


from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase											=       logging.get_logger(__name__)

lowerCamelCase											=       {
    'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json',
}




class A  (   UpperCamelCase_     ):
      UpperCamelCase__							:  Dict      ='roc_bert'






      def __init__(					self						:     List[Any]			,   lowercase_						:     List[str]=3_0522			,   lowercase_						:     str=768			,   lowercase_						:     Dict=12			,   lowercase_						:     Optional[int]=12			,   lowercase_						:     int=3072			,   lowercase_						:     List[str]="gelu"			,   lowercase_						:     Optional[Any]=0.1			,   lowercase_						:     List[str]=0.1			,   lowercase_						:     Union[str, Any]=512			,   lowercase_						:     int=2			,   lowercase_						:     Optional[int]=0.02			,   lowercase_						:     Optional[Any]=1E-12			,   lowercase_						:     List[Any]=True			,   lowercase_						:     str=0			,   lowercase_						:     Union[str, Any]="absolute"			,   lowercase_						:     Optional[int]=None			,   lowercase_						:     Dict=True			,   lowercase_						:     Union[str, Any]=True			,   lowercase_						:     int=768			,   lowercase_						:     List[str]=910			,   lowercase_						:     List[str]=512			,   lowercase_						:     List[Any]=2_4858			,   lowercase_						:     Dict=True			,   **lowercase_						:     str			,   )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		Dict		=vocab_size
             _lowerCamelCase       :		str		=max_position_embeddings
             _lowerCamelCase       :		int		=hidden_size
             _lowerCamelCase       :		Optional[int]		=num_hidden_layers
             _lowerCamelCase       :		Optional[int]		=num_attention_heads
             _lowerCamelCase       :		Optional[Any]		=intermediate_size
             _lowerCamelCase       :		Union[str, Any]		=hidden_act
             _lowerCamelCase       :		Optional[Any]		=hidden_dropout_prob
             _lowerCamelCase       :		Optional[int]		=attention_probs_dropout_prob
             _lowerCamelCase       :		Optional[Any]		=initializer_range
             _lowerCamelCase       :		Any		=type_vocab_size
             _lowerCamelCase       :		Any		=layer_norm_eps
             _lowerCamelCase       :		Any		=use_cache
             _lowerCamelCase       :		str		=enable_pronunciation
             _lowerCamelCase       :		Optional[Any]		=enable_shape
             _lowerCamelCase       :		Optional[Any]		=pronunciation_embed_dim
             _lowerCamelCase       :		List[str]		=pronunciation_vocab_size
             _lowerCamelCase       :		Optional[Any]		=shape_embed_dim
             _lowerCamelCase       :		int		=shape_vocab_size
             _lowerCamelCase       :		List[str]		=concat_input
             _lowerCamelCase       :		Optional[int]		=position_embedding_type
             _lowerCamelCase       :		List[Any]		=classifier_dropout
             super().__init__(pad_token_id=lowercase_			,   **lowercase_ )






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 A  :
      UpperCamelCase__							:  Union[str, Any]      =XGLMConfig
      UpperCamelCase__							:  Dict      ={}
      UpperCamelCase__							:  Tuple      ='gelu'






