






import pickle

import numpy as np
from matplotlib import pyplot as plt







class 	_UpperCAmelCase  :




      """simple docstring"""



      def __init__(   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase=0.2    ,	_lowerCAmelCase=0.2		):



          '''simple docstring'''
          lowerCAmelCase__   :Tuple      							=				bp_numa
          lowerCAmelCase__   :Tuple      							=				bp_numa
          lowerCAmelCase__   :List[str]      							=				bp_numa
          lowerCAmelCase__   :List[Any]      							=				conva_get[:2]
          lowerCAmelCase__   :List[str]      							=				conva_get[2]
          lowerCAmelCase__   :Dict      							=				size_pa
          lowerCAmelCase__   :List[str]      							=				rate_w
          lowerCAmelCase__   :Union[str, Any]      							=				rate_t
          lowerCAmelCase__   :Dict      							=				[
              np.mat(-1 * np.random.rand(self.conva[0]    ,	self.conva[0]		) + 0.5		)
              for i in range(self.conva[1]		)
          ]
          lowerCAmelCase__   :Any      							=				np.mat(-1 * np.random.rand(self.num_bpa    ,	self.num_bpa		) + 0.5		)
          lowerCAmelCase__   :Union[str, Any]      							=				np.mat(-1 * np.random.rand(self.num_bpa    ,	self.num_bpa		) + 0.5		)
          lowerCAmelCase__   :Tuple      							=				-2 * np.random.rand(self.conva[1]		) + 1
          lowerCAmelCase__   :int      							=				-2 * np.random.rand(self.num_bpa		) + 1
          lowerCAmelCase__   :Dict      							=				-2 * np.random.rand(self.num_bpa		) + 1
      def 			snake_case_      (   self    ,	_lowerCAmelCase		):



          '''simple docstring'''
          # save model dict with pickle
          lowerCAmelCase__   :Optional[int]      							=				{
              "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 			snake_case_      (   cls    ,	_lowerCAmelCase		):



          '''simple docstring'''
          # read saved model
          with open(_lowerCamelCase    ,	"rb"		) as f:
              lowerCAmelCase__   :Tuple      							=				pickle.load(_lowerCamelCase		)  # noqa: S301

          lowerCAmelCase__   :Tuple      							=				model_dic.get("conv1"		)
          conv_get.append(model_dic.get("step_conv1"		)		)
          lowerCAmelCase__   :Tuple      							=				model_dic.get("size_pooling1"		)
          lowerCAmelCase__   :Tuple      							=				model_dic.get("num_bp1"		)
          lowerCAmelCase__   :List[str]      							=				model_dic.get("num_bp2"		)
          lowerCAmelCase__   :Dict      							=				model_dic.get("num_bp3"		)
          lowerCAmelCase__   :List[str]      							=				model_dic.get("rate_weight"		)
          lowerCAmelCase__   :Optional[int]      							=				model_dic.get("rate_thre"		)
          # create model instance
          lowerCAmelCase__   :Dict      							=				CNN(_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase		)
          # modify model parameter
          lowerCAmelCase__   :List[str]      							=				model_dic.get("w_conv1"		)
          lowerCAmelCase__   :Optional[Any]      							=				model_dic.get("wkj"		)
          lowerCAmelCase__   :Any      							=				model_dic.get("vji"		)
          lowerCAmelCase__   :int      							=				model_dic.get("thre_conv1"		)
          lowerCAmelCase__   :Optional[Any]      							=				model_dic.get("thre_bp2"		)
          lowerCAmelCase__   :Union[str, Any]      							=				model_dic.get("thre_bp3"		)
          return conv_ins
      def 			snake_case_      (   self    ,	_lowerCAmelCase		):



          '''simple docstring'''
          return 1 / (1 + np.exp(-1 * x		))
      def 			snake_case_      (   self    ,	_lowerCAmelCase		):



