





'''simple docstring'''






import pickle

import numpy as np
from matplotlib import pyplot as plt




class  UpperCAmelCase     :




      def __init__(			self			: Union[str, Any]			,			__snake_case			: Tuple			,			__snake_case			: Dict			,			__snake_case			: List[Any]			,			__snake_case			: Any			,			__snake_case			: str			,			__snake_case			: str=0.2			,			__snake_case			: Union[str, Any]=0.2  )				->      Union[str, Any]:
            _lowerCAmelCase							     =							bp_numa
            _lowerCAmelCase							     =							bp_numa
            _lowerCAmelCase							     =							bp_numa
            _lowerCAmelCase							     =							conva_get[:2]
            _lowerCAmelCase							     =							conva_get[2]
            _lowerCAmelCase							     =							size_pa
            _lowerCAmelCase							     =							rate_w
            _lowerCAmelCase							     =							rate_t
            _lowerCAmelCase							     =							[
                np.mat(-1 * np.random.rand(self.conva[0]			,			self.conva[0]  ) + 0.5  )
                for i in range(self.conva[1]  )
            ]
            _lowerCAmelCase							     =							np.mat(-1 * np.random.rand(self.num_bpa			,			self.num_bpa  ) + 0.5  )
            _lowerCAmelCase							     =							np.mat(-1 * np.random.rand(self.num_bpa			,			self.num_bpa  ) + 0.5  )
            _lowerCAmelCase							     =							-2 * np.random.rand(self.conva[1]  ) + 1
            _lowerCAmelCase							     =							-2 * np.random.rand(self.num_bpa  ) + 1
            _lowerCAmelCase							     =							-2 * np.random.rand(self.num_bpa  ) + 1




      def 				lowercase__    (			self			: Any			,			__snake_case			: List[Any]  )				->      Union[str, Any]:
            # save model dict with pickle
            _lowerCAmelCase							     =							{
                """num_bp1""": self.num_bpa,
                """num_bp2""": self.num_bpa,
                """num_bp3""": self.num_bpa,
                """conv1""": self.conva,
                """step_conv1""": self.step_conva,
                """size_pooling1""": self.size_poolinga,
                """rate_weight""": self.rate_weight,
                """rate_thre""": self.rate_thre,
                """w_conv1""": self.w_conva,
                """wkj""": self.wkj,
                """vji""": self.vji,
                """thre_conv1""": self.thre_conva,
                """thre_bp2""": self.thre_bpa,
                """thre_bp3""": self.thre_bpa,
            }
            with open(__lowerCamelCase			,			"""wb"""  ) as f:
                  pickle.dump(__lowerCamelCase			,			__lowerCamelCase  )

            print(f"Model saved： {save_path}"  )




      @classmethod
      def 				lowercase__    (			cls			: int			,			__snake_case			: Optional[int]  )				->      Union[str, Any]:
            # read saved model
            with open(__lowerCamelCase			,			"""rb"""  ) as f:
                  _lowerCAmelCase							     =							pickle.load(__lowerCamelCase  )  # noqa: S301

            _lowerCAmelCase							     =							model_dic.get("""conv1"""  )
            conv_get.append(model_dic.get("""step_conv1"""  )  )
            _lowerCAmelCase							     =							model_dic.get("""size_pooling1"""  )
            _lowerCAmelCase							     =							model_dic.get("""num_bp1"""  )
            _lowerCAmelCase							     =							model_dic.get("""num_bp2"""  )
            _lowerCAmelCase							     =							model_dic.get("""num_bp3"""  )
            _lowerCAmelCase							     =							model_dic.get("""rate_weight"""  )
            _lowerCAmelCase							     =							model_dic.get("""rate_thre"""  )
            # create model instance
            _lowerCAmelCase							     =							CNN(__lowerCamelCase			,			__lowerCamelCase			,			__lowerCamelCase			,			__lowerCamelCase			,			__lowerCamelCase			,			__lowerCamelCase			,			__lowerCamelCase  )
            # modify model parameter
            _lowerCAmelCase							     =							model_dic.get("""w_conv1"""  )
            _lowerCAmelCase							     =							model_dic.get("""wkj"""  )
            _lowerCAmelCase							     =							model_dic.get("""vji"""  )
            _lowerCAmelCase							     =							model_dic.get("""thre_conv1"""  )
            _lowerCAmelCase							     =							model_dic.get("""thre_bp2"""  )
            _lowerCAmelCase							     =							model_dic.get("""thre_bp3"""  )
            return conv_ins




