"""simple docstring"""


import gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
        CTRLForSequenceClassification,
        CTRLLMHeadModel,
        CTRLModel,
    )


class 				a :


    def __init__(    self		:     Dict      , __lowerCAmelCase		:     int      , __lowerCAmelCase		:     Any=14      , __lowerCAmelCase		:     int=7      , __lowerCAmelCase		:     Tuple=True      , __lowerCAmelCase		:     Tuple=True      , __lowerCAmelCase		:     Optional[Any]=True      , __lowerCAmelCase		:     Dict=True      , __lowerCAmelCase		:     Union[str, Any]=True      , __lowerCAmelCase		:     Dict=99      , __lowerCAmelCase		:     Any=32      , __lowerCAmelCase		:     str=5      , __lowerCAmelCase		:     Optional[int]=4      , __lowerCAmelCase		:     str=37      , __lowerCAmelCase		:     Union[str, Any]="gelu"      , __lowerCAmelCase		:     List[str]=0.1      , __lowerCAmelCase		:     List[Any]=0.1      , __lowerCAmelCase		:     List[str]=512      , __lowerCAmelCase		:     int=16      , __lowerCAmelCase		:     Optional[int]=2      , __lowerCAmelCase		:     str=0.02      , __lowerCAmelCase		:     Any=3      , __lowerCAmelCase		:     List[Any]=4      , __lowerCAmelCase		:     Optional[Any]=None      , ):
      _UpperCAmelCase         =					parent
      _UpperCAmelCase         =					batch_size
      _UpperCAmelCase         =					seq_length
      _UpperCAmelCase         =					is_training
      _UpperCAmelCase         =					use_token_type_ids
      _UpperCAmelCase         =					use_input_mask
      _UpperCAmelCase         =					use_labels
      _UpperCAmelCase         =					use_mc_token_ids
      _UpperCAmelCase         =					vocab_size
      _UpperCAmelCase         =					hidden_size
      _UpperCAmelCase         =					num_hidden_layers
      _UpperCAmelCase         =					num_attention_heads
      _UpperCAmelCase         =					intermediate_size
      _UpperCAmelCase         =					hidden_act
      _UpperCAmelCase         =					hidden_dropout_prob
      _UpperCAmelCase         =					attention_probs_dropout_prob
      _UpperCAmelCase         =					max_position_embeddings
      _UpperCAmelCase         =					type_vocab_size
      _UpperCAmelCase         =					type_sequence_label_size
      _UpperCAmelCase         =					initializer_range
      _UpperCAmelCase         =					num_labels
      _UpperCAmelCase         =					num_choices
      _UpperCAmelCase         =					scope
      _UpperCAmelCase         =					self.vocab_size - 1


    def    lowerCAmelCase_     (    self		:     Tuple    ):
      _UpperCAmelCase         =					ids_tensor([self.batch_size, self.seq_length]      , self.vocab_size    )

      _UpperCAmelCase         =					None
      if self.use_input_mask:
        _UpperCAmelCase         =					random_attention_mask([self.batch_size, self.seq_length]    )

      _UpperCAmelCase         =					None
      if self.use_token_type_ids:
        _UpperCAmelCase         =					ids_tensor([self.batch_size, self.seq_length]      , self.type_vocab_size    )

      _UpperCAmelCase         =					None
      if self.use_mc_token_ids:
        _UpperCAmelCase         =					ids_tensor([self.batch_size, self.num_choices]      , self.seq_length    )

      _UpperCAmelCase         =					None
      _UpperCAmelCase         =					None
      _UpperCAmelCase         =					None
      if self.use_labels:
        _UpperCAmelCase         =					ids_tensor([self.batch_size]      , self.type_sequence_label_size    )
        _UpperCAmelCase         =					ids_tensor([self.batch_size, self.seq_length]      , self.num_labels    )
        _UpperCAmelCase         =					ids_tensor([self.batch_size]      , self.num_choices    )