          '''simple docstring'''
          return round(_lowerCamelCase    ,	3		)
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		):



          '''simple docstring'''
          # convolution process
          lowerCAmelCase__   :Any      							=				convs[0]
          lowerCAmelCase__   :Dict      							=				convs[1]
          lowerCAmelCase__   :int      							=				np.shape(_lowerCamelCase		)[0]
          # get the data slice of original image data, data_focus
          lowerCAmelCase__   :str      							=				[]
          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__   :str      							=				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__   :str      							=				[]
          lowerCAmelCase__   :Optional[Any]      							=				int((size_data - size_conv) / conv_step + 1		)
          for i_map in range(_lowerCamelCase		):
              lowerCAmelCase__   :List[str]      							=				[]
              for i_focus in range(len(_lowerCamelCase		)		):
                  lowerCAmelCase__   :Dict      							=				(
                      np.sum(np.multiply(data_focus[i_focus]    ,	w_convs[i_map]		)		)
                      - thre_convs[i_map]
                  )
                  featuremap.append(self.sig(_lowerCamelCase		)		)
              lowerCAmelCase__   :Optional[Any]      							=				np.asmatrix(_lowerCamelCase		).reshape(
                  _lowerCamelCase    ,	_lowerCamelCase		)
              data_featuremap.append(_lowerCamelCase		)

          # expanding the data slice to One dimenssion
          lowerCAmelCase__   :Tuple      							=				[]
          for each_focus in data_focus:
              focusa_list.extend(self.Expand_Mat(_lowerCamelCase		)		)
          lowerCAmelCase__   :Union[str, Any]      							=				np.asarray(_lowerCamelCase		)
          return focus_list, data_featuremap
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase="average_pool"		):



          '''simple docstring'''
          # pooling process
          lowerCAmelCase__   :Union[str, Any]      							=				len(featuremaps[0]		)
          lowerCAmelCase__   :List[Any]      							=				int(size_map / size_pooling		)
          lowerCAmelCase__   :List[str]      							=				[]
          for i_map in range(len(_lowerCamelCase		)		):
              lowerCAmelCase__   :Any      							=				featuremaps[i_map]
              lowerCAmelCase__   :Dict      							=				[]
              for i_focus in range(0    ,	_lowerCamelCase    ,	_lowerCamelCase		):
                  for j_focus in range(0    ,	_lowerCamelCase    ,	_lowerCamelCase		):
                      lowerCAmelCase__   :Any      							=				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__   :int      							=				np.asmatrix(_lowerCamelCase		).reshape(_lowerCamelCase    ,	_lowerCamelCase		)
              featuremap_pooled.append(_lowerCamelCase		)
          return featuremap_pooled
      def 			snake_case_      (   self    ,	_lowerCAmelCase		):



          '''simple docstring'''
          # expanding three dimension data to one dimension list
          lowerCAmelCase__   :List[str]      							=				[]
          for i in range(len(_lowerCamelCase		)		):
              lowerCAmelCase__   :str      							=				np.shape(data[i]		)
              lowerCAmelCase__   :Optional[int]      							=				data[i].reshape(1    ,	shapes[0] * shapes[1]		)
              lowerCAmelCase__   :Optional[Any]      							=				data_listed.getA().tolist()[0]
              data_expanded.extend(_lowerCamelCase		)
          lowerCAmelCase__   :Any      							=				np.asarray(_lowerCamelCase		)
          return data_expanded
      def 			snake_case_      (   self    ,	_lowerCAmelCase		):