      def 				lowercase__    (			self			: List[str]			,			__snake_case			: List[str]  )				->      str:
            return 1 / (1 + np.exp(-1 * x  ))




      def 				lowercase__    (			self			: List[Any]			,			__snake_case			: Dict  )				->      Optional[int]:
            return round(__lowerCamelCase			,			3  )




      def 				lowercase__    (			self			: Tuple			,			__snake_case			: Dict			,			__snake_case			: Optional[int]			,			__snake_case			: int			,			__snake_case			: List[str]			,			__snake_case			: int  )				->      Any:
            # convolution process
            _lowerCAmelCase							     =							convs[0]
            _lowerCAmelCase							     =							convs[1]
            _lowerCAmelCase							     =							np.shape(__lowerCamelCase  )[0]
            # get the data slice of original image data, data_focus
            _lowerCAmelCase							     =							[]
            for i_focus in range(0			,			size_data - size_conv + 1			,			__lowerCamelCase  ):
                  for j_focus in range(0			,			size_data - size_conv + 1			,			__lowerCamelCase  ):
                        _lowerCAmelCase							     =							data[
                            i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
                        ]
                        data_focus.append(__lowerCamelCase  )
        # calculate the feature map of every single kernel, and saved as list of matrix
            _lowerCAmelCase							     =							[]
            _lowerCAmelCase							     =							int((size_data - size_conv) / conv_step + 1  )
            for i_map in range(__lowerCamelCase  ):
                  _lowerCAmelCase							     =							[]
                  for i_focus in range(len(__lowerCamelCase  )  ):
                        _lowerCAmelCase							     =							(
                            np.sum(np.multiply(data_focus[i_focus]			,			w_convs[i_map]  )  )
                            - thre_convs[i_map]
                        )
                        featuremap.append(self.sig(__lowerCamelCase  )  )
                  _lowerCAmelCase							     =							np.asmatrix(__lowerCamelCase  ).reshape(
                      __lowerCamelCase			,			__lowerCamelCase  )
                  data_featuremap.append(__lowerCamelCase  )

            # expanding the data slice to One dimenssion
            _lowerCAmelCase							     =							[]
            for each_focus in data_focus:
                  focusa_list.extend(self.Expand_Mat(__lowerCamelCase  )  )
            _lowerCAmelCase							     =							np.asarray(__lowerCamelCase  )
            return focus_list, data_featuremap




      def 				lowercase__    (			self			: Optional[int]			,			__snake_case			: List[str]			,			__snake_case			: Dict			,			__snake_case			: List[str]="average_pool"  )				->      Tuple:
            # pooling process
            _lowerCAmelCase							     =							len(featuremaps[0]  )
            _lowerCAmelCase							     =							int(size_map / size_pooling  )
            _lowerCAmelCase							     =							[]
            for i_map in range(len(__lowerCamelCase  )  ):
                  _lowerCAmelCase							     =							featuremaps[i_map]
                  _lowerCAmelCase							     =							[]
                  for i_focus in range(0			,			__lowerCamelCase			,			__lowerCamelCase  ):
                        for j_focus in range(0			,			__lowerCamelCase			,			__lowerCamelCase  ):
                              _lowerCAmelCase							     =							feature_map[
                                  i_focus : i_focus + size_pooling,
                                  j_focus : j_focus + size_pooling,
                              ]
                              if pooling_type == "average_pool":
                                    # average pooling
                                    map_pooled.append(np.average(__lowerCamelCase  )  )
                              elif pooling_type == "max_pooling":
                                    # max pooling
                                    map_pooled.append(np.max(__lowerCamelCase  )  )
                  _lowerCAmelCase							     =							np.asmatrix(__lowerCamelCase  ).reshape(__lowerCamelCase			,			__lowerCamelCase  )
                  featuremap_pooled.append(__lowerCamelCase  )
            return featuremap_pooled




      def 				lowercase__    (			self			: Optional[int]			,			__snake_case			: Any  )				->      Any:
            # expanding three dimension data to one dimension list
            _lowerCAmelCase							     =							[]
            for i in range(len(__lowerCamelCase  )  ):
                  _lowerCAmelCase							     =							np.shape(data[i]  )
                  _lowerCAmelCase							     =							data[i].reshape(1			,			shapes[0] * shapes[1]  )
                  _lowerCAmelCase							     =							data_listed.getA().tolist()[0]
                  data_expanded.extend(__lowerCamelCase  )
            _lowerCAmelCase							     =							np.asarray(__lowerCamelCase  )
            return data_expanded




