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   lowercase			(   a   ):




							def __init__(  self      :    List[Any]		,							_UpperCamelCase      :    Optional[Any]		,							)    -> Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				parent
												SCREAMING_SNAKE_CASE				=				13
												SCREAMING_SNAKE_CASE				=				7
												SCREAMING_SNAKE_CASE				=				True
												SCREAMING_SNAKE_CASE				=				True
												SCREAMING_SNAKE_CASE				=				True
												SCREAMING_SNAKE_CASE				=				True
												SCREAMING_SNAKE_CASE				=				True
												SCREAMING_SNAKE_CASE				=				99
												SCREAMING_SNAKE_CASE				=				32
												SCREAMING_SNAKE_CASE				=				2
												SCREAMING_SNAKE_CASE				=				4
												SCREAMING_SNAKE_CASE				=				37
												SCREAMING_SNAKE_CASE				=				"gelu"
												SCREAMING_SNAKE_CASE				=				0.1
												SCREAMING_SNAKE_CASE				=				0.1
												SCREAMING_SNAKE_CASE				=				512
												SCREAMING_SNAKE_CASE				=				16
												SCREAMING_SNAKE_CASE				=				2
												SCREAMING_SNAKE_CASE				=				0.0_2
												SCREAMING_SNAKE_CASE				=				3
												SCREAMING_SNAKE_CASE				=				4
												SCREAMING_SNAKE_CASE				=				None
												SCREAMING_SNAKE_CASE				=				self.vocab_size - 1




							def 		__snake_case(  self      :    Optional[int]					)    -> List[str]:



												'''simple docstring'''

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

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

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

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

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

												SCREAMING_SNAKE_CASE				=				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		,							)

												SCREAMING_SNAKE_CASE				=				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      :    List[Any]		,							_UpperCamelCase      :    Optional[int]		,							_UpperCamelCase      :    Optional[int]		,							_UpperCamelCase      :    Optional[Any]		,							_UpperCamelCase      :    Dict		,							_UpperCamelCase      :    Any		,							*_UpperCamelCase      :    str					)    -> Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				TFCTRLModel(config=_UpperCamelCase					)
												SCREAMING_SNAKE_CASE				=				{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
												SCREAMING_SNAKE_CASE				=				model(_UpperCamelCase					)

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

												SCREAMING_SNAKE_CASE				=				model(_UpperCamelCase					)

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




							def 		__snake_case(  self      :    str		,							_UpperCamelCase      :    List[Any]		,							_UpperCamelCase      :    Dict		,							_UpperCamelCase      :    List[str]		,							_UpperCamelCase      :    Any		,							_UpperCamelCase      :    Any		,							*_UpperCamelCase      :    List[Any]					)    -> Dict:



												'''simple docstring'''

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




							def 		__snake_case(  self      :    Union[str, Any]		,							_UpperCamelCase      :    str		,							_UpperCamelCase      :    List[Any]		,							_UpperCamelCase      :    Any		,							_UpperCamelCase      :    Any		,							_UpperCamelCase      :    Union[str, Any]		,							*_UpperCamelCase      :    str					)    -> List[str]:



												'''simple docstring'''

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





							def 		__snake_case(  self      :    int					)    -> Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				self.prepare_config_and_inputs()

												(
												    (
												    SCREAMING_SNAKE_CASE
												)	,      (
												    SCREAMING_SNAKE_CASE
												)	,      (
												    SCREAMING_SNAKE_CASE
												)	,      (
												    SCREAMING_SNAKE_CASE
												)	,      (
												    SCREAMING_SNAKE_CASE
												)	,      (
												    SCREAMING_SNAKE_CASE
												)	,      (
												    SCREAMING_SNAKE_CASE
												)	,      (
												    SCREAMING_SNAKE_CASE
												)	,      (
												    SCREAMING_SNAKE_CASE
												)	,      
												)				=				config_and_inputs

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



@require_tf
class   lowercase			(   a ,				a ,				unittest.TestCase   ):
							lowercase__  :				Dict									=							(TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
							lowercase__  :				List[Any]									=							(TFCTRLLMHeadModel,) if is_tf_available() else ()
							lowercase__  :				Any									=							(
							    {
							        """feature-extraction""": TFCTRLModel,
							        """text-classification""": TFCTRLForSequenceClassification,
							        """text-generation""": TFCTRLLMHeadModel,
							        """zero-shot""": TFCTRLForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)
							lowercase__  :				Dict									=							False
							lowercase__  :				int									=							False




							def 		__snake_case(  self      :    Any		,							_UpperCamelCase      :    List[Any]		,							_UpperCamelCase      :    Any		,							_UpperCamelCase      :    Dict		,							_UpperCamelCase      :    List[str]		,							_UpperCamelCase      :    Dict					)    -> List[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 		__snake_case(  self      :    List[Any]					)    -> Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				TFCTRLModelTester(self					)
												SCREAMING_SNAKE_CASE				=				ConfigTester(self		,							config_class=_UpperCamelCase		,							n_embd=37					)




