.save_pretrained(snake_case			)
																		snake_case_     							=       RagRetriever.from_pretrained(snake_case			)
																		self.assertIsInstance(snake_case ,  snake_case			)
																		snake_case_     							=       np.array(
																		    [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)] ,  dtype=np.floataa			)
																		snake_case_     							=       retriever.retrieve(snake_case ,  n_docs=1			)
																		self.assertTrue(out is not None			)




				def        a				(   self			):
											snake_case_     							=       1
											snake_case_     							=       self.get_dummy_custom_hf_index_retriever(from_disk=snake_case			)
											snake_case_     							=       np.array(
											    [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)] ,  dtype=np.floataa			)
											snake_case_							,       snake_case_							,       snake_case_     							=       retriever.retrieve(snake_case ,  n_docs=snake_case			)
											self.assertEqual(retrieved_doc_embeds.shape ,  (2, n_docs, self.retrieval_vector_size)			)
											self.assertEqual(len(snake_case			) ,  2			)
											self.assertEqual(sorted(doc_dicts[0]			) ,  ['embeddings', 'id', 'text', 'title']			)
											self.assertEqual(len(doc_dicts[0]['id']			) ,  snake_case			)
											self.assertEqual(doc_dicts[0]['id'][0] ,  '1'			)  # max inner product is reached with second doc
											self.assertEqual(doc_dicts[1]['id'][0] ,  '0'			)  # max inner product is reached with first doc
											self.assertListEqual(doc_ids.tolist() ,  [[1], [0]]			)




				def        a				(   self			):
											snake_case_     							=       self.get_dummy_custom_hf_index_retriever(from_disk=snake_case			)
											with tempfile.TemporaryDirectory() as tmp_dirname:
																		retriever.save_pretrained(snake_case			)
																		snake_case_     							=       RagRetriever.from_pretrained(snake_case			)
																		self.assertIsInstance(snake_case ,  snake_case			)
																		snake_case_     							=       np.array(
																		    [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)] ,  dtype=np.floataa			)
																		snake_case_     							=       retriever.retrieve(snake_case ,  n_docs=1			)
																		self.assertTrue(out is not None			)




				def        a				(   self			):
											snake_case_     							=       1
											snake_case_     							=       self.get_dummy_legacy_index_retriever()
											snake_case_     							=       np.array(
											    [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)] ,  dtype=np.floataa			)
											snake_case_							,       snake_case_							,       snake_case_     							=       retriever.retrieve(snake_case ,  n_docs=snake_case			)
											self.assertEqual(retrieved_doc_embeds.shape ,  (2, n_docs, self.retrieval_vector_size)			)
											self.assertEqual(len(snake_case			) ,  2			)
											self.assertEqual(sorted(doc_dicts[0]			) ,  ['text', 'title']			)
											self.assertEqual(len(doc_dicts[0]['text']			) ,  snake_case			)
											self.assertEqual(doc_dicts[0]['text'][0] ,  'bar'			)  # max inner product is reached with second doc
											self.assertEqual(doc_dicts[1]['text'][0] ,  'foo'			)  # max inner product is reached with first doc
											self.assertListEqual(doc_ids.tolist() ,  [[1], [0]]			)




				def        a				(   self			):
											snake_case_     							=       self.get_dummy_legacy_index_retriever()
											with tempfile.TemporaryDirectory() as tmp_dirname:
																		retriever.save_pretrained(snake_case			)
																		snake_case_     							=       RagRetriever.from_pretrained(snake_case			)
																		self.assertIsInstance(snake_case ,  snake_case			)
																		snake_case_     							=       np.array(
																		    [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)] ,  dtype=np.floataa			)
																		snake_case_     							=       retriever.retrieve(snake_case ,  n_docs=1			)
																		self.assertTrue(out is not None			)




				@require_torch
				@require_tokenizers
				@require_sentencepiece
				def        a				(   self			):
											import torch

											snake_case_     							=       1
											snake_case_     							=       self.get_dummy_canonical_hf_index_retriever()
											snake_case_     							=       [[5, 7], [10, 11]]
											snake_case_     							=       np.array(
											    [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)] ,  dtype=np.floataa			)
											snake_case_     							=       retriever(snake_case ,  snake_case ,  prefix=retriever.config.generator.prefix ,  n_docs=snake_case			)
											snake_case_							,       snake_case_							,       snake_case_     							=       (
											    out['context_input_ids'],
											    out['context_attention_mask'],
											    out['retrieved_doc_embeds'],
											)
											self.assertEqual(retrieved_doc_embeds.shape ,  (2, n_docs, self.retrieval_vector_size)			)
											self.assertIsInstance(snake_case ,  snake_case			)
											self.assertIsInstance(snake_case ,  snake_case			)
											self.assertIsInstance(snake_case ,  np.ndarray			)

											snake_case_     							=       retriever(
											    snake_case ,  snake_case ,  prefix=retriever.config.generator.prefix ,  n_docs=snake_case ,  return_tensors='pt' ,  )
											snake_case_							,       snake_case_							,       snake_case_							,       snake_case_     							=       (  # noqa: F841
											    out['context_input_ids'],
											    out['context_attention_mask'],
											    out['retrieved_doc_embeds'],
											    out['doc_ids'],
											)
											self.assertEqual(retrieved_doc_embeds.shape ,  (2, n_docs, self.retrieval_vector_size)			)
											self.assertIsInstance(snake_case ,  torch.Tensor			)
											self.assertIsInstance(snake_case ,  torch.Tensor			)
											self.assertIsInstance(snake_case ,  torch.Tensor			)




				@require_torch
				@require_tokenizers
				@require_sentencepiece
				def        a				(   self			):
											snake_case_     							=       self.get_dpr_ctx_encoder_tokenizer()
											snake_case_     							=       1
											snake_case_     							=       self.get_dummy_custom_hf_index_retriever(from_disk=snake_case			)
											retriever.set_ctx_encoder_tokenizer(snake_case			)

											snake_case_     							=       [[5, 7], [10, 11]]
											snake_case_     							=       np.array(
											    [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)] ,  dtype=np.floataa			)
											snake_case_     							=       retriever(snake_case ,  snake_case ,  prefix=retriever.config.generator.prefix ,  n_docs=snake_case			)

											self.assertEqual(
											    len(snake_case			) ,  6			)  # check whether the retriever output consist of 6 attributes including tokenized docs
											self.assertEqual(
											    all(k in out for k in ('tokenized_doc_ids', 'tokenized_doc_attention_mask')			) ,  snake_case			)  # check for doc token related keys in dictionary.




