


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 		_SCREAMING_SNAKE_CASE     :
     __SCREAMING_SNAKE_CASE     					=    XGLMConfig
     __SCREAMING_SNAKE_CASE     					=    {}
     __SCREAMING_SNAKE_CASE     					=    '''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_=5_12							,					A_=0.0_2							,					):
       _UpperCAmelCase		:							Union[str, Any]							=     parent
       _UpperCAmelCase		:							Tuple							=     batch_size
       _UpperCAmelCase		:							Any							=     seq_length
       _UpperCAmelCase		:							Any							=     is_training
       _UpperCAmelCase		:							int							=     use_input_mask
       _UpperCAmelCase		:							List[Any]							=     use_labels
       _UpperCAmelCase		:							Union[str, Any]							=     vocab_size
       _UpperCAmelCase		:							List[str]							=     d_model
       _UpperCAmelCase		:							List[Any]							=     num_hidden_layers
       _UpperCAmelCase		:							Optional[Any]							=     num_attention_heads
       _UpperCAmelCase		:							str							=     ffn_dim
       _UpperCAmelCase		:							Any							=     activation_function
       _UpperCAmelCase		:							int							=     activation_dropout
       _UpperCAmelCase		:							Union[str, Any]							=     attention_dropout
       _UpperCAmelCase		:							List[Any]							=     max_position_embeddings
       _UpperCAmelCase		:							List[Any]							=     initializer_range
       _UpperCAmelCase		:							Dict							=     None
       _UpperCAmelCase		:							int							=     0
       _UpperCAmelCase		:							Any							=     2
       _UpperCAmelCase		:							List[str]							=     1


     def        __snake_case(  self    ):
       return XGLMConfig.from_pretrained("""facebook/xglm-564M"""    )


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							List[Any]							=     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[int]							=     None
       if self.use_input_mask:
         _UpperCAmelCase		:							Any							=     random_attention_mask([self.batch_size, self.seq_length]    )

       _UpperCAmelCase		:							Dict							=     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        __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=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        __snake_case(  self    ):
       _UpperCAmelCase		:							Tuple							=     self.prepare_config_and_inputs()

       (
           (
           _UpperCAmelCase
       ),(
           _UpperCAmelCase
       ),(
           _UpperCAmelCase
       ),(
           _UpperCAmelCase
       ),
       )		:							Any							=     config_and_inputs

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

       return config, inputs_dict


@require_tf
class 		_SCREAMING_SNAKE_CASE     (						A							,	A							,	unittest.TestCase     ):
     __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        __snake_case(  self    ):
       _UpperCAmelCase		:							Optional[Any]							=     TFXGLMModelTester(self    )
       _UpperCAmelCase		:							str							=     ConfigTester(self							,					config_class=A_							,					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]:
         _UpperCAmelCase		:							Tuple							=     TFXGLMModel.from_pretrained(A_    )
         self.assertIsNotNone(A_    )




     @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 		_SCREAMING_SNAKE_CASE     (						unittest.TestCase     ):


     @slow
     def        __snake_case(  self							,					A_=True    ):
       _UpperCAmelCase		:							int							=     TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""    )
       _UpperCAmelCase		:							Dict							=     tf.convert_to_tensor([[2, 2_68, 98_65]]							,					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		:							Tuple							=     [2, 2_68, 98_65, 67, 11, 19_88, 5_72_52, 98_65, 5, 9_84, 67, 19_88, 21_38_38, 16_58, 53, 7_04_46, 33, 66_57, 2_78, 15_81]
       # 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        __snake_case(  self    ):
       _UpperCAmelCase		:							str							=     XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""    )
       _UpperCAmelCase		:							str							=     TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""    )

       tf.random.set_seed(0    )
       _UpperCAmelCase		:							List[Any]							=     tokenizer("""Today is a nice day and"""							,					return_tensors="""tf"""    )
       _UpperCAmelCase		:							Dict							=     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		:							List[str]							=     model.generate(A_							,					do_sample=A_							,					seed=[7, 0]    )
       _UpperCAmelCase		:							Optional[int]							=     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        __snake_case(  self    ):
       _UpperCAmelCase		:							Optional[int]							=     TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""    )
       _UpperCAmelCase		:							int							=     XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""    )

       _UpperCAmelCase		:							str							=     """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		:							Optional[int]							=     tokenizer(A_							,					return_tensors="""tf"""							,					padding=A_    )
       _UpperCAmelCase		:							Optional[Any]							=     inputs["""input_ids"""]

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

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

       _UpperCAmelCase		:							int							=     tokenizer(sentences[1]							,					return_tensors="""tf"""    ).input_ids
       _UpperCAmelCase		:							Dict							=     model.generate(input_ids=A_							,					max_new_tokens=12    )

       _UpperCAmelCase		:							Optional[int]							=     tokenizer.batch_decode(A_							,					skip_special_tokens=A_    )
       _UpperCAmelCase		:							Optional[int]							=     tokenizer.decode(output_non_padded[0]							,					skip_special_tokens=A_    )
       _UpperCAmelCase		:							Any							=     tokenizer.decode(output_padded[0]							,					skip_special_tokens=A_    )

       _UpperCAmelCase		:							Optional[int]							=     [
           """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 copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate


@dataclass(frozen=A     )
class 		_SCREAMING_SNAKE_CASE     (						A     ):
     __SCREAMING_SNAKE_CASE     					=    field(default='''image-classification'''							,	metadata={'''include_in_asdict_even_if_is_default''': True}     )
     __SCREAMING_SNAKE_CASE     					=    Features({'''image''': Image()}     )
     __SCREAMING_SNAKE_CASE     					=    Features({'''labels''': ClassLabel}     )
     __SCREAMING_SNAKE_CASE     					=    "image"
     __SCREAMING_SNAKE_CASE     					=    "labels"


     def        __snake_case(  self							,					A_    ):
       if self.label_column not in features:
         raise ValueError(F'''Column {self.label_column} is not present in features.'''    )
       if not isinstance(features[self.label_column]							,					A_    ):
         raise ValueError(F'''Column {self.label_column} is not a ClassLabel.'''    )
       _UpperCAmelCase		:							Tuple							=     copy.deepcopy(self    )
       _UpperCAmelCase		:							str							=     self.label_schema.copy()
       _UpperCAmelCase		:							Optional[Any]							=     features[self.label_column]
       _UpperCAmelCase		:							int							=     label_schema
       return task_template




     @property
     def        __snake_case(  self    ):
       return {
           self.image_column: "image",
           self.label_column: "labels",
       }





