


'''simple docstring'''
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile

import pyarrow as pa
import pyarrow.parquet as pq
import pytest

import datasets
import datasets.config







@pytest.fixture(scope="session" )
def       a_				(	):
							A_			    =		1_0
							A_			    =		datasets.Features(
							    {
							        "tokens": datasets.Sequence(datasets.Value("string" ) ),
							        "labels": datasets.Sequence(datasets.ClassLabel(names=["negative", "positive"] ) ),
							        "answers": datasets.Sequence(
							            {
							                "text": datasets.Value("string" ),
							                "answer_start": datasets.Value("int32" ),
							            } ),
							        "id": datasets.Value("int64" ),
							    } )
							A_			    =		datasets.Dataset.from_dict(
							    {
							        "tokens": [["foo"] * 5] * n,
							        "labels": [[1] * 5] * n,
							        "answers": [{"answer_start": [9_7], "text": ["1976"]}] * 1_0,
							        "id": list(range(UpperCamelCase_ ) ),
							    }						,  features=UpperCamelCase_						,  )
							return dataset







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		str(tmp_path_factory.mktemp("data" ) / "file.arrow" )
							dataset.map(cache_file_name=UpperCamelCase_ )
							return filename


# FILE_CONTENT + files


__SCREAMING_SNAKE_CASE					:				Any									=		'''\
    Text data.
    Second line of data.'''







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_ ):
							A_			    =		tmp_path_factory.mktemp("data" ) / "file.txt"
							A_			    =		FILE_CONTENT
							with open(UpperCamelCase_						,  "w" ) as f:
														f.write(UpperCamelCase_ )
							return filename







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_ ):
							import bza

							A_			    =		tmp_path_factory.mktemp("data" ) / "file.txt.bz2"
							A_			    =		bytes(UpperCamelCase_						,  "utf-8" )
							with bza.open(UpperCamelCase_						,  "wb" ) as f:
														f.write(UpperCamelCase_ )
							return path







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_ ):
							import gzip

							A_			    =		str(tmp_path_factory.mktemp("data" ) / "file.txt.gz" )
							A_			    =		bytes(UpperCamelCase_						,  "utf-8" )
							with gzip.open(UpperCamelCase_						,  "wb" ) as f:
														f.write(UpperCamelCase_ )
							return path







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_ ):
							if datasets.config.LZ4_AVAILABLE:
														import lza.frame

														A_			    =		tmp_path_factory.mktemp("data" ) / "file.txt.lz4"
														A_			    =		bytes(UpperCamelCase_						,  "utf-8" )
														with lza.frame.open(UpperCamelCase_						,  "wb" ) as f:
																					f.write(UpperCamelCase_ )
														return path







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							if datasets.config.PY7ZR_AVAILABLE:
														import pyazr

														A_			    =		tmp_path_factory.mktemp("data" ) / "file.txt.7z"
														with pyazr.SevenZipFile(UpperCamelCase_						,  "w" ) as archive:
																					archive.write(UpperCamelCase_						,  arcname=os.path.basename(UpperCamelCase_ ) )
														return path







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							import tarfile

							A_			    =		tmp_path_factory.mktemp("data" ) / "file.txt.tar"
							with tarfile.TarFile(UpperCamelCase_						,  "w" ) as f:
														f.add(UpperCamelCase_						,  arcname=os.path.basename(UpperCamelCase_ ) )
							return path







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_ ):
							import lzma

							A_			    =		tmp_path_factory.mktemp("data" ) / "file.txt.xz"
							A_			    =		bytes(UpperCamelCase_						,  "utf-8" )
							with lzma.open(UpperCamelCase_						,  "wb" ) as f:
														f.write(UpperCamelCase_ )
							return path







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							import zipfile

							A_			    =		tmp_path_factory.mktemp("data" ) / "file.txt.zip"
							with zipfile.ZipFile(UpperCamelCase_						,  "w" ) as f:
														f.write(UpperCamelCase_						,  arcname=os.path.basename(UpperCamelCase_ ) )
							return path







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_ ):
							if datasets.config.ZSTANDARD_AVAILABLE:
														import zstandard as zstd

														A_			    =		tmp_path_factory.mktemp("data" ) / "file.txt.zst"
														A_			    =		bytes(UpperCamelCase_						,  "utf-8" )
														with zstd.open(UpperCamelCase_						,  "wb" ) as f:
																					f.write(UpperCamelCase_ )
														return path