          '''simple docstring'''
          # expanding matrix to one dimension list
          lowerCAmelCase__   :List[Any]      							=				np.asarray(_lowerCamelCase		)
          lowerCAmelCase__   :Any      							=				np.shape(_lowerCamelCase		)
          lowerCAmelCase__   :List[str]      							=				data_mat.reshape(1    ,	shapes[0] * shapes[1]		)
          return data_expanded
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :Dict      							=				[]
          lowerCAmelCase__   :str      							=				0
          for i_map in range(_lowerCamelCase		):
              lowerCAmelCase__   :str      							=				np.ones((size_map, size_map)		)
              for i in range(0    ,	_lowerCamelCase    ,	_lowerCamelCase		):
                  for j in range(0    ,	_lowerCamelCase    ,	_lowerCamelCase		):
                      lowerCAmelCase__   :int      							=				pd_pool[
                          i_pool
                      ]
                      lowerCAmelCase__   :int      							=				i_pool + 1
              lowerCAmelCase__   :Tuple      							=				np.multiply(
                  _lowerCamelCase    ,	np.multiply(out_map[i_map]    ,	(1 - out_map[i_map])		)		)
              pd_all.append(_lowerCamelCase		)
          return pd_all
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase=bool		):



          '''simple docstring'''
          # model traning
          print("----------------------Start Training-------------------------"		)
          print((" - - Shape: Train_Data  ", np.shape(_lowerCamelCase		))		)
          print((" - - Shape: Teach_Data  ", np.shape(_lowerCamelCase		))		)
          lowerCAmelCase__   :int      							=				0
          lowerCAmelCase__   :str      							=				[]
          lowerCAmelCase__   :Tuple      							=				10_000
          while rp < n_repeat and mse >= error_accuracy:
              lowerCAmelCase__   :Optional[Any]      							=				0
              print(f'''-------------Learning Time {rp}--------------'''		)
              for p in range(len(_lowerCamelCase		)		):
                  # print('------------Learning Image: %d--------------'%p)
                  lowerCAmelCase__   :Optional[Any]      							=				np.asmatrix(datas_train[p]		)
                  lowerCAmelCase__   :Tuple      							=				np.asarray(datas_teach[p]		)
                  lowerCAmelCase__							,lowerCAmelCase__   :Dict      							=				self.convolute(
                      _lowerCamelCase    ,	self.conva    ,	self.w_conva    ,	self.thre_conva    ,	conv_step=self.step_conva    ,	)
                  lowerCAmelCase__   :Union[str, Any]      							=				self.pooling(_lowerCamelCase    ,	self.size_poolinga		)
                  lowerCAmelCase__   :Tuple      							=				np.shape(_lowerCamelCase		)
                  lowerCAmelCase__   :str      							=				self._expand(_lowerCamelCase		)
                  lowerCAmelCase__   :List[str]      							=				data_bp_input

                  lowerCAmelCase__   :Any      							=				np.dot(_lowerCamelCase    ,	self.vji.T		) - self.thre_bpa
                  lowerCAmelCase__   :Any      							=				self.sig(_lowerCamelCase		)
                  lowerCAmelCase__   :Optional[int]      							=				np.dot(_lowerCamelCase    ,	self.wkj.T		) - self.thre_bpa
                  lowerCAmelCase__   :List[str]      							=				self.sig(_lowerCamelCase		)

                  # --------------Model Leaning ------------------------
                  # calculate error and gradient---------------
                  lowerCAmelCase__   :List[Any]      							=				np.multiply(
                      (data_teach - bp_outa)    ,	np.multiply(_lowerCamelCase    ,	(1 - bp_outa)		)		)
                  lowerCAmelCase__   :str      							=				np.multiply(
                      np.dot(_lowerCamelCase    ,	self.wkj		)    ,	np.multiply(_lowerCamelCase    ,	(1 - bp_outa)		)		)
                  lowerCAmelCase__   :str      							=				np.dot(_lowerCamelCase    ,	self.vji		)

                  lowerCAmelCase__   :List[str]      							=				pd_i_all / (self.size_poolinga * self.size_poolinga)
                  lowerCAmelCase__   :int      							=				pd_conva_pooled.T.getA().tolist()
                  lowerCAmelCase__   :Optional[int]      							=				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__   :Union[str, Any]      							=				self._expand_mat(pd_conva_all[k_conv]		)
                      lowerCAmelCase__   :List[Any]      							=				self.rate_weight * np.dot(_lowerCamelCase    ,	_lowerCamelCase		)

                      lowerCAmelCase__   :Union[str, Any]      							=				self.w_conva[k_conv] + delta_w.reshape(
                          (self.conva[0], self.conva[0])		)

