



"""simple docstring"""

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

			a_			   =					'''0.12'''  # assumed parallelism: 8


if is_torch_available():
			import torch







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Union[str, Any]				,    SCREAMING_SNAKE_CASE__ :  Tuple				,    SCREAMING_SNAKE_CASE__ :  Optional[int]=None			):



					"""simple docstring"""





					if rng is None:
										snake_case_					:		Union[str, Any] 					=					random.Random()

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

					snake_case_					:		List[Any] 					=					[]
					for _ in range(SCREAMING_SNAKE_CASE__			):
										values.append(rng.randint(0				,    vocab_size - 1			)			)

					snake_case_					:		Dict 					=					np.array(SCREAMING_SNAKE_CASE__				,    dtype=jnp.intaa			).reshape(SCREAMING_SNAKE_CASE__			)

					return output







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Union[str, Any]				,    SCREAMING_SNAKE_CASE__ :  List[str]=None			):



					"""simple docstring"""





					snake_case_					:		Tuple 					=					ids_tensor(SCREAMING_SNAKE_CASE__				,    vocab_size=2				,    rng=SCREAMING_SNAKE_CASE__			)
					# make sure that at least one token is attended to for each batch
					snake_case_					:		int 					=					1
					return attn_mask







@require_flax
class    __lowercase     :







						"""simple docstring"""







						_A		:							Optional[int]							       =		None
						_A		:							Tuple							       =		()







						def 						__UpperCamelCase							(self       ):
											snake_case_					,			snake_case_					:		Union[str, Any] 					=					self.model_tester.prepare_config_and_inputs_for_common()

											# cut to half length & take max batch_size 3
											snake_case_					:		List[Any] 					=					2
											snake_case_					:		Tuple 					=					inputs["""input_ids"""].shape[-1] // 2
											snake_case_					:		Tuple 					=					inputs["""input_ids"""][:max_batch_size, :sequence_length]

											snake_case_					:		int 					=					jnp.ones_like(lowercase__       )
											snake_case_					:		List[str] 					=					attention_mask[:max_batch_size, :sequence_length]

											# generate max 5 tokens
											snake_case_					:		Dict 					=					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()`
																snake_case_					:		Union[str, Any] 					=					config.eos_token_id
											return config, input_ids, attention_mask, max_length







						@is_pt_flax_cross_test
						def 						__UpperCamelCase							(self       ):
											snake_case_					,			snake_case_					,			snake_case_					,			snake_case_					:		List[Any] 					=					self._get_input_ids_and_config()
											snake_case_					:		Any 					=					False
											snake_case_					:		int 					=					max_length
											snake_case_					:		Dict 					=					0

											for model_class in self.all_generative_model_classes:
																snake_case_					:		Tuple 					=					model_class(lowercase__       )

																snake_case_					:		Any 					=					model_class.__name__[4:]  # Skip the "Flax" at the beginning
																snake_case_					:		Dict 					=					getattr(lowercase__   ,		lowercase__       )
																snake_case_					:		Optional[Any] 					=					pt_model_class(lowercase__       ).eval()
																snake_case_					:		Optional[Any] 					=					load_flax_weights_in_pytorch_model(lowercase__   ,		flax_model.params       )

																snake_case_					:		Tuple 					=					flax_model.generate(lowercase__       ).sequences
																snake_case_					:		Dict 					=					pt_model.generate(torch.tensor(lowercase__   ,		dtype=torch.long       )       )

																if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
																					snake_case_					:		Optional[int] 					=					flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

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







						def 						__UpperCamelCase							(self       ):
											snake_case_					,			snake_case_					,			snake_case_					,			snake_case_					:		str 					=					self._get_input_ids_and_config()
											snake_case_					:		List[str] 					=					False
											snake_case_					:		List[Any] 					=					max_length

											for model_class in self.all_generative_model_classes:
																snake_case_					:		Tuple 					=					model_class(lowercase__       )

																snake_case_					:		Tuple 					=					model.generate(lowercase__       ).sequences
																self.assertEqual(generation_outputs.shape[-1]   ,		lowercase__       )

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

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







						def 						__UpperCamelCase							(self       ):
											snake_case_					,			snake_case_					,			snake_case_					,			snake_case_					:		str 					=					self._get_input_ids_and_config()
											snake_case_					:		List[Any] 					=					True
											snake_case_					:		Tuple 					=					max_length

											for model_class in self.all_generative_model_classes:
																snake_case_					:		Optional[Any] 					=					model_class(lowercase__       )

																snake_case_					:		List[Any] 					=					model.generate(lowercase__       ).sequences
																self.assertEqual(generation_outputs.shape[-1]   ,		lowercase__       )

																snake_case_					:		List[str] 					=					jit(model.generate       )
																snake_case_					:		Optional[int] 					=					jit_generate(lowercase__       ).sequences

