
import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	return EnvironmentCommand()






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	return EnvironmentCommand(args.accelerate_config_file							)







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	@staticmethod
	def 	SCREAMING_SNAKE_CASE__						(       snake_case       ):
		lowercase       =    parser.add_parser('env'       )
		download_parser.set_defaults(func=snake_case       )
		download_parser.add_argument(
		    '--accelerate-config_file'		,    default=snake_case		,    help='The accelerate config file to use for the default values in the launching script.'		,    )
		download_parser.set_defaults(func=snake_case       )





	def __init__(       self		,    snake_case		,    *snake_case       ):
		lowercase       =    accelerate_config_file





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    'not installed'
		if is_safetensors_available():
			import safetensors

			lowercase       =    safetensors.__version__
		elif importlib.util.find_spec('safetensors'       ) is not None:
			import safetensors

			lowercase       =    F'''{safetensors.__version__} but is ignored because of PyTorch version too old.'''

		lowercase       =    'not installed'
		lowercase       =    lowercase       =    'not found'
		if is_accelerate_available():
			import accelerate
			from accelerate.commands.config import default_config_file, load_config_from_file

			lowercase       =    accelerate.__version__
			# Get the default from the config file.
			if self._accelerate_config_file is not None or os.path.isfile(snake_case       ):
				lowercase       =    load_config_from_file(self._accelerate_config_file       ).to_dict()

			lowercase       =    (
			    '\n'.join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]       )
			    if isinstance(snake_case		,    snake_case       )
			    else F'''\t{accelerate_config}'''
			)

		lowercase       =    'not installed'
		lowercase       =    'NA'
		if is_torch_available():
			import torch

			lowercase       =    torch.__version__
			lowercase       =    torch.cuda.is_available()

		lowercase       =    'not installed'
		lowercase       =    'NA'
		if is_tf_available():
			import tensorflow as tf

			lowercase       =    tf.__version__
			try:
				# deprecated in v2.1
				lowercase       =    tf.test.is_gpu_available()
			except AttributeError:
				# returns list of devices, convert to bool
				lowercase       =    bool(tf.config.list_physical_devices('GPU'       )       )

		lowercase       =    'not installed'
		lowercase       =    'not installed'
		lowercase       =    'not installed'
		lowercase       =    'NA'
		if is_flax_available():
			import flax
			import jax
			import jaxlib

			lowercase       =    flax.__version__
			lowercase       =    jax.__version__
			lowercase       =    jaxlib.__version__
			lowercase       =    jax.lib.xla_bridge.get_backend().platform

		lowercase       =    {
		    '`transformers` version': version,
		    'Platform': platform.platform(),
		    'Python version': platform.python_version(),
		    'Huggingface_hub version': huggingface_hub.__version__,
		    'Safetensors version': F'''{safetensors_version}''',
		    'Accelerate version': F'''{accelerate_version}''',
		    'Accelerate config': F'''{accelerate_config_str}''',
		    'PyTorch version (GPU?)': F'''{pt_version} ({pt_cuda_available})''',
		    'Tensorflow version (GPU?)': F'''{tf_version} ({tf_cuda_available})''',
		    'Flax version (CPU?/GPU?/TPU?)': F'''{flax_version} ({jax_backend})''',
		    'Jax version': F'''{jax_version}''',
		    'JaxLib version': F'''{jaxlib_version}''',
		    'Using GPU in script?': '<fill in>',
		    'Using distributed or parallel set-up in script?': '<fill in>',
		}

		print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'       )
		print(self.format_dict(snake_case       )       )

		return info





	@staticmethod
	def 	SCREAMING_SNAKE_CASE__						(       snake_case       ):
		return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()]       ) + "\n"



import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
	from PIL import Image

	from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor







@require_vision
class        A_   (			unittest.TestCase   ):




	'''simple docstring'''


	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    tempfile.mkdtemp()

		lowercase       =    [
		    '[UNK]',
		    '[CLS]',
		    '[SEP]',
		    '[PAD]',
		    '[MASK]',
		    '的',
		    '价',
		    '格',
		    '是',
		    '15',
		    '便',
		    'alex',
		    '##andra',
		    '，',
		    '。',
		    '-',
		    't',
		    'shirt',
		]
		lowercase       =    os.path.join(self.tmpdirname		,    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]       )       )

		lowercase       =    {
		    'do_resize': True,
		    'size': {'height': 224, 'width': 224},
		    'do_center_crop': True,
		    'crop_size': {'height': 18, 'width': 18},
		    'do_normalize': True,
		    'image_mean': [0.48_145_466, 0.4_578_275, 0.40_821_073],
		    'image_std': [0.26_862_954, 0.26_130_258, 0.27_577_711],
		    'do_convert_rgb': True,
		}
		lowercase       =    os.path.join(self.tmpdirname		,    snake_case       )
		with open(self.image_processor_file		,    'w'		,    encoding='utf-8'       ) as fp:
			json.dump(snake_case		,    snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    **snake_case       ):
		return BertTokenizer.from_pretrained(self.tmpdirname		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    **snake_case       ):
		return BertTokenizerFast.from_pretrained(self.tmpdirname		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    **snake_case       ):
		return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname		,    **snake_case       )





