
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     __A(				a    ):




				def __init__(  self	,  _snake_case	,  )					->   List[Any]:


								'''simple docstring'''




								__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:


								'''simple docstring'''




								__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	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case	,  *_snake_case       )					->   List[Any]:


								'''simple docstring'''




								__a    							=					TFCTRLModel(config=_snake_case       )
								__a    							=					{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
								__a    							=					model(_snake_case       )

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

								__a    							=					model(_snake_case       )

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




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


								'''simple docstring'''




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




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case	,  *_snake_case       )					->   Tuple:


								'''simple docstring'''




								__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(_snake_case       )
								__a    							=					model(_snake_case       )
								self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.num_labels)       )




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Tuple:


								'''simple docstring'''




								__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     __A(				a						,     a						,     unittest.TestCase    ):
				snake_case_		     =					(TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
				snake_case_		     =					(TFCTRLLMHeadModel,) if is_tf_available() else ()
				snake_case_		     =					(
				    {
				        '''feature-extraction''': TFCTRLModel,
				        '''text-classification''': TFCTRLForSequenceClassification,
				        '''text-generation''': TFCTRLLMHeadModel,
				        '''zero-shot''': TFCTRLForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				snake_case_		     =					False
				snake_case_		     =					False




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


								'''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       )					->   str:


								'''simple docstring'''




								__a    							=					TFCTRLModelTester(self       )
								__a    							=					ConfigTester(self	,  config_class=_snake_case	,  n_embd=37       )




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


								'''simple docstring'''




								self.config_tester.run_common_tests()




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   str:


								'''simple docstring'''




								__a    							=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_ctrl_model(*_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   str:


								'''simple docstring'''




								__a    							=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_ctrl_lm_head(*_snake_case       )




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


								'''simple docstring'''




								__a    							=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_ctrl_for_sequence_classification(*_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   int:


								'''simple docstring'''




								__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(_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:
																__a    							=					model.get_output_embeddings()
																assert isinstance(_snake_case	,  tf.keras.layers.Layer       )
																__a    							=					model.get_bias()
																assert isinstance(_snake_case	,  _snake_case       )
																for k, v in name.items():
																				assert isinstance(_snake_case	,  tf.Variable       )
												elif model_class in list_other_models_with_output_ebd:
																__a    							=					model.get_output_embeddings()
																assert isinstance(_snake_case	,  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:


								'''simple docstring'''




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







@require_tf
class     __A(				unittest.TestCase    ):




				@slow
				def        SCREAMING_SNAKE_CASE_	(  self       )					->   int:


								'''simple docstring'''




								__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(_snake_case	,  do_sample=_snake_case       )
								self.assertListEqual(output_ids[0].numpy().tolist()	,  _snake_case       )

import importlib
import importlib.metadata
import os
import warnings
from distutils.util import strtobool
from functools import lru_cache

import torch
from packaging import version
from packaging.version import parse

from .environment import parse_flag_from_env
from .versions import compare_versions, is_torch_version


try:
					import torch_xla.core.xla_model as xm  # noqa: F401

					A  :			List[str]     				=				True
except ImportError:
					A  :			List[Any]     				=				False


# Cache this result has it's a C FFI call which can be pretty time-consuming
A  :			str     				=				torch.distributed.is_available()


def 					__lowerCAmelCase    (    a__						) ->						List[str]:
				# Check we're not importing a "pkg_name" directory somewhere but the actual library by trying to grab the version
				__a    							=					importlib.util.find_spec(a__						) is not None
				if package_exists:
								try:
												__a    							=					importlib.metadata.metadata(a__						)
												return True
								except importlib.metadata.PackageNotFoundError:
												return False


def 					__lowerCAmelCase    (    ) ->						bool:
				return _torch_distributed_available


def 					__lowerCAmelCase    (    ) ->						Tuple:
				try:
								pass
				except ImportError:
								print(
								    '''Intel(R) oneCCL Bindings for PyTorch* is required to run DDP on Intel(R) GPUs, but it is not'''
								    ''' detected. If you see \"ValueError: Invalid backend: \'ccl\'\" error, please install Intel(R) oneCCL'''
								    ''' Bindings for PyTorch*.'''						)
				return (
				    importlib.util.find_spec('''torch_ccl'''						) is not None
				    or importlib.util.find_spec('''oneccl_bindings_for_pytorch'''						) is not None
				)


def 					__lowerCAmelCase    (    ) ->						Union[str, Any]:
				return importlib.metadata.version('''oneccl_bind_pt'''						)


def 					__lowerCAmelCase    (    ) ->						Dict:
				return _is_package_available('''transformer_engine'''						)


@lru_cache
def 					__lowerCAmelCase    (    a__=True						) ->						Union[str, Any]:
				# Due to bugs on the amp series GPUs, we disable torch-xla on them
				if torch.cuda.is_available():
								return False
				if _tpu_available and check_device:
								try:
												# Will raise a RuntimeError if no XLA configuration is found
												__a    							=					xm.xla_device()
												return True
								except RuntimeError:
												return False
				return _tpu_available


def 					__lowerCAmelCase    (    ) ->						Any:
				return _is_package_available('''deepspeed'''						)


def 					__lowerCAmelCase    (    a__=False						) ->						Optional[Any]:
				if is_tpu_available():
								return not ignore_tpu
				if torch.cuda.is_available():
								return torch.cuda.is_bfaa_supported()
				if is_npu_available():
								return False
				return True


def 					__lowerCAmelCase    (    ) ->						List[str]:
				__a    							=					_is_package_available('''bitsandbytes'''						)
				if package_exists:
								__a    							=					version.parse(importlib.metadata.version('''bitsandbytes'''						)						)
								return compare_versions(a__		,					'''>='''		,					'''0.39.0'''						)
				return False