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







						def 						__UpperCamelCase							(self       ):
											snake_case_					,			snake_case_					,			snake_case_					,			snake_case_					:		List[str] 					=					self._get_input_ids_and_config()
											snake_case_					:		Tuple 					=					False
											snake_case_					:		Union[str, Any] 					=					max_length
											snake_case_					:		Optional[Any] 					=					2

											for model_class in self.all_generative_model_classes:
																snake_case_					:		Dict 					=					model_class(lowercase__       )

																snake_case_					:		Optional[int] 					=					model.generate(lowercase__       ).sequences
																self.assertEqual(generation_outputs.shape[-1]   ,		lowercase__       )

																snake_case_					:		List[Any] 					=					jit(model.generate       )
																snake_case_					:		Optional[Any] 					=					jit_generate(lowercase__       ).sequences

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







						def 						__UpperCamelCase							(self       ):
											snake_case_					,			snake_case_					,			snake_case_					,			snake_case_					:		Optional[int] 					=					self._get_input_ids_and_config()
											snake_case_					:		Optional[Any] 					=					False
											snake_case_					:		Tuple 					=					max_length
											snake_case_					:		Union[str, Any] 					=					2
											snake_case_					:		int 					=					2

											for model_class in self.all_generative_model_classes:
																snake_case_					:		int 					=					model_class(lowercase__       )

																snake_case_					:		Optional[int] 					=					model.generate(lowercase__       ).sequences
																self.assertEqual(generation_outputs.shape[0]   ,		input_ids.shape[0] * config.num_return_sequences       )







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

											for model_class in self.all_generative_model_classes:
																snake_case_					:		int 					=					model_class(lowercase__       )

																snake_case_					:		Optional[int] 					=					model.generate(lowercase__       ).sequences
																self.assertEqual(generation_outputs.shape[-1]   ,		lowercase__       )

																snake_case_					:		Optional[int] 					=					jit(model.generate       )
																snake_case_					:		str 					=					jit_generate(lowercase__       ).sequences

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







						def 						__UpperCamelCase							(self       ):
											snake_case_					,			snake_case_					,			snake_case_					,			snake_case_					:		Any 					=					self._get_input_ids_and_config()
											snake_case_					:		str 					=					max_length
											snake_case_					:		Optional[Any] 					=					1
											snake_case_					:		Optional[int] 					=					8
											snake_case_					:		Union[str, Any] 					=					9

											for model_class in self.all_generative_model_classes:
																snake_case_					:		str 					=					model_class(lowercase__       )

																snake_case_					:		Any 					=					model.generate(lowercase__       ).sequences
																self.assertEqual(generation_outputs.shape[-1]   ,		lowercase__       )

																snake_case_					:		Any 					=					jit(model.generate       )
																snake_case_					:		List[str] 					=					jit_generate(lowercase__       ).sequences

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







						def 						__UpperCamelCase							(self       ):
											snake_case_					,			snake_case_					,			snake_case_					,			snake_case_					:		Tuple 					=					self._get_input_ids_and_config()
											snake_case_					:		List[str] 					=					max_length
											snake_case_					:		Optional[int] 					=					2
											snake_case_					:		List[Any] 					=					1
											snake_case_					:		Optional[Any] 					=					8
											snake_case_					:		str 					=					9

											for model_class in self.all_generative_model_classes:
																snake_case_					:		Tuple 					=					model_class(lowercase__       )

																snake_case_					:		Optional[int] 					=					model.generate(lowercase__       ).sequences
																self.assertEqual(generation_outputs.shape[-1]   ,		lowercase__       )

																snake_case_					:		List[Any] 					=					jit(model.generate       )
																snake_case_					:		List[str] 					=					jit_generate(lowercase__       ).sequences

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







						def 						__UpperCamelCase							(self       ):
											snake_case_					,			snake_case_					,			snake_case_					,			snake_case_					:		Union[str, Any] 					=					self._get_input_ids_and_config()

