





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     UpperCamelCase__					:






		'''simple docstring'''






		__a       :       Tuple       =	XGLMConfig
		__a       :       int       =	{}
		__a       :       str       =	"""gelu"""






		def __init__(							self,   snake_case__,   snake_case__=14,   snake_case__=7,   snake_case__=True,   snake_case__=True,   snake_case__=True,   snake_case__=99,   snake_case__=32,   snake_case__=2,   snake_case__=4,   snake_case__=37,   snake_case__="gelu",   snake_case__=0.1,   snake_case__=0.1,   snake_case__=5_12,   snake_case__=0.02,   )       -> Union[str, Any]:






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






		def 						snake_case__				(							self  )       -> str:






									"""simple docstring"""
									return XGLMConfig.from_pretrained("""facebook/xglm-564M"""  )






		def 						snake_case__				(							self  )       -> Any:






									"""simple docstring"""
									lowercase_	:   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  )

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

									lowercase_	:   Dict  				=     self.get_config()

									lowercase_	:   List[Any]  				=     floats_tensor([self.num_hidden_layers, self.num_attention_heads],   2  )

									return (
									    config,
									    input_ids,
									    input_mask,
									    head_mask,
									)






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






									"""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=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=snake_case__,   )






		def 						snake_case__				(							self  )       -> Tuple:






									"""simple docstring"""
									lowercase_	:   Tuple  				=     self.prepare_config_and_inputs()

									(
									    (
									    lowercase_
									)				,		(
									    lowercase_
									)				,		(
									    lowercase_
									)				,		(
									    lowercase_
									)				,		
									)	:   Tuple  				=     config_and_inputs

									lowercase_	:   int  				=     {
									    """input_ids""": input_ids,
									    """head_mask""": head_mask,
									}

									return config, inputs_dict




@require_tf
class     UpperCamelCase__					(     lowerCamelCase__				, lowerCamelCase__				, unittest.TestCase				):






		'''simple docstring'''






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






		def 						snake_case__				(							self  )       -> List[Any]:






									"""simple docstring"""
									lowercase_	:   Any  				=     TFXGLMModelTester(self  )
									lowercase_	:   Any  				=     ConfigTester(self,   config_class=snake_case__,   n_embd=37  )






		def 						snake_case__				(							self  )       -> Union[str, Any]:






									"""simple docstring"""
									self.config_tester.run_common_tests()






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






									"""simple docstring"""
									for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																lowercase_	:   Any  				=     TFXGLMModel.from_pretrained(snake_case__  )
																self.assertIsNotNone(snake_case__  )






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






									"""simple docstring"""
									super().test_resize_token_embeddings()




@require_tf
class     UpperCamelCase__					(     unittest.TestCase				):






		'''simple docstring'''






		@slow
		def 						snake_case__				(							self,   snake_case__=True  )       -> Optional[int]:






									"""simple docstring"""
									lowercase_	:   int  				=     TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""  )
									lowercase_	:   Dict  				=     tf.convert_to_tensor([[2, 2_68, 98_65]],   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_	:   List[str]  				=     [2, 2_68, 98_65, 67, 11, 19_88, 5_72_52, 98_65, 5, 9_84, 67, 19_88, 21_38_38, 16_58, 53, 7_04_46, 33, 66_57, 2_78, 15_81]
									# fmt: on
									lowercase_	:   List[str]  				=     model.generate(snake_case__,   do_sample=snake_case__,   num_beams=1  )
									if verify_outputs:
																self.assertListEqual(output_ids[0].numpy().tolist(),   snake_case__  )






		@slow
		def 						snake_case__				(							self  )       -> List[str]:






									"""simple docstring"""
									lowercase_	:   Optional[Any]  				=     XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""  )
									lowercase_	:   List[str]  				=     TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""  )

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

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






		@slow
		def 						snake_case__				(							self  )       -> Tuple:






									"""simple docstring"""
									lowercase_	:   Union[str, Any]  				=     TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""  )
									lowercase_	:   Any  				=     XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""  )

									lowercase_	:   int  				=     """left"""

									# use different length sentences to test batching
									lowercase_	:   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""",
									]

									lowercase_	:   Optional[int]  				=     tokenizer(snake_case__,   return_tensors="""tf""",   padding=snake_case__  )
									lowercase_	:   List[str]  				=     inputs["""input_ids"""]

									lowercase_	:   int  				=     model.generate(input_ids=snake_case__,   attention_mask=inputs["""attention_mask"""],   max_new_tokens=12  )

									lowercase_	:   str  				=     tokenizer(sentences[0],   return_tensors="""tf"""  ).input_ids
									lowercase_	:   Dict  				=     model.generate(input_ids=snake_case__,   max_new_tokens=12  )

									lowercase_	:   Optional[int]  				=     tokenizer(sentences[1],   return_tensors="""tf"""  ).input_ids
									lowercase_	:   List[str]  				=     model.generate(input_ids=snake_case__,   max_new_tokens=12  )

									lowercase_	:   List[Any]  				=     tokenizer.batch_decode(snake_case__,   skip_special_tokens=snake_case__  )
									lowercase_	:   Any  				=     tokenizer.decode(output_non_padded[0],   skip_special_tokens=snake_case__  )
									lowercase_	:   List[Any]  				=     tokenizer.decode(output_padded[0],   skip_special_tokens=snake_case__  )

									lowercase_	:   Optional[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(snake_case__,   snake_case__  )
									self.assertListEqual(snake_case__,   [non_padded_sentence, padded_sentence]  )






from string import ascii_uppercase

UpperCAmelCase_							=				{char: i for i, char in enumerate(ascii_uppercase)}
UpperCAmelCase_							=				dict(enumerate(ascii_uppercase))





def  __magic_name__  (				lowercase				,       lowercase				)				-> str:

							"""simple docstring"""



							lowercase_	:   Dict  				=     len(lowercase				)
							lowercase_	:   Tuple  				=     0
							while True:
														if x == i:
																					lowercase_	:   Dict  				=     0
														if len(lowercase				) == len(lowercase				):
																					break
														key += key[i]
														i += 1
							return key





def  __magic_name__  (				lowercase				,       lowercase				)				-> str:

							"""simple docstring"""



							lowercase_	:   Any  				=     """"""
							lowercase_	:   Optional[Any]  				=     0
							for letter in message:
														if letter == " ":
																					cipher_text += " "
														else:
																					lowercase_	:   Any  				=     (dicta[letter] - dicta[key_new[i]]) % 26
																					i += 1
																					cipher_text += dicta[x]
							return cipher_text





def  __magic_name__  (				lowercase				,       lowercase				)				-> str:

							"""simple docstring"""



							lowercase_	:   Optional[Any]  				=     """"""
							lowercase_	:   Optional[Any]  				=     0
							for letter in cipher_text:
														if letter == " ":
																					or_txt += " "
														else:
																					lowercase_	:   Optional[int]  				=     (dicta[letter] + dicta[key_new[i]] + 26) % 26
																					i += 1
																					or_txt += dicta[x]
							return or_txt





def  __magic_name__  (				)				-> None:

							"""simple docstring"""



							lowercase_	:   Union[str, Any]  				=     """THE GERMAN ATTACK"""
							lowercase_	:   str  				=     """SECRET"""
							lowercase_	:   int  				=     generate_key(lowercase				,       lowercase				)
							lowercase_	:   List[str]  				=     cipher_text(lowercase				,       lowercase				)
							print(f"""Encrypted Text = {s}"""				)
							print(f"""Original Text = {original_text(lowercase				,       lowercase				)}"""				)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()