
"""simple docstring"""




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  ):


				"""simple docstring"""



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   tempfile.mkdtemp()

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

											SCREAMING_SNAKE_CASE__						:       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""": [0.48145466, 0.4578275, 0.40821073],
											    """image_std""": [0.26862954, 0.26130258, 0.27577711],
											    """do_convert_rgb""": True,
											}
											SCREAMING_SNAKE_CASE__						:       Tuple           =   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   ,       **SCREAMING_SNAKE_CASE__    )					->		Any:



											"""simple docstring"""




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



				def 	__magic_name__    (self   ,       **SCREAMING_SNAKE_CASE__    )					->		int:



											"""simple docstring"""




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



				def 	__magic_name__    (self   ,       **SCREAMING_SNAKE_CASE__    )					->		int:



											"""simple docstring"""




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



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



											"""simple docstring"""




											shutil.rmtree(self.tmpdirname    )



				def 	__magic_name__    (self    )					->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[str]           =   [np.random.randint(2_55   ,       size=(3, 30, 4_00)   ,       dtype=np.uinta    )]

											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__   ,       0   ,       -1    )    ) for x in image_inputs]

											return image_inputs



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   self.get_tokenizer()
											SCREAMING_SNAKE_CASE__						:       Any           =   self.get_rust_tokenizer()
											SCREAMING_SNAKE_CASE__						:       List[str]           =   self.get_image_processor()

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__   ,       image_processor=SCREAMING_SNAKE_CASE__    )
											processor_slow.save_pretrained(self.tmpdirname    )
											SCREAMING_SNAKE_CASE__						:       str           =   ChineseCLIPProcessor.from_pretrained(self.tmpdirname   ,       use_fast=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__   ,       image_processor=SCREAMING_SNAKE_CASE__    )
											processor_fast.save_pretrained(self.tmpdirname    )
											SCREAMING_SNAKE_CASE__						:       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    )					->		Dict:



											"""simple docstring"""




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

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

											SCREAMING_SNAKE_CASE__						:       Tuple           =   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    )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Any           =   self.get_image_processor()
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   self.get_tokenizer()

											SCREAMING_SNAKE_CASE__						:       Tuple           =   ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__   ,       image_processor=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Dict           =   self.prepare_image_inputs()

											SCREAMING_SNAKE_CASE__						:       Dict           =   image_processor(SCREAMING_SNAKE_CASE__   ,       return_tensors="""np"""    )
											SCREAMING_SNAKE_CASE__						:       Dict           =   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    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[str]           =   self.get_image_processor()
											SCREAMING_SNAKE_CASE__						:       Dict           =   self.get_tokenizer()

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

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   """Alexandra，T-shirt的价格是15便士。"""

											SCREAMING_SNAKE_CASE__						:       Tuple           =   processor(text=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       str           =   tokenizer(SCREAMING_SNAKE_CASE__    )

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



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.get_image_processor()
											SCREAMING_SNAKE_CASE__						:       int           =   self.get_tokenizer()

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

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

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   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    )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       int           =   self.get_image_processor()
											SCREAMING_SNAKE_CASE__						:       str           =   self.get_tokenizer()

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__   ,       image_processor=SCREAMING_SNAKE_CASE__    )

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

											SCREAMING_SNAKE_CASE__						:       Any           =   processor.batch_decode(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Tuple           =   tokenizer.batch_decode(SCREAMING_SNAKE_CASE__    )

											self.assertListEqual(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.get_image_processor()
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.get_tokenizer()

											SCREAMING_SNAKE_CASE__						:       str           =   ChineseCLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__   ,       image_processor=SCREAMING_SNAKE_CASE__    )

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

											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   processor(text=SCREAMING_SNAKE_CASE__   ,       images=SCREAMING_SNAKE_CASE__    )

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







import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
						import os

						import jax.numpy as jnp
						from jax import jit

						from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
						from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

						lowerCAmelCase							:  Any   	=						"""0.12"""  # assumed parallelism: 8


if is_torch_available():
						import torch







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase=None    ):
						if rng is None:
												SCREAMING_SNAKE_CASE_: List[Any]  		=       random.Random()

						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       1
						for dim in shape:
												total_dims *= dim

						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       []
						for _ in range(_UpperCAmelCase    ):
												values.append(rng.randint(0				,	vocab_size - 1    )    )

						SCREAMING_SNAKE_CASE_: List[Any]  		=       np.array(_UpperCAmelCase				,	dtype=jnp.intaa    ).reshape(_UpperCAmelCase    )

						return output







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase=None    ):
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       ids_tensor(_UpperCAmelCase				,	vocab_size=2				,	rng=_UpperCAmelCase    )
						# make sure that at least one token is attended to for each batch
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       1
						return attn_mask



@require_flax
class   __lowercase				:




			"""simple docstring"""







			_UpperCAmelCase   :							Any										=     None
			_UpperCAmelCase   :							List[Any]										=     ()