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

          def draw_error():
              lowerCAmelCase__   :Tuple      							=				[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 			snake_case_      (   self    ,	_lowerCAmelCase		):



          '''simple docstring'''
          # model predict
          lowerCAmelCase__   :Optional[int]      							=				[]
          print("-------------------Start Testing-------------------------"		)
          print((" - - Shape: Test_Data  ", np.shape(_lowerCamelCase		))		)
          for p in range(len(_lowerCamelCase		)		):
              lowerCAmelCase__   :Tuple      							=				np.asmatrix(datas_test[p]		)
              lowerCAmelCase__							,lowerCAmelCase__   :Optional[int]      							=				self.convolute(
                  _lowerCamelCase    ,	self.conva    ,	self.w_conva    ,	self.thre_conva    ,	conv_step=self.step_conva    ,	)
              lowerCAmelCase__   :Union[str, Any]      							=				self.pooling(_lowerCamelCase    ,	self.size_poolinga		)
              lowerCAmelCase__   :Any      							=				self._expand(_lowerCamelCase		)

              lowerCAmelCase__   :List[str]      							=				data_bp_input
              lowerCAmelCase__   :Optional[Any]      							=				bp_outa * self.vji.T - self.thre_bpa
              lowerCAmelCase__   :Optional[int]      							=				self.sig(_lowerCamelCase		)
              lowerCAmelCase__   :Any      							=				bp_outa * self.wkj.T - self.thre_bpa
              lowerCAmelCase__   :str      							=				self.sig(_lowerCamelCase		)
              produce_out.extend(bp_outa.getA().tolist()		)
          lowerCAmelCase__   :Optional[int]      							=				[list(map(self.do_round    ,	_lowerCamelCase		)		) for each in produce_out]
          return np.asarray(_lowerCamelCase		)



      def 			snake_case_      (   self    ,	_lowerCAmelCase		):



          '''simple docstring'''
          # return the data of image after convoluting process so we can check it out
          lowerCAmelCase__   :Tuple      							=				np.asmatrix(_lowerCamelCase		)
          lowerCAmelCase__							,lowerCAmelCase__   :Tuple      							=				self.convolute(
              _lowerCamelCase    ,	self.conva    ,	self.w_conva    ,	self.thre_conva    ,	conv_step=self.step_conva    ,	)
          lowerCAmelCase__   :int      							=				self.pooling(_lowerCamelCase    ,	self.size_poolinga		)

          return data_conveda, data_pooleda


if __name__ == "__main__":
 pass









from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel







@require_tf
class 	_UpperCAmelCase  :




      """simple docstring"""



      A          =    MBartConfig
      A          =    {}
      A          =    '''gelu'''
      def __init__(   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase=13    ,	_lowerCAmelCase=7    ,	_lowerCAmelCase=True    ,	_lowerCAmelCase=False    ,	_lowerCAmelCase=99    ,	_lowerCAmelCase=32    ,	_lowerCAmelCase=2    ,	_lowerCAmelCase=4    ,	_lowerCAmelCase=37    ,	_lowerCAmelCase=0.1    ,	_lowerCAmelCase=0.1    ,	_lowerCAmelCase=20    ,	_lowerCAmelCase=2    ,	_lowerCAmelCase=1    ,	_lowerCAmelCase=0    ,	):



