





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						:







 '''simple docstring'''

 def __init__(self    :       int	,			UpperCAmelCase_    :       Optional[int]	,			UpperCAmelCase_    :       List[Any]=14	,			UpperCAmelCase_    :       Tuple=7	,			UpperCAmelCase_    :       Union[str, Any]=True	,			UpperCAmelCase_    :       Dict=True	,			UpperCAmelCase_    :       Optional[Any]=True	,			UpperCAmelCase_    :       int=True	,			UpperCAmelCase_    :       int=True	,			UpperCAmelCase_    :       List[str]=99	,			UpperCAmelCase_    :       str=32	,			UpperCAmelCase_    :       List[str]=5	,			UpperCAmelCase_    :       Union[str, Any]=4	,			UpperCAmelCase_    :       Optional[int]=37	,			UpperCAmelCase_    :       Dict="gelu"	,			UpperCAmelCase_    :       List[str]=0.1	,			UpperCAmelCase_    :       List[str]=0.1	,			UpperCAmelCase_    :       Optional[Any]=512	,			UpperCAmelCase_    :       Optional[Any]=16	,			UpperCAmelCase_    :       int=2	,			UpperCAmelCase_    :       List[Any]=0.02	,			UpperCAmelCase_    :       Tuple=3	,			UpperCAmelCase_    :       int=4	,			UpperCAmelCase_    :       List[Any]=None	,			)	->Dict:



   '''simple docstring'''



   lowerCamelCase__:				int        =parent
   lowerCamelCase__:				Optional[int]        =batch_size
   lowerCamelCase__:				Any        =seq_length
   lowerCamelCase__:				Union[str, Any]        =is_training
   lowerCamelCase__:				int        =use_token_type_ids
   lowerCamelCase__:				str        =use_input_mask
   lowerCamelCase__:				Union[str, Any]        =use_labels
   lowerCamelCase__:				List[str]        =use_mc_token_ids
   lowerCamelCase__:				Optional[Any]        =vocab_size
   lowerCamelCase__:				str        =hidden_size
   lowerCamelCase__:				List[Any]        =num_hidden_layers
   lowerCamelCase__:				Optional[int]        =num_attention_heads
   lowerCamelCase__:				Tuple        =intermediate_size
   lowerCamelCase__:				Optional[Any]        =hidden_act
   lowerCamelCase__:				Union[str, Any]        =hidden_dropout_prob
   lowerCamelCase__:				Tuple        =attention_probs_dropout_prob
   lowerCamelCase__:				Union[str, Any]        =max_position_embeddings
   lowerCamelCase__:				Optional[Any]        =type_vocab_size
   lowerCamelCase__:				Any        =type_sequence_label_size
   lowerCamelCase__:				List[str]        =initializer_range
   lowerCamelCase__:				Any        =num_labels
   lowerCamelCase__:				List[Any]        =num_choices
   lowerCamelCase__:				Any        =scope
   lowerCamelCase__:				str        =self.vocab_size - 1

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any])	->str:



   '''simple docstring'''



   lowerCamelCase__:				Optional[int]        =ids_tensor([self.batch_size, self.seq_length]	,			self.vocab_size)

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

   lowerCamelCase__:				str        =None
   if self.use_token_type_ids:
     lowerCamelCase__:				Optional[Any]        =ids_tensor([self.batch_size, self.seq_length]	,			self.type_vocab_size)

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

   lowerCamelCase__:				List[Any]        =None
   lowerCamelCase__:				Dict        =None
   lowerCamelCase__:				Optional[int]        =None
   if self.use_labels:
     lowerCamelCase__:				Optional[Any]        =ids_tensor([self.batch_size]	,			self.type_sequence_label_size)
     lowerCamelCase__:				Tuple        =ids_tensor([self.batch_size, self.seq_length]	,			self.num_labels)
     lowerCamelCase__:				Optional[int]        =ids_tensor([self.batch_size]	,			self.num_choices)

   lowerCamelCase__:				Any        =self.get_config()

   lowerCamelCase__:				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 							SCREAMING_SNAKE_CASE_ (self    :       List[Any])	->int:



   '''simple docstring'''



   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 							SCREAMING_SNAKE_CASE_ (self    :       Tuple	,			UpperCAmelCase_    :       Union[str, Any]	,			UpperCAmelCase_    :       Union[str, Any]	,			UpperCAmelCase_    :       Optional[int]	,			UpperCAmelCase_    :       Any	,			UpperCAmelCase_    :       Union[str, Any]	,			*UpperCAmelCase_    :       Any)	->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__:				Optional[Any]        =CTRLModel(config=UpperCAmelCase_)
   model.to(UpperCAmelCase_)
   model.eval()

   model(UpperCAmelCase_	,			token_type_ids=UpperCAmelCase_	,			head_mask=UpperCAmelCase_)
   model(UpperCAmelCase_	,			token_type_ids=UpperCAmelCase_)
   lowerCamelCase__:				Union[str, Any]        =model(UpperCAmelCase_)
   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 							SCREAMING_SNAKE_CASE_ (self    :       Any	,			UpperCAmelCase_    :       Any	,			UpperCAmelCase_    :       Dict	,			UpperCAmelCase_    :       List[str]	,			UpperCAmelCase_    :       str	,			UpperCAmelCase_    :       Optional[Any]	,			*UpperCAmelCase_    :       int)	->str:



   '''simple docstring'''



   lowerCamelCase__:				List[Any]        =CTRLLMHeadModel(UpperCAmelCase_)
   model.to(UpperCAmelCase_)
   model.eval()

   lowerCamelCase__:				List[Any]        =model(UpperCAmelCase_	,			token_type_ids=UpperCAmelCase_	,			labels=UpperCAmelCase_)
   self.parent.assertEqual(result.loss.shape	,			())
   self.parent.assertEqual(result.logits.shape	,			(self.batch_size, self.seq_length, self.vocab_size))

 def 							SCREAMING_SNAKE_CASE_ (self    :       Any)	->Dict:



   '''simple docstring'''



   lowerCamelCase__:				str        =self.prepare_config_and_inputs()

   (
       (
       lowerCamelCase__
   )  , (
       lowerCamelCase__
   )  , (
       lowerCamelCase__
   )  , (
       lowerCamelCase__
   )  , (
       lowerCamelCase__
   )  , (
       lowerCamelCase__
   )  , (
       lowerCamelCase__
   )  , (
       lowerCamelCase__
   )  , (
       lowerCamelCase__
   )  , 
   ):				List[str]        =config_and_inputs

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

   return config, inputs_dict







 def 							SCREAMING_SNAKE_CASE_ (self    :       Any	,			UpperCAmelCase_    :       List[str]	,			UpperCAmelCase_    :       Optional[int]	,			UpperCAmelCase_    :       Union[str, Any]	,			UpperCAmelCase_    :       str	,			*UpperCAmelCase_    :       Tuple)	->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__:				Dict        =self.num_labels
   lowerCamelCase__:				Union[str, Any]        =CTRLForSequenceClassification(UpperCAmelCase_)
   model.to(UpperCAmelCase_)
   model.eval()
   lowerCamelCase__:				Optional[int]        =ids_tensor([self.batch_size]	,			self.type_sequence_label_size)
   lowerCamelCase__:				Dict        =model(UpperCAmelCase_	,			token_type_ids=UpperCAmelCase_	,			labels=UpperCAmelCase_)
   self.parent.assertEqual(result.logits.shape	,			(self.batch_size, self.num_labels))



@require_torch
class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ,							__SCREAMING_SNAKE_CASE   ,							__SCREAMING_SNAKE_CASE   ,							unittest.TestCase   ):







 '''simple docstring'''
 lowercase_		=      (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
 lowercase_		=      (CTRLLMHeadModel,) if is_torch_available() else ()
 lowercase_		=      (
     {
         "feature-extraction": CTRLModel,
         "text-classification": CTRLForSequenceClassification,
         "text-generation": CTRLLMHeadModel,
         "zero-shot": CTRLForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 lowercase_		=      True
 lowercase_		=      False
 lowercase_		=      False

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any]	,			UpperCAmelCase_    :       Any	,			UpperCAmelCase_    :       List[str]	,			UpperCAmelCase_    :       Optional[int]	,			UpperCAmelCase_    :       List[str]	,			UpperCAmelCase_    :       Optional[Any])	->List[str]:



   '''simple docstring'''



   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 							SCREAMING_SNAKE_CASE_ (self    :       List[Any])	->Dict:



   '''simple docstring'''



   lowerCamelCase__:				Union[str, Any]        =CTRLModelTester(self)
   lowerCamelCase__:				int        =ConfigTester(self	,			config_class=UpperCAmelCase_	,			n_embd=37)

 def 							SCREAMING_SNAKE_CASE_ (self    :       str)	->Optional[Any]:



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_ (self    :       Any)	->Union[str, Any]:



   '''simple docstring'''



   self.config_tester.run_common_tests()

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int])	->Dict:



   '''simple docstring'''



   lowerCamelCase__:				str        =self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_ctrl_model(*UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any])	->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:				int        =self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_lm_head_model(*UpperCAmelCase_)

 @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
 def 							SCREAMING_SNAKE_CASE_ (self    :       str)	->List[Any]:



   '''simple docstring'''



   pass

 @slow
 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any])	->Union[str, Any]:



   '''simple docstring'''



   for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     lowerCamelCase__:				Any        =CTRLModel.from_pretrained(UpperCAmelCase_)
     self.assertIsNotNone(UpperCAmelCase_)







 @unittest.skip("The model doesn't support left padding")  # and it's not used enough to be worth fixing :)
 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any])	->Tuple:



   '''simple docstring'''



   pass



@require_torch
class        _SCREAMING_SNAKE_CASE						(     unittest.TestCase   ):







 '''simple docstring'''

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any])	->int:



   '''simple docstring'''



