



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

	_UpperCAmelCase            =		'0.12'  # assumed parallelism: 8


if is_torch_available():
	import torch




def     UpperCamelCase						(	__lowercase						:			str						,__lowercase						:			Tuple						,__lowercase						:			Tuple=None     ):
		'''simple docstring'''




		if rng is None:
				A_		:    str         =				random.Random()

		A_		:    List[Any]         =				1
		for dim in shape:
				total_dims *= dim

		A_		:    int         =				[]
		for _ in range(__snake_case     ):
				values.append(rng.randint(0						,vocab_size - 1     )     )

		A_		:    Dict         =				np.array(__snake_case						,dtype=jnp.intaa     ).reshape(__snake_case     )

		return output




def     UpperCamelCase						(	__lowercase						:			Tuple						,__lowercase						:			str=None     ):
		'''simple docstring'''




		A_		:    List[str]         =				ids_tensor(__snake_case						,vocab_size=2						,rng=__snake_case     )
		# make sure that at least one token is attended to for each batch
		A_		:    str         =				1
		return attn_mask





@require_flax
class UpperCAmelCase						:


				'''simple docstring'''






				lowerCamelCase_    = None
				lowerCamelCase_    = ()
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Union[str, Any]         =				self.model_tester.prepare_config_and_inputs_for_common()

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

						A_		:    str         =				jnp.ones_like(_SCREAMING_SNAKE_CASE )
						A_		:    str         =				attention_mask[:max_batch_size, :sequence_length]

						# generate max 5 tokens
						A_		:    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()`
								A_		:    int         =				config.eos_token_id
						return config, input_ids, attention_mask, max_length
				@is_pt_flax_cross_test
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    str         =				self._get_input_ids_and_config()
						A_		:    Dict         =				False
						A_		:    Dict         =				max_length
						A_		:    Any         =				0

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

								A_		:    List[Any]         =				model_class.__name__[4:]  # Skip the "Flax" at the beginning
								A_		:    int         =				getattr(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE )
								A_		:    Tuple         =				pt_model_class(_SCREAMING_SNAKE_CASE ).eval()
								A_		:    Optional[int]         =				load_flax_weights_in_pytorch_model(_SCREAMING_SNAKE_CASE		,     flax_model.params )

								A_		:    int         =				flax_model.generate(_SCREAMING_SNAKE_CASE ).sequences
								A_		:    Any         =				pt_model.generate(torch.tensor(_SCREAMING_SNAKE_CASE		,     dtype=torch.long ) )

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

								self.assertListEqual(pt_generation_outputs.numpy().tolist()		,     flax_generation_outputs.tolist() )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Optional[Any]         =				self._get_input_ids_and_config()
						A_		:    List[str]         =				False
						A_		:    Optional[Any]         =				max_length

						for model_class in self.all_generative_model_classes:
								A_		:    List[str]         =				model_class(_SCREAMING_SNAKE_CASE )

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

								A_		:    List[Any]         =				jit(model.generate )
								A_		:    List[str]         =				jit_generate(_SCREAMING_SNAKE_CASE ).sequences

								self.assertListEqual(generation_outputs.tolist()		,     jit_generation_outputs.tolist() )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Optional[Any]         =				self._get_input_ids_and_config()
						A_		:    Dict         =				True
						A_		:    List[str]         =				max_length

						for model_class in self.all_generative_model_classes:
								A_		:    Tuple         =				model_class(_SCREAMING_SNAKE_CASE )

								A_		:    Optional[Any]         =				model.generate(_SCREAMING_SNAKE_CASE ).sequences
								self.assertEqual(generation_outputs.shape[-1]		,     _SCREAMING_SNAKE_CASE )

								A_		:    Union[str, Any]         =				jit(model.generate )
								A_		:    Optional[Any]         =				jit_generate(_SCREAMING_SNAKE_CASE ).sequences

								self.assertListEqual(generation_outputs.tolist()		,     jit_generation_outputs.tolist() )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Any         =				self._get_input_ids_and_config()
						A_		:    Tuple         =				False
						A_		:    Tuple         =				max_length
						A_		:    Any         =				2

						for model_class in self.all_generative_model_classes:
								A_		:    Optional[int]         =				model_class(_SCREAMING_SNAKE_CASE )

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

								A_		:    str         =				jit(model.generate )
								A_		:    Dict         =				jit_generate(_SCREAMING_SNAKE_CASE ).sequences

								self.assertListEqual(generation_outputs.tolist()		,     jit_generation_outputs.tolist() )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Optional[int]         =				self._get_input_ids_and_config()
						A_		:    List[Any]         =				False
						A_		:    Any         =				max_length
						A_		:    Dict         =				2
						A_		:    int         =				2

						for model_class in self.all_generative_model_classes:
								A_		:    str         =				model_class(_SCREAMING_SNAKE_CASE )