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_ ):
							A_			    =		tmp_path_factory.mktemp("data" ) / "file.xml"
							A_			    =		textwrap.dedent(
							    "\\n    <?xml version=\"1.0\" encoding=\"UTF-8\" ?>\n    <tmx version=\"1.4\">\n      <header segtype=\"sentence\" srclang=\"ca\" />\n      <body>\n        <tu>\n          <tuv xml:lang=\"ca\"><seg>Contingut 1</seg></tuv>\n          <tuv xml:lang=\"en\"><seg>Content 1</seg></tuv>\n        </tu>\n        <tu>\n          <tuv xml:lang=\"ca\"><seg>Contingut 2</seg></tuv>\n          <tuv xml:lang=\"en\"><seg>Content 2</seg></tuv>\n        </tu>\n        <tu>\n          <tuv xml:lang=\"ca\"><seg>Contingut 3</seg></tuv>\n          <tuv xml:lang=\"en\"><seg>Content 3</seg></tuv>\n        </tu>\n        <tu>\n          <tuv xml:lang=\"ca\"><seg>Contingut 4</seg></tuv>\n          <tuv xml:lang=\"en\"><seg>Content 4</seg></tuv>\n        </tu>\n        <tu>\n          <tuv xml:lang=\"ca\"><seg>Contingut 5</seg></tuv>\n          <tuv xml:lang=\"en\"><seg>Content 5</seg></tuv>\n        </tu>\n      </body>\n    </tmx>" )
							with open(UpperCamelCase_						,  "w" ) as f:
														f.write(UpperCamelCase_ )
							return filename


__SCREAMING_SNAKE_CASE					:				List[str]									=		[
    {'''col_1''': '''0''', '''col_2''': 0, '''col_3''': 0.0},
    {'''col_1''': '''1''', '''col_2''': 1, '''col_3''': 1.0},
    {'''col_1''': '''2''', '''col_2''': 2, '''col_3''': 2.0},
    {'''col_1''': '''3''', '''col_2''': 3, '''col_3''': 3.0},
]
__SCREAMING_SNAKE_CASE					:				Tuple									=		[
    {'''col_1''': '''4''', '''col_2''': 4, '''col_3''': 4.0},
    {'''col_1''': '''5''', '''col_2''': 5, '''col_3''': 5.0},
]
__SCREAMING_SNAKE_CASE					:				Optional[Any]									=		{
    '''col_1''': ['''0''', '''1''', '''2''', '''3'''],
    '''col_2''': [0, 1, 2, 3],
    '''col_3''': [0.0, 1.0, 2.0, 3.0],
}

__SCREAMING_SNAKE_CASE					:				Union[str, Any]									=		[
    {'''col_3''': 0.0, '''col_1''': '''0''', '''col_2''': 0},
    {'''col_3''': 1.0, '''col_1''': '''1''', '''col_2''': 1},
]

__SCREAMING_SNAKE_CASE					:				int									=		[
    {'''col_1''': '''s0''', '''col_2''': 0, '''col_3''': 0.0},
    {'''col_1''': '''s1''', '''col_2''': 1, '''col_3''': 1.0},
    {'''col_1''': '''s2''', '''col_2''': 2, '''col_3''': 2.0},
    {'''col_1''': '''s3''', '''col_2''': 3, '''col_3''': 3.0},
]







@pytest.fixture(scope="session" )
def       a_				(	):
							return DATA_DICT_OF_LISTS







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_ ):
							A_			    =		datasets.Dataset.from_dict(UpperCamelCase_ )
							A_			    =		str(tmp_path_factory.mktemp("data" ) / "dataset.arrow" )
							dataset.map(cache_file_name=UpperCamelCase_ )
							return path







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_ ):
							A_			    =		str(tmp_path_factory.mktemp("data" ) / "dataset.sqlite" )
							with contextlib.closing(sqlitea.connect(UpperCamelCase_ ) ) as con:
														A_			    =		con.cursor()
														cur.execute("CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)" )
														for item in DATA:
																					cur.execute("INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)"						,  tuple(item.values() ) )
														con.commit()
							return path







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_ ):
							A_			    =		str(tmp_path_factory.mktemp("data" ) / "dataset.csv" )
							with open(UpperCamelCase_						,  "w"						,  newline="" ) as f:
														A_			    =		csv.DictWriter(UpperCamelCase_						,  fieldnames=["col_1", "col_2", "col_3"] )
														writer.writeheader()
														for item in DATA:
																					writer.writerow(UpperCamelCase_ )
							return path