      def 				lowercase__    (			self			: Optional[Any]			,			__snake_case			: List[Any]  )				->      Tuple:
            # expanding matrix to one dimension list
            _lowerCAmelCase							     =							np.asarray(__lowerCamelCase  )
            _lowerCAmelCase							     =							np.shape(__lowerCamelCase  )
            _lowerCAmelCase							     =							data_mat.reshape(1			,			shapes[0] * shapes[1]  )
            return data_expanded




      def 				lowercase__    (			self			: Optional[Any]			,			__snake_case			: Optional[Any]			,			__snake_case			: Optional[Any]			,			__snake_case			: Union[str, Any]			,			__snake_case			: Optional[Any]			,			__snake_case			: int  )				->      str:
            _lowerCAmelCase							     =							[]
            _lowerCAmelCase							     =							0
            for i_map in range(__lowerCamelCase  ):
                  _lowerCAmelCase							     =							np.ones((size_map, size_map)  )
                  for i in range(0			,			__lowerCamelCase			,			__lowerCamelCase  ):
                        for j in range(0			,			__lowerCamelCase			,			__lowerCamelCase  ):
                              _lowerCAmelCase							     =							pd_pool[
                                  i_pool
                              ]
                              _lowerCAmelCase							     =							i_pool + 1
                  _lowerCAmelCase							     =							np.multiply(
                      __lowerCamelCase			,			np.multiply(out_map[i_map]			,			(1 - out_map[i_map])  )  )
                  pd_all.append(__lowerCamelCase  )
            return pd_all




      def 				lowercase__    (			self			: List[str]			,			__snake_case			: int			,			__snake_case			: Tuple			,			__snake_case			: Optional[Any]			,			__snake_case			: Optional[int]			,			__snake_case			: Any			,			__snake_case			: Dict=bool  )				->      str:
            # model traning
            print("""----------------------Start Training-------------------------"""  )
            print((""" - - Shape: Train_Data  """, np.shape(__lowerCamelCase  ))  )
            print((""" - - Shape: Teach_Data  """, np.shape(__lowerCamelCase  ))  )
            _lowerCAmelCase							     =							0
            _lowerCAmelCase							     =							[]
            _lowerCAmelCase							     =							1_00_00
            while rp < n_repeat and mse >= error_accuracy:
                  _lowerCAmelCase							     =							0
                  print(f"-------------Learning Time {rp}--------------"  )
                  for p in range(len(__lowerCamelCase  )  ):
                        # print('------------Learning Image: %d--------------'%p)
                        _lowerCAmelCase							     =							np.asmatrix(datas_train[p]  )
                        _lowerCAmelCase							     =							np.asarray(datas_teach[p]  )
                        _lowerCAmelCase     ,							_lowerCAmelCase							     =							self.convolute(
                            __lowerCamelCase			,			self.conva			,			self.w_conva			,			self.thre_conva			,			conv_step=self.step_conva			,			)
                        _lowerCAmelCase							     =							self.pooling(__lowerCamelCase			,			self.size_poolinga  )
                        _lowerCAmelCase							     =							np.shape(__lowerCamelCase  )
                        _lowerCAmelCase							     =							self._expand(__lowerCamelCase  )
                        _lowerCAmelCase							     =							data_bp_input

                        _lowerCAmelCase							     =							np.dot(__lowerCamelCase			,			self.vji.T  ) - self.thre_bpa
                        _lowerCAmelCase							     =							self.sig(__lowerCamelCase  )
                        _lowerCAmelCase							     =							np.dot(__lowerCamelCase			,			self.wkj.T  ) - self.thre_bpa
                        _lowerCAmelCase							     =							self.sig(__lowerCamelCase  )

                        # --------------Model Leaning ------------------------
                        # calculate error and gradient---------------
                        _lowerCAmelCase							     =							np.multiply(
                            (data_teach - bp_outa)			,			np.multiply(__lowerCamelCase			,			(1 - bp_outa)  )  )
                        _lowerCAmelCase							     =							np.multiply(
                            np.dot(__lowerCamelCase			,			self.wkj  )			,			np.multiply(__lowerCamelCase			,			(1 - bp_outa)  )  )
                        _lowerCAmelCase							     =							np.dot(__lowerCamelCase			,			self.vji  )

