




"""simple docstring"""

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 					UpperCamelCase__       (						lowercase__					:       int    ,       lowercase__					:       int = 512				):
		snake_case						:  Optional[int]        =			x.shape[1] if len(x.shape				) > 1 else 1
		snake_case						:  List[str]        =			1 + np.arange(0    ,       feature_num * offset    ,       lowercase__    ,       dtype=np.intaa				)
		snake_case						:  Tuple        =			x + feature_offset
		return x



def 					UpperCamelCase__       (						lowercase__					:       int    ,       lowercase__					:       List[Any]=True				):
		requires_backends(lowercase__    ,       ["cython"]				)

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

		snake_case						:  int        =			np.asarray(item["edge_index"]    ,       dtype=np.intaa				)

		snake_case						:  List[Any]        =			convert_to_single_emb(lowercase__				) + 1
		snake_case						:  Any        =			item["num_nodes"]

		if len(edge_attr.shape				) == 1:
				snake_case						:  int        =			edge_attr[:, None]
		snake_case						:  int        =			np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]    ,       dtype=np.intaa				)
		snake_case						:  Optional[Any]        =			convert_to_single_emb(lowercase__				) + 1

		# node adj matrix [num_nodes, num_nodes] bool
		snake_case						:  Union[str, Any]        =			np.zeros([num_nodes, num_nodes]    ,       dtype=lowercase__				)
		snake_case						:  Union[str, Any]        =			True

		snake_case     ,			snake_case						:  List[str]        =			algos_graphormer.floyd_warshall(lowercase__				)
		snake_case						:  Union[str, Any]        =			np.amax(lowercase__				)

		snake_case						:  Dict        =			algos_graphormer.gen_edge_input(lowercase__    ,       lowercase__    ,       lowercase__				)
		snake_case						:  int        =			np.zeros([num_nodes + 1, num_nodes + 1]    ,       dtype=np.single				)  # with graph token

		# combine
		snake_case						:  Dict        =			input_nodes + 1  # we shift all indices by one for padding
		snake_case						:  int        =			attn_bias
		snake_case						:  List[str]        =			attn_edge_type
		snake_case						:  Tuple        =			shortest_path_result.astype(np.intaa				) + 1  # we shift all indices by one for padding
		snake_case						:  List[Any]        =			np.sum(lowercase__    ,       axis=1				).reshape(-1				) + 1  # we shift all indices by one for padding
		snake_case						:  Union[str, Any]        =			item["in_degree"]  # for undirected graph
		snake_case						:  Optional[Any]        =			input_edges + 1  # we shift all indices by one for padding
		if "labels" not in item:
				snake_case						:  List[Any]        =			item["y"]

		return item
class      lowerCamelCase__      :


			def __init__(       self			,	SCREAMING_SNAKE_CASE=20			,	SCREAMING_SNAKE_CASE=False							):



					"""simple docstring"""







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

					snake_case						:  Optional[Any]        =			spatial_pos_max
					snake_case						:  List[Any]        =			on_the_fly_processing



			def __call__(       self			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







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

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

					snake_case						:  Union[str, Any]        =			max(len(i["input_nodes"]							) for i in features							)
					snake_case						:  Tuple        =			len(features[0]["input_nodes"][0]							)
					snake_case						:  Optional[Any]        =			len(features[0]["attn_edge_type"][0][0]							)
					snake_case						:  Tuple        =			max(len(i["input_edges"][0][0]							) for i in features							)
					snake_case						:  Optional[int]        =			len(features[0]["input_edges"][0][0][0]							)
					snake_case						:  List[str]        =			len(SCREAMING_SNAKE_CASE							)

					snake_case						:  Tuple        =			torch.zeros(SCREAMING_SNAKE_CASE			,	max_node_num + 1			,	max_node_num + 1			,	dtype=torch.float							)
					snake_case						:  Dict        =			torch.zeros(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	dtype=torch.long							)
					snake_case						:  Optional[int]        =			torch.zeros(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	dtype=torch.long							)
					snake_case						:  Dict        =			torch.zeros(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	dtype=torch.long							)
					snake_case						:  str        =			torch.zeros(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	dtype=torch.long							)
					snake_case						:  Tuple        =			torch.zeros(
					    SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	dtype=torch.long							)

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

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

							snake_case						:  Any        =			f["attn_bias"]
							snake_case						:  List[Any]        =			f[
							    "attn_edge_type"
							]
							snake_case						:  Tuple        =			f["spatial_pos"]
							snake_case						:  Optional[int]        =			f["in_degree"]
							snake_case						:  int        =			f["input_nodes"]
							snake_case						:  List[Any]        =			f["input_edges"]

					snake_case						:  Tuple        =			batch["in_degree"]

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

					return batch







"""simple docstring"""

import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY
@is_pipeline_test
class      lowerCamelCase__      (					unittest.TestCase     ):
			a__	: List[str]        =       MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
			a__	: List[Any]        =       TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					if isinstance(model.config			,	SCREAMING_SNAKE_CASE							):
							snake_case     ,			snake_case						:  Optional[Any]        =			list(tokenizer.lang_code_to_id.keys()							)[:2]
							snake_case						:  Tuple        =			TranslationPipeline(model=SCREAMING_SNAKE_CASE			,	tokenizer=SCREAMING_SNAKE_CASE			,	src_lang=SCREAMING_SNAKE_CASE			,	tgt_lang=SCREAMING_SNAKE_CASE							)
					else:
							snake_case						:  List[Any]        =			TranslationPipeline(model=SCREAMING_SNAKE_CASE			,	tokenizer=SCREAMING_SNAKE_CASE							)
					return translator, ["Some string", "Some other text"]


