

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       SCREAMING_SNAKE_CASE__					:





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





    def       snake_case_				(      self):
         lowercase__    :   str      =       ids_tensor([self.batch_size, self.seq_length]						,   self.vocab_size)

         lowercase__    :   Union[str, Any]      =       None
         if self.use_input_mask:
              lowercase__    :   Tuple      =       random_attention_mask([self.batch_size, self.seq_length])

         lowercase__    :   List[Any]      =       None
         if self.use_token_type_ids:
              lowercase__    :   Tuple      =       ids_tensor([self.batch_size, self.seq_length]						,   self.type_vocab_size)

         lowercase__    :   List[Any]      =       None
         if self.use_mc_token_ids:
              lowercase__    :   Optional[Any]      =       ids_tensor([self.batch_size, self.num_choices]						,   self.seq_length)

         lowercase__    :   Optional[Any]      =       None
         lowercase__    :   Union[str, Any]      =       None
         lowercase__    :   str      =       None
         if self.use_labels:
              lowercase__    :   Any      =       ids_tensor([self.batch_size]						,   self.type_sequence_label_size)
              lowercase__    :   List[Any]      =       ids_tensor([self.batch_size, self.seq_length]						,   self.num_labels)
              lowercase__    :   str      =       ids_tensor([self.batch_size]						,   self.num_choices)

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

         lowercase__    :   int      =       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       snake_case_				(      self):
         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       snake_case_				(      self						,   a						,   a						,   a						,   a						,   a						,   *a):
         lowercase__    :   Tuple      =       CTRLModel(config=a)
         model.to(a)
         model.eval()

         model(a						,   token_type_ids=a						,   head_mask=a)
         model(a						,   token_type_ids=a)
         lowercase__    :   Any      =       model(a)
         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       snake_case_				(      self						,   a						,   a						,   a						,   a						,   a						,   *a):
         lowercase__    :   Optional[int]      =       CTRLLMHeadModel(a)
         model.to(a)
         model.eval()

         lowercase__    :   Any      =       model(a						,   token_type_ids=a						,   labels=a)
         self.parent.assertEqual(result.loss.shape						,   ())
         self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.seq_length, self.vocab_size))





    def       snake_case_				(      self):
         lowercase__    :   Optional[Any]      =       self.prepare_config_and_inputs()

         (
             (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , 
         )    :   str      =       config_and_inputs

         lowercase__    :   Dict      =       {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'head_mask': head_mask}

         return config, inputs_dict





    def       snake_case_				(      self						,   a						,   a						,   a						,   a						,   *a):
         lowercase__    :   str      =       self.num_labels
         lowercase__    :   Any      =       CTRLForSequenceClassification(a)
         model.to(a)
         model.eval()
         lowercase__    :   Tuple      =       ids_tensor([self.batch_size]						,   self.type_sequence_label_size)
         lowercase__    :   Union[str, Any]      =       model(a						,   token_type_ids=a						,   labels=a)
         self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.num_labels))



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





    def       snake_case_				(      self						,   a						,   a						,   a						,   a						,   a):
         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       snake_case_				(      self):
         lowercase__    :   Any      =       CTRLModelTester(self)
         lowercase__    :   Union[str, Any]      =       ConfigTester(self						,   config_class=a						,   n_embd=37)





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





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





    def       snake_case_				(      self):
         lowercase__    :   List[str]      =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_ctrl_model(*a)





    def       snake_case_				(      self):
         lowercase__    :   List[Any]      =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_lm_head_model(*a)





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





    @slow
    def       snake_case_				(      self):
         for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowercase__    :   Any      =       CTRLModel.from_pretrained(a)
              self.assertIsNotNone(a)





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



@require_torch
class       SCREAMING_SNAKE_CASE__					(unittest.TestCase     ):





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





    @slow
    def       snake_case_				(      self):
         lowercase__    :   List[str]      =       CTRLLMHeadModel.from_pretrained('ctrl')
         model.to(a)
         lowercase__    :   Tuple      =       torch.tensor(
             [[1_1859, 0, 1611, 8]]						,   dtype=torch.long						,   device=a)  # Legal the president is
         lowercase__    :   Any      =       [
             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

         lowercase__    :   str      =       model.generate(a						,   do_sample=a)
         self.assertListEqual(output_ids[0].tolist()						,   a)




def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    str = "The quick brown fox jumps over the lazy dog"	,     ):



     '''simple docstring'''





     lowercase__    :   str      =       set()

     # Replace all the whitespace in our sentence
     lowercase__    :   Tuple      =       input_str.replace(' '	,     ''			)
     for alpha in input_str:
          if "a" <= alpha.lower() <= "z":
               frequency.add(alpha.lower()			)
     return len(SCREAMING_SNAKE_CASE_			) == 26

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    str = "The quick brown fox jumps over the lazy dog"	,     ):



     '''simple docstring'''





     lowercase__    :   Dict      =       [False] * 26
     for char in input_str:
          if char.islower():
               lowercase__    :   List[Any]      =       True
          elif char.isupper():
               lowercase__    :   Optional[Any]      =       True
     return all(SCREAMING_SNAKE_CASE_			)

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    str = "The quick brown fox jumps over the lazy dog"	,     ):



     '''simple docstring'''





     return len({char for char in input_str.lower() if char.isalpha()}			) == 26

def 	snake_case__			(    ):



     '''simple docstring'''





     from timeit import timeit

     lowercase__    :   Union[str, Any]      =       'from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest'
     print(timeit('is_pangram()'	,     setup=SCREAMING_SNAKE_CASE_			)			)
     print(timeit('is_pangram_faster()'	,     setup=SCREAMING_SNAKE_CASE_			)			)
     print(timeit('is_pangram_fastest()'	,     setup=SCREAMING_SNAKE_CASE_			)			)
     # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
     # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       benchmark()

