

"""simple docstring"""





import pickle

import numpy as np
from matplotlib import pyplot as plt




class 					_lowerCAmelCase  :







 """simple docstring"""







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


 def 					_lowercase (			self							:			Union[str, Any],					UpperCAmelCase__							:			Tuple	):
    # save model dict with pickle
    __lowercase								=						{
        "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(UpperCAmelCase__,					"wb"	) as f:
       pickle.dump(UpperCAmelCase__,					UpperCAmelCase__	)

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


 @classmethod
 def 					_lowercase (			cls							:			int,					UpperCAmelCase__							:			int	):
    # read saved model
    with open(UpperCAmelCase__,					"rb"	) as f:
       __lowercase								=						pickle.load(UpperCAmelCase__	)  # noqa: S301

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


 def 					_lowercase (			self							:			List[str],					UpperCAmelCase__							:			Dict	):
    return 1 / (1 + np.exp(-1 * x	))


 def 					_lowercase (			self							:			Union[str, Any],					UpperCAmelCase__							:			Any	):
    return round(UpperCAmelCase__,					3	)


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

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


 def 					_lowercase (			self							:			Optional[int],					UpperCAmelCase__							:			Dict,					UpperCAmelCase__							:			Union[str, Any],					UpperCAmelCase__							:			Optional[int]="average_pool"	):
    # pooling process
    __lowercase								=						len(featuremaps[0]	)
    __lowercase								=						int(size_map / size_pooling	)
    __lowercase								=						[]
    for i_map in range(len(UpperCAmelCase__	)	):
       __lowercase								=						featuremaps[i_map]
       __lowercase								=						[]
       for i_focus in range(0,					UpperCAmelCase__,					UpperCAmelCase__	):
          for j_focus in range(0,					UpperCAmelCase__,					UpperCAmelCase__	):
             __lowercase								=						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(UpperCAmelCase__	)	)
             elif pooling_type == "max_pooling":
                # max pooling
                map_pooled.append(np.max(UpperCAmelCase__	)	)
       __lowercase								=						np.asmatrix(UpperCAmelCase__	).reshape(UpperCAmelCase__,					UpperCAmelCase__	)
       featuremap_pooled.append(UpperCAmelCase__	)
    return featuremap_pooled


 def 					_lowercase (			self							:			str,					UpperCAmelCase__							:			List[Any]	):
    # expanding three dimension data to one dimension list
    __lowercase								=						[]
    for i in range(len(UpperCAmelCase__	)	):
       __lowercase								=						np.shape(data[i]	)
       __lowercase								=						data[i].reshape(1,					shapes[0] * shapes[1]	)
       __lowercase								=						data_listed.getA().tolist()[0]
       data_expanded.extend(UpperCAmelCase__	)
    __lowercase								=						np.asarray(UpperCAmelCase__	)
    return data_expanded


 def 					_lowercase (			self							:			Union[str, Any],					UpperCAmelCase__							:			Dict	):
    # expanding matrix to one dimension list
    __lowercase								=						np.asarray(UpperCAmelCase__	)
    __lowercase								=						np.shape(UpperCAmelCase__	)
    __lowercase								=						data_mat.reshape(1,					shapes[0] * shapes[1]	)
    return data_expanded


 def 					_lowercase (			self							:			Tuple,					UpperCAmelCase__							:			Any,					UpperCAmelCase__							:			Tuple,					UpperCAmelCase__							:			Optional[Any],					UpperCAmelCase__							:			List[str],					UpperCAmelCase__							:			str	):
    __lowercase								=						[]
    __lowercase								=						0
    for i_map in range(UpperCAmelCase__	):
       __lowercase								=						np.ones((size_map, size_map)	)
       for i in range(0,					UpperCAmelCase__,					UpperCAmelCase__	):
          for j in range(0,					UpperCAmelCase__,					UpperCAmelCase__	):
             __lowercase								=						pd_pool[
                 i_pool
             ]
             __lowercase								=						i_pool + 1
       __lowercase								=						np.multiply(
           UpperCAmelCase__,					np.multiply(out_map[i_map],					(1 - out_map[i_map])	)	)
       pd_all.append(UpperCAmelCase__	)
    return pd_all