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  List[str]        =			translator("Some string"							)
					self.assertEqual(SCREAMING_SNAKE_CASE			,	[{"translation_text": ANY(SCREAMING_SNAKE_CASE							)}]							)

					snake_case						:  Optional[Any]        =			translator(["Some string"]							)
					self.assertEqual(SCREAMING_SNAKE_CASE			,	[{"translation_text": ANY(SCREAMING_SNAKE_CASE							)}]							)

					snake_case						:  List[Any]        =			translator(["Some string", "other string"]							)
					self.assertEqual(SCREAMING_SNAKE_CASE			,	[{"translation_text": ANY(SCREAMING_SNAKE_CASE							)}, {"translation_text": ANY(SCREAMING_SNAKE_CASE							)}]							)


			@require_torch
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Optional[int]        =			pipeline("translation_en_to_ro"			,	model="patrickvonplaten/t5-tiny-random"			,	framework="pt"							)
					snake_case						:  Union[str, Any]        =			translator("This is a test string"			,	max_length=20							)
					self.assertEqual(
					    SCREAMING_SNAKE_CASE			,	[
					        {
					            "translation_text": (
					                "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
					                " Beide Beide"
					            )
					        }
					    ]			,	)


			@require_tf
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  int        =			pipeline("translation_en_to_ro"			,	model="patrickvonplaten/t5-tiny-random"			,	framework="tf"							)
					snake_case						:  int        =			translator("This is a test string"			,	max_length=20							)
					self.assertEqual(
					    SCREAMING_SNAKE_CASE			,	[
					        {
					            "translation_text": (
					                "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
					                " Beide Beide"
					            )
					        }
					    ]			,	)


			@require_torch
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  List[Any]        =			pipeline("translation_en_to_de"			,	model="patrickvonplaten/t5-tiny-random"			,	framework="pt"							)
					snake_case						:  List[str]        =			translator("This is a test string"			,	max_length=20							)
					self.assertEqual(
					    SCREAMING_SNAKE_CASE			,	[
					        {
					            "translation_text": (
					                "monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"
					                " urine urine urine urine urine urine urine"
					            )
					        }
					    ]			,	)



			@require_tf
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  List[str]        =			pipeline("translation_en_to_de"			,	model="patrickvonplaten/t5-tiny-random"			,	framework="tf"							)
					snake_case						:  Tuple        =			translator("This is a test string"			,	max_length=20							)
					self.assertEqual(
					    SCREAMING_SNAKE_CASE			,	[
					        {
					            "translation_text": (
					                "monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"
					                " urine urine urine urine urine urine urine"
					            )
					        }
					    ]			,	)







class      lowerCamelCase__      (					unittest.TestCase     ):


			@require_torch
			@slow
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					with self.assertRaises(SCREAMING_SNAKE_CASE							):
							pipeline(task="translation_cn_to_ar"							)

					# but we do for this one
					snake_case						:  List[str]        =			pipeline(task="translation_en_to_de"							)
					self.assertEqual(translator._preprocess_params["src_lang"]			,	"en"							)
					self.assertEqual(translator._preprocess_params["tgt_lang"]			,	"de"							)


			@require_torch
			@slow
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Any        =			MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"							)
					snake_case						:  Union[str, Any]        =			MBartaaTokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"							)

					snake_case						:  Dict        =			pipeline(task="translation"			,	model=SCREAMING_SNAKE_CASE			,	tokenizer=SCREAMING_SNAKE_CASE							)
					# Missing src_lang, tgt_lang
					with self.assertRaises(SCREAMING_SNAKE_CASE							):
							translator("This is a test"							)

					snake_case						:  Optional[int]        =			translator("This is a test"			,	src_lang="en_XX"			,	tgt_lang="ar_AR"							)
					self.assertEqual(SCREAMING_SNAKE_CASE			,	[{"translation_text": "هذا إختبار"}]							)

					snake_case						:  Dict        =			translator("This is a test"			,	src_lang="en_XX"			,	tgt_lang="hi_IN"							)
					self.assertEqual(SCREAMING_SNAKE_CASE			,	[{"translation_text": "यह एक परीक्षण है"}]							)

					# src_lang, tgt_lang can be defined at pipeline call time
					snake_case						:  Tuple        =			pipeline(task="translation"			,	model=SCREAMING_SNAKE_CASE			,	tokenizer=SCREAMING_SNAKE_CASE			,	src_lang="en_XX"			,	tgt_lang="ar_AR"							)
					snake_case						:  int        =			translator("This is a test"							)
					self.assertEqual(SCREAMING_SNAKE_CASE			,	[{"translation_text": "هذا إختبار"}]							)


			@require_torch
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  int        =			"patrickvonplaten/t5-tiny-random"
					snake_case						:  Tuple        =			pipeline(task="translation_cn_to_ar"			,	model=SCREAMING_SNAKE_CASE							)
					self.assertEqual(translator._preprocess_params["src_lang"]			,	"cn"							)
					self.assertEqual(translator._preprocess_params["tgt_lang"]			,	"ar"							)


			@require_torch
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  str        =			"patrickvonplaten/t5-tiny-random"
					with pytest.warns(SCREAMING_SNAKE_CASE			,	match=r".*translation_en_to_de.*"							):
							snake_case						:  Dict        =			pipeline(task="translation"			,	model=SCREAMING_SNAKE_CASE							)
					self.assertEqual(translator.task			,	"translation_en_to_de"							)
					self.assertEqual(translator._preprocess_params["src_lang"]			,	"en"							)
					self.assertEqual(translator._preprocess_params["tgt_lang"]			,	"de"							)



			@require_torch
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					with self.assertRaises(SCREAMING_SNAKE_CASE							):
							pipeline(task="translation"							)