      _UpperCAmelCase         =					self.get_config()

      _UpperCAmelCase         =					ids_tensor([self.num_hidden_layers, self.num_attention_heads]      , 2    )

      return (
          config,
          input_ids,
          input_mask,
          head_mask,
          token_type_ids,
          mc_token_ids,
          sequence_labels,
          token_labels,
          choice_labels,
      )


    def    lowerCAmelCase_     (    self		:     str    ):
      return CTRLConfig(
          vocab_size=self.vocab_size      , n_embd=self.hidden_size      , n_layer=self.num_hidden_layers      , n_head=self.num_attention_heads      , n_positions=self.max_position_embeddings      , pad_token_id=self.pad_token_id      , )


    def    lowerCAmelCase_     (    self		:     Optional[int]      , __lowerCAmelCase		:     int      , __lowerCAmelCase		:     Optional[int]      , __lowerCAmelCase		:     Optional[int]      , __lowerCAmelCase		:     str      , __lowerCAmelCase		:     Optional[Any]      , *__lowerCAmelCase		:     Union[str, Any]    ):
      _UpperCAmelCase         =					CTRLModel(config=__lowerCAmelCase    )
      model.to(__lowerCAmelCase    )
      model.eval()

      model(__lowerCAmelCase      , token_type_ids=__lowerCAmelCase      , head_mask=__lowerCAmelCase    )
      model(__lowerCAmelCase      , token_type_ids=__lowerCAmelCase    )
      _UpperCAmelCase         =					model(__lowerCAmelCase    )
      self.parent.assertEqual(result.last_hidden_state.shape      , (self.batch_size, self.seq_length, self.hidden_size)    )
      self.parent.assertEqual(len(result.past_key_values    )      , config.n_layer    )


    def    lowerCAmelCase_     (    self		:     Any      , __lowerCAmelCase		:     List[Any]      , __lowerCAmelCase		:     int      , __lowerCAmelCase		:     List[str]      , __lowerCAmelCase		:     Dict      , __lowerCAmelCase		:     Dict      , *__lowerCAmelCase		:     List[str]    ):
      _UpperCAmelCase         =					CTRLLMHeadModel(__lowerCAmelCase    )
      model.to(__lowerCAmelCase    )
      model.eval()

      _UpperCAmelCase         =					model(__lowerCAmelCase      , token_type_ids=__lowerCAmelCase      , labels=__lowerCAmelCase    )
      self.parent.assertEqual(result.loss.shape      , ()    )
      self.parent.assertEqual(result.logits.shape      , (self.batch_size, self.seq_length, self.vocab_size)    )


    def    lowerCAmelCase_     (    self		:     Optional[int]    ):
      _UpperCAmelCase         =					self.prepare_config_and_inputs()

      (
          _UpperCAmelCase 
      )         =					config_and_inputs

      _UpperCAmelCase         =					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'head_mask': head_mask}

      return config, inputs_dict


    def    lowerCAmelCase_     (    self		:     Tuple      , __lowerCAmelCase		:     List[Any]      , __lowerCAmelCase		:     int      , __lowerCAmelCase		:     Union[str, Any]      , __lowerCAmelCase		:     Union[str, Any]      , *__lowerCAmelCase		:     List[Any]    ):
      _UpperCAmelCase         =					self.num_labels
      _UpperCAmelCase         =					CTRLForSequenceClassification(__lowerCAmelCase    )
      model.to(__lowerCAmelCase    )
      model.eval()
      _UpperCAmelCase         =					ids_tensor([self.batch_size]      , self.type_sequence_label_size    )
      _UpperCAmelCase         =					model(__lowerCAmelCase      , token_type_ids=__lowerCAmelCase      , labels=__lowerCAmelCase    )
      self.parent.assertEqual(result.logits.shape      , (self.batch_size, self.num_labels)    )