                        _lowerCAmelCase							     =							pd_i_all / (self.size_poolinga * self.size_poolinga)
                        _lowerCAmelCase							     =							pd_conva_pooled.T.getA().tolist()
                        _lowerCAmelCase							     =							self._calculate_gradient_from_pool(
                            __lowerCamelCase			,			__lowerCamelCase			,			shape_featuremapa[0]			,			shape_featuremapa[1]			,			self.size_poolinga			,			)
                        # weight and threshold learning process---------
                        # convolution layer
                        for k_conv in range(self.conva[1]  ):
                              _lowerCAmelCase							     =							self._expand_mat(pd_conva_all[k_conv]  )
                              _lowerCAmelCase							     =							self.rate_weight * np.dot(__lowerCamelCase			,			__lowerCamelCase  )

                              _lowerCAmelCase							     =							self.w_conva[k_conv] + delta_w.reshape(
                                  (self.conva[0], self.conva[0])  )

                              _lowerCAmelCase							     =							(
                                  self.thre_conva[k_conv]
                                  - np.sum(pd_conva_all[k_conv]  ) * self.rate_thre
                              )
                        # all connected layer
                        _lowerCAmelCase							     =							self.wkj + pd_k_all.T * bp_outa * self.rate_weight
                        _lowerCAmelCase							     =							self.vji + pd_j_all.T * bp_outa * self.rate_weight
                        _lowerCAmelCase							     =							self.thre_bpa - pd_k_all * self.rate_thre
                        _lowerCAmelCase							     =							self.thre_bpa - pd_j_all * self.rate_thre
                        # calculate the sum error of all single image
                        _lowerCAmelCase							     =							np.sum(abs(data_teach - bp_outa  )  )
                        error_count += errors
                        # print('   ----Teach      ',data_teach)
                        # print('   ----BP_output  ',bp_out3)
                  _lowerCAmelCase							     =							rp + 1
                  _lowerCAmelCase							     =							error_count / patterns
                  all_mse.append(__lowerCamelCase  )

            def draw_error():
                  _lowerCAmelCase							     =							[error_accuracy for i in range(int(n_repeat * 1.2  )  )]
                  plt.plot(__lowerCamelCase			,			"""+-"""  )
                  plt.plot(__lowerCamelCase			,			"""r--"""  )
                  plt.xlabel("""Learning Times"""  )
                  plt.ylabel("""All_mse"""  )
                  plt.grid(__lowerCamelCase			,			alpha=0.5  )
                  plt.show()

            print("""------------------Training Complished---------------------"""  )
            print((""" - - Training epoch: """, rp, f"     - - Mse: {mse:.6f}")  )
            if draw_e:
                  draw_error()
            return mse




      def 				lowercase__    (			self			: Any			,			__snake_case			: str  )				->      Dict:
            # model predict
            _lowerCAmelCase							     =							[]
            print("""-------------------Start Testing-------------------------"""  )
            print((""" - - Shape: Test_Data  """, np.shape(__lowerCamelCase  ))  )
            for p in range(len(__lowerCamelCase  )  ):
                  _lowerCAmelCase							     =							np.asmatrix(datas_test[p]  )
                  _lowerCAmelCase     ,							_lowerCAmelCase							     =							self.convolute(
                      __lowerCamelCase			,			self.conva			,			self.w_conva			,			self.thre_conva			,			conv_step=self.step_conva			,			)
                  _lowerCAmelCase							     =							self.pooling(__lowerCamelCase			,			self.size_poolinga  )
                  _lowerCAmelCase							     =							self._expand(__lowerCamelCase  )

                  _lowerCAmelCase							     =							data_bp_input
                  _lowerCAmelCase							     =							bp_outa * self.vji.T - self.thre_bpa
                  _lowerCAmelCase							     =							self.sig(__lowerCamelCase  )
                  _lowerCAmelCase							     =							bp_outa * self.wkj.T - self.thre_bpa
                  _lowerCAmelCase							     =							self.sig(__lowerCamelCase  )
                  produce_out.extend(bp_outa.getA().tolist()  )
            _lowerCAmelCase							     =							[list(map(self.do_round			,			__lowerCamelCase  )  ) for each in produce_out]
            return np.asarray(__lowerCamelCase  )




