"""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						:



    """simple docstring"""

    _UpperCAmelCase	:Tuple						    = XGLMConfig
    _UpperCAmelCase	:List[str]						    = {}
    _UpperCAmelCase	:Tuple						    = "gelu"






    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase=14    ,			_UpperCAmelCase=7    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=99    ,			_UpperCAmelCase=32    ,			_UpperCAmelCase=2    ,			_UpperCAmelCase=4    ,			_UpperCAmelCase=37    ,			_UpperCAmelCase="gelu"    ,			_UpperCAmelCase=0.1    ,			_UpperCAmelCase=0.1    ,			_UpperCAmelCase=512    ,			_UpperCAmelCase=0.02    ,			):
       lowercase__:   str					=      parent
       lowercase__:   str					=      batch_size
       lowercase__:   Any					=      seq_length
       lowercase__:   Any					=      is_training
       lowercase__:   List[Any]					=      use_input_mask
       lowercase__:   Optional[Any]					=      use_labels
       lowercase__:   int					=      vocab_size
       lowercase__:   int					=      d_model
       lowercase__:   str					=      num_hidden_layers
       lowercase__:   Dict					=      num_attention_heads
       lowercase__:   Dict					=      ffn_dim
       lowercase__:   Union[str, Any]					=      activation_function
       lowercase__:   Union[str, Any]					=      activation_dropout
       lowercase__:   Union[str, Any]					=      attention_dropout
       lowercase__:   Optional[Any]					=      max_position_embeddings
       lowercase__:   Dict					=      initializer_range
       lowercase__:   str					=      None
       lowercase__:   Any					=      0
       lowercase__:   List[Any]					=      2
       lowercase__:   Optional[int]					=      1






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






    def _snake_case			(   self							):
       lowercase__:   str					=      tf.clip_by_value(
           ids_tensor([self.batch_size, self.seq_length]    ,			self.vocab_size							)    ,			clip_value_min=0    ,			clip_value_max=3							)

       lowercase__:   Optional[int]					=      None
       if self.use_input_mask:
          lowercase__:   str					=      random_attention_mask([self.batch_size, self.seq_length]							)

       lowercase__:   List[Any]					=      self.get_config()

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

       return (
           config,
           input_ids,
           input_mask,
           head_mask,
       )






    def _snake_case			(   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=_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 _snake_case			(   self							):
       lowercase__:   List[Any]					=      self.prepare_config_and_inputs()

       (
           (
           lowercase__
       ), (
           lowercase__
       ), (
           lowercase__
       ), (
           lowercase__
       ), 
       ):   Optional[int]					=      config_and_inputs

       lowercase__:   Tuple					=      {
           '''input_ids''': input_ids,
           '''head_mask''': head_mask,
       }

       return config, inputs_dict



@require_tf
class     UpperCAmelCase						(_UpperCAmelCase    ,_UpperCAmelCase    ,unittest.TestCase  ):



    """simple docstring"""

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






    def _snake_case			(   self							):
       lowercase__:   Union[str, Any]					=      TFXGLMModelTester(self							)
       lowercase__:   Any					=      ConfigTester(self    ,			config_class=_UpperCAmelCase    ,			n_embd=37							)






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






    @slow
    def _snake_case			(   self							):
       for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowercase__:   Optional[int]					=      TFXGLMModel.from_pretrained(_UpperCAmelCase							)
          self.assertIsNotNone(_UpperCAmelCase							)






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



@require_tf
class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

    @slow
    def _snake_case			(   self    ,			_UpperCAmelCase=True							):
       lowercase__:   Union[str, Any]					=      TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''							)
       lowercase__:   Any					=      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__:   List[Any]					=      [2, 268, 9865, 67, 11, 1988, 57252, 9865, 5, 984, 67, 1988, 213838, 1658, 53, 70446, 33, 6657, 278, 1581]
       # fmt: on
       lowercase__:   List[str]					=      model.generate(_UpperCAmelCase    ,			do_sample=_UpperCAmelCase    ,			num_beams=1							)
       if verify_outputs:
          self.assertListEqual(output_ids[0].numpy().tolist()    ,			_UpperCAmelCase							)






    @slow
    def _snake_case			(   self							):
       lowercase__:   Optional[Any]					=      XGLMTokenizer.from_pretrained('''facebook/xglm-564M'''							)
       lowercase__:   str					=      TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''							)

       tf.random.set_seed(0							)
       lowercase__:   str					=      tokenizer('''Today is a nice day and'''    ,			return_tensors='''tf'''							)
       lowercase__:   Tuple					=      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__:   Optional[Any]					=      model.generate(_UpperCAmelCase    ,			do_sample=_UpperCAmelCase    ,			seed=[7, 0]							)
       lowercase__:   Optional[int]					=      tokenizer.decode(output_ids[0]    ,			skip_special_tokens=_UpperCAmelCase							)

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






    @slow
    def _snake_case			(   self							):
       lowercase__:   str					=      TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''							)
       lowercase__:   int					=      XGLMTokenizer.from_pretrained('''facebook/xglm-564M'''							)

       lowercase__:   Any					=      '''left'''

       # use different length sentences to test batching
       lowercase__:   List[Any]					=      [
           '''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__:   Union[str, Any]					=      tokenizer(_UpperCAmelCase    ,			return_tensors='''tf'''    ,			padding=_UpperCAmelCase							)
       lowercase__:   Union[str, Any]					=      inputs['''input_ids''']

       lowercase__:   Dict					=      model.generate(input_ids=_UpperCAmelCase    ,			attention_mask=inputs['''attention_mask''']    ,			max_new_tokens=12							)

       lowercase__:   int					=      tokenizer(sentences[0]    ,			return_tensors='''tf'''							).input_ids
       lowercase__:   Tuple					=      model.generate(input_ids=_UpperCAmelCase    ,			max_new_tokens=12							)

       lowercase__:   Any					=      tokenizer(sentences[1]    ,			return_tensors='''tf'''							).input_ids
       lowercase__:   List[Any]					=      model.generate(input_ids=_UpperCAmelCase    ,			max_new_tokens=12							)

       lowercase__:   Dict					=      tokenizer.batch_decode(_UpperCAmelCase    ,			skip_special_tokens=_UpperCAmelCase							)
       lowercase__:   Union[str, Any]					=      tokenizer.decode(output_non_padded[0]    ,			skip_special_tokens=_UpperCAmelCase							)
       lowercase__:   int					=      tokenizer.decode(output_padded[0]    ,			skip_special_tokens=_UpperCAmelCase							)

       lowercase__:   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 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 ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A			   = {
    "configuration_lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __A			   = [
           "LILT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "LiltForQuestionAnswering",
           "LiltForSequenceClassification",
           "LiltForTokenClassification",
           "LiltModel",
           "LiltPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_lilt import (
                  LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LiltForQuestionAnswering,
                  LiltForSequenceClassification,
                  LiltForTokenClassification,
                  LiltModel,
                  LiltPreTrainedModel,
              )

else:
       import sys

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