






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       :			Optional[Any] ,	UpperCamelCase_       :			List[str] ,	UpperCamelCase_       :			List[str]=14 ,	UpperCamelCase_       :			int=7 ,	UpperCamelCase_       :			Tuple=True ,	UpperCamelCase_       :			Optional[int]=True ,	UpperCamelCase_       :			Union[str, Any]=True ,	UpperCamelCase_       :			Tuple=99 ,	UpperCamelCase_       :			List[Any]=32 ,	UpperCamelCase_       :			List[str]=2 ,	UpperCamelCase_       :			Union[str, Any]=4 ,	UpperCamelCase_       :			List[str]=37 ,	UpperCamelCase_       :			List[str]="gelu" ,	UpperCamelCase_       :			List[str]=0.1 ,	UpperCamelCase_       :			List[str]=0.1 ,	UpperCamelCase_       :			Optional[Any]=512 ,	UpperCamelCase_       :			int=0.02 ,	):


         """simple docstring"""



         __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       :			Dict				):


         """simple docstring"""



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




     def 		lowerCAmelCase_     (				self       :			Optional[int]				):


         """simple docstring"""



         __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				):


         """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=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       :			Any				):


         """simple docstring"""



         __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      (	_snake_case			,      _snake_case			,      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       :			Optional[Any]				):


         """simple docstring"""



         __A         =	TFXGLMModelTester(self				)
         __A         =	ConfigTester(self ,	config_class=UpperCamelCase_ ,	n_embd=37				)




     def 		lowerCAmelCase_     (				self       :			Union[str, Any]				):


         """simple docstring"""



         self.config_tester.run_common_tests()




     @slow
     def 		lowerCAmelCase_     (				self       :			str				):


         """simple docstring"""



         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       :			int				):


         """simple docstring"""



         super().test_resize_token_embeddings()


@require_tf
class       __lowercase      (	unittest.TestCase  ):







     '''simple docstring'''




     @slow
     def 		lowerCAmelCase_     (				self       :			str ,	UpperCamelCase_       :			str=True				):


         """simple docstring"""



         __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       :			Optional[int]				):


         """simple docstring"""



         __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]				):


         """simple docstring"""



         __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]				)












import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


__a      :       List[str]				  =   pytest.mark.integration



@pytest.mark.parametrize("""path"""			,				["""paws""", """csv"""]    )
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			List[Any]			,				__lowercase   :			Optional[int]    )						->      Tuple:





    """simple docstring"""

    inspect_dataset(__lowercase			,				__lowercase    )
    __A         =	path + """.py"""
    assert script_name in os.listdir(__lowercase    )
    assert "__pycache__" not in os.listdir(__lowercase    )



@pytest.mark.filterwarnings("""ignore:inspect_metric is deprecated:FutureWarning"""    )
@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning"""    )
@pytest.mark.parametrize("""path"""			,				["""accuracy"""]    )
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[int]			,				__lowercase   :			Tuple    )						->      Any:





    """simple docstring"""

    inspect_metric(__lowercase			,				__lowercase    )
    __A         =	path + """.py"""
    assert script_name in os.listdir(__lowercase    )
    assert "__pycache__" not in os.listdir(__lowercase    )



@pytest.mark.parametrize(
    """path, config_name, expected_splits"""			,				[
        ("""squad""", """plain_text""", ["""train""", """validation"""]),
        ("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
        ("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
    ]			,				)
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Dict			,				__lowercase   :			Optional[int]			,				__lowercase   :			List[Any]    )						->      Optional[Any]:





    """simple docstring"""

    __A         =	get_dataset_config_info(__lowercase			,				config_name=__lowercase    )
    assert info.config_name == config_name
    assert list(info.splits.keys()    ) == expected_splits



@pytest.mark.parametrize(
    """path, config_name, expected_exception"""			,				[
        ("""paws""", None, ValueError),
    ]			,				)
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			List[Any]			,				__lowercase   :			int			,				__lowercase   :			List[str]    )						->      int:





    """simple docstring"""

    with pytest.raises(__lowercase    ):
        get_dataset_config_info(__lowercase			,				config_name=__lowercase    )



@pytest.mark.parametrize(
    """path, expected"""			,				[
        ("""squad""", """plain_text"""),
        ("""acronym_identification""", """default"""),
        ("""lhoestq/squad""", """plain_text"""),
        ("""lhoestq/test""", """default"""),
        ("""lhoestq/demo1""", """lhoestq--demo1"""),
        ("""dalle-mini/wit""", """dalle-mini--wit"""),
    ]			,				)
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Tuple			,				__lowercase   :			Optional[Any]    )						->      Any:





    """simple docstring"""

    __A         =	get_dataset_config_names(__lowercase    )
    assert expected in config_names



@pytest.mark.parametrize(
    """path, expected_configs, expected_splits_in_first_config"""			,				[
        ("""squad""", ["""plain_text"""], ["""train""", """validation"""]),
        ("""dalle-mini/wit""", ["""dalle-mini--wit"""], ["""train"""]),
        ("""paws""", ["""labeled_final""", """labeled_swap""", """unlabeled_final"""], ["""train""", """test""", """validation"""]),
    ]			,				)
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			int			,				__lowercase   :			Tuple			,				__lowercase   :			List[Any]    )						->      List[str]:





    """simple docstring"""

    __A         =	get_dataset_infos(__lowercase    )
    assert list(infos.keys()    ) == expected_configs
    __A         =	expected_configs[0]
    assert expected_config in infos
    __A         =	infos[expected_config]
    assert info.config_name == expected_config
    assert list(info.splits.keys()    ) == expected_splits_in_first_config



@pytest.mark.parametrize(
    """path, expected_config, expected_splits"""			,				[
        ("""squad""", """plain_text""", ["""train""", """validation"""]),
        ("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
        ("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
    ]			,				)
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[int]			,				__lowercase   :			str			,				__lowercase   :			Union[str, Any]    )						->      Dict:





    """simple docstring"""

    __A         =	get_dataset_infos(__lowercase    )
    assert expected_config in infos
    __A         =	infos[expected_config]
    assert info.config_name == expected_config
    assert list(info.splits.keys()    ) == expected_splits






@pytest.mark.parametrize(
    """path, config_name, expected_exception"""			,				[
        ("""paws""", None, ValueError),
    ]			,				)
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			List[str]			,				__lowercase   :			Optional[int]			,				__lowercase   :			Tuple    )						->      Union[str, Any]:





    """simple docstring"""

    with pytest.raises(__lowercase    ):
        get_dataset_split_names(__lowercase			,				config_name=__lowercase    )




