


'''simple docstring'''



import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
      from PIL import Image

      from transformers import CLIPImageProcessor, CLIPProcessor





@require_vision
class   UpperCAmelCase_    (unittest.TestCase    ):





    """simple docstring"""






    def  lowercase_							(				self      )							->	Optional[int]:
         __lowerCamelCase  :      Any														=						tempfile.mkdtemp()

         # fmt: off
         __lowerCamelCase  :      Optional[Any]														=						['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
         # fmt: on
         __lowerCamelCase  :      str														=						dict(zip(SCREAMING_SNAKE_CASE_     ,	range(len(SCREAMING_SNAKE_CASE_      )      )      )      )
         __lowerCamelCase  :      Tuple														=						['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
         __lowerCamelCase  :      str														=						{'unk_token': '<unk>'}

         __lowerCamelCase  :      Any														=						os.path.join(self.tmpdirname     ,	VOCAB_FILES_NAMES['vocab_file']      )
         __lowerCamelCase  :      Union[str, Any]														=						os.path.join(self.tmpdirname     ,	VOCAB_FILES_NAMES['merges_file']      )
         with open(self.vocab_file     ,	'w'     ,	encoding='utf-8'      ) as fp:
              fp.write(json.dumps(SCREAMING_SNAKE_CASE_      ) + '\n'      )
         with open(self.merges_file     ,	'w'     ,	encoding='utf-8'      ) as fp:
              fp.write('\n'.join(SCREAMING_SNAKE_CASE_      )      )

         __lowerCamelCase  :      Union[str, Any]														=						{
             'do_resize': True,
             'size': 20,
             'do_center_crop': True,
             'crop_size': 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],
         }
         __lowerCamelCase  :      Optional[Any]														=						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  lowercase_							(				self     ,	**SCREAMING_SNAKE_CASE_      )							->	Tuple:
         return CLIPTokenizer.from_pretrained(self.tmpdirname     ,	**SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	**SCREAMING_SNAKE_CASE_      )							->	Union[str, Any]:
         return CLIPTokenizerFast.from_pretrained(self.tmpdirname     ,	**SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	**SCREAMING_SNAKE_CASE_      )							->	Union[str, Any]:
         return CLIPImageProcessor.from_pretrained(self.tmpdirname     ,	**SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	int:
         shutil.rmtree(self.tmpdirname      )







    def  lowercase_							(				self      )							->	Any:
         __lowerCamelCase  :      Optional[Any]														=						[np.random.randint(2_55     ,	size=(3, 30, 4_00)     ,	dtype=np.uinta      )]

         __lowerCamelCase  :      Optional[Any]														=						[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_     ,	0     ,	-1      )      ) for x in image_inputs]

         return image_inputs







    def  lowercase_							(				self      )							->	Tuple:
         __lowerCamelCase  :      str														=						self.get_tokenizer()
         __lowerCamelCase  :      Any														=						self.get_rust_tokenizer()
         __lowerCamelCase  :      int														=						self.get_image_processor()

         __lowerCamelCase  :      Tuple														=						CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE_     ,	image_processor=SCREAMING_SNAKE_CASE_      )
         processor_slow.save_pretrained(self.tmpdirname      )
         __lowerCamelCase  :      Optional[Any]														=						CLIPProcessor.from_pretrained(self.tmpdirname     ,	use_fast=SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Optional[Any]														=						CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE_     ,	image_processor=SCREAMING_SNAKE_CASE_      )
         processor_fast.save_pretrained(self.tmpdirname      )
         __lowerCamelCase  :      Dict														=						CLIPProcessor.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  lowercase_							(				self      )							->	Dict:
         __lowerCamelCase  :      Tuple														=						CLIPProcessor(tokenizer=self.get_tokenizer()     ,	image_processor=self.get_image_processor()      )
         processor.save_pretrained(self.tmpdirname      )

         __lowerCamelCase  :      Any														=						self.get_tokenizer(bos_token='(BOS)'     ,	eos_token='(EOS)'      )
         __lowerCamelCase  :      Any														=						self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE_     ,	padding_value=1.0      )

         __lowerCamelCase  :      Optional[Any]														=						CLIPProcessor.from_pretrained(
             self.tmpdirname     ,	bos_token='(BOS)'     ,	eos_token='(EOS)'     ,	do_normalize=SCREAMING_SNAKE_CASE_     ,	padding_value=1.0      )

         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  lowercase_							(				self      )							->	str:
         __lowerCamelCase  :      Union[str, Any]														=						self.get_image_processor()
         __lowerCamelCase  :      Dict														=						self.get_tokenizer()

         __lowerCamelCase  :      Optional[Any]														=						CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE_     ,	image_processor=SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      int														=						self.prepare_image_inputs()

         __lowerCamelCase  :      List[Any]														=						image_processor(SCREAMING_SNAKE_CASE_     ,	return_tensors='np'      )
         __lowerCamelCase  :      Tuple														=						processor(images=SCREAMING_SNAKE_CASE_     ,	return_tensors='np'      )

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







    def  lowercase_							(				self      )							->	Any:
         __lowerCamelCase  :      Dict														=						self.get_image_processor()
         __lowerCamelCase  :      Any														=						self.get_tokenizer()

         __lowerCamelCase  :      str														=						CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE_     ,	image_processor=SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Optional[int]														=						'lower newer'

         __lowerCamelCase  :      Optional[int]														=						processor(text=SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      int														=						tokenizer(SCREAMING_SNAKE_CASE_      )

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







    def  lowercase_							(				self      )							->	List[Any]:
         __lowerCamelCase  :      Dict														=						self.get_image_processor()
         __lowerCamelCase  :      Optional[int]														=						self.get_tokenizer()