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



												'''simple docstring'''

												self.config_tester.run_common_tests()




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_ctrl_model(*_UpperCamelCase					)




							def 		__snake_case(  self      :    Optional[Any]					)    -> Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_ctrl_lm_head(*_UpperCamelCase					)




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_ctrl_for_sequence_classification(*_UpperCamelCase					)




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



												'''simple docstring'''

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

												for model_class in self.all_model_classes:
																	SCREAMING_SNAKE_CASE				=				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:
																						SCREAMING_SNAKE_CASE				=				model.get_output_embeddings()
																						assert isinstance(_UpperCamelCase		,							tf.keras.layers.Layer					)
																						SCREAMING_SNAKE_CASE				=				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:
																						SCREAMING_SNAKE_CASE				=				model.get_output_embeddings()
																						assert isinstance(_UpperCamelCase		,							tf.keras.layers.Layer					)
																						SCREAMING_SNAKE_CASE				=				model.get_bias()
																						assert name is None
																	else:
																						SCREAMING_SNAKE_CASE				=				model.get_output_embeddings()
																						assert x is None
																						SCREAMING_SNAKE_CASE				=				model.get_bias()
																						assert name is None





							@slow
							def 		__snake_case(  self      :    Union[str, Any]					)    -> List[str]:



												'''simple docstring'''

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



@require_tf
class   lowercase			(   unittest.TestCase   ):




							@slow
							def 		__snake_case(  self      :    List[Any]					)    -> Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				TFCTRLLMHeadModel.from_pretrained("ctrl"					)
												SCREAMING_SNAKE_CASE				=				tf.convert_to_tensor([[11_859, 0, 1_611, 8]]		,							dtype=tf.intaa					)  # Legal the president is
												SCREAMING_SNAKE_CASE				=				[
												    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

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





from typing import Any, Dict, List, Mapping

import numpy as np
import torch

from ...utils import is_cython_available, requires_backends


if is_cython_available():
							import pyximport

							pyximport.install(setup_args={'''include_dirs''': np.get_include()})
							from . import algos_graphormer  # noqa E402





def 		__lowerCamelCase							(UpperCAmelCase__   :							List[Any]					,       UpperCAmelCase__   :							int = 5_1_2     ):
					SCREAMING_SNAKE_CASE				=				x.shape[1] if len(x.shape     ) > 1 else 1
					SCREAMING_SNAKE_CASE				=				1 + np.arange(0					,       feature_num * offset					,       UpperCAmelCase__					,       dtype=np.intaa     )
					SCREAMING_SNAKE_CASE				=				x + feature_offset
					return x





def 		__lowerCamelCase							(UpperCAmelCase__   :							int					,       UpperCAmelCase__   :							Optional[Any]=True     ):
					requires_backends(UpperCAmelCase__					,       ["cython"]     )

					if keep_features and "edge_attr" in item.keys():  # edge_attr
										SCREAMING_SNAKE_CASE				=				np.asarray(item["edge_attr"]					,       dtype=np.intaa     )
					else:
										SCREAMING_SNAKE_CASE				=				np.ones((len(item["edge_index"][0]     ), 1)					,       dtype=np.intaa     )  # same embedding for all

					if keep_features and "node_feat" in item.keys():  # input_nodes
										SCREAMING_SNAKE_CASE				=				np.asarray(item["node_feat"]					,       dtype=np.intaa     )
					else:
										SCREAMING_SNAKE_CASE				=				np.ones((item["num_nodes"], 1)					,       dtype=np.intaa     )  # same embedding for all

					SCREAMING_SNAKE_CASE				=				np.asarray(item["edge_index"]					,       dtype=np.intaa     )

					SCREAMING_SNAKE_CASE				=				convert_to_single_emb(UpperCAmelCase__     ) + 1
					SCREAMING_SNAKE_CASE				=				item["num_nodes"]

					if len(edge_attr.shape     ) == 1:
										SCREAMING_SNAKE_CASE				=				edge_attr[:, None]
					SCREAMING_SNAKE_CASE				=				np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]					,       dtype=np.intaa     )
					SCREAMING_SNAKE_CASE				=				convert_to_single_emb(UpperCAmelCase__     ) + 1

					# node adj matrix [num_nodes, num_nodes] bool
					SCREAMING_SNAKE_CASE				=				np.zeros([num_nodes, num_nodes]					,       dtype=UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				True

					SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE				=				algos_graphormer.floyd_warshall(UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				np.amax(UpperCAmelCase__     )

					SCREAMING_SNAKE_CASE				=				algos_graphormer.gen_edge_input(UpperCAmelCase__					,       UpperCAmelCase__					,       UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				np.zeros([num_nodes + 1, num_nodes + 1]					,       dtype=np.single     )  # with graph token