          '''simple docstring'''
          lowerCAmelCase__   :Optional[int]      							=				parent
          lowerCAmelCase__   :Dict      							=				batch_size
          lowerCAmelCase__   :Optional[int]      							=				seq_length
          lowerCAmelCase__   :Any      							=				is_training
          lowerCAmelCase__   :List[Any]      							=				use_labels
          lowerCAmelCase__   :Optional[int]      							=				vocab_size
          lowerCAmelCase__   :Optional[int]      							=				hidden_size
          lowerCAmelCase__   :Optional[Any]      							=				num_hidden_layers
          lowerCAmelCase__   :List[str]      							=				num_attention_heads
          lowerCAmelCase__   :List[str]      							=				intermediate_size
          lowerCAmelCase__   :List[str]      							=				hidden_dropout_prob
          lowerCAmelCase__   :Optional[Any]      							=				attention_probs_dropout_prob
          lowerCAmelCase__   :Tuple      							=				max_position_embeddings
          lowerCAmelCase__   :Optional[Any]      							=				eos_token_id
          lowerCAmelCase__   :int      							=				pad_token_id
          lowerCAmelCase__   :Any      							=				bos_token_id
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Tuple      							=				ids_tensor([self.batch_size, self.seq_length - 1]    ,	self.vocab_size		)
          lowerCAmelCase__   :List[Any]      							=				tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		)    ,	1		)
          lowerCAmelCase__   :Union[str, Any]      							=				tf.concat([input_ids, eos_tensor]    ,	axis=1		)

          lowerCAmelCase__   :str      							=				ids_tensor([self.batch_size, self.seq_length]    ,	self.vocab_size		)

          lowerCAmelCase__   :Dict      							=				self.config_cls(
              vocab_size=self.vocab_size    ,	d_model=self.hidden_size    ,	encoder_layers=self.num_hidden_layers    ,	decoder_layers=self.num_hidden_layers    ,	encoder_attention_heads=self.num_attention_heads    ,	decoder_attention_heads=self.num_attention_heads    ,	encoder_ffn_dim=self.intermediate_size    ,	decoder_ffn_dim=self.intermediate_size    ,	dropout=self.hidden_dropout_prob    ,	attention_dropout=self.attention_probs_dropout_prob    ,	max_position_embeddings=self.max_position_embeddings    ,	eos_token_ids=[2]    ,	bos_token_id=self.bos_token_id    ,	pad_token_id=self.pad_token_id    ,	decoder_start_token_id=self.pad_token_id    ,	**self.config_updates    ,	)
          lowerCAmelCase__   :Union[str, Any]      							=				prepare_mbart_inputs_dict(_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		)
          return config, inputs_dict



      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :int      							=				TFMBartModel(config=_lowerCAmelCase		).get_decoder()
          lowerCAmelCase__   :Tuple      							=				inputs_dict["input_ids"]

          lowerCAmelCase__   :Dict      							=				input_ids[:1, :]
          lowerCAmelCase__   :Dict      							=				inputs_dict["attention_mask"][:1, :]
          lowerCAmelCase__   :List[str]      							=				inputs_dict["head_mask"]
          lowerCAmelCase__   :Optional[Any]      							=				1

          # first forward pass
          lowerCAmelCase__   :List[Any]      							=				model(_lowerCAmelCase    ,	attention_mask=_lowerCAmelCase    ,	head_mask=_lowerCAmelCase    ,	use_cache=_lowerCAmelCase		)

          lowerCAmelCase__							,lowerCAmelCase__   :Any      							=				outputs.to_tuple()
          lowerCAmelCase__   :Tuple      							=				past_key_values[1]





