


"""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 __snake_case		(					_SCREAMING_SNAKE_CASE      ):







			"""simple docstring"""




			def __init__(		self							:int				,  UpperCamelCase__							:str				,  ):
										_a          =				parent
										_a          =				13
										_a          =				7
										_a          =				True
										_a          =				True
										_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          =				512
										_a          =				16
										_a          =				2
										_a          =				0.02
										_a          =				3
										_a          =				4
										_a          =				None
										_a          =				self.vocab_size - 1




			def SCREAMING_SNAKE_CASE_      (		self							:str       ):
										_a          =				ids_tensor([self.batch_size, self.seq_length]				,  self.vocab_size       )

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

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

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

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

										_a          =				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				,  )

										_a          =				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]				,  UpperCamelCase__							:Union[str, Any]				,  UpperCamelCase__							:str				,  UpperCamelCase__							:Optional[Any]				,  UpperCamelCase__							:Dict				,  UpperCamelCase__							:str				,  *UpperCamelCase__							:Optional[int]       ):
										_a          =				TFCTRLModel(config=UpperCamelCase__       )
										_a          =				{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
										_a          =				model(UpperCamelCase__       )

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

										_a          =				model(UpperCamelCase__       )

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




			def SCREAMING_SNAKE_CASE_      (		self							:Dict				,  UpperCamelCase__							:Tuple				,  UpperCamelCase__							:Optional[Any]				,  UpperCamelCase__							:Tuple				,  UpperCamelCase__							:Optional[int]				,  UpperCamelCase__							:Any				,  *UpperCamelCase__							:Dict       ):
										_a          =				TFCTRLLMHeadModel(config=UpperCamelCase__       )
										_a          =				{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
										_a          =				model(UpperCamelCase__       )
										self.parent.assertEqual(result.logits.shape				,  (self.batch_size, self.seq_length, self.vocab_size)       )




			def SCREAMING_SNAKE_CASE_      (		self							:List[Any]				,  UpperCamelCase__							:List[Any]				,  UpperCamelCase__							:Any				,  UpperCamelCase__							:Any				,  UpperCamelCase__							:Tuple				,  UpperCamelCase__							:Any				,  *UpperCamelCase__							:Optional[Any]       ):
										_a          =				self.num_labels
										_a          =				ids_tensor([self.batch_size]				,  self.type_sequence_label_size       )
										_a          =				{
										    "input_ids": input_ids,
										    "token_type_ids": token_type_ids,
										    "labels": sequence_labels,
										}
										_a          =				TFCTRLForSequenceClassification(UpperCamelCase__       )
										_a          =				model(UpperCamelCase__       )
										self.parent.assertEqual(result.logits.shape				,  (self.batch_size, self.num_labels)       )




			def SCREAMING_SNAKE_CASE_      (		self							:Optional[Any]       ):
										_a          =				self.prepare_config_and_inputs()

										(
										    (
										    _a 
										)   , (
										    _a 
										)   , (
										    _a 
										)   , (
										    _a 
										)   , (
										    _a 
										)   , (
										    _a 
										)   , (
										    _a 
										)   , (
										    _a 
										)   , (
										    _a 
										)   ,  
										)          =				config_and_inputs

										_a          =				{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
										return config, inputs_dict






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







			"""simple docstring"""




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




			def SCREAMING_SNAKE_CASE_      (		self							:Tuple				,  UpperCamelCase__							:Dict				,  UpperCamelCase__							:Optional[int]				,  UpperCamelCase__							:str				,  UpperCamelCase__							:List[Any]				,  UpperCamelCase__							:int       ):
										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							:Optional[Any]       ):
										_a          =				TFCTRLModelTester(self       )
										_a          =				ConfigTester(self				,  config_class=UpperCamelCase__				,  n_embd=37       )




			def SCREAMING_SNAKE_CASE_      (		self							:List[str]       ):
										self.config_tester.run_common_tests()




			def SCREAMING_SNAKE_CASE_      (		self							:List[Any]       ):
										_a          =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_ctrl_model(*UpperCamelCase__       )




			def SCREAMING_SNAKE_CASE_      (		self							:str       ):
										_a          =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_ctrl_lm_head(*UpperCamelCase__       )




			def SCREAMING_SNAKE_CASE_      (		self							:List[Any]       ):
										_a          =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_ctrl_for_sequence_classification(*UpperCamelCase__       )




			def SCREAMING_SNAKE_CASE_      (		self							:Union[str, Any]       ):
										_a   , _a          =				self.model_tester.prepare_config_and_inputs_for_common()
										_a          =				[TFCTRLLMHeadModel]
										_a          =				[TFCTRLForSequenceClassification]

										for model_class in self.all_model_classes:
																	_a          =				model_class(UpperCamelCase__       )
																	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:
																								_a          =				model.get_output_embeddings()
																								assert isinstance(UpperCamelCase__				,  tf.keras.layers.Layer       )
																								_a          =				model.get_bias()
																								assert isinstance(UpperCamelCase__				,  UpperCamelCase__       )
																								for k, v in name.items():
																															assert isinstance(UpperCamelCase__				,  tf.Variable       )
																	elif model_class in list_other_models_with_output_ebd:
																								_a          =				model.get_output_embeddings()
																								assert isinstance(UpperCamelCase__				,  tf.keras.layers.Layer       )
																								_a          =				model.get_bias()
																								assert name is None
																	else:
																								_a          =				model.get_output_embeddings()
																								assert x is None
																								_a          =				model.get_bias()
																								assert name is None




