



'''simple docstring'''


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       :
	__A			  =					XGLMConfig
	__A			  =					{}
	__A			  =					'''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 ,				)    ->    Optional[Any]:
							'''simple docstring'''






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







	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






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







	def   _a   (    self						)    ->    Optional[int]:
							'''simple docstring'''






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

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

							lowercase        =							self.get_config()

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

							return (
							    config,
							    input_ids,
							    input_mask,
							    head_mask,
							)







	def   _a   (    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=_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   _a   (    self						)    ->    Tuple:
							'''simple docstring'''






							lowercase        =							self.prepare_config_and_inputs()

							(
							    (
							    lowercase
							)     ,   (
							    lowercase
							)     ,   (
							    lowercase
							)     ,   (
							    lowercase
							)     ,   
							)        =							config_and_inputs

							lowercase        =							{
							    """input_ids""": input_ids,
							    """head_mask""": head_mask,
							}

							return config, inputs_dict




@require_tf
class      __UpperCamelCase       (_UpperCAmelCase			,   _UpperCAmelCase			,   unittest.TestCase			):
	__A			  =					(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
	__A			  =					(TFXGLMForCausalLM,) if is_tf_available() else ()
	__A			  =					(
	    {'''feature-extraction''': TFXGLMModel, '''text-generation''': TFXGLMForCausalLM} if is_tf_available() else {}
	)
	__A			  =					False
	__A			  =					False
	__A			  =					False







	def   _a   (    self						)    ->    int:
							'''simple docstring'''






							lowercase        =							TFXGLMModelTester(self						)
							lowercase        =							ConfigTester(self ,				config_class=_lowerCAmelCase ,				n_embd=37						)







	def   _a   (    self						)    ->    Optional[int]:
							'''simple docstring'''






							self.config_tester.run_common_tests()







	@slow
	def   _a   (    self						)    ->    int:
							'''simple docstring'''






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







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






							super().test_resize_token_embeddings()




@require_tf
class      __UpperCamelCase       (unittest.TestCase			):







	@slow
	def   _a   (    self ,				_lowerCAmelCase=True						)    ->    Dict:
							'''simple docstring'''






							lowercase        =							TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""						)
							lowercase        =							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
							lowercase        =							[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
							lowercase        =							model.generate(_lowerCAmelCase ,				do_sample=_lowerCAmelCase ,				num_beams=1						)
							if verify_outputs:
													self.assertListEqual(output_ids[0].numpy().tolist() ,				_lowerCAmelCase						)







	@slow
	def   _a   (    self						)    ->    List[Any]:
							'''simple docstring'''






							lowercase        =							XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""						)
							lowercase        =							TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""						)

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

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







	@slow
	def   _a   (    self						)    ->    int:
							'''simple docstring'''






							lowercase        =							TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""						)
							lowercase        =							XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""						)

							lowercase        =							"""left"""

							# use different length sentences to test batching
							lowercase        =							[
							    """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        =							tokenizer(_lowerCAmelCase ,				return_tensors="""tf""" ,				padding=_lowerCAmelCase						)
							lowercase        =							inputs["""input_ids"""]

							lowercase        =							model.generate(input_ids=_lowerCAmelCase ,				attention_mask=inputs["""attention_mask"""] ,				max_new_tokens=12						)

							lowercase        =							tokenizer(sentences[0] ,				return_tensors="""tf"""						).input_ids
							lowercase        =							model.generate(input_ids=_lowerCAmelCase ,				max_new_tokens=12						)

							lowercase        =							tokenizer(sentences[1] ,				return_tensors="""tf"""						).input_ids
							lowercase        =							model.generate(input_ids=_lowerCAmelCase ,				max_new_tokens=12						)

							lowercase        =							tokenizer.batch_decode(_lowerCAmelCase ,				skip_special_tokens=_lowerCAmelCase						)
							lowercase        =							tokenizer.decode(output_non_padded[0] ,				skip_special_tokens=_lowerCAmelCase						)
							lowercase        =							tokenizer.decode(output_padded[0] ,				skip_special_tokens=_lowerCAmelCase						)

							lowercase        =							[
							    """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]						)







'''simple docstring'''


from __future__ import annotations

import os
from typing import Any

import requests

lowercase_      :				List[str]   					=    '''https://api.github.com'''

# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
lowercase_      :				Any   					=    BASE_URL + '''/user'''

# https://github.com/settings/tokens
lowercase_      :				Union[str, Any]   					=    os.environ.get('''USER_TOKEN''', '''''')







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      str						):
						lowercase        =							{
						    """Authorization""": F"""token {auth_token}""",
						    """Accept""": """application/vnd.github.v3+json""",
						}
						return requests.get(lowercase_ ,  headers=lowercase_						).json()


if __name__ == "__main__":  # pragma: no cover
						if USER_TOKEN:
												for key, value in fetch_github_info(USER_TOKEN).items():
																		print(f'''{key}: {value}''')
						else:
												raise ValueError('''\'USER_TOKEN\' field cannot be empty.''')