def 				snake_case__							(							UpperCAmelCase							:  Union[str, Any]    ,			UpperCAmelCase							:  Optional[Any]    ,			UpperCAmelCase							:  Optional[int]    ,			UpperCAmelCase							:  int=None    ,			UpperCAmelCase							:  Optional[Any]=None    ,			UpperCAmelCase							:  Any=None    ,			UpperCAmelCase							:  Dict=None    ,			UpperCAmelCase							:  Dict=None    ,			):
    if attention_mask is None:
        lowerCAmelCase__   :str      							=				tf.cast(tf.math.not_equal(UpperCAmelCase    ,			config.pad_token_id       )    ,			tf.inta       )
    if decoder_attention_mask is None:
        lowerCAmelCase__   :int      							=				tf.concat(
            [
                tf.ones(decoder_input_ids[:, :1].shape    ,			dtype=tf.inta       ),
                tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]    ,			config.pad_token_id       )    ,			tf.inta       ),
            ]    ,			axis=-1    ,			)
    if head_mask is None:
        lowerCAmelCase__   :str      							=				tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
    if decoder_head_mask is None:
        lowerCAmelCase__   :Tuple      							=				tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
    if cross_attn_head_mask is None:
        lowerCAmelCase__   :Optional[Any]      							=				tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
    return {
        "input_ids": input_ids,
        "decoder_input_ids": decoder_input_ids,
        "attention_mask": attention_mask,
        "decoder_attention_mask": decoder_attention_mask,
        "head_mask": head_mask,
        "decoder_head_mask": decoder_head_mask,
        "cross_attn_head_mask": cross_attn_head_mask,
    }







@require_tf
class 	_UpperCAmelCase  (  _A				,							_A				,							unittest.TestCase		):




      """simple docstring"""



      A          =    (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
      A          =    (TFMBartForConditionalGeneration,) if is_tf_available() else ()
      A          =    (
          {
              '''conversational''': TFMBartForConditionalGeneration,
              '''feature-extraction''': TFMBartModel,
              '''summarization''': TFMBartForConditionalGeneration,
              '''text2text-generation''': TFMBartForConditionalGeneration,
              '''translation''': TFMBartForConditionalGeneration,
          }
          if is_tf_available()
          else {}
      )
      A          =    True
      A          =    False
      A          =    False
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		):



          '''simple docstring'''
          if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
              # Exception encountered when calling layer '...'
              return True

          return False
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :List[str]      							=				TFMBartModelTester(self		)
          lowerCAmelCase__   :Union[str, Any]      							=				ConfigTester(self    ,	config_class=_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          self.config_tester.run_common_tests()



      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Tuple      							=				self.model_tester.prepare_config_and_inputs_for_common()
          self.model_tester.check_decoder_model_past_large_inputs(*_lowerCAmelCase		)







@require_sentencepiece
@require_tokenizers
@require_tf
class 	_UpperCAmelCase  (  unittest.TestCase		):




      """simple docstring"""



      A          =    [
          ''' UN Chief Says There Is No Military Solution in Syria''',
      ]
      A          =    [
          '''Şeful ONU declară că nu există o soluţie militară în Siria''',
      ]
      A          =    '''facebook/mbart-large-en-ro'''
      @cached_property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return AutoTokenizer.from_pretrained(self.model_name		)
      @cached_property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Union[str, Any]      							=				TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
          return model
      def 			snake_case_      (   self    ,	**_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :Tuple      							=				self.translate_src_text(**_lowerCAmelCase		)
          self.assertListEqual(self.expected_text    ,	_lowerCAmelCase		)
      def 			snake_case_      (   self    ,	**_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :Tuple      							=				self.tokenizer(self.src_text    ,	**_lowerCAmelCase    ,	return_tensors="tf"		)
          lowerCAmelCase__   :str      							=				self.model.generate(
              model_inputs.input_ids    ,	attention_mask=model_inputs.attention_mask    ,	num_beams=2		)
          lowerCAmelCase__   :int      							=				self.tokenizer.batch_decode(_lowerCAmelCase    ,	skip_special_tokens=_lowerCAmelCase		)
          return generated_words



      @slow
      def 			snake_case_      (   self		):



          '''simple docstring'''
          self._assert_generated_batch_equal_expected()



