


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 				SCREAMING_SNAKE_CASE__						:
      snake_case__       :  str       =		XGLMConfig
      snake_case__       :  Optional[int]       =		{}
      snake_case__       :  Optional[int]       =		"""gelu"""
      def __init__( self       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : str=1_4  ,							SCREAMING_SNAKE_CASE__       : Dict=7  ,							SCREAMING_SNAKE_CASE__       : int=True  ,							SCREAMING_SNAKE_CASE__       : Tuple=True  ,							SCREAMING_SNAKE_CASE__       : Any=True  ,							SCREAMING_SNAKE_CASE__       : int=9_9  ,							SCREAMING_SNAKE_CASE__       : List[str]=3_2  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=2  ,							SCREAMING_SNAKE_CASE__       : Dict=4  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=3_7  ,							SCREAMING_SNAKE_CASE__       : Optional[int]="gelu"  ,							SCREAMING_SNAKE_CASE__       : Any=0.1  ,							SCREAMING_SNAKE_CASE__       : Any=0.1  ,							SCREAMING_SNAKE_CASE__       : List[str]=5_1_2  ,							SCREAMING_SNAKE_CASE__       : str=0.02  ,							)	->    str:
             a_      :	int							=						parent
             a_      :	Union[str, Any]							=						batch_size
             a_      :	str							=						seq_length
             a_      :	List[Any]							=						is_training
             a_      :	Union[str, Any]							=						use_input_mask
             a_      :	Any							=						use_labels
             a_      :	Optional[Any]							=						vocab_size
             a_      :	str							=						d_model
             a_      :	Optional[Any]							=						num_hidden_layers
             a_      :	int							=						num_attention_heads
             a_      :	List[Any]							=						ffn_dim
             a_      :	Dict							=						activation_function
             a_      :	Tuple							=						activation_dropout
             a_      :	Union[str, Any]							=						attention_dropout
             a_      :	Dict							=						max_position_embeddings
             a_      :	str							=						initializer_range
             a_      :	Union[str, Any]							=						None
             a_      :	int							=						0
             a_      :	Dict							=						2
             a_      :	Tuple							=						1
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    Optional[Any]:
             return XGLMConfig.from_pretrained('facebook/xglm-564M'  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    int:
             a_      :	List[Any]							=						tf.clip_by_value(
                 ids_tensor([self.batch_size, self.seq_length]  ,							self.vocab_size  )  ,							clip_value_min=0  ,							clip_value_max=3  )

             a_      :	int							=						None
             if self.use_input_mask:
                    a_      :	List[str]							=						random_attention_mask([self.batch_size, self.seq_length]  )

             a_      :	Any							=						self.get_config()

             a_      :	int							=						floats_tensor([self.num_hidden_layers, self.num_attention_heads]  ,							2  )

             return (
                 config,
                 input_ids,
                 input_mask,
                 head_mask,
             )
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    List[str]:
             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=SCREAMING_SNAKE_CASE__  ,							bos_token_id=self.bos_token_id  ,							eos_token_id=self.eos_token_id  ,							pad_token_id=self.pad_token_id  ,							return_dict=SCREAMING_SNAKE_CASE__  ,							)

      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    Any:
             a_      :	List[str]							=						self.prepare_config_and_inputs()

             (
                 (
                 a_
             )							,	(
                 a_
             )							,	(
                 a_
             )							,	(
                 a_
             )							,	
             )      :	Optional[int]							=						config_and_inputs

             a_      :	List[str]							=						{
                 'input_ids': input_ids,
                 'head_mask': head_mask,
             }

             return config, inputs_dict
@require_tf
class 				SCREAMING_SNAKE_CASE__						(       lowercase__		,					lowercase__		,					unittest.TestCase    ):
      snake_case__       :  int       =		(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
      snake_case__       :  Optional[int]       =		(TFXGLMForCausalLM,) if is_tf_available() else ()
      snake_case__       :  Optional[int]       =		(
          {"""feature-extraction""": TFXGLMModel, """text-generation""": TFXGLMForCausalLM} if is_tf_available() else {}
      )
      snake_case__       :  Optional[int]       =		False
      snake_case__       :  Dict       =		False
      snake_case__       :  Optional[int]       =		False
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    Union[str, Any]:
             a_      :	List[str]							=						TFXGLMModelTester(self  )
             a_      :	Any							=						ConfigTester(self  ,							config_class=SCREAMING_SNAKE_CASE__  ,							n_embd=3_7  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    List[str]:
             self.config_tester.run_common_tests()
      @slow
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Dict:

             for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    a_      :	Optional[Any]							=						TFXGLMModel.from_pretrained(SCREAMING_SNAKE_CASE__  )
                    self.assertIsNotNone(SCREAMING_SNAKE_CASE__  )

      @unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.'  )
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    Dict:
             super().test_resize_token_embeddings()
@require_tf
class 				SCREAMING_SNAKE_CASE__						(       unittest.TestCase    ):
      @slow
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  ,							SCREAMING_SNAKE_CASE__       : str=True  )	->    Union[str, Any]:
             a_      :	Union[str, Any]							=						TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'  )
             a_      :	Dict							=						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
             a_      :	List[str]							=						[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
             a_      :	Optional[int]							=						model.generate(SCREAMING_SNAKE_CASE__  ,							do_sample=SCREAMING_SNAKE_CASE__  ,							num_beams=1  )
             if verify_outputs:
                    self.assertListEqual(output_ids[0].numpy().tolist()  ,							SCREAMING_SNAKE_CASE__  )
      @slow
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    List[Any]:
             a_      :	Optional[Any]							=						XGLMTokenizer.from_pretrained('facebook/xglm-564M'  )
             a_      :	Optional[int]							=						TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'  )

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

             a_      :	Dict							=						(
                 'Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'
             )
             self.assertEqual(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

      @slow
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    Tuple:
             a_      :	List[Any]							=						TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'  )
             a_      :	str							=						XGLMTokenizer.from_pretrained('facebook/xglm-564M'  )

             a_      :	str							=						'left'

             # use different length sentences to test batching
             a_      :	Any							=						[
                 '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',
             ]

             a_      :	str							=						tokenizer(SCREAMING_SNAKE_CASE__  ,							return_tensors='tf'  ,							padding=SCREAMING_SNAKE_CASE__  )
             a_      :	Optional[int]							=						inputs['input_ids']

             a_      :	Optional[Any]							=						model.generate(input_ids=SCREAMING_SNAKE_CASE__  ,							attention_mask=inputs['attention_mask']  ,							max_new_tokens=1_2  )

             a_      :	Union[str, Any]							=						tokenizer(sentences[0]  ,							return_tensors='tf'  ).input_ids
             a_      :	List[Any]							=						model.generate(input_ids=SCREAMING_SNAKE_CASE__  ,							max_new_tokens=1_2  )

             a_      :	List[str]							=						tokenizer(sentences[1]  ,							return_tensors='tf'  ).input_ids
             a_      :	List[str]							=						model.generate(input_ids=SCREAMING_SNAKE_CASE__  ,							max_new_tokens=1_2  )

             a_      :	Dict							=						tokenizer.batch_decode(SCREAMING_SNAKE_CASE__  ,							skip_special_tokens=SCREAMING_SNAKE_CASE__  )
             a_      :	Optional[Any]							=						tokenizer.decode(output_non_padded[0]  ,							skip_special_tokens=SCREAMING_SNAKE_CASE__  )
             a_      :	Any							=						tokenizer.decode(output_padded[0]  ,							skip_special_tokens=SCREAMING_SNAKE_CASE__  )

             a_      :	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 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(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
             self.assertListEqual(SCREAMING_SNAKE_CASE__  ,							[non_padded_sentence, padded_sentence]  )






def  SCREAMING_SNAKE_CASE_		(    __A				:    str       )			->	list:

       """simple docstring"""

       return [
           txt[:a] + txt[a].upper() + txt[a + 1 :]
           for a in range(len(__A       )       )
           if txt[a].isalpha()
       ]


if __name__ == "__main__":
   __import__('doctest').testmod()