			@slow
			def SCREAMING_SNAKE_CASE_      (		self							:Tuple       ):
										for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_a          =				TFCTRLModel.from_pretrained(UpperCamelCase__       )
																	self.assertIsNotNone(UpperCamelCase__       )






@require_tf
class __snake_case		(					unittest.TestCase      ):







			"""simple docstring"""




			@slow
			def SCREAMING_SNAKE_CASE_      (		self							:Dict       ):
										_a          =				TFCTRLLMHeadModel.from_pretrained("ctrl"       )
										_a          =				tf.convert_to_tensor([[11_859, 0, 1_611, 8]]				,  dtype=tf.intaa       )  # Legal the president is
										_a          =				[
										    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

										_a          =				model.generate(UpperCamelCase__				,  do_sample=UpperCamelCase__       )
										self.assertListEqual(output_ids[0].numpy().tolist()				,  UpperCamelCase__       )




"""simple docstring"""




# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import List



def 						__a      (  a,		a					):





       """simple docstring"""







       if name != "training_function" and name != "main":
              raise ValueError(F'Incorrect function name passed: {name}, choose either \'main\' or \'training_function\''					)
       _a   , _a          =				[], False
       for line in lines:
              if not found_start and F'def {name}' in line:
                     _a          =				True
                     good_lines.append(a					)
                     continue
              if found_start:
                     if name == "training_function" and "def main" in line:
                            return good_lines
                     if name == "main" and "if __name__" in line:
                            return good_lines
                     good_lines.append(a					)



def 						__a      (  a					):





       """simple docstring"""







       return [line for line in lines if not line.lstrip().startswith("#"					) and line != "\n"]



def 						__a      (  a,		a,		a,		a = None					):





       """simple docstring"""







       with open(a,		"r"					) as f:
              _a          =				f.readlines()
       with open(os.path.abspath(os.path.join("examples",		"nlp_example.py"					)					),		"r"					) as f:
              _a          =				f.readlines()
       with open(a,		"r"					) as f:
              _a          =				f.readlines()
       if secondary_filename is not None:
              with open(a,		"r"					) as f:
                     _a          =				f.readlines()

    # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
       if parser_only:
              _a          =				clean_lines(get_function_contents_by_name(a,		"main"					)					)
              _a          =				clean_lines(get_function_contents_by_name(a,		"main"					)					)
              _a          =				clean_lines(get_function_contents_by_name(a,		"main"					)					)
              if secondary_filename is not None:
                     _a          =				clean_lines(get_function_contents_by_name(a,		"main"					)					)
       else:
              _a          =				clean_lines(get_function_contents_by_name(a,		"training_function"					)					)
              _a          =				clean_lines(get_function_contents_by_name(a,		"training_function"					)					)
              _a          =				clean_lines(get_function_contents_by_name(a,		"training_function"					)					)
              if secondary_filename is not None:
                     _a          =				clean_lines(
                         get_function_contents_by_name(a,		"training_function"					)					)

       _a          =				"train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n"

       # Specific code in our script that differs from the full version, aka what is new
       _a          =				[]
       _a          =				[]  # We keep track of the idxs just in case it's a repeated statement
       _a          =				iter(a					)
       for i in range(len(a					) - 1					):
              if i not in passed_idxs:
                     _a          =				next(a					)
                     if (line not in full_file_func) and (line.lstrip() != _dl_line):
                            if "TESTING_MOCKED_DATALOADERS" not in line:
                                   new_feature_code.append(a					)
                                   passed_idxs.append(a					)
                            else:
                                   # Skip over the `config['num_epochs'] = 2` statement
                                   _a          =				next(a					)

    # Extract out just the new parts from the full_file_training_func
       _a          =				[]
       _a          =				[]  # We keep track of the idxs just in case it's a repeated statement
       for i, line in enumerate(a					):
              if i not in passed_idxs:
                     if (line not in full_file_func) and (line.lstrip() != _dl_line):
                            if "TESTING_MOCKED_DATALOADERS" not in line:
                                   new_full_example_parts.append(a					)
                                   passed_idxs.append(a					)

    # Finally, get the overall diff
       _a          =				[line for line in new_feature_code if line not in new_full_example_parts]
       if secondary_filename is not None:
              _a          =				[line for line in full_file_contents if line not in secondary_file_func]
              _a          =				[line for line in diff_from_example if line not in diff_from_two]

       return diff_from_example
