





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  A__       :
      _UpperCAmelCase     :Optional[int]						   =       XGLMConfig
      _UpperCAmelCase     :str						   =       {}
      _UpperCAmelCase     :List[str]						   =       'gelu'





      def __init__(			self	,							A_	,							A_=14	,							A_=7	,							A_=True	,							A_=True	,							A_=True	,							A_=99	,							A_=32	,							A_=2	,							A_=4	,							A_=37	,							A_="gelu"	,							A_=0.1	,							A_=0.1	,							A_=512	,							A_=0.02	,							):
       '''simple docstring'''




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





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




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





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




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

       UpperCamelCase     : Optional[Any]	     =       None
       if self.use_input_mask:
        UpperCamelCase     : Tuple	     =       random_attention_mask([self.batch_size, self.seq_length]       )

       UpperCamelCase     : Any	     =       self.get_config()

       UpperCamelCase     : Union[str, Any]	     =       floats_tensor([self.num_hidden_layers, self.num_attention_heads]	,							2       )

       return (
           config,
           input_ids,
           input_mask,
           head_mask,
       )





      def 	__UpperCamelCase(			self       ):
       '''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=A_	,							bos_token_id=self.bos_token_id	,							eos_token_id=self.eos_token_id	,							pad_token_id=self.pad_token_id	,							return_dict=A_	,							)





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Dict	     =       self.prepare_config_and_inputs()

       (
           (
           UpperCamelCase
       )     ,   (
           UpperCamelCase
       )     ,   (
           UpperCamelCase
       )     ,   (
           UpperCamelCase
       )     ,   
       )     : Dict	     =       config_and_inputs

       UpperCamelCase     : List[str]	     =       {
           "input_ids": input_ids,
           "head_mask": head_mask,
       }

       return config, inputs_dict



@require_tf
class  A__       (	__snake_case	,       __snake_case	,       unittest.TestCase      ):
      _UpperCAmelCase     :Tuple						   =       (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
      _UpperCAmelCase     :Any						   =       (TFXGLMForCausalLM,) if is_tf_available() else ()
      _UpperCAmelCase     :List[str]						   =       (
          {'feature-extraction': TFXGLMModel, 'text-generation': TFXGLMForCausalLM} if is_tf_available() else {}
      )
      _UpperCAmelCase     :int						   =       False
      _UpperCAmelCase     :Dict						   =       False
      _UpperCAmelCase     :Any						   =       False





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Union[str, Any]	     =       TFXGLMModelTester(self       )
       UpperCamelCase     : int	     =       ConfigTester(self	,							config_class=A_	,							n_embd=37       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       self.config_tester.run_common_tests()





      @slow
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCamelCase     : List[str]	     =       TFXGLMModel.from_pretrained(A_       )
        self.assertIsNotNone(A_       )





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




       super().test_resize_token_embeddings()



@require_tf
class  A__       (	unittest.TestCase      ):





      @slow
      def 	__UpperCamelCase(			self	,							A_=True       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[int]	     =       TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"       )
       UpperCamelCase     : Tuple	     =       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
       UpperCamelCase     : Optional[Any]	     =       [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
       UpperCamelCase     : Any	     =       model.generate(A_	,							do_sample=A_	,							num_beams=1       )
       if verify_outputs:
        self.assertListEqual(output_ids[0].numpy().tolist()	,							A_       )





      @slow
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




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

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

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





      @slow
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : str	     =       TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"       )
       UpperCamelCase     : List[str]	     =       XGLMTokenizer.from_pretrained("facebook/xglm-564M"       )

       UpperCamelCase     : int	     =       "left"

       # use different length sentences to test batching
       UpperCamelCase     : Optional[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",
       ]

       UpperCamelCase     : List[str]	     =       tokenizer(A_	,							return_tensors="tf"	,							padding=A_       )
       UpperCamelCase     : List[str]	     =       inputs["input_ids"]

       UpperCamelCase     : str	     =       model.generate(input_ids=A_	,							attention_mask=inputs["attention_mask"]	,							max_new_tokens=12       )

       UpperCamelCase     : int	     =       tokenizer(sentences[0]	,							return_tensors="tf"       ).input_ids
       UpperCamelCase     : int	     =       model.generate(input_ids=A_	,							max_new_tokens=12       )

       UpperCamelCase     : Optional[Any]	     =       tokenizer(sentences[1]	,							return_tensors="tf"       ).input_ids
       UpperCamelCase     : str	     =       model.generate(input_ids=A_	,							max_new_tokens=12       )

       UpperCamelCase     : Any	     =       tokenizer.batch_decode(A_	,							skip_special_tokens=A_       )
       UpperCamelCase     : Dict	     =       tokenizer.decode(output_non_padded[0]	,							skip_special_tokens=A_       )
       UpperCamelCase     : str	     =       tokenizer.decode(output_padded[0]	,							skip_special_tokens=A_       )

       UpperCamelCase     : Tuple	     =       [
           "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(A_	,							A_       )
       self.assertListEqual(A_	,							[non_padded_sentence, padded_sentence]       )







import sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

__lowerCamelCase	:					Optional[int]			  =					"""python tqdm regex requests packaging filelock numpy tokenizers""".split()
if sys.version_info < (3, 7):
    pkgs_to_check_at_runtime.append("""dataclasses""")
if sys.version_info < (3, 8):
    pkgs_to_check_at_runtime.append("""importlib_metadata""")

for pkg in pkgs_to_check_at_runtime:
    if pkg in deps:
        if pkg == "tokenizers":
            # must be loaded here, or else tqdm check may fail
            from .utils import is_tokenizers_available

            if not is_tokenizers_available():
                continue  # not required, check version only if installed

        require_version_core(deps[pkg])
    else:
        raise ValueError(f"""can't find {pkg} in {deps.keys()}, check dependency_versions_table.py""")



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase=None					)						->			Optional[Any]:
 require_version(deps[pkg]							,       _lowerCAmelCase					)
