'''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"""






     SCREAMING_SNAKE_CASE__		   =						XGLMConfig
     SCREAMING_SNAKE_CASE__		   =						{}
     SCREAMING_SNAKE_CASE__		   =						"""gelu"""







     def __init__(		self     ,			_lowerCamelCase     ,			_lowerCamelCase=1_4     ,			_lowerCamelCase=7     ,			_lowerCamelCase=True     ,			_lowerCamelCase=True     ,			_lowerCamelCase=True     ,			_lowerCamelCase=9_9     ,			_lowerCamelCase=3_2     ,			_lowerCamelCase=2     ,			_lowerCamelCase=4     ,			_lowerCamelCase=3_7     ,			_lowerCamelCase="gelu"     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase=5_1_2     ,			_lowerCamelCase=0.0_2     ,			):
         __UpperCamelCase	:	int		    =  parent
         __UpperCamelCase	:	List[str]		    =  batch_size
         __UpperCamelCase	:	List[Any]		    =  seq_length
         __UpperCamelCase	:	str		    =  is_training
         __UpperCamelCase	:	Dict		    =  use_input_mask
         __UpperCamelCase	:	Any		    =  use_labels
         __UpperCamelCase	:	List[Any]		    =  vocab_size
         __UpperCamelCase	:	Optional[Any]		    =  d_model
         __UpperCamelCase	:	Dict		    =  num_hidden_layers
         __UpperCamelCase	:	int		    =  num_attention_heads
         __UpperCamelCase	:	Optional[Any]		    =  ffn_dim
         __UpperCamelCase	:	Optional[Any]		    =  activation_function
         __UpperCamelCase	:	Any		    =  activation_dropout
         __UpperCamelCase	:	List[str]		    =  attention_dropout
         __UpperCamelCase	:	Any		    =  max_position_embeddings
         __UpperCamelCase	:	Any		    =  initializer_range
         __UpperCamelCase	:	Union[str, Any]		    =  None
         __UpperCamelCase	:	str		    =  0
         __UpperCamelCase	:	str		    =  2
         __UpperCamelCase	:	str		    =  1







     def lowerCAmelCase     (		self    ):
         return XGLMConfig.from_pretrained('facebook/xglm-564M'    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Tuple		    =  tf.clip_by_value(
             ids_tensor([self.batch_size, self.seq_length]     ,			self.vocab_size    )     ,			clip_value_min=0     ,			clip_value_max=3    )

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

         __UpperCamelCase	:	List[str]		    =  self.get_config()

         __UpperCamelCase	:	str		    =  floats_tensor([self.num_hidden_layers, self.num_attention_heads]     ,			2    )

         return (
             config,
             input_ids,
             input_mask,
             head_mask,
         )







     def lowerCAmelCase     (		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 lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	List[Any]		    =  self.prepare_config_and_inputs()

         (
             (
             __UpperCamelCase
         )    ,					(
             __UpperCamelCase
         )    ,					(
             __UpperCamelCase
         )    ,					(
             __UpperCamelCase
         )    ,					
         )	:	Tuple		    =  config_and_inputs

         __UpperCamelCase	:	Union[str, Any]		    =  {
             'input_ids': input_ids,
             'head_mask': head_mask,
         }

         return config, inputs_dict






@require_tf
class 			__lowercase		(						_lowerCamelCase			,							_lowerCamelCase			,							unittest.TestCase						):







     """simple docstring"""






     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 lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Union[str, Any]		    =  TFXGLMModelTester(self    )
         __UpperCamelCase	:	int		    =  ConfigTester(self     ,			config_class=_lowerCamelCase     ,			n_embd=3_7    )







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







     @slow
     def lowerCAmelCase     (		self    ):

         for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             __UpperCamelCase	:	Dict		    =  TFXGLMModel.from_pretrained(_lowerCamelCase    )
             self.assertIsNotNone(_lowerCamelCase    )







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






@require_tf
class 			__lowercase		(						unittest.TestCase						):







     """simple docstring"""






     @slow
     def lowerCAmelCase     (		self     ,			_lowerCamelCase=True    ):
         __UpperCamelCase	:	Optional[Any]		    =  TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'    )
         __UpperCamelCase	:	int		    =  tf.convert_to_tensor([[2, 2_6_8, 9_8_6_5]]     ,			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
         __UpperCamelCase	:	Optional[Any]		    =  [2, 2_6_8, 9_8_6_5, 6_7, 1_1, 1_9_8_8, 5_7_2_5_2, 9_8_6_5, 5, 9_8_4, 6_7, 1_9_8_8, 2_1_3_8_3_8, 1_6_5_8, 5_3, 7_0_4_4_6, 3_3, 6_6_5_7, 2_7_8, 1_5_8_1]
         # fmt: on
         __UpperCamelCase	:	int		    =  model.generate(_lowerCamelCase     ,			do_sample=_lowerCamelCase     ,			num_beams=1    )
         if verify_outputs:
             self.assertListEqual(output_ids[0].numpy().tolist()     ,			_lowerCamelCase    )







     @slow
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	List[Any]		    =  XGLMTokenizer.from_pretrained('facebook/xglm-564M'    )
         __UpperCamelCase	:	Tuple		    =  TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'    )

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

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







     @slow
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Tuple		    =  TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'    )
         __UpperCamelCase	:	str		    =  XGLMTokenizer.from_pretrained('facebook/xglm-564M'    )

         __UpperCamelCase	:	Optional[Any]		    =  'left'

         # use different length sentences to test batching
         __UpperCamelCase	:	Dict		    =  [
             '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',
         ]

         __UpperCamelCase	:	Tuple		    =  tokenizer(_lowerCamelCase     ,			return_tensors='tf'     ,			padding=_lowerCamelCase    )
         __UpperCamelCase	:	Dict		    =  inputs['input_ids']

         __UpperCamelCase	:	Any		    =  model.generate(input_ids=_lowerCamelCase     ,			attention_mask=inputs['attention_mask']     ,			max_new_tokens=1_2    )

         __UpperCamelCase	:	List[Any]		    =  tokenizer(sentences[0]     ,			return_tensors='tf'    ).input_ids
         __UpperCamelCase	:	Optional[Any]		    =  model.generate(input_ids=_lowerCamelCase     ,			max_new_tokens=1_2    )

         __UpperCamelCase	:	List[Any]		    =  tokenizer(sentences[1]     ,			return_tensors='tf'    ).input_ids
         __UpperCamelCase	:	Tuple		    =  model.generate(input_ids=_lowerCamelCase     ,			max_new_tokens=1_2    )

         __UpperCamelCase	:	str		    =  tokenizer.batch_decode(_lowerCamelCase     ,			skip_special_tokens=_lowerCamelCase    )
         __UpperCamelCase	:	Optional[int]		    =  tokenizer.decode(output_non_padded[0]     ,			skip_special_tokens=_lowerCamelCase    )
         __UpperCamelCase	:	Any		    =  tokenizer.decode(output_padded[0]     ,			skip_special_tokens=_lowerCamelCase    )

         __UpperCamelCase	:	List[str]		    =  [
             '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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a=  {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a=  [
      '''VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''ViTMSNModel''',
      '''ViTMSNForImageClassification''',
      '''ViTMSNPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vit_msn import (
        VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
        ViTMSNForImageClassification,
        ViTMSNModel,
        ViTMSNPreTrainedModel,
    )

else:
  import sys

  a=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