 def 					_lowercase (			self							:			Optional[Any],					UpperCAmelCase__							:			List[str],					UpperCAmelCase__							:			str,					UpperCAmelCase__							:			Dict,					UpperCAmelCase__							:			str,					UpperCAmelCase__							:			Optional[Any],					UpperCAmelCase__							:			Tuple=bool	):
    # model traning
    print("----------------------Start Training-------------------------"	)
    print((" - - Shape: Train_Data  ", np.shape(UpperCAmelCase__	))	)
    print((" - - Shape: Teach_Data  ", np.shape(UpperCAmelCase__	))	)
    __lowercase								=						0
    __lowercase								=						[]
    __lowercase								=						1_0_0_0_0
    while rp < n_repeat and mse >= error_accuracy:
       __lowercase								=						0
       print(F"""-------------Learning Time {rp}--------------"""	)
       for p in range(len(UpperCAmelCase__	)	):
          # print('------------Learning Image: %d--------------'%p)
          __lowercase								=						np.asmatrix(datas_train[p]	)
          __lowercase								=						np.asarray(datas_teach[p]	)
          __lowercase		,__lowercase								=						self.convolute(
              UpperCAmelCase__,					self.conva,					self.w_conva,					self.thre_conva,					conv_step=self.step_conva,					)
          __lowercase								=						self.pooling(UpperCAmelCase__,					self.size_poolinga	)
          __lowercase								=						np.shape(UpperCAmelCase__	)
          __lowercase								=						self._expand(UpperCAmelCase__	)
          __lowercase								=						data_bp_input

          __lowercase								=						np.dot(UpperCAmelCase__,					self.vji.T	) - self.thre_bpa
          __lowercase								=						self.sig(UpperCAmelCase__	)
          __lowercase								=						np.dot(UpperCAmelCase__,					self.wkj.T	) - self.thre_bpa
          __lowercase								=						self.sig(UpperCAmelCase__	)

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

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

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

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

    def draw_error():
       __lowercase								=						[error_accuracy for i in range(int(n_repeat * 1.2	)	)]
       plt.plot(UpperCAmelCase__,					"+-"	)
       plt.plot(UpperCAmelCase__,					"r--"	)
       plt.xlabel("Learning Times"	)
       plt.ylabel("All_mse"	)
       plt.grid(UpperCAmelCase__,					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							:			List[str],					UpperCAmelCase__							:			Any	):
    # model predict
    __lowercase								=						[]
    print("-------------------Start Testing-------------------------"	)
    print((" - - Shape: Test_Data  ", np.shape(UpperCAmelCase__	))	)
    for p in range(len(UpperCAmelCase__	)	):
       __lowercase								=						np.asmatrix(datas_test[p]	)
       __lowercase		,__lowercase								=						self.convolute(
           UpperCAmelCase__,					self.conva,					self.w_conva,					self.thre_conva,					conv_step=self.step_conva,					)
       __lowercase								=						self.pooling(UpperCAmelCase__,					self.size_poolinga	)
       __lowercase								=						self._expand(UpperCAmelCase__	)

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



 def 					_lowercase (			self							:			Optional[int],					UpperCAmelCase__							:			str	):
    # return the data of image after convoluting process so we can check it out
    __lowercase								=						np.asmatrix(UpperCAmelCase__	)
    __lowercase		,__lowercase								=						self.convolute(
        UpperCAmelCase__,					self.conva,					self.w_conva,					self.thre_conva,					conv_step=self.step_conva,					)
    __lowercase								=						self.pooling(UpperCAmelCase__,					self.size_poolinga	)

    return data_conveda, data_pooleda


if __name__ == "__main__":
   pass







"""simple docstring"""





from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
   import tensorflow as tf

   from transformers.models.xglm.modeling_tf_xglm import (
       TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
       TFXGLMForCausalLM,
       TFXGLMModel,
   )




@require_tf
class 					_lowerCAmelCase  :







 """simple docstring"""