      def __init__(					self						:     List[Any]			,   lowercase_						:     List[str]			,   lowercase_						:     Union[str, Any]=14			,   lowercase_						:     Dict=7			,   lowercase_						:     Union[str, Any]=True			,   lowercase_						:     Optional[Any]=True			,   lowercase_						:     Any=True			,   lowercase_						:     Optional[int]=99			,   lowercase_						:     List[Any]=32			,   lowercase_						:     List[Any]=2			,   lowercase_						:     Dict=4			,   lowercase_						:     List[str]=37			,   lowercase_						:     int="gelu"			,   lowercase_						:     List[Any]=0.1			,   lowercase_						:     Union[str, Any]=0.1			,   lowercase_						:     List[str]=512			,   lowercase_						:     Union[str, Any]=0.02			,   )					->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase       :		Dict		=parent
             _lowerCamelCase       :		Optional[Any]		=batch_size
             _lowerCamelCase       :		Optional[int]		=seq_length
             _lowerCamelCase       :		Union[str, Any]		=is_training
             _lowerCamelCase       :		Tuple		=use_input_mask
             _lowerCamelCase       :		str		=use_labels
             _lowerCamelCase       :		Any		=vocab_size
             _lowerCamelCase       :		List[str]		=d_model
             _lowerCamelCase       :		List[Any]		=num_hidden_layers
             _lowerCamelCase       :		Union[str, Any]		=num_attention_heads
             _lowerCamelCase       :		List[Any]		=ffn_dim
             _lowerCamelCase       :		Optional[Any]		=activation_function
             _lowerCamelCase       :		Dict		=activation_dropout
             _lowerCamelCase       :		Tuple		=attention_dropout
             _lowerCamelCase       :		List[str]		=max_position_embeddings
             _lowerCamelCase       :		int		=initializer_range
             _lowerCamelCase       :		Optional[int]		=None
             _lowerCamelCase       :		Optional[Any]		=0
             _lowerCamelCase       :		List[str]		=2
             _lowerCamelCase       :		Any		=1






      def        lowerCamelCase       (					self						:     str )					->						int:
             """simple docstring"""




             return XGLMConfig.from_pretrained('facebook/xglm-564M' )






      def        lowerCamelCase       (					self						:     List[Any] )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		Union[str, Any]		=tf.clip_by_value(
                 ids_tensor([self.batch_size, self.seq_length]			,   self.vocab_size )			,   clip_value_min=0			,   clip_value_max=3 )

             _lowerCamelCase       :		Any		=None
             if self.use_input_mask:
                    _lowerCamelCase       :		str		=random_attention_mask([self.batch_size, self.seq_length] )

             _lowerCamelCase       :		Optional[int]		=self.get_config()

             _lowerCamelCase       :		Optional[Any]		=floats_tensor([self.num_hidden_layers, self.num_attention_heads]			,   2 )

             return (
                 config,
                 input_ids,
                 input_mask,
                 head_mask,
             )






      def        lowerCamelCase       (					self						:     List[str] )					->						Dict:
             """simple docstring"""




             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=lowercase_			,   bos_token_id=self.bos_token_id			,   eos_token_id=self.eos_token_id			,   pad_token_id=self.pad_token_id			,   return_dict=lowercase_			,   )






      def        lowerCamelCase       (					self						:     Optional[int] )					->						str:
             """simple docstring"""




             _lowerCamelCase       :		str		=self.prepare_config_and_inputs()

             (
                 (
                 _lowerCamelCase
             )	,     (
                 _lowerCamelCase
             )	,     (
                 _lowerCamelCase
             )	,     (
                 _lowerCamelCase
             )	,     
             )       :		Any		=config_and_inputs

             _lowerCamelCase       :		Union[str, Any]		={
                 'input_ids': input_ids,
                 'head_mask': head_mask,
             }

             return config, inputs_dict




@require_tf
class A  (   UpperCamelCase_    ,							UpperCamelCase_    ,							unittest.TestCase     ):
      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__							:  str      =False
      UpperCamelCase__							:  int      =False
      UpperCamelCase__							:  int      =False






      def        lowerCamelCase       (					self						:     Optional[int] )					->						List[Any]:
             """simple docstring"""




             _lowerCamelCase       :		Tuple		=TFXGLMModelTester(self )
             _lowerCamelCase       :		str		=ConfigTester(self			,   config_class=lowercase_			,   n_embd=37 )






      def        lowerCamelCase       (					self						:     str )					->						Dict:
             """simple docstring"""




             self.config_tester.run_common_tests()






