"""simple docstring"""







import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import MODEL_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
     from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import GLPNImageProcessor






class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.config_class(**self.inputs_dict	)
      self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,				'hidden_sizes'	)	)
      self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,				'num_attention_heads'	)	)
      self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,				'num_encoder_blocks'	)	)






class      _SCREAMING_SNAKE_CASE							:



   '''simple docstring'''



   def __init__(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE=13 ,				_SCREAMING_SNAKE_CASE=64 ,				_SCREAMING_SNAKE_CASE=3 ,				_SCREAMING_SNAKE_CASE=4 ,				_SCREAMING_SNAKE_CASE=[2, 2, 2, 2] ,				_SCREAMING_SNAKE_CASE=[8, 4, 2, 1] ,				_SCREAMING_SNAKE_CASE=[16, 32, 64, 128] ,				_SCREAMING_SNAKE_CASE=[1, 4, 8, 16] ,				_SCREAMING_SNAKE_CASE=[1, 2, 4, 8] ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE="gelu" ,				_SCREAMING_SNAKE_CASE=0.1 ,				_SCREAMING_SNAKE_CASE=0.1 ,				_SCREAMING_SNAKE_CASE=0.02 ,				_SCREAMING_SNAKE_CASE=16 ,				_SCREAMING_SNAKE_CASE=3 ,				_SCREAMING_SNAKE_CASE=None ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					parent
      __lowerCamelCase    		=					batch_size
      __lowerCamelCase    		=					image_size
      __lowerCamelCase    		=					num_channels
      __lowerCamelCase    		=					num_encoder_blocks
      __lowerCamelCase    		=					sr_ratios
      __lowerCamelCase    		=					depths
      __lowerCamelCase    		=					hidden_sizes
      __lowerCamelCase    		=					downsampling_rates
      __lowerCamelCase    		=					num_attention_heads
      __lowerCamelCase    		=					is_training
      __lowerCamelCase    		=					use_labels
      __lowerCamelCase    		=					hidden_act
      __lowerCamelCase    		=					hidden_dropout_prob
      __lowerCamelCase    		=					attention_probs_dropout_prob
      __lowerCamelCase    		=					initializer_range
      __lowerCamelCase    		=					decoder_hidden_size
      __lowerCamelCase    		=					num_labels
      __lowerCamelCase    		=					scope







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

      __lowerCamelCase    		=					None
      if self.use_labels:
         __lowerCamelCase    		=					ids_tensor([self.batch_size, self.image_size, self.image_size] ,				self.num_labels	)

      __lowerCamelCase    		=					self.get_config()
      return config, pixel_values, labels







   def    _A				(	self	):


      '''simple docstring'''

      return GLPNConfig(
          image_size=self.image_size ,				num_channels=self.num_channels ,				num_encoder_blocks=self.num_encoder_blocks ,				depths=self.depths ,				hidden_sizes=self.hidden_sizes ,				num_attention_heads=self.num_attention_heads ,				hidden_act=self.hidden_act ,				hidden_dropout_prob=self.hidden_dropout_prob ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob ,				initializer_range=self.initializer_range ,				decoder_hidden_size=self.decoder_hidden_size ,				)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					GLPNModel(config=_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					__lowerCamelCase    		=					self.image_size // (self.downsampling_rates[-1] * 2)
      self.parent.assertEqual(
          result.last_hidden_state.shape ,				(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.num_labels
      __lowerCamelCase    		=					GLPNForDepthEstimation(_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.predicted_depth.shape ,				(self.batch_size, self.image_size, self.image_size)	)
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE ,				labels=_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.predicted_depth.shape ,				(self.batch_size, self.image_size, self.image_size)	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.prepare_config_and_inputs()
      __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase    		=					config_and_inputs
      __lowerCamelCase    		=					{'pixel_values': pixel_values}
      return config, inputs_dict






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



   '''simple docstring'''



   __UpperCAmelCase	:							Union[str, Any]    =    (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
   __UpperCAmelCase	:							Any    =    (
       {"depth-estimation": GLPNForDepthEstimation, "feature-extraction": GLPNModel} if is_torch_available() else {}
   )

   __UpperCAmelCase	:							Optional[Any]    =    False
   __UpperCAmelCase	:							Optional[int]    =    False
   __UpperCAmelCase	:							int    =    False