@require_torch
class 				a (						__snake_case		,							__snake_case		,							__snake_case		,							unittest.TestCase      ):
    _snake_case   :     str   							=							(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
    _snake_case   :     Optional[int]   							=							(CTRLLMHeadModel,) if is_torch_available() else ()
    _snake_case   :     List[Any]   							=							(
        {
            """feature-extraction""": CTRLModel,
            """text-classification""": CTRLForSequenceClassification,
            """text-generation""": CTRLLMHeadModel,
            """zero-shot""": CTRLForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    _snake_case   :     int   							=							True
    _snake_case   :     List[Any]   							=							False
    _snake_case   :     Optional[int]   							=							False


    def    lowerCAmelCase_     (    self		:     List[str]      , __lowerCAmelCase		:     Tuple      , __lowerCAmelCase		:     Union[str, Any]      , __lowerCAmelCase		:     Optional[int]      , __lowerCAmelCase		:     str      , __lowerCAmelCase		:     str    ):
      if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
        # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
        # `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
        # config could not be created.
        return True

      return False


    def    lowerCAmelCase_     (    self		:     List[Any]    ):
      _UpperCAmelCase         =					CTRLModelTester(self    )
      _UpperCAmelCase         =					ConfigTester(self      , config_class=__lowerCAmelCase      , n_embd=37    )


    def    lowerCAmelCase_     (    self		:     List[Any]    ):
      super().tearDown()
      # clean-up as much as possible GPU memory occupied by PyTorch
      gc.collect()
      torch.cuda.empty_cache()


    def    lowerCAmelCase_     (    self		:     Optional[int]    ):
      self.config_tester.run_common_tests()


    def    lowerCAmelCase_     (    self		:     Optional[int]    ):
      _UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_ctrl_model(*__lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     Tuple    ):
      _UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_lm_head_model(*__lowerCAmelCase    )


    @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""    )
    def    lowerCAmelCase_     (    self		:     int    ):
      pass


    @slow
    def    lowerCAmelCase_     (    self		:     Optional[Any]    ):
      for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        _UpperCAmelCase         =					CTRLModel.from_pretrained(__lowerCAmelCase    )
        self.assertIsNotNone(__lowerCAmelCase    )


    @unittest.skip("""The model doesn\'t support left padding"""    )  # and it's not used enough to be worth fixing :)
    def    lowerCAmelCase_     (    self		:     Optional[int]    ):
      pass



@require_torch
class 				a (						unittest.TestCase      ):


    def    lowerCAmelCase_     (    self		:     List[Any]    ):
      super().tearDown()
      # clean-up as much as possible GPU memory occupied by PyTorch
      gc.collect()
      torch.cuda.empty_cache()


    @slow
    def    lowerCAmelCase_     (    self		:     Optional[Any]    ):
      _UpperCAmelCase         =					CTRLLMHeadModel.from_pretrained("""ctrl"""    )
      model.to(__lowerCAmelCase    )
      _UpperCAmelCase         =					torch.tensor(
          [[1_1859, 0, 1611, 8]]      , dtype=torch.long      , device=__lowerCAmelCase    )  # Legal the president is
      _UpperCAmelCase         =					[
          1_1859,
          0,
          1611,
          8,
          5,
          150,
          2_6449,
          2,
          19,
          348,
          469,
          3,
          2595,
          48,
          2_0740,
          24_6533,
          24_6533,
          19,
          30,
          5,
      ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

      _UpperCAmelCase         =					model.generate(__lowerCAmelCase      , do_sample=__lowerCAmelCase    )
      self.assertListEqual(output_ids[0].tolist()      , __lowerCAmelCase    )



import warnings

from ...utils import logging
from .image_processing_flava import FlavaImageProcessor


snake_case_			  = logging.get_logger(__name__)


class       SCREAMING_SNAKE_CASE__					(__snake_case     ):





    def __init__(      self						,   *a						,   **a):
         warnings.warn(
             'The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
             ' use FlavaImageProcessor instead.'						,   a						,   )
         super().__init__(*a						,   **a)