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_ ):
							A_			    =		str(tmp_path_factory.mktemp("data" ) / "dataset2.csv" )
							with open(UpperCamelCase_						,  "w"						,  newline="" ) as f:
														A_			    =		csv.DictWriter(UpperCamelCase_						,  fieldnames=["col_1", "col_2", "col_3"] )
														writer.writeheader()
														for item in DATA:
																					writer.writerow(UpperCamelCase_ )
							return path







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							import bza

							A_			    =		tmp_path_factory.mktemp("data" ) / "dataset.csv.bz2"
							with open(UpperCamelCase_						,  "rb" ) as f:
														A_			    =		f.read()
							# data = bytes(FILE_CONTENT, "utf-8")
							with bza.open(UpperCamelCase_						,  "wb" ) as f:
														f.write(UpperCamelCase_ )
							return path







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		tmp_path_factory.mktemp("data" ) / "dataset.csv.zip"
							with zipfile.ZipFile(UpperCamelCase_						,  "w" ) as f:
														f.write(UpperCamelCase_						,  arcname=os.path.basename(UpperCamelCase_ ) )
														f.write(UpperCamelCase_						,  arcname=os.path.basename(UpperCamelCase_ ) )
							return path







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		tmp_path_factory.mktemp("data" ) / "dataset.csv.zip"
							with zipfile.ZipFile(UpperCamelCase_						,  "w" ) as f:
														f.write(UpperCamelCase_						,  arcname=os.path.basename(csv_path.replace(".csv"						,  ".CSV" ) ) )
														f.write(UpperCamelCase_						,  arcname=os.path.basename(csva_path.replace(".csv"						,  ".CSV" ) ) )
							return path







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		tmp_path_factory.mktemp("data" ) / "dataset_with_dir.csv.zip"
							with zipfile.ZipFile(UpperCamelCase_						,  "w" ) as f:
														f.write(UpperCamelCase_						,  arcname=os.path.join("main_dir"						,  os.path.basename(UpperCamelCase_ ) ) )
														f.write(UpperCamelCase_						,  arcname=os.path.join("main_dir"						,  os.path.basename(UpperCamelCase_ ) ) )
							return path







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_ ):
							A_			    =		str(tmp_path_factory.mktemp("data" ) / "dataset.parquet" )
							A_			    =		pa.schema(
							    {
							        "col_1": pa.string(),
							        "col_2": pa.intaa(),
							        "col_3": pa.floataa(),
							    } )
							with open(UpperCamelCase_						,  "wb" ) as f:
														A_			    =		pq.ParquetWriter(UpperCamelCase_						,  schema=UpperCamelCase_ )
														A_			    =		pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(UpperCamelCase_ ) )] for k in DATA[0]}						,  schema=UpperCamelCase_ )
														writer.write_table(UpperCamelCase_ )
														writer.close()
							return path







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_ ):
							A_			    =		str(tmp_path_factory.mktemp("data" ) / "dataset.json" )
							A_			    =		{"data": DATA}
							with open(UpperCamelCase_						,  "w" ) as f:
														json.dump(UpperCamelCase_						,  UpperCamelCase_ )
							return path







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_ ):
							A_			    =		str(tmp_path_factory.mktemp("data" ) / "dataset.json" )
							A_			    =		{"data": DATA_DICT_OF_LISTS}
							with open(UpperCamelCase_						,  "w" ) as f:
														json.dump(UpperCamelCase_						,  UpperCamelCase_ )
							return path







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_ ):
							A_			    =		str(tmp_path_factory.mktemp("data" ) / "dataset.jsonl" )
							with open(UpperCamelCase_						,  "w" ) as f:
														for item in DATA:
																					f.write(json.dumps(UpperCamelCase_ ) + "\n" )
							return path







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_ ):
							A_			    =		str(tmp_path_factory.mktemp("data" ) / "dataset2.jsonl" )
							with open(UpperCamelCase_						,  "w" ) as f:
														for item in DATA:
																					f.write(json.dumps(UpperCamelCase_ ) + "\n" )
							return path







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_ ):
							A_			    =		str(tmp_path_factory.mktemp("data" ) / "dataset_312.jsonl" )
							with open(UpperCamelCase_						,  "w" ) as f:
														for item in DATA_312:
																					f.write(json.dumps(UpperCamelCase_ ) + "\n" )
							return path







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_ ):
							A_			    =		str(tmp_path_factory.mktemp("data" ) / "dataset-str.jsonl" )
							with open(UpperCamelCase_						,  "w" ) as f:
														for item in DATA_STR:
																					f.write(json.dumps(UpperCamelCase_ ) + "\n" )
							return path