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       self.model_tester.prepare_config_and_inputs_for_common()

									# cut to half length & take max batch_size 3
									SCREAMING_SNAKE_CASE_: str  		=       2
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       inputs["input_ids"].shape[-1] // 2
									SCREAMING_SNAKE_CASE_: List[str]  		=       inputs["input_ids"][:max_batch_size, :sequence_length]

									SCREAMING_SNAKE_CASE_: Any  		=       jnp.ones_like(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       attention_mask[:max_batch_size, :sequence_length]

									# generate max 5 tokens
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       input_ids.shape[-1] + 5
									if config.eos_token_id is not None and config.pad_token_id is None:
															# hack to allow generate for models such as GPT2 as is done in `generate()`
															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       config.eos_token_id
									return config, input_ids, attention_mask, max_length



			@is_pt_flax_cross_test
			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[int]  		=       self._get_input_ids_and_config()
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       False
									SCREAMING_SNAKE_CASE_: Dict  		=       max_length
									SCREAMING_SNAKE_CASE_: List[Any]  		=       0

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: int  		=       model_class(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Tuple  		=       model_class.__name__[4:]  # Skip the "Flax" at the beginning
															SCREAMING_SNAKE_CASE_: List[Any]  		=       getattr(lowerCAmelCase__   ,						lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       pt_model_class(lowerCAmelCase__).eval()
															SCREAMING_SNAKE_CASE_: str  		=       load_flax_weights_in_pytorch_model(lowerCAmelCase__   ,						flax_model.params)

															SCREAMING_SNAKE_CASE_: List[Any]  		=       flax_model.generate(lowerCAmelCase__).sequences
															SCREAMING_SNAKE_CASE_: str  		=       pt_model.generate(torch.tensor(lowerCAmelCase__   ,						dtype=torch.long))

															if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
																					SCREAMING_SNAKE_CASE_: List[Any]  		=       flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

															self.assertListEqual(pt_generation_outputs.numpy().tolist()   ,						flax_generation_outputs.tolist())



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self._get_input_ids_and_config()
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       False
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       max_length

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       model_class(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: str  		=       model.generate(lowerCAmelCase__).sequences
															self.assertEqual(generation_outputs.shape[-1]   ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: List[Any]  		=       jit(model.generate)
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       jit_generate(lowerCAmelCase__).sequences

															self.assertListEqual(generation_outputs.tolist()   ,						jit_generation_outputs.tolist())



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       self._get_input_ids_and_config()
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       True
									SCREAMING_SNAKE_CASE_: Dict  		=       max_length

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: Tuple  		=       model_class(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: int  		=       model.generate(lowerCAmelCase__).sequences
															self.assertEqual(generation_outputs.shape[-1]   ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: List[str]  		=       jit(model.generate)
															SCREAMING_SNAKE_CASE_: Dict  		=       jit_generate(lowerCAmelCase__).sequences

															self.assertListEqual(generation_outputs.tolist()   ,						jit_generation_outputs.tolist())



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Any  		=       self._get_input_ids_and_config()
									SCREAMING_SNAKE_CASE_: int  		=       False
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       max_length
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       2

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: List[str]  		=       model_class(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: str  		=       model.generate(lowerCAmelCase__).sequences
															self.assertEqual(generation_outputs.shape[-1]   ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Any  		=       jit(model.generate)
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       jit_generate(lowerCAmelCase__).sequences

															self.assertListEqual(generation_outputs.tolist()   ,						jit_generation_outputs.tolist())



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Dict  		=       self._get_input_ids_and_config()
									SCREAMING_SNAKE_CASE_: str  		=       False
									SCREAMING_SNAKE_CASE_: int  		=       max_length
									SCREAMING_SNAKE_CASE_: str  		=       2
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       2

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: str  		=       model_class(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       model.generate(lowerCAmelCase__).sequences
															self.assertEqual(generation_outputs.shape[0]   ,						input_ids.shape[0] * config.num_return_sequences)



			def        _SCREAMING_SNAKE_CASE			( self  :     Any):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       self._get_input_ids_and_config()
									SCREAMING_SNAKE_CASE_: Tuple  		=       True
									SCREAMING_SNAKE_CASE_: List[str]  		=       max_length
									SCREAMING_SNAKE_CASE_: Any  		=       0.8
									SCREAMING_SNAKE_CASE_: Any  		=       10
									SCREAMING_SNAKE_CASE_: List[str]  		=       0.3
									SCREAMING_SNAKE_CASE_: Tuple  		=       1
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       8
									SCREAMING_SNAKE_CASE_: int  		=       9

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: List[str]  		=       model_class(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: str  		=       model.generate(lowerCAmelCase__).sequences
															self.assertEqual(generation_outputs.shape[-1]   ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Dict  		=       jit(model.generate)
															SCREAMING_SNAKE_CASE_: List[Any]  		=       jit_generate(lowerCAmelCase__).sequences

															self.assertListEqual(generation_outputs.tolist()   ,						jit_generation_outputs.tolist())