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





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    [np.random.randint(255		,    size=(3, 30, 400)		,    dtype=np.uinta       )]

		lowercase       =    [Image.fromarray(np.moveaxis(snake_case		,    0		,    -1       )       ) for x in image_inputs]

		return image_inputs





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.get_tokenizer()
		lowercase       =    self.get_rust_tokenizer()
		lowercase       =    self.get_image_processor()

		lowercase       =    ChineseCLIPProcessor(tokenizer=snake_case		,    image_processor=snake_case       )
		processor_slow.save_pretrained(self.tmpdirname       )
		lowercase       =    ChineseCLIPProcessor.from_pretrained(self.tmpdirname		,    use_fast=snake_case       )

		lowercase       =    ChineseCLIPProcessor(tokenizer=snake_case		,    image_processor=snake_case       )
		processor_fast.save_pretrained(self.tmpdirname       )
		lowercase       =    ChineseCLIPProcessor.from_pretrained(self.tmpdirname       )

		self.assertEqual(processor_slow.tokenizer.get_vocab()		,    tokenizer_slow.get_vocab()       )
		self.assertEqual(processor_fast.tokenizer.get_vocab()		,    tokenizer_fast.get_vocab()       )
		self.assertEqual(tokenizer_slow.get_vocab()		,    tokenizer_fast.get_vocab()       )
		self.assertIsInstance(processor_slow.tokenizer		,    snake_case       )
		self.assertIsInstance(processor_fast.tokenizer		,    snake_case       )

		self.assertEqual(processor_slow.image_processor.to_json_string()		,    image_processor.to_json_string()       )
		self.assertEqual(processor_fast.image_processor.to_json_string()		,    image_processor.to_json_string()       )
		self.assertIsInstance(processor_slow.image_processor		,    snake_case       )
		self.assertIsInstance(processor_fast.image_processor		,    snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    ChineseCLIPProcessor(tokenizer=self.get_tokenizer()		,    image_processor=self.get_image_processor()       )
		processor.save_pretrained(self.tmpdirname       )

		lowercase       =    self.get_tokenizer(cls_token='(CLS)'		,    sep_token='(SEP)'       )
		lowercase       =    self.get_image_processor(do_normalize=snake_case       )

		lowercase       =    ChineseCLIPProcessor.from_pretrained(
		    self.tmpdirname		,    cls_token='(CLS)'		,    sep_token='(SEP)'		,    do_normalize=snake_case       )

		self.assertEqual(processor.tokenizer.get_vocab()		,    tokenizer_add_kwargs.get_vocab()       )
		self.assertIsInstance(processor.tokenizer		,    snake_case       )

		self.assertEqual(processor.image_processor.to_json_string()		,    image_processor_add_kwargs.to_json_string()       )
		self.assertIsInstance(processor.image_processor		,    snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.get_image_processor()
		lowercase       =    self.get_tokenizer()

		lowercase       =    ChineseCLIPProcessor(tokenizer=snake_case		,    image_processor=snake_case       )

		lowercase       =    self.prepare_image_inputs()

		lowercase       =    image_processor(snake_case		,    return_tensors='np'       )
		lowercase       =    processor(images=snake_case		,    return_tensors='np'       )

		for key in input_feat_extract.keys():
			self.assertAlmostEqual(input_feat_extract[key].sum()		,    input_processor[key].sum()		,    delta=1E-2       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.get_image_processor()
		lowercase       =    self.get_tokenizer()

		lowercase       =    ChineseCLIPProcessor(tokenizer=snake_case		,    image_processor=snake_case       )

		lowercase       =    'Alexandra，T-shirt的价格是15便士。'

		lowercase       =    processor(text=snake_case       )

		lowercase       =    tokenizer(snake_case       )

		for key in encoded_tok.keys():
			self.assertListEqual(encoded_tok[key]		,    encoded_processor[key]       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.get_image_processor()
		lowercase       =    self.get_tokenizer()

		lowercase       =    ChineseCLIPProcessor(tokenizer=snake_case		,    image_processor=snake_case       )

		lowercase       =    'Alexandra，T-shirt的价格是15便士。'
		lowercase       =    self.prepare_image_inputs()

		lowercase       =    processor(text=snake_case		,    images=snake_case       )

		self.assertListEqual(list(inputs.keys()       )		,    ['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']       )

		# test if it raises when no input is passed
		with pytest.raises(snake_case       ):
			processor()





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.get_image_processor()
		lowercase       =    self.get_tokenizer()

		lowercase       =    ChineseCLIPProcessor(tokenizer=snake_case		,    image_processor=snake_case       )

		lowercase       =    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

		lowercase       =    processor.batch_decode(snake_case       )
		lowercase       =    tokenizer.batch_decode(snake_case       )

		self.assertListEqual(snake_case		,    snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.get_image_processor()
		lowercase       =    self.get_tokenizer()

		lowercase       =    ChineseCLIPProcessor(tokenizer=snake_case		,    image_processor=snake_case       )

		lowercase       =    'Alexandra，T-shirt的价格是15便士。'
		lowercase       =    self.prepare_image_inputs()

		lowercase       =    processor(text=snake_case		,    images=snake_case       )

		self.assertListEqual(list(inputs.keys()       )		,    processor.model_input_names       )