      @slow
      def        lowerCamelCase       (					self						:     Any )					->						int:
             """simple docstring"""




             for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    _lowerCamelCase       :		int		=TFXGLMModel.from_pretrained(lowercase_ )
                    self.assertIsNotNone(lowercase_ )






      @unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.' )
      def        lowerCamelCase       (					self						:     Optional[int] )					->						str:
             """simple docstring"""




             super().test_resize_token_embeddings()




@require_tf
class A  (   unittest.TestCase     ):






      @slow
      def        lowerCamelCase       (					self						:     str			,   lowercase_						:     str=True )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		Any		=TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
             _lowerCamelCase       :		List[Any]		=tf.convert_to_tensor([[2, 268, 9865]]			,   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
             _lowerCamelCase       :		int		=[2, 268, 9865, 67, 11, 1988, 5_7252, 9865, 5, 984, 67, 1988, 21_3838, 1658, 53, 7_0446, 33, 6657, 278, 1581]
             # fmt: on
             _lowerCamelCase       :		Dict		=model.generate(lowercase_			,   do_sample=lowercase_			,   num_beams=1 )
             if verify_outputs:
                    self.assertListEqual(output_ids[0].numpy().tolist()			,   lowercase_ )






      @slow
      def        lowerCamelCase       (					self						:     List[Any] )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		List[str]		=XGLMTokenizer.from_pretrained('facebook/xglm-564M' )
             _lowerCamelCase       :		Any		=TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )

             tf.random.set_seed(0 )
             _lowerCamelCase       :		Tuple		=tokenizer('Today is a nice day and'			,   return_tensors='tf' )
             _lowerCamelCase       :		Optional[int]		=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' ):
                    _lowerCamelCase       :		List[Any]		=model.generate(lowercase_			,   do_sample=lowercase_			,   seed=[7, 0] )
             _lowerCamelCase       :		Union[str, Any]		=tokenizer.decode(output_ids[0]			,   skip_special_tokens=lowercase_ )

             _lowerCamelCase       :		Union[str, Any]		=(
                 'Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'
             )
             self.assertEqual(lowercase_			,   lowercase_ )






      @slow
      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		int		=TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
             _lowerCamelCase       :		Any		=XGLMTokenizer.from_pretrained('facebook/xglm-564M' )

             _lowerCamelCase       :		Optional[Any]		='left'

             # use different length sentences to test batching
             _lowerCamelCase       :		int		=[
                 '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',
             ]

             _lowerCamelCase       :		List[Any]		=tokenizer(lowercase_			,   return_tensors='tf'			,   padding=lowercase_ )
             _lowerCamelCase       :		int		=inputs['input_ids']

             _lowerCamelCase       :		str		=model.generate(input_ids=lowercase_			,   attention_mask=inputs['attention_mask']			,   max_new_tokens=12 )

             _lowerCamelCase       :		Optional[Any]		=tokenizer(sentences[0]			,   return_tensors='tf' ).input_ids
             _lowerCamelCase       :		List[str]		=model.generate(input_ids=lowercase_			,   max_new_tokens=12 )

             _lowerCamelCase       :		Tuple		=tokenizer(sentences[1]			,   return_tensors='tf' ).input_ids
             _lowerCamelCase       :		Dict		=model.generate(input_ids=lowercase_			,   max_new_tokens=12 )

             _lowerCamelCase       :		str		=tokenizer.batch_decode(lowercase_			,   skip_special_tokens=lowercase_ )
             _lowerCamelCase       :		str		=tokenizer.decode(output_non_padded[0]			,   skip_special_tokens=lowercase_ )
             _lowerCamelCase       :		int		=tokenizer.decode(output_padded[0]			,   skip_special_tokens=lowercase_ )

             _lowerCamelCase       :		List[str]		=[
                 '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(lowercase_			,   lowercase_ )
             self.assertListEqual(lowercase_			,   [non_padded_sentence, padded_sentence] )