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					GLPNModelTester(self	)
      __lowerCamelCase    		=					GLPNConfigTester(self ,				config_class=_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      self.config_tester.run_common_tests()







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_depth_estimation(*_SCREAMING_SNAKE_CASE	)







   @unittest.skip('GLPN does not use inputs_embeds'	)
   def    _A				(	self	):


      '''simple docstring'''

      pass







   @unittest.skip('GLPN does not have get_input_embeddings method and get_output_embeddings methods'	)
   def    _A				(	self	):


      '''simple docstring'''

      pass







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase		,__lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         __lowerCamelCase    		=					model_class(_SCREAMING_SNAKE_CASE	)
         __lowerCamelCase    		=					inspect.signature(model.forward	)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         __lowerCamelCase    		=					[*signature.parameters.keys()]

         __lowerCamelCase    		=					['pixel_values']
         self.assertListEqual(arg_names[:1] ,				_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase		,__lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs_for_common()
      __lowerCamelCase    		=					True

      for model_class in self.all_model_classes:
         __lowerCamelCase    		=					True
         __lowerCamelCase    		=					False
         __lowerCamelCase    		=					True
         __lowerCamelCase    		=					model_class(_SCREAMING_SNAKE_CASE	)
         model.to(_SCREAMING_SNAKE_CASE	)
         model.eval()
         with torch.no_grad():
            __lowerCamelCase    		=					model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)	)
         __lowerCamelCase    		=					outputs.attentions

         __lowerCamelCase    		=					sum(self.model_tester.depths	)
         self.assertEqual(len(_SCREAMING_SNAKE_CASE	) ,				_SCREAMING_SNAKE_CASE	)

         # check that output_attentions also work using config
         del inputs_dict["output_attentions"]
         __lowerCamelCase    		=					True
         __lowerCamelCase    		=					model_class(_SCREAMING_SNAKE_CASE	)
         model.to(_SCREAMING_SNAKE_CASE	)
         model.eval()
         with torch.no_grad():
            __lowerCamelCase    		=					model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)	)
         __lowerCamelCase    		=					outputs.attentions

         self.assertEqual(len(_SCREAMING_SNAKE_CASE	) ,				_SCREAMING_SNAKE_CASE	)

         # verify the first attentions (first block, first layer)
         __lowerCamelCase    		=					(self.model_tester.image_size // 4) ** 2
         __lowerCamelCase    		=					(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
         self.assertListEqual(
             list(attentions[0].shape[-3:]	) ,				[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len] ,				)

         # verify the last attentions (last block, last layer)
         __lowerCamelCase    		=					(self.model_tester.image_size // 32) ** 2
         __lowerCamelCase    		=					(self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
         self.assertListEqual(
             list(attentions[-1].shape[-3:]	) ,				[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len] ,				)
         __lowerCamelCase    		=					len(_SCREAMING_SNAKE_CASE	)

         # Check attention is always last and order is fine
         __lowerCamelCase    		=					True
         __lowerCamelCase    		=					True
         __lowerCamelCase    		=					model_class(_SCREAMING_SNAKE_CASE	)
         model.to(_SCREAMING_SNAKE_CASE	)
         model.eval()
         with torch.no_grad():
            __lowerCamelCase    		=					model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)	)

         self.assertEqual(out_len + 1 ,				len(_SCREAMING_SNAKE_CASE	)	)

         __lowerCamelCase    		=					outputs.attentions

         self.assertEqual(len(_SCREAMING_SNAKE_CASE	) ,				_SCREAMING_SNAKE_CASE	)
         # verify the first attentions (first block, first layer)
         __lowerCamelCase    		=					(self.model_tester.image_size // 4) ** 2
         __lowerCamelCase    		=					(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
         self.assertListEqual(
             list(self_attentions[0].shape[-3:]	) ,				[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len] ,				)







   def    _A				(	self	):


      '''simple docstring'''

      def check_hidden_states_output(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):
         __lowerCamelCase    		=					model_class(_SCREAMING_SNAKE_CASE	)
         model.to(_SCREAMING_SNAKE_CASE	)
         model.eval()

         with torch.no_grad():
            __lowerCamelCase    		=					model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)	)

         __lowerCamelCase    		=					outputs.hidden_states

         __lowerCamelCase    		=					self.model_tester.num_encoder_blocks
         self.assertEqual(len(_SCREAMING_SNAKE_CASE	) ,				_SCREAMING_SNAKE_CASE	)

