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 	snake_case__ (							unittest.TestCase				):
	def       __magic_name__    (    self			)							->				List[Any]:
							__magic_name__      :       str       						= tempfile.mkdtemp()

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

							__magic_name__      :       Optional[int]       						= {
							    """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""": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
							    """image_std""": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
							    """do_convert_rgb""": True,
							}
							__magic_name__      :       str       						= os.path.join(self.tmpdirname      ,       __SCREAMING_SNAKE_CASE			)
							with open(self.image_processor_file      ,       """w"""      ,       encoding="""utf-8"""			) as fp:
													json.dump(__SCREAMING_SNAKE_CASE      ,       __SCREAMING_SNAKE_CASE			)
	def       __magic_name__    (    self      ,       **lowerCAmelCase__			)							->				Any:
							return BertTokenizer.from_pretrained(self.tmpdirname      ,       **__SCREAMING_SNAKE_CASE			)
	def       __magic_name__    (    self      ,       **lowerCAmelCase__			)							->				Any:
							return BertTokenizerFast.from_pretrained(self.tmpdirname      ,       **__SCREAMING_SNAKE_CASE			)
	def       __magic_name__    (    self      ,       **lowerCAmelCase__			)							->				str:
							return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname      ,       **__SCREAMING_SNAKE_CASE			)
	def       __magic_name__    (    self			)							->				Any:
							shutil.rmtree(self.tmpdirname			)
	def       __magic_name__    (    self			)							->				int:
							__magic_name__      :       Optional[int]       						= [np.random.randint(2_55      ,       size=(3, 30, 4_00)      ,       dtype=np.uinta			)]

							__magic_name__      :       Optional[int]       						= [Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE      ,       0      ,       -1			)			) for x in image_inputs]

							return image_inputs
	def       __magic_name__    (    self			)							->				Tuple:
							__magic_name__      :       Dict       						= self.get_tokenizer()
							__magic_name__      :       List[str]       						= self.get_rust_tokenizer()
							__magic_name__      :       Tuple       						= self.get_image_processor()

							__magic_name__      :       List[str]       						= ChineseCLIPProcessor(tokenizer=__SCREAMING_SNAKE_CASE      ,       image_processor=__SCREAMING_SNAKE_CASE			)
							processor_slow.save_pretrained(self.tmpdirname			)
							__magic_name__      :       Dict       						= ChineseCLIPProcessor.from_pretrained(self.tmpdirname      ,       use_fast=__SCREAMING_SNAKE_CASE			)

							__magic_name__      :       str       						= ChineseCLIPProcessor(tokenizer=__SCREAMING_SNAKE_CASE      ,       image_processor=__SCREAMING_SNAKE_CASE			)
							processor_fast.save_pretrained(self.tmpdirname			)
							__magic_name__      :       List[Any]       						= 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      ,       __SCREAMING_SNAKE_CASE			)
							self.assertIsInstance(processor_fast.tokenizer      ,       __SCREAMING_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      ,       __SCREAMING_SNAKE_CASE			)
							self.assertIsInstance(processor_fast.image_processor      ,       __SCREAMING_SNAKE_CASE			)
	def       __magic_name__    (    self			)							->				List[str]:
							__magic_name__      :       str       						= ChineseCLIPProcessor(tokenizer=self.get_tokenizer()      ,       image_processor=self.get_image_processor()			)
							processor.save_pretrained(self.tmpdirname			)

							__magic_name__      :       Optional[int]       						= self.get_tokenizer(cls_token="""(CLS)"""      ,       sep_token="""(SEP)"""			)
							__magic_name__      :       Any       						= self.get_image_processor(do_normalize=__SCREAMING_SNAKE_CASE			)

							__magic_name__      :       Optional[Any]       						= ChineseCLIPProcessor.from_pretrained(
							    self.tmpdirname      ,       cls_token="""(CLS)"""      ,       sep_token="""(SEP)"""      ,       do_normalize=__SCREAMING_SNAKE_CASE			)

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

							self.assertEqual(processor.image_processor.to_json_string()      ,       image_processor_add_kwargs.to_json_string()			)
							self.assertIsInstance(processor.image_processor      ,       __SCREAMING_SNAKE_CASE			)
	def       __magic_name__    (    self			)							->				Dict:
							__magic_name__      :       Union[str, Any]       						= self.get_image_processor()
							__magic_name__      :       List[str]       						= self.get_tokenizer()

							__magic_name__      :       Any       						= ChineseCLIPProcessor(tokenizer=__SCREAMING_SNAKE_CASE      ,       image_processor=__SCREAMING_SNAKE_CASE			)