								A_		:    Any         =				model.generate(_SCREAMING_SNAKE_CASE ).sequences
								self.assertEqual(generation_outputs.shape[0]		,     input_ids.shape[0] * config.num_return_sequences )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    int         =				self._get_input_ids_and_config()
						A_		:    str         =				True
						A_		:    Tuple         =				max_length
						A_		:    Tuple         =				0.8
						A_		:    Any         =				1_0
						A_		:    Any         =				0.3
						A_		:    List[Any]         =				1
						A_		:    int         =				8
						A_		:    Optional[int]         =				9

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

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

								A_		:    Optional[int]         =				jit(model.generate )
								A_		:    str         =				jit_generate(_SCREAMING_SNAKE_CASE ).sequences

								self.assertListEqual(generation_outputs.tolist()		,     jit_generation_outputs.tolist() )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[str]         =				self._get_input_ids_and_config()
						A_		:    int         =				max_length
						A_		:    Tuple         =				1
						A_		:    List[str]         =				8
						A_		:    str         =				9

						for model_class in self.all_generative_model_classes:
								A_		:    Dict         =				model_class(_SCREAMING_SNAKE_CASE )

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

								A_		:    Any         =				jit(model.generate )
								A_		:    str         =				jit_generate(_SCREAMING_SNAKE_CASE ).sequences

								self.assertListEqual(generation_outputs.tolist()		,     jit_generation_outputs.tolist() )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    int         =				self._get_input_ids_and_config()
						A_		:    Union[str, Any]         =				max_length
						A_		:    Dict         =				2
						A_		:    Tuple         =				1
						A_		:    int         =				8
						A_		:    str         =				9

						for model_class in self.all_generative_model_classes:
								A_		:    str         =				model_class(_SCREAMING_SNAKE_CASE )

								A_		:    Dict         =				model.generate(_SCREAMING_SNAKE_CASE ).sequences
								self.assertEqual(generation_outputs.shape[-1]		,     _SCREAMING_SNAKE_CASE )

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

								self.assertListEqual(generation_outputs.tolist()		,     jit_generation_outputs.tolist() )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Union[str, Any]         =				self._get_input_ids_and_config()

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

						A_		:    Optional[Any]         =				False
						A_		:    int         =				max_length

						for model_class in self.all_generative_model_classes:
								A_		:    str         =				model_class(_SCREAMING_SNAKE_CASE )

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

								A_		:    Tuple         =				jit(model.generate )
								A_		:    Dict         =				jit_generate(_SCREAMING_SNAKE_CASE		,     attention_mask=_SCREAMING_SNAKE_CASE ).sequences

								self.assertListEqual(generation_outputs.tolist()		,     jit_generation_outputs.tolist() )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Any         =				self._get_input_ids_and_config()

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

						A_		:    Optional[int]         =				True
						A_		:    Optional[Any]         =				max_length

						for model_class in self.all_generative_model_classes:
								A_		:    Any         =				model_class(_SCREAMING_SNAKE_CASE )

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

								A_		:    Tuple         =				jit(model.generate )
								A_		:    Any         =				jit_generate(_SCREAMING_SNAKE_CASE		,     attention_mask=_SCREAMING_SNAKE_CASE ).sequences

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







				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Union[str, Any]         =				self._get_input_ids_and_config()

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

						A_		:    Tuple         =				2
						A_		:    Dict         =				max_length

						for model_class in self.all_generative_model_classes:
								A_		:    Tuple         =				model_class(_SCREAMING_SNAKE_CASE )

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

								A_		:    Dict         =				jit(model.generate )
								A_		:    int         =				jit_generate(_SCREAMING_SNAKE_CASE		,     attention_mask=_SCREAMING_SNAKE_CASE ).sequences

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





@require_flax
class UpperCAmelCase						( unittest.TestCase  ):


				'''simple docstring'''






				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Tuple         =				AutoTokenizer.from_pretrained('hf-internal-testing/tiny-bert' )
						A_		:    Optional[int]         =				FlaxAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-bert-flax-only' )

						A_		:    Optional[Any]         =				"Hello world"
						A_		:    str         =				tokenizer(_SCREAMING_SNAKE_CASE		,     return_tensors='np' ).input_ids

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

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







from ..utils import DummyObject, requires_backends





class UpperCAmelCase						( metaclass=__A  ):


				'''simple docstring'''






				lowerCamelCase_    = ['''onnx''']
				def __init__(						self		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						requires_backends(self		,     ['onnx'] )
				@classmethod
				def   lowerCAmelCase_       (						cls		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						requires_backends(cls		,     ['onnx'] )







				@classmethod
				def   lowerCAmelCase_       (						cls		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						requires_backends(cls		,     ['onnx'] )