         __lowerCamelCase  :      List[Any]														=						CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE_     ,	image_processor=SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Dict														=						'lower newer'
         __lowerCamelCase  :      Optional[Any]														=						self.prepare_image_inputs()

         __lowerCamelCase  :      Any														=						processor(text=SCREAMING_SNAKE_CASE_     ,	images=SCREAMING_SNAKE_CASE_      )

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

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







    def  lowercase_							(				self      )							->	int:
         __lowerCamelCase  :      Tuple														=						self.get_image_processor()
         __lowerCamelCase  :      Any														=						self.get_tokenizer()

         __lowerCamelCase  :      Tuple														=						CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE_     ,	image_processor=SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Optional[Any]														=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         __lowerCamelCase  :      Tuple														=						processor.batch_decode(SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      List[Any]														=						tokenizer.batch_decode(SCREAMING_SNAKE_CASE_      )

         self.assertListEqual(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	str:
         __lowerCamelCase  :      int														=						self.get_image_processor()
         __lowerCamelCase  :      Union[str, Any]														=						self.get_tokenizer()

         __lowerCamelCase  :      int														=						CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE_     ,	image_processor=SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Tuple														=						'lower newer'
         __lowerCamelCase  :      List[str]														=						self.prepare_image_inputs()

         __lowerCamelCase  :      List[str]														=						processor(text=SCREAMING_SNAKE_CASE_     ,	images=SCREAMING_SNAKE_CASE_      )

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











'''simple docstring'''
import unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
					import jax

					from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
					from transformers.models.bert.modeling_flax_bert import FlaxBertModel
					from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel






@require_flax
class 				lowercase__      (				unittest.TestCase  ):





	@slow
	def       UpperCamelCase_    (					self   :							Union[str, Any]					):







							'''simple docstring'''
							for model_name in ["bert-base-cased", "bert-large-uncased"]:
													with self.subTest(lowerCamelCase__					):
																			_UpperCamelCase   :						Optional[int]												=  AutoConfig.from_pretrained(lowerCamelCase__					)
																			self.assertIsNotNone(lowerCamelCase__					)
																			self.assertIsInstance(lowerCamelCase__					,lowerCamelCase__					)

																			_UpperCamelCase   :						Dict												=  FlaxAutoModel.from_pretrained(lowerCamelCase__					)
																			self.assertIsNotNone(lowerCamelCase__					)
																			self.assertIsInstance(lowerCamelCase__					,lowerCamelCase__					)





	@slow
	def       UpperCamelCase_    (					self   :							List[Any]					):







							'''simple docstring'''
							for model_name in ["roberta-base", "roberta-large"]:
													with self.subTest(lowerCamelCase__					):
																			_UpperCamelCase   :						Any												=  AutoConfig.from_pretrained(lowerCamelCase__					)
																			self.assertIsNotNone(lowerCamelCase__					)
																			self.assertIsInstance(lowerCamelCase__					,lowerCamelCase__					)

																			_UpperCamelCase   :						Any												=  FlaxAutoModel.from_pretrained(lowerCamelCase__					)
																			self.assertIsNotNone(lowerCamelCase__					)
																			self.assertIsInstance(lowerCamelCase__					,lowerCamelCase__					)





	@slow
	def       UpperCamelCase_    (					self   :							Union[str, Any]					):







							'''simple docstring'''
							for model_name in ["bert-base-cased", "bert-large-uncased"]:
													_UpperCamelCase   :						Optional[int]												=  AutoTokenizer.from_pretrained(lowerCamelCase__					)
													_UpperCamelCase   :						List[Any]												=  FlaxBertModel.from_pretrained(lowerCamelCase__					)
													_UpperCamelCase   :						Optional[int]												=  tokenizer('Do you support jax jitted function?'					,return_tensors=TensorType.JAX					)

													@jax.jit
													def eval(**lowerCamelCase__   :							Union[str, Any]					):
																			return model(**lowerCamelCase__					)

													eval(**lowerCamelCase__					).block_until_ready()





	@slow
	def       UpperCamelCase_    (					self   :							Dict					):







							'''simple docstring'''
							for model_name in ["roberta-base", "roberta-large"]:
													_UpperCamelCase   :						Optional[int]												=  AutoTokenizer.from_pretrained(lowerCamelCase__					)
													_UpperCamelCase   :						Tuple												=  FlaxRobertaModel.from_pretrained(lowerCamelCase__					)
													_UpperCamelCase   :						Optional[Any]												=  tokenizer('Do you support jax jitted function?'					,return_tensors=TensorType.JAX					)

													@jax.jit
													def eval(**lowerCamelCase__   :							Union[str, Any]					):
																			return model(**lowerCamelCase__					)

													eval(**lowerCamelCase__					).block_until_ready()





	def       UpperCamelCase_    (					self   :							Union[str, Any]					):







							'''simple docstring'''
							with self.assertRaisesRegex(
							    lowerCamelCase__					,'bert-base is not a local folder and is not a valid model identifier'					):
													_UpperCamelCase   :						int												=  FlaxAutoModel.from_pretrained('bert-base'					)





	def       UpperCamelCase_    (					self   :							str					):







							'''simple docstring'''
							with self.assertRaisesRegex(
							    lowerCamelCase__					,R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'					):
													_UpperCamelCase   :						Tuple												=  FlaxAutoModel.from_pretrained(lowerCamelCase__					,revision='aaaaaa'					)





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							with self.assertRaisesRegex(
							    lowerCamelCase__					,'hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack'					,):
													_UpperCamelCase   :						List[Any]												=  FlaxAutoModel.from_pretrained('hf-internal-testing/config-no-model'					)





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							with self.assertRaisesRegex(lowerCamelCase__					,'Use `from_pt=True` to load this model'					):
													_UpperCamelCase   :						Tuple												=  FlaxAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only'					)