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							import gzip

							A_			    =		str(tmp_path_factory.mktemp("data" ) / "dataset.txt.gz" )
							with open(UpperCamelCase_						,  "rb" ) as orig_file:
														with gzip.open(UpperCamelCase_						,  "wb" ) as zipped_file:
																					zipped_file.writelines(UpperCamelCase_ )
							return path







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							import gzip

							A_			    =		str(tmp_path_factory.mktemp("data" ) / "dataset.jsonl.gz" )
							with open(UpperCamelCase_						,  "rb" ) as orig_file:
														with gzip.open(UpperCamelCase_						,  "wb" ) as zipped_file:
																					zipped_file.writelines(UpperCamelCase_ )
							return path







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		tmp_path_factory.mktemp("data" ) / "dataset.jsonl.zip"
							with zipfile.ZipFile(UpperCamelCase_						,  "w" ) as f:
														f.write(UpperCamelCase_						,  arcname=os.path.basename(UpperCamelCase_ ) )
														f.write(UpperCamelCase_						,  arcname=os.path.basename(UpperCamelCase_ ) )
							return path







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		tmp_path_factory.mktemp("data" ) / "dataset_nested.jsonl.zip"
							with zipfile.ZipFile(UpperCamelCase_						,  "w" ) as f:
														f.write(UpperCamelCase_						,  arcname=os.path.join("nested"						,  os.path.basename(UpperCamelCase_ ) ) )
							return path







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		tmp_path_factory.mktemp("data" ) / "dataset_with_dir.jsonl.zip"
							with zipfile.ZipFile(UpperCamelCase_						,  "w" ) as f:
														f.write(UpperCamelCase_						,  arcname=os.path.join("main_dir"						,  os.path.basename(UpperCamelCase_ ) ) )
														f.write(UpperCamelCase_						,  arcname=os.path.join("main_dir"						,  os.path.basename(UpperCamelCase_ ) ) )
							return path







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		tmp_path_factory.mktemp("data" ) / "dataset.jsonl.tar"
							with tarfile.TarFile(UpperCamelCase_						,  "w" ) as f:
														f.add(UpperCamelCase_						,  arcname=os.path.basename(UpperCamelCase_ ) )
														f.add(UpperCamelCase_						,  arcname=os.path.basename(UpperCamelCase_ ) )
							return path







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		tmp_path_factory.mktemp("data" ) / "dataset_nested.jsonl.tar"
							with tarfile.TarFile(UpperCamelCase_						,  "w" ) as f:
														f.add(UpperCamelCase_						,  arcname=os.path.join("nested"						,  os.path.basename(UpperCamelCase_ ) ) )
							return path







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_ ):
							A_			    =		["0", "1", "2", "3"]
							A_			    =		str(tmp_path_factory.mktemp("data" ) / "dataset.txt" )
							with open(UpperCamelCase_						,  "w" ) as f:
														for item in data:
																					f.write(item + "\n" )
							return path







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_ ):
							A_			    =		["0", "1", "2", "3"]
							A_			    =		str(tmp_path_factory.mktemp("data" ) / "dataset2.txt" )
							with open(UpperCamelCase_						,  "w" ) as f:
														for item in data:
																					f.write(item + "\n" )
							return path







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_ ):
							A_			    =		["0", "1", "2", "3"]
							A_			    =		tmp_path_factory.mktemp("data" ) / "dataset.abc"
							with open(UpperCamelCase_						,  "w" ) as f:
														for item in data:
																					f.write(item + "\n" )
							return path







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		tmp_path_factory.mktemp("data" ) / "dataset.text.zip"
							with zipfile.ZipFile(UpperCamelCase_						,  "w" ) as f:
														f.write(UpperCamelCase_						,  arcname=os.path.basename(UpperCamelCase_ ) )
														f.write(UpperCamelCase_						,  arcname=os.path.basename(UpperCamelCase_ ) )
							return path







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		tmp_path_factory.mktemp("data" ) / "dataset_with_dir.text.zip"
							with zipfile.ZipFile(UpperCamelCase_						,  "w" ) as f:
														f.write(UpperCamelCase_						,  arcname=os.path.join("main_dir"						,  os.path.basename(UpperCamelCase_ ) ) )
														f.write(UpperCamelCase_						,  arcname=os.path.join("main_dir"						,  os.path.basename(UpperCamelCase_ ) ) )
							return path