 __UpperCAmelCase	:					Tuple   		=	XGLMConfig
 __UpperCAmelCase	:					Optional[Any]   		=	{}
 __UpperCAmelCase	:					Union[str, Any]   		=	"gelu"


 def __init__(			self							:			Optional[int],					UpperCAmelCase__							:			List[str],					UpperCAmelCase__							:			Optional[int]=1_4,					UpperCAmelCase__							:			str=7,					UpperCAmelCase__							:			Optional[Any]=True,					UpperCAmelCase__							:			List[Any]=True,					UpperCAmelCase__							:			int=True,					UpperCAmelCase__							:			List[str]=9_9,					UpperCAmelCase__							:			Union[str, Any]=3_2,					UpperCAmelCase__							:			Union[str, Any]=2,					UpperCAmelCase__							:			Union[str, Any]=4,					UpperCAmelCase__							:			Tuple=3_7,					UpperCAmelCase__							:			List[Any]="gelu",					UpperCAmelCase__							:			List[str]=0.1,					UpperCAmelCase__							:			Optional[int]=0.1,					UpperCAmelCase__							:			Tuple=5_1_2,					UpperCAmelCase__							:			Optional[Any]=0.02,					):
    __lowercase								=						parent
    __lowercase								=						batch_size
    __lowercase								=						seq_length
    __lowercase								=						is_training
    __lowercase								=						use_input_mask
    __lowercase								=						use_labels
    __lowercase								=						vocab_size
    __lowercase								=						d_model
    __lowercase								=						num_hidden_layers
    __lowercase								=						num_attention_heads
    __lowercase								=						ffn_dim
    __lowercase								=						activation_function
    __lowercase								=						activation_dropout
    __lowercase								=						attention_dropout
    __lowercase								=						max_position_embeddings
    __lowercase								=						initializer_range
    __lowercase								=						None
    __lowercase								=						0
    __lowercase								=						2
    __lowercase								=						1


 def 					_lowercase (			self							:			Union[str, Any]	):
    return XGLMConfig.from_pretrained("facebook/xglm-564M"	)


 def 					_lowercase (			self							:			Tuple	):
    __lowercase								=						tf.clip_by_value(
        ids_tensor([self.batch_size, self.seq_length],					self.vocab_size	),					clip_value_min=0,					clip_value_max=3	)

    __lowercase								=						None
    if self.use_input_mask:
       __lowercase								=						random_attention_mask([self.batch_size, self.seq_length]	)

    __lowercase								=						self.get_config()

    __lowercase								=						floats_tensor([self.num_hidden_layers, self.num_attention_heads],					2	)

    return (
        config,
        input_ids,
        input_mask,
        head_mask,
    )


 def 					_lowercase (			self							:			List[Any]	):
    return XGLMConfig(
        vocab_size=self.vocab_size,					d_model=self.hidden_size,					num_layers=self.num_hidden_layers,					attention_heads=self.num_attention_heads,					ffn_dim=self.ffn_dim,					activation_function=self.activation_function,					activation_dropout=self.activation_dropout,					attention_dropout=self.attention_dropout,					max_position_embeddings=self.max_position_embeddings,					initializer_range=self.initializer_range,					use_cache=UpperCAmelCase__,					bos_token_id=self.bos_token_id,					eos_token_id=self.eos_token_id,					pad_token_id=self.pad_token_id,					return_dict=UpperCAmelCase__,					)



 def 					_lowercase (			self							:			Dict	):
    __lowercase								=						self.prepare_config_and_inputs()

    (
        (
        __lowercase
    )		,(
        __lowercase
    )		,(
        __lowercase
    )		,(
        __lowercase
    )		,
    )								=						config_and_inputs

    __lowercase								=						{
        "input_ids": input_ids,
        "head_mask": head_mask,
    }

    return config, inputs_dict







@require_tf
class 					_lowerCAmelCase  (	lowercase     ,lowercase     ,unittest.TestCase   ):







 """simple docstring"""







 __UpperCAmelCase	:					Union[str, Any]   		=	(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
 __UpperCAmelCase	:					List[str]   		=	(TFXGLMForCausalLM,) if is_tf_available() else ()
 __UpperCAmelCase	:					Any   		=	(
     {"feature-extraction": TFXGLMModel, "text-generation": TFXGLMForCausalLM} if is_tf_available() else {}
 )
 __UpperCAmelCase	:					Optional[Any]   		=	False
 __UpperCAmelCase	:					List[str]   		=	False
 __UpperCAmelCase	:					int   		=	False


 def 					_lowercase (			self							:			Optional[Any]	):
    __lowercase								=						TFXGLMModelTester(self	)
    __lowercase								=						ConfigTester(self,					config_class=UpperCAmelCase__,					n_embd=3_7	)


 def 					_lowercase (			self							:			Any	):
    self.config_tester.run_common_tests()