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







 @slow
 def 							SCREAMING_SNAKE_CASE_ (self    :       str)	->Tuple:



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =CTRLLMHeadModel.from_pretrained("ctrl")
   model.to(UpperCAmelCase_)
   lowerCamelCase__:				str        =torch.tensor(
       [[11_859, 0, 1_611, 8]]	,			dtype=torch.long	,			device=UpperCAmelCase_)  # Legal the president is
   lowerCamelCase__:				List[Any]        =[
       11_859,
       0,
       1_611,
       8,
       5,
       150,
       26_449,
       2,
       19,
       348,
       469,
       3,
       2_595,
       48,
       20_740,
       246_533,
       246_533,
       19,
       30,
       5,
   ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

   lowerCamelCase__:				List[Any]        =model.generate(UpperCAmelCase_	,			do_sample=UpperCAmelCase_)
   self.assertListEqual(output_ids[0].tolist()	,			UpperCAmelCase_)








from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image


__A     =    ["text", "image", "audio"]





def 					lowerCAmelCase_			(	__a		)					->		Optional[Any]:







  """simple docstring"""





  lowerCamelCase__:				Tuple        =[]

  for input_type in input_types:
    if input_type == "text":
      inputs.append("Text input"		)
    elif input_type == "image":
      inputs.append(
          Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"		)		) / "000000039769.png"		).resize((512, 512)		)		)
    elif input_type == "audio":
      inputs.append(torch.ones(3000		)		)
    elif isinstance(__a     ,   __a		):
      inputs.append(create_inputs(__a		)		)
    else:
      raise ValueError(F"""Invalid type requested: {input_type}"""		)

  return inputs





def 					lowerCAmelCase_			(	__a		)					->		Union[str, Any]:







  """simple docstring"""





  lowerCamelCase__:				Union[str, Any]        =[]

  for output in outputs:
    if isinstance(__a     ,   (str, AgentText)		):
      output_types.append("text"		)
    elif isinstance(__a     ,   (Image.Image, AgentImage)		):
      output_types.append("image"		)
    elif isinstance(__a     ,   (torch.Tensor, AgentAudio)		):
      output_types.append("audio"		)
    else:
      raise ValueError(F"""Invalid output: {output}"""		)

  return output_types






@is_tool_test
class        _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str])	->Dict:



   '''simple docstring'''



   self.assertTrue(hasattr(self.tool	,			"inputs"))
   self.assertTrue(hasattr(self.tool	,			"outputs"))

   lowerCamelCase__:				Tuple        =self.tool.inputs
   for _input in inputs:
     if isinstance(_input	,			UpperCAmelCase_):
       for __input in _input:
         self.assertTrue(__input in authorized_types)
     else:
       self.assertTrue(_input in authorized_types)

   lowerCamelCase__:				Optional[Any]        =self.tool.outputs
   for _output in outputs:
     self.assertTrue(_output in authorized_types)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any])	->str:



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =create_inputs(self.tool.inputs)
   lowerCamelCase__:				str        =self.tool(*UpperCAmelCase_)

   # There is a single output
   if len(self.tool.outputs) == 1:
     lowerCamelCase__:				Optional[Any]        =[outputs]

   self.assertListEqual(output_types(UpperCAmelCase_)	,			self.tool.outputs)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict)	->Any:



   '''simple docstring'''



   self.assertTrue(hasattr(self.tool	,			"description"))
   self.assertTrue(hasattr(self.tool	,			"default_checkpoint"))
   self.assertTrue(self.tool.description.startswith("This is a tool that"))

 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:				str        =create_inputs(self.tool.inputs)
   lowerCamelCase__:				Dict        =self.tool(*UpperCAmelCase_)

   if not isinstance(UpperCAmelCase_	,			UpperCAmelCase_):
     lowerCamelCase__:				Tuple        =[outputs]

   self.assertEqual(len(UpperCAmelCase_)	,			len(self.tool.outputs))

   for output, output_type in zip(UpperCAmelCase_	,			self.tool.outputs):
     lowerCamelCase__:				Any        =AGENT_TYPE_MAPPING[output_type]
     self.assertTrue(isinstance(UpperCAmelCase_	,			UpperCAmelCase_))







 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict)	->str:



   '''simple docstring'''



   lowerCamelCase__:				Any        =create_inputs(self.tool.inputs)

   lowerCamelCase__:				int        =[]

   for _input, input_type in zip(UpperCAmelCase_	,			self.tool.inputs):
     if isinstance(UpperCAmelCase_	,			UpperCAmelCase_):
       _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input) for _input_type in input_type])
     else:
       _inputs.append(AGENT_TYPE_MAPPING[input_type](_input))

        # Should not raise an error
   lowerCamelCase__:				Union[str, Any]        =self.tool(*UpperCAmelCase_)

   if not isinstance(UpperCAmelCase_	,			UpperCAmelCase_):
     lowerCamelCase__:				str        =[outputs]

   self.assertEqual(len(UpperCAmelCase_)	,			len(self.tool.outputs))

