




"""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 							lowercase_			:



 '''simple docstring'''
 UpperCAmelCase       :	List[Any]     =      XGLMConfig
 UpperCAmelCase       :	List[str]     =      {}
 UpperCAmelCase       :	Dict     =      '''gelu'''

 def __init__(					self			:			Optional[Any]       ,				_UpperCAmelCase			:			Any       ,				_UpperCAmelCase			:			int=14       ,				_UpperCAmelCase			:			List[Any]=7       ,				_UpperCAmelCase			:			str=True       ,				_UpperCAmelCase			:			Optional[int]=True       ,				_UpperCAmelCase			:			List[Any]=True       ,				_UpperCAmelCase			:			Any=99       ,				_UpperCAmelCase			:			List[str]=32       ,				_UpperCAmelCase			:			List[Any]=2       ,				_UpperCAmelCase			:			int=4       ,				_UpperCAmelCase			:			List[str]=37       ,				_UpperCAmelCase			:			Any="gelu"       ,				_UpperCAmelCase			:			Union[str, Any]=0.1       ,				_UpperCAmelCase			:			Optional[Any]=0.1       ,				_UpperCAmelCase			:			int=512       ,				_UpperCAmelCase			:			List[str]=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      lowerCAmelCase_				(					self			:			Any						):
       return XGLMConfig.from_pretrained('facebook/xglm-564M'						)

 def      lowerCAmelCase_				(					self			:			str						):
       _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			:			Dict						):
       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			:			Optional[Any]						):
       _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_			(	__lowerCAmelCase     ,      __lowerCAmelCase     ,      unittest.TestCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Any     =      (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
 UpperCAmelCase       :	List[str]     =      (TFXGLMForCausalLM,) if is_tf_available() else ()
 UpperCAmelCase       :	str     =      (
     {'''feature-extraction''': TFXGLMModel, '''text-generation''': TFXGLMForCausalLM} if is_tf_available() else {}
 )
 UpperCAmelCase       :	Optional[Any]     =      False
 UpperCAmelCase       :	Tuple     =      False
 UpperCAmelCase       :	int     =      False

 def      lowerCAmelCase_				(					self			:			List[Any]						):
       _A     =  TFXGLMModelTester(self						)
       _A     =  ConfigTester(self       ,				config_class=_UpperCAmelCase       ,				n_embd=37						)

 def      lowerCAmelCase_				(					self			:			List[str]						):
       self.config_tester.run_common_tests()

 @slow
 def      lowerCAmelCase_				(					self			:			List[Any]						):
       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			:			List[str]						):
       super().test_resize_token_embeddings()





@require_tf
class 							lowercase_			(	unittest.TestCase				):



 '''simple docstring'''

 @slow
 def      lowerCAmelCase_				(					self			:			Any       ,				_UpperCAmelCase			:			str=True						):
       _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			:			List[Any]						):
       _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			:			Union[str, Any]						):
       _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]						)








'''simple docstring'''




from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


__UpperCAmelCase            =       {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCAmelCase            =       ["""GPTNeoXTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCAmelCase            =       [
							    """GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """GPTNeoXForCausalLM""",
							    """GPTNeoXForQuestionAnswering""",
							    """GPTNeoXForSequenceClassification""",
							    """GPTNeoXForTokenClassification""",
							    """GPTNeoXLayer""",
							    """GPTNeoXModel""",
							    """GPTNeoXPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gpt_neox import (
														    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTNeoXForCausalLM,
														    GPTNeoXForQuestionAnswering,
														    GPTNeoXForSequenceClassification,
														    GPTNeoXForTokenClassification,
														    GPTNeoXLayer,
														    GPTNeoXModel,
														    GPTNeoXPreTrainedModel,
														)


else:
							import sys

							__UpperCAmelCase            =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