							__magic_name__      :       Optional[Any]       						= self.prepare_image_inputs()

							__magic_name__      :       List[str]       						= image_processor(__SCREAMING_SNAKE_CASE      ,       return_tensors="""np"""			)
							__magic_name__      :       int       						= processor(images=__SCREAMING_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       __magic_name__    (    self			)							->				List[Any]:
							__magic_name__      :       Any       						= self.get_image_processor()
							__magic_name__      :       Dict       						= self.get_tokenizer()

							__magic_name__      :       List[Any]       						= ChineseCLIPProcessor(tokenizer=__SCREAMING_SNAKE_CASE      ,       image_processor=__SCREAMING_SNAKE_CASE			)

							__magic_name__      :       Union[str, Any]       						= """Alexandra，T-shirt的价格是15便士。"""

							__magic_name__      :       Any       						= processor(text=__SCREAMING_SNAKE_CASE			)

							__magic_name__      :       Dict       						= tokenizer(__SCREAMING_SNAKE_CASE			)

							for key in encoded_tok.keys():
													self.assertListEqual(encoded_tok[key]      ,       encoded_processor[key]			)
	def       __magic_name__    (    self			)							->				List[str]:
							__magic_name__      :       Union[str, Any]       						= self.get_image_processor()
							__magic_name__      :       Tuple       						= self.get_tokenizer()

							__magic_name__      :       Any       						= ChineseCLIPProcessor(tokenizer=__SCREAMING_SNAKE_CASE      ,       image_processor=__SCREAMING_SNAKE_CASE			)

							__magic_name__      :       List[Any]       						= """Alexandra，T-shirt的价格是15便士。"""
							__magic_name__      :       List[str]       						= self.prepare_image_inputs()

							__magic_name__      :       Optional[int]       						= processor(text=__SCREAMING_SNAKE_CASE      ,       images=__SCREAMING_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(__SCREAMING_SNAKE_CASE			):
													processor()
	def       __magic_name__    (    self			)							->				Tuple:
							__magic_name__      :       List[str]       						= self.get_image_processor()
							__magic_name__      :       Any       						= self.get_tokenizer()

							__magic_name__      :       Union[str, Any]       						= ChineseCLIPProcessor(tokenizer=__SCREAMING_SNAKE_CASE      ,       image_processor=__SCREAMING_SNAKE_CASE			)

							__magic_name__      :       int       						= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							__magic_name__      :       Dict       						= processor.batch_decode(__SCREAMING_SNAKE_CASE			)
							__magic_name__      :       Optional[int]       						= tokenizer.batch_decode(__SCREAMING_SNAKE_CASE			)

							self.assertListEqual(__SCREAMING_SNAKE_CASE      ,       __SCREAMING_SNAKE_CASE			)







	def       __magic_name__    (    self			)							->				int:
							__magic_name__      :       Tuple       						= self.get_image_processor()
							__magic_name__      :       List[str]       						= self.get_tokenizer()

							__magic_name__      :       str       						= ChineseCLIPProcessor(tokenizer=__SCREAMING_SNAKE_CASE      ,       image_processor=__SCREAMING_SNAKE_CASE			)

							__magic_name__      :       Optional[Any]       						= """Alexandra，T-shirt的价格是15便士。"""
							__magic_name__      :       List[str]       						= self.prepare_image_inputs()

							__magic_name__      :       List[Any]       						= processor(text=__SCREAMING_SNAKE_CASE      ,       images=__SCREAMING_SNAKE_CASE			)

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








import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
		import optax
		from flax.training.common_utils import onehot

		from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
		from transformers.models.ta.modeling_flax_ta import shift_tokens_right





@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 	snake_case__ (							unittest.TestCase				):
	@slow
	def       __magic_name__    (    self			)							->				Optional[Any]:
							__magic_name__      :       Any       						= FlaxMTaForConditionalGeneration.from_pretrained("""google/mt5-small"""			)
							__magic_name__      :       Dict       						= AutoTokenizer.from_pretrained("""google/mt5-small"""			)

							__magic_name__      :       Tuple       						= tokenizer("""Hello there"""      ,       return_tensors="""np"""			).input_ids
							__magic_name__      :       List[Any]       						= tokenizer("""Hi I am"""      ,       return_tensors="""np"""			).input_ids

							__magic_name__      :       Any       						= shift_tokens_right(lowerCAmelCase__      ,       model.config.pad_token_id      ,       model.config.decoder_start_token_id			)

							__magic_name__      :       List[Any]       						= model(lowerCAmelCase__      ,       decoder_input_ids=lowerCAmelCase__			).logits
							__magic_name__      :       Tuple       						= optax.softmax_cross_entropy(lowerCAmelCase__      ,       onehot(lowerCAmelCase__      ,       logits.shape[-1]			)			).mean()

							__magic_name__      :       List[Any]       						= -(labels.shape[-1] * loss.item())

							__magic_name__      :       List[Any]       						= -8_4.9_1_2_7
							self.assertTrue(abs(mtf_score - EXPECTED_SCORE			) < 1e-4			)