         # verify the first hidden states (first block)
         self.assertListEqual(
             list(hidden_states[0].shape[-3:]	) ,				[
                 self.model_tester.hidden_sizes[0],
                 self.model_tester.image_size // 4,
                 self.model_tester.image_size // 4,
             ] ,				)

      __lowerCamelCase		,__lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         __lowerCamelCase    		=					True
         check_hidden_states_output(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         __lowerCamelCase    		=					True

         check_hidden_states_output(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      if not self.model_tester.is_training:
         return

      __lowerCamelCase		,__lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs_for_common()
      __lowerCamelCase    		=					True

      for model_class in self.all_model_classes:
         if model_class in get_values(_SCREAMING_SNAKE_CASE	):
            continue
         # TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
         # this can then be incorporated into _prepare_for_class in test_modeling_common.py
         if model_class.__name__ == "GLPNForDepthEstimation":
            __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase    		=					inputs_dict['pixel_values'].shape
            __lowerCamelCase    		=					torch.zeros(
                [self.model_tester.batch_size, height, width] ,				device=_SCREAMING_SNAKE_CASE	).long()
         __lowerCamelCase    		=					model_class(_SCREAMING_SNAKE_CASE	)
         model.to(_SCREAMING_SNAKE_CASE	)
         model.train()
         __lowerCamelCase    		=					self._prepare_for_class(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				return_labels=_SCREAMING_SNAKE_CASE	)
         __lowerCamelCase    		=					model(**_SCREAMING_SNAKE_CASE	).loss
         loss.backward()







   @slow
   def    _A				(	self	):


      '''simple docstring'''

      for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         __lowerCamelCase    		=					GLPNModel.from_pretrained(_SCREAMING_SNAKE_CASE	)
         self.assertIsNotNone(_SCREAMING_SNAKE_CASE	)



def       A__    (	)  ->							Union[str, Any]:
   __lowerCamelCase    		=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
   return image






@require_torch
@require_vision
@slow
class      _SCREAMING_SNAKE_CASE							(			unittest.TestCase				):



   '''simple docstring'''



   @slow
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)
      __lowerCamelCase    		=					GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]	).to(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					prepare_img()
      __lowerCamelCase    		=					image_processor(images=_SCREAMING_SNAKE_CASE ,				return_tensors='pt'	).to(_SCREAMING_SNAKE_CASE	)

      # forward pass
      with torch.no_grad():
         __lowerCamelCase    		=					model(**_SCREAMING_SNAKE_CASE	)

      # verify the predicted depth
      __lowerCamelCase    		=					torch.Size([1, 480, 640]	)
      self.assertEqual(outputs.predicted_depth.shape ,				_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					torch.tensor(
          [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]	).to(_SCREAMING_SNAKE_CASE	)

      self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3] ,				_SCREAMING_SNAKE_CASE ,				atol=1E-4	)	)







"""simple docstring"""







from __future__ import annotations

import unittest

from transformers import CTRLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers.models.ctrl.modeling_tf_ctrl import (
         TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
         TFCTRLForSequenceClassification,
         TFCTRLLMHeadModel,
         TFCTRLModel,
     )






class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   def __init__(	self ,				_SCREAMING_SNAKE_CASE ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					parent
      __lowerCamelCase    		=					13
      __lowerCamelCase    		=					7
      __lowerCamelCase    		=					True
      __lowerCamelCase    		=					True
      __lowerCamelCase    		=					True
      __lowerCamelCase    		=					True
      __lowerCamelCase    		=					True
      __lowerCamelCase    		=					99
      __lowerCamelCase    		=					32
      __lowerCamelCase    		=					2
      __lowerCamelCase    		=					4
      __lowerCamelCase    		=					37
      __lowerCamelCase    		=					'gelu'
      __lowerCamelCase    		=					0.1
      __lowerCamelCase    		=					0.1
      __lowerCamelCase    		=					512
      __lowerCamelCase    		=					16
      __lowerCamelCase    		=					2
      __lowerCamelCase    		=					0.02
      __lowerCamelCase    		=					3
      __lowerCamelCase    		=					4
      __lowerCamelCase    		=					None
      __lowerCamelCase    		=					self.vocab_size - 1







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					ids_tensor([self.batch_size, self.seq_length] ,				self.vocab_size	)

