
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        lowerCAmelCase__      (   unittest.TestCase	):

						def  A_				(			self				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	tempfile.mkdtemp()

								_UpperCamelCase						    =	[
								    """[UNK]""",
								    """[CLS]""",
								    """[SEP]""",
								    """[PAD]""",
								    """[MASK]""",
								    """的""",
								    """价""",
								    """格""",
								    """是""",
								    """15""",
								    """便""",
								    """alex""",
								    """##andra""",
								    """，""",
								    """。""",
								    """-""",
								    """t""",
								    """shirt""",
								]
								_UpperCamelCase						    =	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]				)				)

								_UpperCamelCase						    =	{
								    """do_resize""": True,
								    """size""": {"""height""": 2_24, """width""": 2_24},
								    """do_center_crop""": True,
								    """crop_size""": {"""height""": 18, """width""": 18},
								    """do_normalize""": True,
								    """image_mean""": [0.4814_5466, 0.457_8275, 0.4082_1073],
								    """image_std""": [0.2686_2954, 0.2613_0258, 0.2757_7711],
								    """do_convert_rgb""": True,
								}
								_UpperCamelCase						    =	os.path.join(self.tmpdirname       ,							a				)
								with open(self.image_processor_file       ,							"""w"""       ,							encoding="""utf-8"""				) as fp:
										json.dump(a       ,							a				)

						def  A_				(			self       ,							**a				)					->      Dict:


								'''simple docstring'''

								return BertTokenizer.from_pretrained(self.tmpdirname       ,							**a				)

						def  A_				(			self       ,							**a				)					->      Union[str, Any]:


								'''simple docstring'''

								return BertTokenizerFast.from_pretrained(self.tmpdirname       ,							**a				)

						def  A_				(			self       ,							**a				)					->      Any:


								'''simple docstring'''

								return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname       ,							**a				)

						def  A_				(			self				)					->      List[str]:


								'''simple docstring'''

								shutil.rmtree(self.tmpdirname				)

						def  A_				(			self				)					->      Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	[np.random.randint(2_55       ,							size=(3, 30, 4_00)       ,							dtype=np.uinta				)]

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

								return image_inputs

						def  A_				(			self				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	self.get_tokenizer()
								_UpperCamelCase						    =	self.get_rust_tokenizer()
								_UpperCamelCase						    =	self.get_image_processor()

								_UpperCamelCase						    =	ChineseCLIPProcessor(tokenizer=a       ,							image_processor=a				)
								processor_slow.save_pretrained(self.tmpdirname				)
								_UpperCamelCase						    =	ChineseCLIPProcessor.from_pretrained(self.tmpdirname       ,							use_fast=a				)

								_UpperCamelCase						    =	ChineseCLIPProcessor(tokenizer=a       ,							image_processor=a				)
								processor_fast.save_pretrained(self.tmpdirname				)
								_UpperCamelCase						    =	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       ,							a				)
								self.assertIsInstance(processor_fast.tokenizer       ,							a				)

								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       ,							a				)
								self.assertIsInstance(processor_fast.image_processor       ,							a				)

						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								_UpperCamelCase						    =	ChineseCLIPProcessor(tokenizer=self.get_tokenizer()       ,							image_processor=self.get_image_processor()				)
								processor.save_pretrained(self.tmpdirname				)

								_UpperCamelCase						    =	self.get_tokenizer(cls_token="""(CLS)"""       ,							sep_token="""(SEP)"""				)
								_UpperCamelCase						    =	self.get_image_processor(do_normalize=a				)

								_UpperCamelCase						    =	ChineseCLIPProcessor.from_pretrained(
								    self.tmpdirname       ,							cls_token="""(CLS)"""       ,							sep_token="""(SEP)"""       ,							do_normalize=a				)

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

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

						def  A_				(			self				)					->      Union[str, Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	self.get_image_processor()
								_UpperCamelCase						    =	self.get_tokenizer()

								_UpperCamelCase						    =	ChineseCLIPProcessor(tokenizer=a       ,							image_processor=a				)