      def 				lowercase__    (			self			: Any			,			__snake_case			: List[str]  )				->      Union[str, Any]:
            # return the data of image after convoluting process so we can check it out
            _lowerCAmelCase							     =							np.asmatrix(__lowerCamelCase  )
            _lowerCAmelCase     ,							_lowerCAmelCase							     =							self.convolute(
                __lowerCamelCase			,			self.conva			,			self.w_conva			,			self.thre_conva			,			conv_step=self.step_conva			,			)
            _lowerCAmelCase							     =							self.pooling(__lowerCamelCase			,			self.size_poolinga  )

            return data_conveda, data_pooleda


if __name__ == "__main__":
      pass












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		:				List[Any]       			=		'0.12'  # assumed parallelism: 8


if is_torch_available():
		import torch




def 			__magic_name__				( A		:					Dict,  A		:					Union[str, Any],  A		:					Optional[int]=None   ):






			'''simple docstring'''







			if rng is None:
						a    =			random.Random()

			a    =			1
			for dim in shape:
						total_dims *= dim

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

			a    =			np.array(A,  dtype=jnp.intaa   ).reshape(A   )

			return output




def 			__magic_name__				( A		:					Dict,  A		:					Union[str, Any]=None   ):






			'''simple docstring'''







			a    =			ids_tensor(A,  vocab_size=2,  rng=A   )
			# make sure that at least one token is attended to for each batch
			a    =			1
			return attn_mask




@require_flax
class     snake_case__							:
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							List[str]							 =					None
				SCREAMING_SNAKE_CASE_       :							Any							 =					()





				def 							__UpperCAmelCase							(      self				:     int							)					->     List[str]:
							a    ,		a    =			self.model_tester.prepare_config_and_inputs_for_common()

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

							a    =			jnp.ones_like(__lowerCamelCase							)
							a    =			attention_mask[:max_batch_size, :sequence_length]

							# generate max 5 tokens
							a    =			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    =			config.eos_token_id
							return config, input_ids, attention_mask, max_length





				@is_pt_flax_cross_test
				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     int:
							a    ,		a    ,		a    ,		a    =			self._get_input_ids_and_config()
							a    =			False
							a    =			max_length
							a    =			0

							for model_class in self.all_generative_model_classes:
										a    =			model_class(__lowerCamelCase							)

										a    =			model_class.__name__[4:]  # Skip the "Flax" at the beginning
										a    =			getattr(__lowerCamelCase					,      __lowerCamelCase							)
										a    =			pt_model_class(__lowerCamelCase							).eval()
										a    =			load_flax_weights_in_pytorch_model(__lowerCamelCase					,      flax_model.params							)

										a    =			flax_model.generate(__lowerCamelCase							).sequences
										a    =			pt_model.generate(torch.tensor(__lowerCamelCase					,      dtype=torch.long							)							)

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

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





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Optional[int]:
							a    ,		a    ,		a    ,		a    =			self._get_input_ids_and_config()
							a    =			False
							a    =			max_length

							for model_class in self.all_generative_model_classes:
										a    =			model_class(__lowerCamelCase							)

										a    =			model.generate(__lowerCamelCase							).sequences
										self.assertEqual(generation_outputs.shape[-1]					,      __lowerCamelCase							)

										a    =			jit(model.generate							)
										a    =			jit_generate(__lowerCamelCase							).sequences

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





				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     Any:
							a    ,		a    ,		a    ,		a    =			self._get_input_ids_and_config()
							a    =			True
							a    =			max_length

							for model_class in self.all_generative_model_classes:
										a    =			model_class(__lowerCamelCase							)

										a    =			model.generate(__lowerCamelCase							).sequences
										self.assertEqual(generation_outputs.shape[-1]					,      __lowerCamelCase							)

										a    =			jit(model.generate							)
										a    =			jit_generate(__lowerCamelCase							).sequences

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





				def 							__UpperCAmelCase							(      self				:     int							)					->     Dict:
							a    ,		a    ,		a    ,		a    =			self._get_input_ids_and_config()
							a    =			False
							a    =			max_length
							a    =			2

							for model_class in self.all_generative_model_classes:
										a    =			model_class(__lowerCamelCase							)

										a    =			model.generate(__lowerCamelCase							).sequences
										self.assertEqual(generation_outputs.shape[-1]					,      __lowerCamelCase							)

										a    =			jit(model.generate							)
										a    =			jit_generate(__lowerCamelCase							).sequences