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

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

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

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

      __lowerCamelCase    		=					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 ,				)

      __lowerCamelCase    		=					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    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				*_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					TFCTRLModel(config=_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					[input_ids, None, input_mask]  # None is the input for 'past'
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)

      self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				*_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					TFCTRLLMHeadModel(config=_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.vocab_size)	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				*_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.num_labels
      __lowerCamelCase    		=					ids_tensor([self.batch_size] ,				self.type_sequence_label_size	)
      __lowerCamelCase    		=					{
          'input_ids': input_ids,
          'token_type_ids': token_type_ids,
          'labels': sequence_labels,
      }
      __lowerCamelCase    		=					TFCTRLForSequenceClassification(_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.prepare_config_and_inputs()

      (
          (
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,
      )    		=					config_and_inputs

      __lowerCamelCase    		=					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
      return config, inputs_dict






@require_tf
class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase    ,				UpperCAmelCase    ,				unittest.TestCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Union[str, Any]    =    (TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
   __UpperCAmelCase	:							List[str]    =    (TFCTRLLMHeadModel,) if is_tf_available() else ()
   __UpperCAmelCase	:							Dict    =    (
       {
           "feature-extraction": TFCTRLModel,
           "text-classification": TFCTRLForSequenceClassification,
           "text-generation": TFCTRLLMHeadModel,
           "zero-shot": TFCTRLForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   __UpperCAmelCase	:							str    =    False
   __UpperCAmelCase	:							Tuple    =    False







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''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    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					TFCTRLModelTester(self	)
      __lowerCamelCase    		=					ConfigTester(self ,				config_class=_SCREAMING_SNAKE_CASE ,				n_embd=37	)







   def    _A				(	self	):


      '''simple docstring'''

      self.config_tester.run_common_tests()







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_ctrl_model(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_ctrl_lm_head(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_ctrl_for_sequence_classification(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase		,__lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs_for_common()
      __lowerCamelCase    		=					[TFCTRLLMHeadModel]
      __lowerCamelCase    		=					[TFCTRLForSequenceClassification]

      for model_class in self.all_model_classes:
         __lowerCamelCase    		=					model_class(_SCREAMING_SNAKE_CASE	)
         model.build()  # may be needed for the get_bias() call below
         assert isinstance(model.get_input_embeddings() ,				tf.keras.layers.Layer	)

         if model_class in list_lm_models:
            __lowerCamelCase    		=					model.get_output_embeddings()
            assert isinstance(_SCREAMING_SNAKE_CASE ,				tf.keras.layers.Layer	)
            __lowerCamelCase    		=					model.get_bias()
            assert isinstance(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)
            for k, v in name.items():
               assert isinstance(_SCREAMING_SNAKE_CASE ,				tf.Variable	)
         elif model_class in list_other_models_with_output_ebd:
            __lowerCamelCase    		=					model.get_output_embeddings()
            assert isinstance(_SCREAMING_SNAKE_CASE ,				tf.keras.layers.Layer	)
            __lowerCamelCase    		=					model.get_bias()
            assert name is None
         else:
            __lowerCamelCase    		=					model.get_output_embeddings()
            assert x is None
            __lowerCamelCase    		=					model.get_bias()
            assert name is None







   @slow
   def    _A				(	self	):


      '''simple docstring'''

      for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         __lowerCamelCase    		=					TFCTRLModel.from_pretrained(_SCREAMING_SNAKE_CASE	)
         self.assertIsNotNone(_SCREAMING_SNAKE_CASE	)






@require_tf
class      _SCREAMING_SNAKE_CASE							(			unittest.TestCase				):



   '''simple docstring'''



   @slow
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					TFCTRLLMHeadModel.from_pretrained('ctrl'	)
      __lowerCamelCase    		=					tf.convert_to_tensor([[11859, 0, 1611, 8]] ,				dtype=tf.intaa	)  # Legal the president is
      __lowerCamelCase    		=					[
          11859,
          0,
          1611,
          8,
          5,
          150,
          26449,
          2,
          19,
          348,
          469,
          3,
          2595,
          48,
          20740,
          246533,
          246533,
          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    		=					model.generate(_SCREAMING_SNAKE_CASE ,				do_sample=_SCREAMING_SNAKE_CASE	)
      self.assertListEqual(output_ids[0].numpy().tolist() ,				_SCREAMING_SNAKE_CASE	)