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: str  		=       self._get_input_ids_and_config()
									SCREAMING_SNAKE_CASE_: Any  		=       max_length
									SCREAMING_SNAKE_CASE_: int  		=       1
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       8
									SCREAMING_SNAKE_CASE_: List[Any]  		=       9

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: int  		=       model_class(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       model.generate(lowerCAmelCase__).sequences
															self.assertEqual(generation_outputs.shape[-1]   ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       jit(model.generate)
															SCREAMING_SNAKE_CASE_: List[str]  		=       jit_generate(lowerCAmelCase__).sequences

															self.assertListEqual(generation_outputs.tolist()   ,						jit_generation_outputs.tolist())



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Dict  		=       self._get_input_ids_and_config()
									SCREAMING_SNAKE_CASE_: Any  		=       max_length
									SCREAMING_SNAKE_CASE_: List[str]  		=       2
									SCREAMING_SNAKE_CASE_: str  		=       1
									SCREAMING_SNAKE_CASE_: Tuple  		=       8
									SCREAMING_SNAKE_CASE_: List[Any]  		=       9

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       model_class(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: str  		=       model.generate(lowerCAmelCase__).sequences
															self.assertEqual(generation_outputs.shape[-1]   ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: int  		=       jit(model.generate)
															SCREAMING_SNAKE_CASE_: List[str]  		=       jit_generate(lowerCAmelCase__).sequences

															self.assertListEqual(generation_outputs.tolist()   ,						jit_generation_outputs.tolist())



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       self._get_input_ids_and_config()

									# pad attention mask on the left
									SCREAMING_SNAKE_CASE_: Dict  		=       attention_mask.at[(0, 0)].set(0)

									SCREAMING_SNAKE_CASE_: Dict  		=       False
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       max_length

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: Any  		=       model_class(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: List[Any]  		=       model.generate(lowerCAmelCase__   ,						attention_mask=lowerCAmelCase__).sequences
															self.assertEqual(generation_outputs.shape[-1]   ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Optional[int]  		=       jit(model.generate)
															SCREAMING_SNAKE_CASE_: List[Any]  		=       jit_generate(lowerCAmelCase__   ,						attention_mask=lowerCAmelCase__).sequences

															self.assertListEqual(generation_outputs.tolist()   ,						jit_generation_outputs.tolist())



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       self._get_input_ids_and_config()

									# pad attention mask on the left
									SCREAMING_SNAKE_CASE_: List[Any]  		=       attention_mask.at[(0, 0)].set(0)

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       True
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       max_length

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: str  		=       model_class(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Dict  		=       model.generate(lowerCAmelCase__   ,						attention_mask=lowerCAmelCase__).sequences
															self.assertEqual(generation_outputs.shape[-1]   ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Optional[int]  		=       jit(model.generate)
															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       jit_generate(lowerCAmelCase__   ,						attention_mask=lowerCAmelCase__).sequences

															self.assertListEqual(generation_outputs.tolist()   ,						jit_generation_outputs.tolist())




			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       self._get_input_ids_and_config()

									# pad attention mask on the left
									SCREAMING_SNAKE_CASE_: Dict  		=       attention_mask.at[(0, 0)].set(0)

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       2
									SCREAMING_SNAKE_CASE_: Any  		=       max_length

									for model_class in self.all_generative_model_classes:
															SCREAMING_SNAKE_CASE_: Tuple  		=       model_class(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Optional[int]  		=       model.generate(lowerCAmelCase__   ,						attention_mask=lowerCAmelCase__).sequences
															self.assertEqual(generation_outputs.shape[-1]   ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: str  		=       jit(model.generate)
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       jit_generate(lowerCAmelCase__   ,						attention_mask=lowerCAmelCase__).sequences

															self.assertListEqual(generation_outputs.tolist()   ,						jit_generation_outputs.tolist())







@require_flax
class   __lowercase				( unittest.TestCase	):




			"""simple docstring"""







			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									SCREAMING_SNAKE_CASE_: Tuple  		=       AutoTokenizer.from_pretrained("hf-internal-testing/tiny-bert")
									SCREAMING_SNAKE_CASE_: List[Any]  		=       FlaxAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-bert-flax-only")

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       "Hello world"
									SCREAMING_SNAKE_CASE_: List[Any]  		=       tokenizer(lowerCAmelCase__   ,						return_tensors="np").input_ids

									# typos are quickly detected (the correct argument is `do_sample`)
									with self.assertRaisesRegex(lowerCAmelCase__   ,						"do_samples"):
															model.generate(lowerCAmelCase__   ,						do_samples=lowerCAmelCase__)

									# arbitrary arguments that will not be used anywhere are also not accepted
									with self.assertRaisesRegex(lowerCAmelCase__   ,						"foo"):
															SCREAMING_SNAKE_CASE_: str  		=       {"foo": "bar"}
															model.generate(lowerCAmelCase__   ,						**lowerCAmelCase__)






