





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__					:
 __SCREAMING_SNAKE_CASE							=      XGLMConfig
 __SCREAMING_SNAKE_CASE							=      {}
 __SCREAMING_SNAKE_CASE							=      '''gelu'''



 def __init__(				self,__lowerCamelCase,__lowerCamelCase=14,__lowerCamelCase=7,__lowerCamelCase=True,__lowerCamelCase=True,__lowerCamelCase=True,__lowerCamelCase=99,__lowerCamelCase=32,__lowerCamelCase=2,__lowerCamelCase=4,__lowerCamelCase=37,__lowerCamelCase="gelu",__lowerCamelCase=0.1,__lowerCamelCase=0.1,__lowerCamelCase=512,__lowerCamelCase=0.02,):
        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 							UpperCamelCase     (				self					):
        return XGLMConfig.from_pretrained('''facebook/xglm-564M'''					)



 def 							UpperCamelCase     (				self					):
        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 							UpperCamelCase     (				self					):
        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=__lowerCamelCase,bos_token_id=self.bos_token_id,eos_token_id=self.eos_token_id,pad_token_id=self.pad_token_id,return_dict=__lowerCamelCase,)




 def 							UpperCamelCase     (				self					):
        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        SCREAMING_SNAKE_CASE__					(		UpperCamelCase__							,   UpperCamelCase__							,   unittest.TestCase ):
 __SCREAMING_SNAKE_CASE							=      (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
 __SCREAMING_SNAKE_CASE							=      (TFXGLMForCausalLM,) if is_tf_available() else ()
 __SCREAMING_SNAKE_CASE							=      (
     {'''feature-extraction''': TFXGLMModel, '''text-generation''': TFXGLMForCausalLM} if is_tf_available() else {}
 )
 __SCREAMING_SNAKE_CASE							=      False
 __SCREAMING_SNAKE_CASE							=      False
 __SCREAMING_SNAKE_CASE							=      False



 def 							UpperCamelCase     (				self					):
        A__		   =		TFXGLMModelTester(self					)
        A__		   =		ConfigTester(self,config_class=__lowerCamelCase,n_embd=37					)



 def 							UpperCamelCase     (				self					):
        self.config_tester.run_common_tests()



 @slow
 def 							UpperCamelCase     (				self					):
        for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               A__		   =		TFXGLMModel.from_pretrained(__lowerCamelCase					)
               self.assertIsNotNone(__lowerCamelCase					)




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




@require_tf
class        SCREAMING_SNAKE_CASE__					(		unittest.TestCase ):



 @slow
 def 							UpperCamelCase     (				self,__lowerCamelCase=True					):
        A__		   =		TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''					)
        A__		   =		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
        A__		   =		[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
        A__		   =		model.generate(__lowerCamelCase,do_sample=__lowerCamelCase,num_beams=1					)
        if verify_outputs:
               self.assertListEqual(output_ids[0].numpy().tolist(),__lowerCamelCase					)



 @slow
 def 							UpperCamelCase     (				self					):
        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(__lowerCamelCase,do_sample=__lowerCamelCase,seed=[7, 0]					)
        A__		   =		tokenizer.decode(output_ids[0],skip_special_tokens=__lowerCamelCase					)

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




 @slow
 def 							UpperCamelCase     (				self					):
        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(__lowerCamelCase,return_tensors='''tf''',padding=__lowerCamelCase					)
        A__		   =		inputs['''input_ids''']

        A__		   =		model.generate(input_ids=__lowerCamelCase,attention_mask=inputs['''attention_mask'''],max_new_tokens=12					)

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

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

        A__		   =		tokenizer.batch_decode(__lowerCamelCase,skip_special_tokens=__lowerCamelCase					)
        A__		   =		tokenizer.decode(output_non_padded[0],skip_special_tokens=__lowerCamelCase					)
        A__		   =		tokenizer.decode(output_padded[0],skip_special_tokens=__lowerCamelCase					)

        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(__lowerCamelCase,__lowerCamelCase					)
        self.assertListEqual(__lowerCamelCase,[non_padded_sentence, padded_sentence]					)












class        SCREAMING_SNAKE_CASE__					:



 def __init__(				self,__lowerCamelCase					):
        A__		   =		size
        A__		   =		[0] * size
        A__		   =		[0] * size



 @staticmethod
 def 							UpperCamelCase     (				__lowerCamelCase					):
        return index | (index + 1)



 @staticmethod
 def 							UpperCamelCase     (				__lowerCamelCase					):
        return (index & (index + 1)) - 1



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase					):
        A__		   =		value
        while index < self.size:
               A__		   =		self.get_prev(__lowerCamelCase					) + 1
               if current_left_border == index:
                      A__		   =		value
               else:
                      A__		   =		max(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase					)
               A__		   =		self.get_next(__lowerCamelCase					)




 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase					):
        right -= 1  # Because of right is exclusive
        A__		   =		0
        while left <= right:
               A__		   =		self.get_prev(__lowerCamelCase					)
               if left <= current_left:
                      A__		   =		max(__lowerCamelCase,self.tree[right]					)
                      A__		   =		current_left
               else:
                      A__		   =		max(__lowerCamelCase,self.arr[right]					)
                      right -= 1
        return result


if __name__ == "__main__":
       import doctest

       doctest.testmod()