 @slow
 def 					_lowercase (			self							:			List[str]	):
    for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __lowercase								=						TFXGLMModel.from_pretrained(UpperCAmelCase__	)
       self.assertIsNotNone(UpperCAmelCase__	)



 @unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor."	)
 def 					_lowercase (			self							:			int	):
    super().test_resize_token_embeddings()







@require_tf
class 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







 @slow
 def 					_lowercase (			self							:			Dict,					UpperCAmelCase__							:			Optional[int]=True	):
    __lowercase								=						TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"	)
    __lowercase								=						tf.convert_to_tensor([[2, 2_6_8, 9_8_6_5]],					dtype=tf.intaa	)  # The dog
    # </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
    # fmt: off
    __lowercase								=						[2, 2_6_8, 9_8_6_5, 6_7, 1_1, 1_9_8_8, 5_7_2_5_2, 9_8_6_5, 5, 9_8_4, 6_7, 1_9_8_8, 2_1_3_8_3_8, 1_6_5_8, 5_3, 7_0_4_4_6, 3_3, 6_6_5_7, 2_7_8, 1_5_8_1]
    # fmt: on
    __lowercase								=						model.generate(UpperCAmelCase__,					do_sample=UpperCAmelCase__,					num_beams=1	)
    if verify_outputs:
       self.assertListEqual(output_ids[0].numpy().tolist(),					UpperCAmelCase__	)


 @slow
 def 					_lowercase (			self							:			List[Any]	):
    __lowercase								=						XGLMTokenizer.from_pretrained("facebook/xglm-564M"	)
    __lowercase								=						TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"	)

    tf.random.set_seed(0	)
    __lowercase								=						tokenizer("Today is a nice day and",					return_tensors="tf"	)
    __lowercase								=						tokenized.input_ids
    # forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
    with tf.device(":/CPU:0"	):
       __lowercase								=						model.generate(UpperCAmelCase__,					do_sample=UpperCAmelCase__,					seed=[7, 0]	)
    __lowercase								=						tokenizer.decode(output_ids[0],					skip_special_tokens=UpperCAmelCase__	)

    __lowercase								=						(
        "Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"
    )
    self.assertEqual(UpperCAmelCase__,					UpperCAmelCase__	)



 @slow
 def 					_lowercase (			self							:			Dict	):
    __lowercase								=						TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"	)
    __lowercase								=						XGLMTokenizer.from_pretrained("facebook/xglm-564M"	)

    __lowercase								=						"left"

    # use different length sentences to test batching
    __lowercase								=						[
        "This is an extremelly long sentence that only exists to test the ability of the model to cope with "
        "left-padding, such as in batched generation. The output for the sequence below should be the same "
        "regardless of whether left padding is applied or not. When",
        "Hello, my dog is a little",
    ]

    __lowercase								=						tokenizer(UpperCAmelCase__,					return_tensors="tf",					padding=UpperCAmelCase__	)
    __lowercase								=						inputs["input_ids"]

    __lowercase								=						model.generate(input_ids=UpperCAmelCase__,					attention_mask=inputs["attention_mask"],					max_new_tokens=1_2	)

    __lowercase								=						tokenizer(sentences[0],					return_tensors="tf"	).input_ids
    __lowercase								=						model.generate(input_ids=UpperCAmelCase__,					max_new_tokens=1_2	)

    __lowercase								=						tokenizer(sentences[1],					return_tensors="tf"	).input_ids
    __lowercase								=						model.generate(input_ids=UpperCAmelCase__,					max_new_tokens=1_2	)

    __lowercase								=						tokenizer.batch_decode(UpperCAmelCase__,					skip_special_tokens=UpperCAmelCase__	)
    __lowercase								=						tokenizer.decode(output_non_padded[0],					skip_special_tokens=UpperCAmelCase__	)
    __lowercase								=						tokenizer.decode(output_padded[0],					skip_special_tokens=UpperCAmelCase__	)

    __lowercase								=						[
        "This is an extremelly long sentence that only exists to test the ability of the model to cope with "
        "left-padding, such as in batched generation. The output for the sequence below should be the same "
        "regardless of whether left padding is applied or not. When left padding is applied, the sequence will be "
        "a single",
        "Hello, my dog is a little bit of a shy one, but he is very friendly",
    ]
    self.assertListEqual(UpperCAmelCase__,					UpperCAmelCase__	)
    self.assertListEqual(UpperCAmelCase__,					[non_padded_sentence, padded_sentence]	)