					# combine
					SCREAMING_SNAKE_CASE				=				input_nodes + 1  # we shift all indices by one for padding
					SCREAMING_SNAKE_CASE				=				attn_bias
					SCREAMING_SNAKE_CASE				=				attn_edge_type
					SCREAMING_SNAKE_CASE				=				shortest_path_result.astype(np.intaa     ) + 1  # we shift all indices by one for padding
					SCREAMING_SNAKE_CASE				=				np.sum(UpperCAmelCase__					,       axis=1     ).reshape(-1     ) + 1  # we shift all indices by one for padding
					SCREAMING_SNAKE_CASE				=				item["in_degree"]  # for undirected graph
					SCREAMING_SNAKE_CASE				=				input_edges + 1  # we shift all indices by one for padding
					if "labels" not in item:
										SCREAMING_SNAKE_CASE				=				item["y"]

					return item


class   lowercase			:




							def __init__(  self      :    int		,							_UpperCamelCase      :    Any=20		,							_UpperCamelCase      :    List[str]=False					)    -> Union[str, Any]:



												'''simple docstring'''

												if not is_cython_available():
																	raise ImportError("Graphormer preprocessing needs Cython (pyximport)"					)

												SCREAMING_SNAKE_CASE				=				spatial_pos_max
												SCREAMING_SNAKE_CASE				=				on_the_fly_processing





							def __call__(  self      :    Union[str, Any]		,							_UpperCamelCase      :    List[dict]					)    -> Dict[str, Any]:



												'''simple docstring'''

												if self.on_the_fly_processing:
																	SCREAMING_SNAKE_CASE				=				[preprocess_item(_UpperCamelCase					) for i in features]

												if not isinstance(features[0]		,							_UpperCamelCase					):
																	SCREAMING_SNAKE_CASE				=				[vars(_UpperCamelCase					) for f in features]
												SCREAMING_SNAKE_CASE				=				{}

												SCREAMING_SNAKE_CASE				=				max(len(i["input_nodes"]					) for i in features					)
												SCREAMING_SNAKE_CASE				=				len(features[0]["input_nodes"][0]					)
												SCREAMING_SNAKE_CASE				=				len(features[0]["attn_edge_type"][0][0]					)
												SCREAMING_SNAKE_CASE				=				max(len(i["input_edges"][0][0]					) for i in features					)
												SCREAMING_SNAKE_CASE				=				len(features[0]["input_edges"][0][0][0]					)
												SCREAMING_SNAKE_CASE				=				len(_UpperCamelCase					)

												SCREAMING_SNAKE_CASE				=				torch.zeros(_UpperCamelCase		,							max_node_num + 1		,							max_node_num + 1		,							dtype=torch.float					)
												SCREAMING_SNAKE_CASE				=				torch.zeros(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							dtype=torch.long					)
												SCREAMING_SNAKE_CASE				=				torch.zeros(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							dtype=torch.long					)
												SCREAMING_SNAKE_CASE				=				torch.zeros(_UpperCamelCase		,							_UpperCamelCase		,							dtype=torch.long					)
												SCREAMING_SNAKE_CASE				=				torch.zeros(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							dtype=torch.long					)
												SCREAMING_SNAKE_CASE				=				torch.zeros(
												    _UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							dtype=torch.long					)

												for ix, f in enumerate(_UpperCamelCase					):
																	for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
																						SCREAMING_SNAKE_CASE				=				torch.tensor(f[k]					)

																	if len(f["attn_bias"][1:, 1:][f["spatial_pos"] >= self.spatial_pos_max]					) > 0:
																						SCREAMING_SNAKE_CASE				=				float("-inf"					)

																	SCREAMING_SNAKE_CASE				=				f["attn_bias"]
																	SCREAMING_SNAKE_CASE				=				f[
																	    "attn_edge_type"
																	]
																	SCREAMING_SNAKE_CASE				=				f["spatial_pos"]
																	SCREAMING_SNAKE_CASE				=				f["in_degree"]
																	SCREAMING_SNAKE_CASE				=				f["input_nodes"]
																	SCREAMING_SNAKE_CASE				=				f["input_edges"]

												SCREAMING_SNAKE_CASE				=				batch["in_degree"]

												SCREAMING_SNAKE_CASE				=				features[0]["labels"]
												if len(_UpperCamelCase					) == 1:  # one task
																	if isinstance(sample[0]		,							_UpperCamelCase					):  # regression
																						SCREAMING_SNAKE_CASE				=				torch.from_numpy(np.concatenate([i["labels"] for i in features]					)					)
																	else:  # binary classification
																						SCREAMING_SNAKE_CASE				=				torch.from_numpy(np.concatenate([i["labels"] for i in features]					)					)
												else:  # multi task classification, left to float to keep the NaNs
																	SCREAMING_SNAKE_CASE				=				torch.from_numpy(np.stack([i["labels"] for i in features]		,							axis=0					)					)

												return batch




