






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 		lowercase_					:






      '''simple docstring'''




      __snake_case       =  XGLMConfig
      __snake_case       =  {}
      __snake_case       =  '''gelu'''







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




       """simple docstring"""


       a		  =      parent
       a		  =      batch_size
       a		  =      seq_length
       a		  =      is_training
       a		  =      use_input_mask
       a		  =      use_labels
       a		  =      vocab_size
       a		  =      d_model
       a		  =      num_hidden_layers
       a		  =      num_attention_heads
       a		  =      ffn_dim
       a		  =      activation_function
       a		  =      activation_dropout
       a		  =      attention_dropout
       a		  =      max_position_embeddings
       a		  =      initializer_range
       a		  =      None
       a		  =      0
       a		  =      2
       a		  =      1







      def       __lowerCAmelCase					(      self      :						Optional[Any]  )    ->Any:




       """simple docstring"""


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







      def       __lowerCAmelCase					(      self      :						Dict  )    ->str:




       """simple docstring"""


       a		  =      tf.clip_by_value(
           ids_tensor([self.batch_size, self.seq_length] ,					self.vocab_size  ) ,					clip_value_min=0 ,					clip_value_max=3  )

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

       a		  =      self.get_config()

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

       return (
           config,
           input_ids,
           input_mask,
           head_mask,
       )







      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->Tuple:




       """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=__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       __lowerCAmelCase					(      self      :						Any  )    ->str:




       """simple docstring"""


       a		  =      self.prepare_config_and_inputs()

       (
           (
           a
       )  ,     (
           a
       )  ,     (
           a
       )  ,     (
           a
       )  ,     
       )		  =      config_and_inputs

       a		  =      {
           '''input_ids''': input_ids,
           '''head_mask''': head_mask,
       }

       return config, inputs_dict






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






      '''simple docstring'''




      __snake_case       =  (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
      __snake_case       =  (TFXGLMForCausalLM,) if is_tf_available() else ()
      __snake_case       =  (
          {'''feature-extraction''': TFXGLMModel, '''text-generation''': TFXGLMForCausalLM} if is_tf_available() else {}
      )
      __snake_case       =  False
      __snake_case       =  False
      __snake_case       =  False







      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->int:




       """simple docstring"""


       a		  =      TFXGLMModelTester(self  )
       a		  =      ConfigTester(self ,					config_class=__UpperCAmelCase ,					n_embd=37  )







      def       __lowerCAmelCase					(      self      :						Optional[Any]  )    ->int:




       """simple docstring"""


       self.config_tester.run_common_tests()







      @slow
      def       __lowerCAmelCase					(      self      :						Dict  )    ->Optional[int]:




       """simple docstring"""


       for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        a		  =      TFXGLMModel.from_pretrained(__UpperCAmelCase  )
        self.assertIsNotNone(__UpperCAmelCase  )







      @unittest.skip(reason='''Currently, model embeddings are going to undergo a major refactor.'''  )
      def       __lowerCAmelCase					(      self      :						Any  )    ->int:




       """simple docstring"""


       super().test_resize_token_embeddings()






@require_tf
class 		lowercase_					(	unittest.TestCase   ):






      '''simple docstring'''




      @slow
      def       __lowerCAmelCase					(      self      :						Union[str, Any] ,					__UpperCAmelCase      :						Any=True  )    ->Optional[int]:




       """simple docstring"""


       a		  =      TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''  )
       a		  =      tf.convert_to_tensor([[2, 268, 9_865]] ,					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		  =      [2, 268, 9_865, 67, 11, 1_988, 57_252, 9_865, 5, 984, 67, 1_988, 213_838, 1_658, 53, 70_446, 33, 6_657, 278, 1_581]
       # fmt: on
       a		  =      model.generate(__UpperCAmelCase ,					do_sample=__UpperCAmelCase ,					num_beams=1  )
       if verify_outputs:
        self.assertListEqual(output_ids[0].numpy().tolist() ,					__UpperCAmelCase  )







      @slow
      def       __lowerCAmelCase					(      self      :						str  )    ->str:




       """simple docstring"""


       a		  =      XGLMTokenizer.from_pretrained('''facebook/xglm-564M'''  )
       a		  =      TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''  )

       tf.random.set_seed(0  )
       a		  =      tokenizer('''Today is a nice day and''' ,					return_tensors='''tf'''  )
       a		  =      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		  =      model.generate(__UpperCAmelCase ,					do_sample=__UpperCAmelCase ,					seed=[7, 0]  )
       a		  =      tokenizer.decode(output_ids[0] ,					skip_special_tokens=__UpperCAmelCase  )

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







      @slow
      def       __lowerCAmelCase					(      self      :						Optional[int]  )    ->Any:




       """simple docstring"""


       a		  =      TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''  )
       a		  =      XGLMTokenizer.from_pretrained('''facebook/xglm-564M'''  )

       a		  =      '''left'''

       # use different length sentences to test batching
       a		  =      [
           '''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		  =      tokenizer(__UpperCAmelCase ,					return_tensors='''tf''' ,					padding=__UpperCAmelCase  )
       a		  =      inputs['''input_ids''']

       a		  =      model.generate(input_ids=__UpperCAmelCase ,					attention_mask=inputs['''attention_mask'''] ,					max_new_tokens=12  )

       a		  =      tokenizer(sentences[0] ,					return_tensors='''tf'''  ).input_ids
       a		  =      model.generate(input_ids=__UpperCAmelCase ,					max_new_tokens=12  )

       a		  =      tokenizer(sentences[1] ,					return_tensors='''tf'''  ).input_ids
       a		  =      model.generate(input_ids=__UpperCAmelCase ,					max_new_tokens=12  )

       a		  =      tokenizer.batch_decode(__UpperCAmelCase ,					skip_special_tokens=__UpperCAmelCase  )
       a		  =      tokenizer.decode(output_non_padded[0] ,					skip_special_tokens=__UpperCAmelCase  )
       a		  =      tokenizer.decode(output_padded[0] ,					skip_special_tokens=__UpperCAmelCase  )

       a		  =      [
           '''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]  )










def 						_a    (      a :float	,							a :float )							->			float:
 return price * (1 + tax_rate)


if __name__ == "__main__":
     print(f"""{price_plus_tax(100, 0.25) = }""")
     print(f"""{price_plus_tax(125.50, 0.05) = }""")