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

											snake_case_					:		List[str] 					=					False
											snake_case_					:		Tuple 					=					max_length

											for model_class in self.all_generative_model_classes:
																snake_case_					:		Dict 					=					model_class(lowercase__       )

																snake_case_					:		Optional[Any] 					=					model.generate(lowercase__   ,		attention_mask=lowercase__       ).sequences
																self.assertEqual(generation_outputs.shape[-1]   ,		lowercase__       )

																snake_case_					:		str 					=					jit(model.generate       )
																snake_case_					:		int 					=					jit_generate(lowercase__   ,		attention_mask=lowercase__       ).sequences

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







						def 						__UpperCamelCase							(self       ):
											snake_case_					,			snake_case_					,			snake_case_					,			snake_case_					:		Any 					=					self._get_input_ids_and_config()

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

											snake_case_					:		Dict 					=					True
											snake_case_					:		Optional[int] 					=					max_length

											for model_class in self.all_generative_model_classes:
																snake_case_					:		str 					=					model_class(lowercase__       )

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

																snake_case_					:		Tuple 					=					jit(model.generate       )
																snake_case_					:		List[str] 					=					jit_generate(lowercase__   ,		attention_mask=lowercase__       ).sequences

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







						def 						__UpperCamelCase							(self       ):
											snake_case_					,			snake_case_					,			snake_case_					,			snake_case_					:		int 					=					self._get_input_ids_and_config()

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

											snake_case_					:		Tuple 					=					2
											snake_case_					:		Any 					=					max_length

											for model_class in self.all_generative_model_classes:
																snake_case_					:		Any 					=					model_class(lowercase__       )

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

																snake_case_					:		List[str] 					=					jit(model.generate       )
																snake_case_					:		Optional[Any] 					=					jit_generate(lowercase__   ,		attention_mask=lowercase__       ).sequences

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







@require_flax
class    __lowercase     (					unittest.TestCase):







						"""simple docstring"""







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

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

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

											# arbitrary arguments that will not be used anywhere are also not accepted
											with self.assertRaisesRegex(lowercase__   ,		"""foo"""       ):
																snake_case_					:		Optional[int] 					=					{"""foo""": """bar"""}
																model.generate(lowercase__   ,		**lowercase__       )









"""simple docstring"""

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							Union[str, Any]							       =		["""image_processor""", """tokenizer"""]
						_A		:							str							       =		"""ChineseCLIPImageProcessor"""
						_A		:							Tuple							       =		("""BertTokenizer""", """BertTokenizerFast""")







						def __init__(self   ,		lowercase__=None   ,		lowercase__=None   ,		**lowercase__       ):
											snake_case_					:		Any 					=					None
											if "feature_extractor" in kwargs:
																warnings.warn(
																    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
																    """ instead."""   ,		lowercase__   ,		)
																snake_case_					:		Optional[Any] 					=					kwargs.pop("""feature_extractor"""       )

											snake_case_					:		str 					=					image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
																raise ValueError("""You need to specify an `image_processor`."""       )
											if tokenizer is None:
																raise ValueError("""You need to specify a `tokenizer`."""       )

											super().__init__(lowercase__   ,		lowercase__       )
											snake_case_					:		Union[str, Any] 					=					self.image_processor







						def __call__(self   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__=None   ,		**lowercase__       ):

											if text is None and images is None:
																raise ValueError("""You have to specify either text or images. Both cannot be none."""       )

											if text is not None:
																snake_case_					:		Any 					=					self.tokenizer(lowercase__   ,		return_tensors=lowercase__   ,		**lowercase__       )

											if images is not None:
																snake_case_					:		Tuple 					=					self.image_processor(lowercase__   ,		return_tensors=lowercase__   ,		**lowercase__       )

											if text is not None and images is not None:
																snake_case_					:		List[Any] 					=					image_features.pixel_values
																return encoding
											elif text is not None:
																return encoding
											else:
																return BatchEncoding(data=dict(**lowercase__       )   ,		tensor_type=lowercase__       )







						def 						__UpperCamelCase							(self   ,		*lowercase__   ,		**lowercase__       ):
											return self.tokenizer.batch_decode(*lowercase__   ,		**lowercase__       )







						def 						__UpperCamelCase							(self   ,		*lowercase__   ,		**lowercase__       ):
											return self.tokenizer.decode(*lowercase__   ,		**lowercase__       )







						@property
						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Optional[int] 					=					self.tokenizer.model_input_names
											snake_case_					:		Union[str, Any] 					=					self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )







						@property
						def 						__UpperCamelCase							(self       ):
											warnings.warn(
											    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""   ,		lowercase__   ,		)
											return self.image_processor_class