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		tmp_path_factory.mktemp("data" ) / "dataset.ext.zip"
							with zipfile.ZipFile(UpperCamelCase_						,  "w" ) as f:
														f.write(UpperCamelCase_						,  arcname=os.path.basename("unsupported.ext" ) )
														f.write(UpperCamelCase_						,  arcname=os.path.basename("unsupported_2.ext" ) )
							return path







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_ ):
							A_			    =		"\n".join(["First", "Second\u2029with Unicode new line", "Third"] )
							A_			    =		str(tmp_path_factory.mktemp("data" ) / "dataset_with_unicode_new_lines.txt" )
							with open(UpperCamelCase_						,  "w"						,  encoding="utf-8" ) as f:
														f.write(UpperCamelCase_ )
							return path







@pytest.fixture(scope="session" )
def       a_				(	):
							return os.path.join("tests"						,  "features"						,  "data"						,  "test_image_rgb.jpg" )







@pytest.fixture(scope="session" )
def       a_				(	):
							return os.path.join("tests"						,  "features"						,  "data"						,  "test_audio_44100.wav" )







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		tmp_path_factory.mktemp("data" ) / "dataset.img.zip"
							with zipfile.ZipFile(UpperCamelCase_						,  "w" ) as f:
														f.write(UpperCamelCase_						,  arcname=os.path.basename(UpperCamelCase_ ) )
														f.write(UpperCamelCase_						,  arcname=os.path.basename(UpperCamelCase_ ).replace(".jpg"						,  "2.jpg" ) )
							return path







@pytest.fixture(scope="session" )
def       a_				(	UpperCamelCase_ ):
							A_			    =		tmp_path_factory.mktemp("data_dir" )

							(data_dir / "subdir").mkdir()
							with open(data_dir / "subdir" / "train.txt"						,  "w" ) as f:
														f.write("foo\n" * 1_0 )
							with open(data_dir / "subdir" / "test.txt"						,  "w" ) as f:
														f.write("bar\n" * 1_0 )
							# hidden file
							with open(data_dir / "subdir" / ".test.txt"						,  "w" ) as f:
														f.write("bar\n" * 1_0 )

							# hidden directory
							(data_dir / ".subdir").mkdir()
							with open(data_dir / ".subdir" / "train.txt"						,  "w" ) as f:
														f.write("foo\n" * 1_0 )
							with open(data_dir / ".subdir" / "test.txt"						,  "w" ) as f:
														f.write("bar\n" * 1_0 )

							return data_dir









'''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()
__SCREAMING_SNAKE_CASE					:				List[Any]									=		logging.get_logger(__name__)







def       a_				(	UpperCamelCase_ ):
							A_			    =		MobileNetVaConfig(layer_norm_eps=0.001 )

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

							A_			    =		re.match(R"^mobilenet_v1_([^_]*)_([^_]*)$"						,  UpperCamelCase_ )
							if matches:
														A_			    =		float(matches[1] )
														A_			    =		int(matches[2] )

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

							return config







def       a_				(	):
							A_			    =		"http://images.cocodataset.org/val2017/000000039769.jpg"
							A_			    =		Image.open(requests.get(UpperCamelCase_						,  stream=UpperCamelCase_ ).raw )
							return im







@torch.no_grad()
def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_=False ):
							A_			    =		get_mobilenet_va_config(UpperCamelCase_ )

							# Load 🤗 model
							A_			    =		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
							A_			    =		MobileNetVaImageProcessor(
							    crop_size={"width": config.image_size, "height": config.image_size}						,  size={"shortest_edge": config.image_size + 3_2}						,  )
							A_			    =		image_processor(images=prepare_img()						,  return_tensors="pt" )
							A_			    =		model(**UpperCamelCase_ )
							A_			    =		outputs.logits

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

							if model_name == "mobilenet_v1_1.0_224":
														A_			    =		torch.tensor([-4.1739, -1.1233, 3.1205] )
							elif model_name == "mobilenet_v1_0.75_192":
														A_			    =		torch.tensor([-3.9440, -2.3141, -0.3333] )
							else:
														A_			    =		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..." )
														A_			    =		"google/" + model_name
														image_processor.push_to_hub(UpperCamelCase_ )
														model.push_to_hub(UpperCamelCase_ )


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE					:				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.'''
							)

							__SCREAMING_SNAKE_CASE					:				Dict									=		parser.parse_args()
							convert_movilevit_checkpoint(
							    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
							)