								_UpperCamelCase						    =	self.prepare_image_inputs()

								_UpperCamelCase						    =	image_processor(a       ,							return_tensors="""np"""				)
								_UpperCamelCase						    =	processor(images=a       ,							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  A_				(			self				)					->      Any:


								'''simple docstring'''

								_UpperCamelCase						    =	self.get_image_processor()
								_UpperCamelCase						    =	self.get_tokenizer()

								_UpperCamelCase						    =	ChineseCLIPProcessor(tokenizer=a       ,							image_processor=a				)

								_UpperCamelCase						    =	"""Alexandra，T-shirt的价格是15便士。"""

								_UpperCamelCase						    =	processor(text=a				)

								_UpperCamelCase						    =	tokenizer(a				)

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

						def  A_				(			self				)					->      Any:


								'''simple docstring'''

								_UpperCamelCase						    =	self.get_image_processor()
								_UpperCamelCase						    =	self.get_tokenizer()

								_UpperCamelCase						    =	ChineseCLIPProcessor(tokenizer=a       ,							image_processor=a				)

								_UpperCamelCase						    =	"""Alexandra，T-shirt的价格是15便士。"""
								_UpperCamelCase						    =	self.prepare_image_inputs()

								_UpperCamelCase						    =	processor(text=a       ,							images=a				)

								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(a				):
										processor()

						def  A_				(			self				)					->      Optional[int]:


								'''simple docstring'''

								_UpperCamelCase						    =	self.get_image_processor()
								_UpperCamelCase						    =	self.get_tokenizer()

								_UpperCamelCase						    =	ChineseCLIPProcessor(tokenizer=a       ,							image_processor=a				)

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

								_UpperCamelCase						    =	processor.batch_decode(a				)
								_UpperCamelCase						    =	tokenizer.batch_decode(a				)

								self.assertListEqual(a       ,							a				)







						def  A_				(			self				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	self.get_image_processor()
								_UpperCamelCase						    =	self.get_tokenizer()

								_UpperCamelCase						    =	ChineseCLIPProcessor(tokenizer=a       ,							image_processor=a				)

								_UpperCamelCase						    =	"""Alexandra，T-shirt的价格是15便士。"""
								_UpperCamelCase						    =	self.prepare_image_inputs()

								_UpperCamelCase						    =	processor(text=a       ,							images=a				)

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





import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand


def 							__A(lowerCAmelCase )		->				Optional[int]:

		"""simple docstring"""






		return EnvironmentCommand()







class        lowerCAmelCase__      (   __lowercase	):

						@staticmethod
						def  A_				(			a				)					->      List[str]:


								'''simple docstring'''

								_UpperCamelCase						    =	parser.add_parser("""env"""				)
								download_parser.set_defaults(func=a				)

						def  A_				(			self				)					->      List[str]:


								'''simple docstring'''

								_UpperCamelCase						    =	huggingface_hub.__version__

								_UpperCamelCase						    =	"""not installed"""
								_UpperCamelCase						    =	"""NA"""
								if is_torch_available():
										import torch

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

								_UpperCamelCase						    =	"""not installed"""
								if is_transformers_available():
										import transformers

										_UpperCamelCase						    =	transformers.__version__

								_UpperCamelCase						    =	"""not installed"""
								if is_accelerate_available():
										import accelerate

										_UpperCamelCase						    =	accelerate.__version__

								_UpperCamelCase						    =	"""not installed"""
								if is_xformers_available():
										import xformers

										_UpperCamelCase						    =	xformers.__version__

								_UpperCamelCase						    =	{
								    """`diffusers` version""": version,
								    """Platform""": platform.platform(),
								    """Python version""": platform.python_version(),
								    """PyTorch version (GPU?)""": F'{pt_version} ({pt_cuda_available})',
								    """Huggingface_hub version""": hub_version,
								    """Transformers version""": transformers_version,
								    """Accelerate version""": accelerate_version,
								    """xFormers version""": xformers_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(a				)				)

								return info







						@staticmethod
						def  A_				(			a				)					->      Dict:


								'''simple docstring'''

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