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





				def 							__UpperCAmelCase							(      self				:     Any							)					->     Union[str, Any]:
							a    ,		a    ,		a    ,		a    =			self._get_input_ids_and_config()
							a    =			False
							a    =			max_length
							a    =			2
							a    =			2

							for model_class in self.all_generative_model_classes:
										a    =			model_class(__lowerCamelCase							)

										a    =			model.generate(__lowerCamelCase							).sequences
										self.assertEqual(generation_outputs.shape[0]					,      input_ids.shape[0] * config.num_return_sequences							)





				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     Dict:
							a    ,		a    ,		a    ,		a    =			self._get_input_ids_and_config()
							a    =			True
							a    =			max_length
							a    =			0.8
							a    =			10
							a    =			0.3
							a    =			1
							a    =			8
							a    =			9

							for model_class in self.all_generative_model_classes:
										a    =			model_class(__lowerCamelCase							)

										a    =			model.generate(__lowerCamelCase							).sequences
										self.assertEqual(generation_outputs.shape[-1]					,      __lowerCamelCase							)

										a    =			jit(model.generate							)
										a    =			jit_generate(__lowerCamelCase							).sequences

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





				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     Optional[Any]:
							a    ,		a    ,		a    ,		a    =			self._get_input_ids_and_config()
							a    =			max_length
							a    =			1
							a    =			8
							a    =			9

							for model_class in self.all_generative_model_classes:
										a    =			model_class(__lowerCamelCase							)

										a    =			model.generate(__lowerCamelCase							).sequences
										self.assertEqual(generation_outputs.shape[-1]					,      __lowerCamelCase							)

										a    =			jit(model.generate							)
										a    =			jit_generate(__lowerCamelCase							).sequences

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





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Union[str, Any]:
							a    ,		a    ,		a    ,		a    =			self._get_input_ids_and_config()
							a    =			max_length
							a    =			2
							a    =			1
							a    =			8
							a    =			9

							for model_class in self.all_generative_model_classes:
										a    =			model_class(__lowerCamelCase							)

										a    =			model.generate(__lowerCamelCase							).sequences
										self.assertEqual(generation_outputs.shape[-1]					,      __lowerCamelCase							)

										a    =			jit(model.generate							)
										a    =			jit_generate(__lowerCamelCase							).sequences

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





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]							)					->     Dict:
							a    ,		a    ,		a    ,		a    =			self._get_input_ids_and_config()

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

							a    =			False
							a    =			max_length

							for model_class in self.all_generative_model_classes:
										a    =			model_class(__lowerCamelCase							)

										a    =			model.generate(__lowerCamelCase					,      attention_mask=__lowerCamelCase							).sequences
										self.assertEqual(generation_outputs.shape[-1]					,      __lowerCamelCase							)

										a    =			jit(model.generate							)
										a    =			jit_generate(__lowerCamelCase					,      attention_mask=__lowerCamelCase							).sequences

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





				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     Tuple:
							a    ,		a    ,		a    ,		a    =			self._get_input_ids_and_config()

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

							a    =			True
							a    =			max_length

							for model_class in self.all_generative_model_classes:
										a    =			model_class(__lowerCamelCase							)

										a    =			model.generate(__lowerCamelCase					,      attention_mask=__lowerCamelCase							).sequences
										self.assertEqual(generation_outputs.shape[-1]					,      __lowerCamelCase							)

										a    =			jit(model.generate							)
										a    =			jit_generate(__lowerCamelCase					,      attention_mask=__lowerCamelCase							).sequences

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





				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     List[Any]:
							a    ,		a    ,		a    ,		a    =			self._get_input_ids_and_config()

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

							a    =			2
							a    =			max_length

							for model_class in self.all_generative_model_classes:
										a    =			model_class(__lowerCamelCase							)

										a    =			model.generate(__lowerCamelCase					,      attention_mask=__lowerCamelCase							).sequences
										self.assertEqual(generation_outputs.shape[-1]					,      __lowerCamelCase							)

										a    =			jit(model.generate							)
										a    =			jit_generate(__lowerCamelCase					,      attention_mask=__lowerCamelCase							).sequences

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




@require_flax
class     snake_case__							(unittest.TestCase   ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(      self				:     Dict							)					->     Optional[Any]:
							a    =			AutoTokenizer.from_pretrained("hf-internal-testing/tiny-bert"							)
							a    =			FlaxAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-bert-flax-only"							)

							a    =			"Hello world"
							a    =			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"							):
										a    =			{"foo": "bar"}
										model.generate(__lowerCamelCase					,      **__lowerCamelCase							)