def 					__lowerCAmelCase    (    ) ->						Any:
				__a    							=					_is_package_available('''bitsandbytes'''						)
				if package_exists:
								__a    							=					version.parse(importlib.metadata.version('''bitsandbytes'''						)						)
								return compare_versions(a__		,					'''>='''		,					'''0.37.2'''						)
				return False


def 					__lowerCAmelCase    (    ) ->						int:
				return _is_package_available('''bitsandbytes'''						)


def 					__lowerCAmelCase    (    ) ->						str:
				if strtobool(os.environ.get('''ACCELERATE_USE_MEGATRON_LM'''		,					'''False'''						)						) == 1:
								__a    							=					importlib.util.find_spec('''megatron'''						) is not None
								if package_exists:
												try:
																__a    							=					parse(importlib.metadata.version('''megatron-lm'''						)						)
																return compare_versions(a__		,					'''>='''		,					'''2.2.0'''						)
												except Exception as e:
																warnings.warn(F"""Parse Megatron version failed. Exception:{e}"""						)
																return False


def 					__lowerCAmelCase    (    ) ->						List[str]:
				return _is_package_available('''safetensors'''						)


def 					__lowerCAmelCase    (    ) ->						Optional[Any]:
				return _is_package_available('''transformers'''						)


def 					__lowerCAmelCase    (    ) ->						int:
				return _is_package_available('''datasets'''						)


def 					__lowerCAmelCase    (    ) ->						Optional[int]:
				return _is_package_available('''aim'''						)


def 					__lowerCAmelCase    (    ) ->						List[Any]:
				return _is_package_available('''tensorboard'''						) or _is_package_available('''tensorboardX'''						)


def 					__lowerCAmelCase    (    ) ->						List[Any]:
				return _is_package_available('''wandb'''						)


def 					__lowerCAmelCase    (    ) ->						Optional[int]:
				return _is_package_available('''comet_ml'''						)


def 					__lowerCAmelCase    (    ) ->						str:
				return _is_package_available('''boto3'''						)


def 					__lowerCAmelCase    (    ) ->						Optional[Any]:
				if _is_package_available('''rich'''						):
								if "ACCELERATE_DISABLE_RICH" in os.environ:
												warnings.warn(
												    '''`ACCELERATE_DISABLE_RICH` is deprecated and will be removed in v0.22.0 and deactivated by default. Please use `ACCELERATE_ENABLE_RICH` if you wish to use `rich`.'''						)
												return not parse_flag_from_env('''ACCELERATE_DISABLE_RICH'''		,					a__						)
								return parse_flag_from_env('''ACCELERATE_ENABLE_RICH'''		,					a__						)
				return False


def 					__lowerCAmelCase    (    ) ->						Any:
				return _is_package_available('''sagemaker'''						)


def 					__lowerCAmelCase    (    ) ->						int:
				return _is_package_available('''tqdm'''						)


def 					__lowerCAmelCase    (    ) ->						Union[str, Any]:
				return _is_package_available('''mlflow'''						)


def 					__lowerCAmelCase    (    ) ->						Optional[Any]:
				return is_torch_version('''>='''		,					'''1.12'''						) and torch.backends.mps.is_available() and torch.backends.mps.is_built()


def 					__lowerCAmelCase    (    ) ->						str:
				def get_major_and_minor_from_version(a__						):
								return str(version.parse(a__						).major						) + "." + str(version.parse(a__						).minor						)

				__a    							=					importlib.metadata.version('''torch'''						)
				if importlib.util.find_spec('''intel_extension_for_pytorch'''						) is None:
								return False
				__a    							=					'''N/A'''
				try:
								__a    							=					importlib.metadata.version('''intel_extension_for_pytorch'''						)
				except importlib.metadata.PackageNotFoundError:
								return False
				__a    							=					get_major_and_minor_from_version(_torch_version						)
				__a    							=					get_major_and_minor_from_version(_ipex_version						)
				if torch_major_and_minor != ipex_major_and_minor:
								warnings.warn(
								    F"""Intel Extension for PyTorch {ipex_major_and_minor} needs to work with PyTorch {ipex_major_and_minor}.*,"""
								    F""" but PyTorch {_torch_version} is found. Please switch to the matching version and run again."""						)
								return False
				return True


@lru_cache
def 					__lowerCAmelCase    (    a__=False						) ->						List[Any]:
				if importlib.util.find_spec('''torch'''						) is None or importlib.util.find_spec('''torch_npu'''						) is None:
								return False

				import torch
				import torch_npu  # noqa: F401

				if check_device:
								try:
												# Will raise a RuntimeError if no NPU is found
												__a    							=					torch.npu.device_count()
												return torch.npu.is_available()
								except RuntimeError:
												return False
				return hasattr(a__		,					'''npu'''						) and torch.npu.is_available()






@lru_cache
def 					__lowerCAmelCase    (    a__=False						) ->						Union[str, Any]:
				if not parse_flag_from_env('''ACCELERATE_USE_XPU'''		,					default=a__						):
								return False
				if is_ipex_available():
								import torch

								if is_torch_version('''<='''		,					'''1.12'''						):
												return False
				else:
								return False

				import intel_extension_for_pytorch  # noqa: F401

				if check_device:
								try:
												# Will raise a RuntimeError if no XPU  is found
												__a    							=					torch.xpu.device_count()
												return torch.xpu.is_available()
								except RuntimeError:
												return False
				return hasattr(a__		,					'''xpu'''						) and torch.xpu.is_available()